quantitative analysis of distributed energy resources in future distribution networks

Degree project in

Quantitative analysis of DistributedEnergy Resources in Future Distribution

Networks

Xue Han

Stockholm Sweden 2012

XR-EE-ICS 2012004

ICSMaster Thesis

Abstract

There has been a large body of statements claiming that the large scale

deployment of Distributed Energy Resources (DERs) will eventually reshape

the future distribution grid operation in numerous ways However there is

a lack of evidence specifying to what extent the power system operation will

be alternated In this project quantitative results in terms of how the future

distribution grid will be changed by the deployment of distributed genera-

tion active demand and electric vehicles are presented The quantitative

analysis is based on the conditions for both a radial and a meshed distri-

bution network The input parameters are on the basis of the current and

envisioned DER deployment scenarios proposed for Sweden

The simulation results indicate that the deployment of DERs can signif-

icantly reduce the power losses and voltage drops by compensating power

from the local energy resources and limiting the power transmitted from the

external grid However it is notable that the opposite results (eg severe

voltage fluctuations larger power losses) can be obtained due to the inter-

mittent characteristics of DERs and the irrational management of different

types of DERs in the DNs Subsequently this will lead to challenges for the

Distribution System Operator (DSO)

Keywords Distribution Network Distributed Generation Electric Vehi-

cle Active Demand Power Losses Voltage Profile

Acknowledgements

The thesis has been implemented in cooperation with Vattenfall Research

and Development and was approved by the Department of Industrial Infor-

mation and Control Systems at KTH - Royal Institute of Technology This

project would have not been completed without all those who helped me

with difficulties and problems

First and foremost I would like to show my gratitude to my supervisor

Claes Sandels who provides the basic idea of this thesis and offers me the

opportunity to work on it I am also grateful for the support and fruitful

discussion from my co-supervisor Kun Zhu All their contributions of time

ideas and important feedback throughout the whole period of thesis work

make me accumulate the experience and knowledge in a stimulating envi-

ronment

I am especially grateful for the encouragement and suggestions on future

plans from Prof Lars Nordstrom

I would like to thank Arshad Saleem Nicholas Honeth Yiming Wu

Davood Babazadeh for their kind help on modelling of DERs and designing

of DNs I also want to show my appreciation to Aquil Amir Jalia Quentin

Lambert and Ying He for their help when collecting data and their valuable

advice

Thanks to all the people at Vattenfall who share their insightful ideas

with me and all my friends in ICS who always support me

In the end I do hope my thesis could help Claes and Kun with their PhD

study in ICS and could give some interesting ideas to Vattenfall for their

research

Xue Han

Stockholm March

Contents

List of Figures iv

List of Tables vi

Abbreviation vii

1 Introduction 1

11 Background 1

12 Goals and Delimitations 3

121 Goals and Objective 3

122 Research Questions 4

123 Delimitation 4

124 Definitions and Nomenclature 5

13 Outline of the Report 7

2 Method 8

21 Study Approach 8

22 Mathematical Method 9

3 Theory 11

31 Basic Power System Theory 11

311 DN 11

312 Components 12

313 Calculations in Power System 13

32 Comparison of Network Topologies 15

321 Description of Several Networks 16

322 Comparison of Key Parameters 17

33 Wind Power as DGs 19

331 Operation Mechanism 19

332 Historical Data 20

34 EV Fleets and Behaviours of Customers 21

35 Load Profiles in the MV Level DNs 23

i

CONTENTS

351 Conventional Residential Load 25

352 Other Types of Loads 25

353 Actions Applied in AD Dimension 26

36 Estimation of the Development of DERs and the Changes of Activities in

DNs 30

4 Construction of the Simulation Toolbox 31

41 Network Model 31

42 DG Model 32

421 Wind Power 32

43 EV Model 33

431 Algorithm of Modelling 33

432 Parameter Sets for Simulations 35

433 Individual Results 36

44 AD Model 36

441 Price Sensitivity 37

442 Energy Efficiency Actions 38

443 Small Scale Productions 39


45 Summary of the Parameters 42

5 Results and Analyses 43

51 Simulation Process 44

52 Phase 1 ndash Simulation of Individual Dimensions 45

53 Phase 2 ndash Estimated Use Cases 47

531 Results of Cases in the Radial Network 47

532 Results of Cases in the Meshed Network 51

533 Analysis 53

54 Phase 3 ndash Sensitive Analysis and Extreme Cases 57

541 DG Dimension 57

542 EV Dimension 58

543 AD Dimension 59

6 Discussion and Future Work 61

61 Discussion 61

62 Future Work 62

7 Conclusion 63

References 64

ii

CONTENTS

A Topologies and Description of Test Networks 69

B Flow charts of models 76

C Load Profile of AD 79

D Pre-study on impacts of DERs 81

E Matlab GUI 83

iii

List of Figures

11 Background of the thesis project 1

12 The scenario space 7

21 The Project Procedure 8

31 Typical network topologies 12

32 π-equivalent circuit of lines 12

33 Equivalent circuit of transformer 13

34 General Structure in WindTurbine Block 19

35 Typical power curve of wind turbine 20

36 Total production of wind turbines on Gotland in 2010 21

37 Starting time for different types trips in 24-hour period 22

38 Typical charging curve 23

39 Structure of the hourly load curve of apartments 24

310 Structure of the hourly load curve of houses 24

311 Aggregated load profiles on a random bus of other types of loads 26

312 The acceptance of customers on different appliances 27

313 The Equivalent Circuit Diagram of Photovoltaic Cell 28

314 V-I Feature Curve of a PV cell 29

315 Historical data of Clearness Index on Gotland 30

41 Wind power production on 24-hour base 32

42 Characteristics of EVs 33

43 Individual Results of the model of EVs 37

44 Typical structure of a house as a flexible demand 37

45 Appliances investigated in the strategy 38

46 Structure of the difference between original and reshaped hourly load curve 41

51 The organization of scenarios 43

52 The study procedure of simulations 44

iv

LIST OF FIGURES

53 The simulation process 44

54 Allocation of different load profiles 45

55 Total wind production in the radial network 46

56 Total consumption of EVs in the network 46

57 Electricity price 47

58 Total consumption of residential customers (radial network) 47

59 Voltage condition in Radial Network 48

510 Voltage on Bus5 49

511 Total power losses in the network 49

512 Voltage condition in Meshed Network 50

513 Voltage on Bus SS10 51


515 Total wind production in the network 57


517 Equivalent load curve of EVs in the network 58

518 The Voltage on Bus5 and power losses in the network 58

519 Total consumption of the ADs in the network 59

520 Consumption of all kinds of loads in the network 59

521 Voltage on Bus5 and power losses in the network 60

A1 Topology of the Rural Bornholm MV Feeder 69

A2 Topology of the IEEE Test Feeder 70

A3 Topology of the Rural network from the Swedish reliability report 71

A4 Topology of the Urban network from the Swedish reliability report 72

A5 Topology of radial network 73

A6 Topology of meshed network 73

A7 Network Description of the Radial Network 74

A8 Network Description of the Mesh Network 75

B1 Detailed description of blocks in the flowchart 76

B2 Flowchart of EVrsquos algorithm 77

B3 Flowchart of load generation procedure 78

C1 Load profiles of apartments and houses 80

D1 Impacts of DGs and EVs on DNs 81


E1 GUI application 83

v

List of Tables

31 Different voltage levels in DNs 11

32 Comparison of demonstrative parameters of test network 17

33 Swedish fleet in traffic in 2010 22

34 Average Commuting distances and time 22

35 Comparison of capacities of different types of EV 22

36 Different types of charing 23

37 Seasonal Coefficients of Appliances 25

38 Estimation of evolution of DERs 30

41 Comparison of demonstrative parameters of DN topologies 31

42 Location and the penetration level of wind power 32

43 Allocation of characteristics of the EV fleet 35

44 Capacity of Battery of each PHEVBEV[kWh] 35

45 Trip Types for different types of EV 36

46 Price sensitivity strategy for appliances 39

47 Modelling Parameters 42

51 Simulation Scenarios 45

52 Summary of Voltage Fluctuation 53

53 The Extent of Voltage Fluctuations in Radial Network 55

54 Summary of Average Power Losses 56

vi

Abbreviations

AD Active DemandAVR Automatic Voltage RegulatorBEV Pure Battery Electric VehicleCHP Combined Heat and Power (plant)CV Commercial VehicleDER Distributed Energy ResourceDG Distributed GenerationDN Distribution NetworkDSM Demand Side ManagementDSO Distribution System OperatorEV Electric VehicleHV High Voltage (level)LV Low Voltage (level)MV Medium Voltage (level)PHEV Plug-in Hybrid Electric VehiclePSS Power System Stabilizerpu per unitPrV Private VehiclePV Photovoltaic panel (cells)RES Renewable Energy SourceSOC State of Charge

vii

Chapter 1

Introduction

In this chapter the scopes and aims of the project are described The preliminary

study on both the characteristics of DNs and the potential problems of DERs are pre-

sented as well Furthermore the outline of the thesis report is given in the last section

11 Background

Figure 11 Background of the thesis project - [1]

A series of environmental goals such as [2][3][4] are proposed worldwide which will

lead to the changes of policies and legislations in different countries [5][6] These adjust-

ments result in a dramatically increased penetration of DERs in the conventional DNs

The continuously growing DG especially powered by intermittent energy resources

poses a potential risk on the power system operation (especially in the situations of the

mismatches between the generation and the demand of customers in the DNs [7]) EVs

as the stars in the future transportation sector are expected to reduce the dependency

in fossil fuels The introduction of EVs does not only cast a burdens on the electricity

grid but also imply a new load pattern which is consumer driving behaviour dependent

Meanwhile the widely use of advanced metering technology gives the opportunity for

customers especially households to respond on price signals from the electricity mar-

1

11 Background

ket These modifications result in challenges and problems in terms of the operation

and planning of the DNs for the DSOs [8][9][10][11] Fig 11 illustrates the discussion

above [1]

Some related concepts such as rdquoDERrdquo rdquoSmart Gridrdquo and rdquoEVrdquo have been drawn

a lot of attention among engineers academic researchers and energy companies A lot

of projects are organized to study DERs[12] However most of the projects focused

on reliability issues of the operation [13][14] and analysing the features of one specific

category of DERs [8][15][16] Therefore it is hard to see a global view on interpreting

the changes in a quantitative way[17] So in this project we try to use the concrete and

quantitative results to indicate the impacts of DERs in DNs and to consult the DSO

into further research domains

The introduction of DERs will significantly influence the operation of the whole

network (see Fig D1 and Fig D2 in Appendix) The impacts are classified below

bull Power Flow and Power Losses DERs at the terminal of feeders can change the

original power flow even result in a bidirectional power flow to some extent [8][18]

The capacity of transmission lines is released by DGs and the peak load may be

reduced by Demand Side Management (DSM) ie strategically managing the

active demand [11][19] However renewable energy production is hard to predict

and control comparing to the conventional generation sources In some conditions

power losses may be larger

bull Power Quality Power quality includes the following aspects voltage fluctuations

and harmonics [8][20] Large deviations of the production or consumption of DERs

in each hour such as the removal of a certain load or generator cause voltage sag

or swell [8][18] Power electronics configured in the DNs inject some high frequency

harmonics into the DNs as well [8][18]

bull Reliability and Availability The integration of DGs reduces power transmis-

sion and improves the availability of grid and power supply in general It also

benefits to the island operation and black-out start when a large disturbance oc-

curs [13] Some DGs equipped with Automatic Voltage Regulators (AVR) and

Power System Stabilizers (PSS) can help to stabilize the power system frequency

and voltage which improve the reliability of the DNs [11] However large pene-

tration of DGs may trigger the instability of the whole system and give rise to a

poor power factor a poor frequency stability and a strong chance of short-circuit

[18]

Thus some critical problems may occur in the power system especially in DNs

2

12 Goals and Delimitations

Some immediate questions can be addressed such as

bull What happens if a large-scale wind production is introduced in a given DN

bull How big are the consequences

bull Will it affect the voltage profile in the DNs

bull How much power is saved in the DNs by applying energy efficiency actions

bull How large load profile will a given EV fleet introduce to the DN

For Vattenfall the assessment of theses questions is urgently needed to be answered

to maintain a strong grid operation At the same time by analysing different energy

resources available in terms of all the roles in DNs (including DSO) some business

cases (eg Aggregators [15][21]) appear very interesting which could provide economical

profits and strengthened electricity supply facing the changes in DNs For example the

isolated grid of Gotland (connected to mainland Sweden with a HVDC line) with a high

penetration of wind energy requires an extensive upgrade (eg the implementation of

advanced grid management system according to smart grid concept) to enhance the

security of the present grid and the quality of power supply It is therefore interesting

from the DSO point of view to look upon the most possible critical problems in the DNs

of Gotland For more information about Gotland see [22] and [23]


It is obvious that all the DER components own their unique and very complicated sys-

tems and can be modelled in different ways for various purposes Considering the size of

this project all components are simplified and integrated as loads or generators in MV-

DNs The dynamic behaviour is neglected during the modelling In the static analysis

of power system power losses and voltage profiles are the two primary concerns on the

grid operation and planning The reason is that they are directly relevant to the opera-

tional investments and are sensitive to changes in the network [8][15][16][19][24][25][26]

Thus the main task of the thesis project is to interpret changes in the power system

onto different scenarios in the future environment of the Swedish DNs The quantita-

tive results with regard to the voltage profiles and power losses are expected from the

simulations Subsequently the analysis on the challenges caused by the changes can be

assessed based on these simulation results

121 Goals and Objective

The master thesis project has three goals

1 The first goal is to design and implement two reference DNs a meshed and a radial

network see Section41) in Simulink

3


2 The second goal is to construct a toolbox that consists of different DER models

This is done in order to make it possible for simulating and analysing different

scenarios A scenario is further defined in Section 124

3 The third and final goal is to analyse different technical problems arising from the

scenarios ie voltage problems and power losses in the DNs

122 Research Questions

After the thesis work the following questions should be answered

bull Which technical problems are observed in the worst case And in which condition

bull How does different factors (eg season network penetration level etc) affect the

DN Are the impacts beneficial or harmful

And some more questions could be discussed

bull Is it necessary to apply some aggregators to improve the behaviour of DNs

bull What type of aggregators are needed

123 Delimitation

Initial delimitations are summed up below

bull Data sets are based on the real conditions of Sweden (specifically solar irradiation

and historical wind production data from Gotland)

bull Only two specific networks (ie radial and meshed) are modelled for the simula-

tions and are designed to represent the condition in Sweden especially in Gotland

to some extent

bull Only two interesting factors are selected as targets of simulations and analyses

ie voltage fluctuations and power losses in the network

bull Dynamic behaviour and protection issues are not considered in the thesis

bull All the models are integrated at a medium voltage (MV) DN ie 10kV

bull Only consider customersrsquo behaviour in a random weekday

bull Wind power is the only component in the DG dimension

bull Two types of vehicles ie private vehicles (PVs) and commercial vehicles (CVs)

with certain behaviour are simulated

bull Two types of electric vehicles ie pure battery vehicles (BEVs) and plug-in hybrid

electric vehicles (PHEVs) are taken into account

bull Charging infrastructure is only available at home and at work

bull The regulating actions of the DSO is out of the scope of this study The price

sensitivity is only concerning the day ahead prices (not any time of use tariffs etc)

4


124 Definitions and Nomenclature

To clarify the contribution and delimitation of the thesis some important definitions

in the whole thesis work are presented

1241 Distributed Energy Resources

The concept of rdquoDistributed Energy Resourcesrdquo is not clearly defined either by an

authoritative organization nor by an academic project team Yet it is widely used in a

lot of papers reports and books In our project the definition of DER is given as

Definition 1 DERs are regarded as the electric equipment installed in DNs which pro-vide energy or participate in the operation of the power system regardless of producingor consuming power The deployment of DERs in the grid reflects a paradigm shiftregarding how electricity power is in a power system [11][17]

1242 Scenario

A scenario is composed by different DERs in a certain DN during a certain period

(eg a random weekday in the winter) The integration of DERs are influenced by some

external forces such as opinions of policy makers and consumers and the parameters of

the DN ie the peak load of the specific DN DERs are grouped into three dimensions

DGs EVs and ADs To some extent if the integration levels of DERs hit the threshold

values the DSO will have no choice but to either (i) refurbish the network (eg

replace wires install bigger transformers etc) orand (ii) contract ancillary services

from generators and loads to secure the network operation (i) - (ii) will require some

kind of investments from the DSO and how he should act towards this issue is part of

an optimal decision policy problem The DSO is supposed to minimize the overall cost

with respect of keeping the system operation safe reliable and efficient To simplify

the model we assume that the dimensions are independent from one another Hence a

scenario is defined as follows

Definition 2 A scenario is a specific future plan in terms of amount of DERs in acertain DN DERs are defined by three dimensions (DG AD and EV viz the scenariospace) and their potential success is only dependent on external factors such as opinionsfrom policy makers and consumers Furthermore the scenario space is variable iethe dimension can be assigned different numbers Meanwhile the properties of the DNis fixed In the end the scenario should reflect some kind of technical issue of theoperation that the DSO should solve from an optimal decision making policy in networkinvestments

1243 Electric Vehicle

Continuous improvements of storage technologies and a dedicated support from policy

makers promise a bright future for EVs From the power systemrsquos perspective EVs are

5


batteries embedded in transportation facilities charged and discharged in some cases

when connected to the grid

How much when and where EVs are charged are mostly depending on (i) driving

patterns (eg the behaviour of commuters) (ii) the type of EVs (eg PHEVs) (iii)

charging availability (eg at home) For example a private car runs two or three trips

per day within the time periods around 900 - 1700 respectively The length of each

trip is about 20 km in cities The locations available for charging are at home and at

work If the car is a BEV the possible driving range is very limited (typically 20 - 25

km before a recharge is needed [27][28])

Definition 3 EVs are modelled as portable batteries whose behaviour and properties aredetermined by the vehicle types driving patterns and availability of charging facilities

1244 Active Demand

The improved technologies such as dispatched smart-metering system enable the

customers to track the price and consumption in households The trends of of market

deregulation provide much more opportunities of their participation in the system At

the same time new feed-in tariffs and plans to increase energy efficiency lead to a

strong will of advanced activities on the demand side (eg installation of solar panels

new appliances with an energystar label etc) Three segments are set to define AD

regardless of the effects of DSOs (eg cutting off the peak load compensating power to

the grid when isolated) AD dimension is consequently defined as

Definition 4 Active Demand is residential consumers who change their load by either(i) shifting consumption with respect to the electricity price on the day-ahead market(ii) producing their own energy by installing PV panels on their roofs or (iii) updatinghousewares with the purpose of improving energy efficiency In the end these changeswill reshape the load profiles of the households

1245 Dimension 3 ndash Distributed Generation

The installed capacity of DGs is growing stably especially powered by renewable

energy such as wind solar biomass etc Reasons to introduce them are listed in

the book [8] such as the trends of deregulation of Electricity Market environmental

concerns and enhancing the margin between peak load and available production etc

According to [24] the definition of DG is given as

Definition 5 Distributed generation is an electric power source directly connected tothe MV-DNs within the scale of 15 MW Specifically in this project the wind powergeneration is studied due to its popularity and notable intermittent nature

Obviously the operations of different kinds of DGs are independent on their primal

drivings and the interfaces with the electricity grids which will be detailed described in

6

13 Outline of the Report

latter part of this section In view of the development of DG technologies in Sweden

wind power is considered as the only resource in the DG dimension to reduce the contents

of the models Some micro or small size production (eg solar panels) are regarded

as part of AD dimension due to the reason that they are directly connected to the

consumers ie on the customer side of the meters

DG

AD EV

Figure 12 The scenario space - DERs are defined by three dimensions (DG AD andEV viz the scenario space)


In chapter 2 common methods used in the thesis work are listed and explained

Furthermore some general knowledge of power system theory which is the basis of sim-

ulation is introduced

In chapter 3 introduction and explanations of different components in the DN are

given (eg mechanism of regulation of power production of DGs load profiles of con-

ventional residential loads state of art of the EV technology etc) Different network

topologies are compared two of which are selected as the networks for simulation

In chapter 4 models of different components participating in the networks are de-

scribed with their mathematical algorithms on the basis of their behaviour Models in

the three dimensions are illustrated independently with the rational level of integration

of individual components

In chapter 5 some results of the performed simulations are presented and analysed

A short summary of conclusions obtained in the thesis work is presented in chapter 6

In Chapter 7 an outlook of potential future work can be found

7

Chapter 2

Method

In the first section the study approach of the thesis work is presented followed by

some basic mathematical methods

21 Study Approach

The procedure of the thesis is presented as follows in Fig 21

In the pre-study phase some potential problems in the DNs are studied based on

the fundamental power system theory (eg power flow calculation mathematical mod-

els of the DERs and the DN components in power system) Two target factors ie

power losses and voltage are studied by simulating the scenarios based on the under-

lying models and theoretical algorithms The parameter inputs are from the empirical

observations and the statistics in the related materials such as [29][28] The models

are improved by detecting the difference between expectation and simulation results

eg the availability of vehicles in the network at a certain time The parameters and

algorithms are adjusted until the simulation results meet the real condition based on

historical data The problems revealed from the simulations could further be studied

and solved by for instance aggregators

Figure 21 The Project Procedure - The process of simulation repeats until the perfor-mances of models are close to the results in pre-study literatures and outputs of simulationsare sufficient for the analyses

8

22 Mathematical Method

Other than the method used in the thesis project ie rdquomodelling and simulationrdquo

some other applied procedures can be found in other studies In the book [8] the author

collected a large amount of reliable data of different DERs analysed the data by using

statistic methods and in the end got the conclusions based on the observations In the

large project rdquoMicrogridrdquo[13] some real cases are studied for example a low voltage

level (LV) network study in Portugal Electricity prices line characteristics and their

reliability are studied on the basis of comparison of different countries In the report

[30] the author modelled a fleet of EVs in Monte Carlo simulation models and drew

some conclusions regarding their impact on eg the peak load

To help the DSO to make decisions in network investments the quantified results are

necessary These results could only be accessed by either simulations or study on real

cases Since the pilot network is costly and is absent of existence the only way is to do

the simulations based on the real conditions and available data


In this section some methods based on probability theory and used in specific algo-

rithms of DERs are described These mathematical methods are carried out to construct

models with specific random factors following certain distributions [31]

Random variable A variable X is a random variable when X is a numerical function

on a probability space Ω function X Ω rarr R ie X is measurable The distribution

of X is described by giving its probability function F (x) = P (X le x) When the

probability function F (x) has the form of F (x) =int xminusinfin f(y)dy X has the density function

f

Normal distribution Normal distribution describes the empirical measurements of

experiments are normally and continuously distributed The density function is given

as following

f(x) =1

σradic

2πeminus (xminusmicro)2

(2σ2) (21)

micro is the mean value and σ is the standard deviation

Lognormal distribution If the random variable χ is normally distributed X =

exp(χ) follows the lognormal distribution The estimation of X EX = emicro+σ2

2

9


Uniform distribution f(x) = 1 in a certain range (a b) and 0 otherwise

F (x) =

0 x le ax a le x le b1 x gt 1

(22)

Binomial Distribution If the random variable X is said following a Binomial (n p)

distribution if

P (X = m) =

(nm

)pm(1minus p)nminusm (23)

Weibull distribution The density function of a Weibull random variable X is

f(xλ κ) =

0 x lt 0κλ(xλ)κminus1eminus( x

λ)κ x ge 0

(24)

where κ gt 0 is the shape parameter and λ gt 0 is the scale parameter κ = 1 indicates

the exponential distribution while κ = 2 indicates the Rayleigh distribution

Monte Carlo method Some mathematical results could not be strictly proved but

could be derived or estimated when facilitated Monte Carlo methods Applications are

utilized in a lot of fields from decisionsrsquo making to simulations of complicated systems

Monte Carlo methods are those methods which use random samples in a calculation

that has a structure of a stochastic process (ie a sequence of states whose evolution

is determined by random events) [32] If there are an amount of variables (ie system

inputs) and the function of these inputs are complicated Monte Carlo methods can be

used to get a good estimation of the observation (ie system output) It is formulated

as

E(Y ) =1

N

Nsumi

Yi (25)

where Y is the observation and implemented in Monte Carlo methods for N times

Each observation Yi is independent and determined by functions and constrains of vari-

ables x12M

10

Chapter 3

Theory

The basic knowledge in power system analysis is introduced in this chapter For the

simulation purpose the underlying foundation of models are presented in the following

sections Data sets used to model elements in the toolbox are proposed

31 Basic Power System Theory

This section is a brief digest from some power system analysis books

311 DN

Voltage level DN is the last stage in electricity delivery path which carries electricity

from transmission networks to the end users The whole DN can be split up into three

voltage levels corresponding to the nominal value of the voltage (see Table 31) [33]

As mentioned in Chapter 1 MV is selected as the voltage level for simulation and

modelling Usually customers in the low voltage levels are connected to MVs with

step-down transformers in substations Some consumers with large consumptions are

directly connected to MVs Common voltages in MV-DNs in Sweden are 10 kV and 20

kV There are voltage level 3 6 and 33 kV but these are rare [34]

Network topology The simulation approach requires the first step that typical DN

topologies should be identified Usually the MV-DNs in urban area are with loop or

mesh topologies while in rural area with mesh or radial topologies (see Fig 31)

System Nominal Voltage [kV]

LV le 1MV 1minus 35HV ge 35

Table 31 Different voltage levels in DNs[33]

11


However considering the breakers in networks are normally open most networks are

operating as radial ones [13]

(a) Loop (b) Mesh (c) Radial

Figure 31 Typical network topologies[13]

312 Components

3121 Transmission lines

A considerable share of power in MV-DNs are consumed by the transmission lines

instead of loads due to lower voltage and higher current In MV-DNs both overhead

lines and underground cables are commonly used [13] In Sweden the ratio between

these two types of transmission lines is 31 [34] Overhead lines are less expensive than

underground cables but more space consuming [34][35]

Transmission lines are commonly characterized as π model by their resistance Rs

Rs Xs

Bsh

2

Bsh

2

Figure 32 π-equivalent circuit of lines - π-equivalent of transmission line with RsXs and Bsh

series inductance Ls and shunt capacitance Csh as shown in Fig 32 Reactance Xs

is obtained by ωLs while subceptance Bsh is obtained by ωCsh Thus the equivalent

model of the transmission line is derived as

Zs = Rs + jXs (31)

Ysh = jBsh (32)

where

ω is the angular frequency of power system

12


Zs is the series impedance of the line

Ysh is the shunt admittance of the line

The fact that underground cables have larger capacitance and smaller inductance

compared with overhead lines may lead to novel phenomena of voltage and power losses

in feeders [35]

3122 Transformer

Transformers as the interface between MV-DNs and HV-DNs or between LV-DNs

and MV-DNs bring the voltage levels down eg from 60 kV to 10 kV Usually on-load

tap-changers are only utilized on HVMV transformers to keep the voltage constant

without disconnecting any loads while manually tap-changers are utilized on MVLV

transformers and adjusted only once during installation [36]

The equivalent circuit (referring to the primary side) of a transformer as shown in Fig

33 consists of in-series resistance Rp and Rs representing power losses on each side in-

series reactance Xp and Xs resulted from flux leakage as well as the magnetizing branch

reactance Xm in parallel with the iron losses component Rc (see Fig 33)

Figure 33 Equivalent circuit of transformer - Secondary impedance is referred tothe primary side [36]

313 Calculations in Power System

3131 Power flow

Power flow calculations in general enable a certain power system to indicate voltage

magnitude and angle in each bus active and reactive power flow between each bus The

identified known and unknown quantities in the system determine types of buses (ie

PQ bus PV bus slack bus) Then calculations could be implemented by power balance

equations

0 = minusPi +Nsumk=1

| Vi || Vk | (Gik cos θik +Bik sin θik) (33)

13


0 = minusQi +

Nsumk=1

| Vi || Vk | (Gik sin θik minusBik cos θik) (34)

where Gik and Bik can be obtained from the admittance matrix YBUS

3132 Power losses

The reason to choose which voltage level of the grid much depends on power losses

which are transformed into heat When power losses decrease the maximal capacity on

lines mainly due to the limit of thermal capacity could increase Power losses on the

level of 10 kV are about 3 times of those on the level of 20 kV [34] Other than the

technical factors the design of system operation could also affect the total losses in the

system Power losses Ploss in power system follow such formulas

Ploss = I2R (35)

I =S

U(36)

where

R is the resistance of the transmission line [Ω]

I is the current flowing through the line [A]

U is the voltage difference between two ends of the line [V]

S is the absolute value of complex power given byradicP 2 +Q2 [VA]

3133 Power factor

In power flow calculation two components are obtained as active power [W] P which

transfer energy and reactive power [Var] Q which move no energy to the loads Hence

power factor is defined as

powerfactor =| cosϕ |= P

S=

PradicP 2 +Q2

(37)

When powerfactor = 0 the power flow is purely reactive When powerfactor = 1

only active power generated by the source is consumed Power factors are expressed as

rdquoleadingrdquo or rdquolaggingrdquo to show the phase angle ϕ

3134 Voltage variation and weakest point

For safety concerns there are limits of network voltage to protect equipments con-

nected to the system According to the European Standard EN50160 [20] the voltage

magnitude variation should not exceed plusmn10 for 95 of one week measured as mean

10 minutes root-mean-square (RMS) values The consumption will result in a voltage

drop on the feeder The further away from the substation bus the lower the voltage

14

32 Comparison of Network Topologies

will be On the contrary the connection of generations will lead to a voltage rise on

the feeder The interaction of consumption and generation in MV-DNs therefore causes

voltage variations

The voltage variation in steady state is approximately equal to

∆U

Usim=RlinePG +XlineQG

U2middot 100 (38)

With a given limit on the variation of the voltage ∆Umax the maximum allowed

produced active power from a connected generator can be derived as

Assume that the power factor is constant Then Qmax = α lowast Pmax

According to the equation (38)

∆Umax =(R+ α middotX)PGmax

U(39)

rArr

PGmax =∆Umax

(R+ α middotX)U(310)

From Equation 39 we can derive that when the nominal voltage value and feed-in

power are constant larger equivalent impedance leads to larger variation in voltage In a

power system the weakest point is the load point where the equivalent impedance is the

largest Usually the most critical voltage problem appears in the weakest point The

equivalent impedance is called Thevenin equivalent impedance which is usually decided

by the distance from the slack bus material of transmission line and the topology of

the network

3135 Penetration levels

The penetration levels of DER components indicates the share of them in the whole

network The penetration level is expressed as the ratio between the installed capacity

of DER components and the peak load value

micropen = 100

(sumPnomG

P peakL

)(311)


The DNs directly connect to the end users Their topologies and sizes are different

among areas depending on the operation concepts utilized devices and the load profiles

Thus voltage quality reliability and power losses in this stage are interesting aspects to

study in DNs Several electrical network models are developed as test systems to analyse

one or more specific characteristics (eg voltage regulation unbalanced load reliability

15


of system etc) However a majority are developed to study power system reliability

which indicates that they are not of sufficient details to the level upon which our research

questions could be addressed Additionally there are some network models for power

flow analysis but their sizes are too large to be implemented in Simulink Matlab For the

purpose of investigating the integration of DERs in a future DN reasonable test networks

are required for a sufficient analysis of the simulation results and a correct direction for

further network studies The DNs are studied at a medium voltage level ie 10 kV

From literature review study several typical existing test networks are acknowledged

described and compared [13][37][38][39] Their topologies can be found in the Appendix

321 Description of Several Networks

bull IEEE Test Feeder ndash IEEE Comprehensive distribution test feeder developed by

IEEE Working Group is presented on the IEEE website It consists of 55 buses

two voltage levels and 25 loads which represent a wide variety of components

in one circuit [40] It is a large radial network It is possible to connect some

switches in the network to form a small loop Some other types of modules (eg

identical feeders connected to the same bus radial structure with loads distributed

on feeders) are presented in the network Examples of all the components in the

network are provided in the work document The network allows all the standard

components of a distribution system to be tested (See Fig A2 in appendix for

the topology)

bull Bornholm Test Network ndash The Bornholm Test Network is modelled based on

the real distribution network in Bornholm an island of Denmark with 2 voltage

levels 11 buses 9 transmission lines 7 loads and 3 distributed generations The

detailed parameters of the network are not available However the general network

data can be found in [37] (refer to the topology in Fig A1 in the appendix)

bull Swedish Test Systems ndash The Swedish Test Systems are developed by Elforsk

a Swedish industry research association to analyse the reliability of Swedish net-

works and the electricity market in Sweden The test systems include two distri-

bution systems one for the urban area (ie SURTS) and the other one for the

rural area (ie SRRTS) Loads component data are well represented by these test

systems due to the support from and involvement of Swedish power distribution

companies SURTS is a 10 kV system with 10 identical feeders in cable-loop de-

sign while SRRTS consists of two modules both of which are radial structure

Reliabilities load data and outage costs are specified in the model [34] (refer to

the topologies in Fig A3 and Fig A4 in the appendix)

16


bull The Gotland Network - An isolated mixed network ie both urban and rural

networks with a big share of wind power generation [41] A typical feeder in

the network consists at least 20 load points Shunt capacitors are installed in

the network at the MV level Wind power is directly and particularly connected

to the substation bus with a transformer Load data and parameters of network

components are available It is accessible for the simulation in future work The

edge of the feeder could be regarded as a normally open switch or a big load Some

typical structures of feeders could be selected to represent the whole MV network

after analyse both topologies and load characteristics of the network

bull The German Network - The German test network is provided for the EU project

More Microgrids The description is in the work package G deliverable 1 of More

Microgirds The test system consists of two MV networks (ie urban and rural)

and a detailed LV network for study Line parameters and load profiles are well

presented in the document The MV networks are taken from existing networks

considered to be typical The LV network is an artificial network with different

structure for different load segments The structure of urban MV network is similar

to the Swedish one but the loop circuit is closed by two paralleled feeders with a

normally open switch in between The rural MV network is a 20kV mesh system

with 2 external connection points and 14 buses 15 loads (with static state values

in the figure) and 2 generations are involved in the network Initial power-flow

calculation is described in the figure for different lines and loads Compared with

Swedish rural one the topology is more complicated but with less components in

the network [13]

322 Comparison of Key Parameters

A discussion on the listed test networks is conducted by comparing their complexity

and representativeness Table 32 shows some key parameters of these networks

Table 32 Comparison of demonstrative parameters of test network

Network Voltage Level Buses Loads GenerationsTopology

IEEE NA 55 25 5 meshBornholm 60 kV10 kV 11 7 3 radialSURT 130 kV10 kV 62 60 0 loopSRRT 40 kV10 kV 19 many 0 radialGerman urban kV20 kV 83 81 NA loopGerman rural kV20 kV 14 15 2 meshGotland 70 kV11 kV hundreds mesh

17


Complexity From the description part it is shown that Bornholm test network

and German rural network are much simpler than the other ones The Bornholm net-

work does only consist of 11 buses and German one of 14 buses which simplifies the

quantities of parameters and the analysis of phenomena in simulations Part of the Ger-

man network is selected as mesh network for simulation with 11 buses 8 load points

and two locations for generations The other part of the network is connected with

another substation bus and is isolated to the selected part during normal conditions

Seven or eight load points in the network are sufficient to implement different consumer

categories (ie residential commercial industrial) and to assign EVs together with

loads randomly Some large scale distributed generation such as wind power can be

directly connected to the 10 kV level or a even higher voltage level Those generations

in small scales such as Photovoltaic (PV) panels can be connected in parallel with the

residential load denoting that connecting points are in lower voltage level

Some other simple test systems developed by IEEE [40] are proposed to study unbal-

anced systems and voltage regulators in a very long feeder which do not fit the condition

in this project and the characteristics of real Swedish DNs

Representativeness The network condition in Gotland is similar to that in Born-

holm The restructured German rural network and the DNs in Gotland have some

mutual features To obtain a better representation typical parameters are chosen ac-

cording to the real condition in Gotland (eg the proportions of cables and overhead

lines the distance between buses the voltage levels) The radial network could repre-

sent a typical radial feeder and the first part of a loop feeder with normally open switch

Besides some features of it are set for comparison Feeder B2 is quite short while feeder

B3 is longer which means that buses in B2 can be regarded as rdquostrong gridrdquo compared

with those in B3 In addition B4 and B5 are compared to show the difference between

cables and overhead lines Thus most of technical effects due to the topology of the

network are considered in the test system Since the meshed network is well presented

in the report [13] all properties and characteristics of the cables are modelled based on

the real condition

The IEEE test system SURT and SRRT are all intended for reliability analysis which

indicates that there is no detailed parameters within the documentation In addition

the size of these test systems is a way to large and is not appropriate for the simulation

in Simulink

18

33 Wind Power as DGs


Wind energy is one of the major growing renewable energy technologies in the recent

years Wind power is intermittent and hard to manage which may lead to the voltage

sag or swell usually in a short period The deviation between the real wind production

and and the predicted production may result in grid outages For those wind turbines

equipped with induction generators reactive power is absorbed by them which bring

about a poor power factor and less active power capacity of the transmission line From

an economic perspective additional reactive power compensating devices are costly and

mostly installed in HV networks (to secure the reliable system operation) Wind speed

always reaches the peak value in the winter However for safety concerns a strict

temperature limit (eg minus40C) and speed limit (eg 25 ms) will force the turbines

to cut off their whole production The extracted power curve fits the demand curve in

Sweden because the peak load in Sweden also appears in the winter time Aggregation

of a large geographically spread wind power can reduce the impact of meteorological

influence and improve the accuracy of the prediction significantly [26]

Figure 34 General Structure in WindTurbine Block

331 Operation Mechanism

The wind turbine is the device that extract mechanical power from wind power going

through its swept area and then to transfer energy to electricity (see Fig 34) The

power output of the turbine is given by the following equation [42]

Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where

Cp is coefficient of extracted wind power

λ is tip speed ratio of rotor blade tip speed and wind speed

β is blade pitch angle

ρ is air density (kgm3)

A is the tip swept area (m2)

vw is wind speed ms

ωel is the rotation speed of generator (rads)

A generic equation is used to model Cp [43]

Cp(λ β) = c1

(c2

λiminus c3β minus c4

)e

minusc5λi + c6λ (314)

in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)

Power curves of different type of turbines are unique due to different control methods

different blade lengths different tower heights etc in which rdquocut-in speedrdquo rdquocut-off

speedrdquo rdquorated speedrdquo and rdquorated powerrdquo are the main features of the power curve A

typical power curve is shown in Fig 35 [44]

Figure 35 Typical power curve of wind turbine - Power curves and other technicaldata of wind turbines are given in the product brochures[44]

332 Historical Data

The occurrence of wind speed follows a Weibull distribution and there is little corre-

lation of wind speeds in neighbouring hours Meanwhile the direction and turbulence

of wind are other two main factors which affect the production of electricity Except for

these factors the turbine structures regulation strategies location of the wind farm

and the distribution of wind turbines have a large impact on the production Hence

as a static model the aggregated wind power is depicted as the generation changing on

20

34 EV Fleets and Behaviours of Customers

an hourly basis in a MV-DN at the generation point in the network Fig 36 shows the

variation of total wind production on Gotland throughout the whole year 2010 [41]

0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]

Figure 36 Total production of wind turbines on Gotland in 2010 - The datarecords production in each hour in the whole year [41]


EVs are vehicles that at least are partly powered by electricity or use electric motors

as the direct propulsion Transport sector is one of the most important and largest part

of energy consumption and the energy use in transport sector is dominated entirely by

oil products (eg petrol diesel etc) in Sweden[45] For the purpose of reducing the

emission of CO2 and improving energy efficiency the deployment of a large amount of

EVs can be envisioned in a near future

On-board batteries the core of the EV technology introduce a large amount of

possibilities to the power system to interact with them via charging facilities (eg

plug-in electric outlets) Without considering the effects of management and control

the charging behaviour is mainly dependent on three factors

I Driving patterns In our case only Private Cars (PrV) and Commercial Vehi-

cles (CV) are considered The statistics of travelling behaviour in Sweden (eg

numbers of trips per day the distance covered by on e trip hours of the trips

etc) are collected based on [28] and [29] Table 33 implies that each household in

Sweden own one car in average and EVs are not commonly deployed until now

Table 34 presents the general trip length in different areas and the time consumed

in each trip In Fig 37 the blue curve and the black one illustrate two types

of driving patterns in a day It gives the primary understanding on how vehicles

behave in Sweden

II Types of EVs Among various types of EVs we took Plug-in Hybrid Electric

Vehicles (PHEVs) and Pure Battery Electric Vehicles (BEVs) into our considera-

21


Table 33 Swedish fleet in traffic in 2010 [29]

Type of Vehicles Private Cars Commercial Cars Heavy Vehicles Others

No of Vehicles 3 446 517 1 376 358 526 441 1 869 683

Type of fuel Petrol Diesel Electricity Other

No of Cars 3 479 607 606 570 190 248 815

Table 34 Average Commuting distances and time[28]

Municipality Groupings Distance [km] Time [mins]

Urban Area 20plusmn 1 42plusmn 3Suburban Area 25plusmn 2 40plusmn 2Rural Area 28plusmn 2 41plusmn 1Total 23 41

Figure 37 Starting time for different types trips in 24-hour period (times103) [28]

Table 35 Comparison of capacities of different types of EV[27] - shown in differentcoloured curves

Type of Vehicles Battery Capcity [kWh]

BEV 25 sim 35City-BEV 10 sim 16PHEV90 12 sim 18PHEV30 6 sim 12

tion Compared to PHEVs (with internal combustion system) BEVs (with pure

electricity propulsion) need a larger battery capacity According to the report [27]

typical capacities are shown in Table35

III Charging patterns Charging facilities for different purpose in different area are

assumed planned well to fulfil the basic requirements of EV charging Depending

22

35 Load Profiles in the MV Level DNs

on the preferences of car owners three possible charging approaches1 are proposed

as listed in Table 36 [27]

In reality the charging curve is not a straight line but with some variations

Table 36 Different types of charging [27]

Type of Charge Load Charging time

Slow le 5 kW ge 4 hoursStandard 5 kW - 20 kW 1 hour - 4 hoursFast ge 40 kW le 1 hour

of the slope along the charging process Fig 38 shows one typical real charging

curve For simplicity we assume that the charging current is constant (ie the

curve is a straight line)

Three stages of charging places are stated in [27] as (1) at home (2) at home

and at work (3) everywhere Different charging approaches correspond to different

charging places

Figure 38 Typical charging curve - This is a charging curve of Li-Ion batteries whichcould be idealized or linearised[27]


Flexible residential loads is an important part of our scope The conventional load

profile is reshaped by modifying actions Loads of other sectors are excluded which are

assumed that they have already made their own optimized modifications on their loads

1Charging time depends on the size of battery

23


Figure 39 Structure of the hourly load curve of apartments - Apartment withtwo people (25-65 years old) Workdays

Figure 310 Structure of the hourly load curve of houses - House with two people(25-65 years old) Workdays)[46]

24


351 Conventional Residential Load

According to the report Statistics Sweden 2011 [29] the distribution of houses and

apartments are quite even especially in cities According to [46] the most representa-

tive pattern is a two persons apartment or house Thus the load profiles on an hourly

basis of these patterns are collected as the original residential load profiles in our study

To perform price sensitive strategies different type of household appliances are classified

(eg Dish washer heating white goods etc) Usually heating and water heating in

apartment buildings are not accounted into the consumption of electricity while direct

electric heating is (eg from heat pumps) Subsequently the profiles are presented in

Fig 39 and Fig 310 with different appliances in different colors

As described in [34] the density of residential customers at the LV-DNs are 20(km

of line) in the urban area and less than 10(km of line) in the rural area respectively

Thus the numbers of customers in each load points in the network model are set to

be around 500 for the urban area and 200 for the rural area The load curve of an

apartment is based on the data set in [47] with 120 units aggregated (ie a whole

building) The load curve of a house is based on the data set for a two person (25-65

years old) household (with direct electric heating) measured and presented in the report

[46] In general rdquoone appliancerdquo of the data set refers to an average value of a group of

appliances in the households

Temperature is an important factor influencing the consumptions of different ap-

pliances of which the seasonality effects coefficients are assigned in the report [46] as

well To simplify the model coefficients of four seasons are selected to represent the

temperature factors (see Table 37)

Table 37 Seasonal Coefficients of Appliances[46]

Appliances Spring Summer Autumn Winter

White goods 09 10 09 07Lighting 14 10 22 16Audio and TV 13 10 15 19Cooking 13 10 13 17Heating 10 03 10 25

352 Other Types of Loads

In [34] some real load profiles of different load types are given Since only the

residential loads are interesting the combination of other types are distributed in the

network As an example Fig 311 shows different loads profiles (industrial public and

agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture

Figure 311 Aggregated load profiles on a random bus of other types of loads -The curve is consistent with the information in report [34]

353 Actions Applied in AD Dimension

In the future DN a large amount of smart metering devices are installed which

provides customers the possibility to behave more positive and to get involved in the

energy markets At the same time customers are more aware of their cost of electricity

as well as the relevant environmental issues especially after suffering some extreme high

prices in recent years (eg price spikes of 1 000 eMWh on 22nd Feb of 2010) A survey

conducted at Maingate shows that a significant portion of the Swedes would change

their load profile if the electricity price and their consumption were displayed on an

hourly basis [1] New legislations and plans in Sweden could increase the interests of

interaction even further To evaluate the consequences of these changes 1 a model of the

flexible load is implemented by performing three main factors (ie price sensitivities

small-scale productions energy efficiency actions) [1] These factors are described more

fine grained below

3531 Price sensitivity

In our case price sensitivity implies that retailers could shift or shed the load of different

appliances in households with respect to the electricity price Whether the price sensi-

tivity action can be performed depends on two factors one is how large the electricity

price varies the other one is the acceptance of the customers to shift or shed one or

more appliances Fig 312 shows the assumed customer acceptance

3532 Energy efficiency actions

The Swedish Energy Agency Energimyndigheten initiated a program that helps some

companies to be successful in their efforts to perform energy efficiency actions There is

no doubt that these actors can both save money and energy by executing these actions

A series of new measures to improve energy efficiency are proposed by European

Commission in June 2011 [7] To save energy and to reduce the emission of greenhouse

1We do not study the exact impacts of demand tariffs

26


Figure 312 The acceptance of customers on different appliances - [19]

gases some products and actions are recommended to improve the efficiency of energy

consumption For example to replace light bulbs with Compact Fluorescent Lamps

(CFLs) at home or in public areas to use energy-efficient housewares to enhance the

insulation of buildings Subsequently the reduction of consumption can be ob-

tained However different actions (like the change of lamps) may lead to other negative

results (eg change of power factor harmonics introduced in the network etc) When

considering an individual household the change is tiny which can be ignored compared

with consumption on the bus

3533 Small scale production ndash PV panels

Small scal production is considered as the production units that have a power output

of typically a couple of kWrsquos and are connected directly to the loads (like PV panels

micro CHPs)

A few decades ago limited by high cost solar photovoltaic is not that prevalent as

nowadays The price of PV cells has significantly been reduced while the efficiency is

increased due to the new technology advancements and the manufacturing process [48]

According to [45] the electricity production of solar in Sweden is about 200 MWh in

2009 And a variety of forms of grants and subsidies for solar cells in Sweden promise

a blooming trend of their development To interpret this trend into the thesis work

the PV model is created as the small scale production to indicate its influence on the

residential load profile Data are collected from [49] where the sizes of PV panels on the

roofs are determined as 20 m2 for each house and 3 m2 for each apartment respectively

A PV cell can produce current under its exposure to the sun Thus it is reasonable

to model it as a diode together with an equivalent current source depending on the

27


Figure 313 The Equivalent Circuit Diagram of Photovoltaic Cell

environment temperature and irradiation of the sun (see Fig 313) In some papers

instead of a current source the author modelled the PV cell as a voltage source of which

the output voltage is stablized around the system voltage and the output current are

influenced by the environmental temperature and the irradiation of the sun [48]

VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)

Iph = CI middot Isc (317)

IPV = IC middotNS (318)

VPV = VC middotNP (319)

where

NS is the amount of series cells in a panel

NP is the amount of paralleled cells in a panel

VC is the output voltage of a PV cell

IC is the output current of a PV cell

Iph is the photocurrent produced by the semiconductor in the PV cell

Isc is the short-circuit current of the equivalent circuit

Io is the reverse satiation current of the diode (00002A)

RS is the series resistor in the equivalent circuit to reshape the I V curve according to

the open circuit voltage

A is a curve adjusting factor to fit the model curve to the real one

k is the Bolzmann constant as 138times 1023 JK

Tref is the reference operation temperature given by the manufacturer

e is the quantity of a single electron as 1602times 10minus19 C

28


CV is the coefficient of voltage caused by the temperature and the irradiation

CI is the coefficient of current caused by the temperature and the irradiation

From [50] the coefficients of the voltage and the current CV and CI can be described

by two decoupled part temperature dependent coefficients and irradiation dependent

coefficients as

CTV = 1 + βT (Tref minus Tx) (320)

CTI = 1minus γTSref

(Tref minus Tx) (321)

CSV = 1minus βTαS(Sref minus Sx) (322)

CSV = 1minus 1

Sref(Sref minus Sx) (323)

where

Sref is the reference operation irradiation given by the manufacturer

βT is the PV cell output voltage versus the temperature coefficient

αS is the slope of the change in the temperature due to a change of the solar irradiation

level

γT is the module efficiency

The V-I characteristics curve is shown in Fig 314

Figure 314 V-I Feature Curve of a PV cell

It is obvious that the PV production is mostly affected by the solar irradiation

and temperature The production in the winter is very low compared to the sum-

mer conditions Some seasonal factors are introduced in the simulation to reflect these

effects The PV panel production is estimated by applying historical irradiation data

from NASA as shown in Fig 315 [51]

29

36 Estimation of the Development of DERs and the Changes of Activitiesin DNs

1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex

Figure 315 Historical data of Clearness Index on Gotland - Clearness Index isproportional to the production of PV cells [kWm2d]

36 Estimation of the Development of DERs and the Changesof Activities in DNs

As mentioned a lot of targets related to energy and environment are set The most

important target is the EU 202020 targets (see [2]) The targets as the short-term

plan are applied and need to be met in 2020 while the long-term planning can be ex-

tended to 2050 Thus it is necessary to estimate how the electricity market and the

future DNs look like

In the annual report Energy in Sweden in 2010 [45] the new climate and energy

policies are presented These are (i) The proportion of energy supplied by RES should

be 50 larger than the annual energy consumption in Sweden by 2020 (ii) Vehicles in

Sweden should be independent of fossil fuels by 2030 (iii) The efficiency of energy use is

required to be improved to reduce 20 in energy consumption between the years 2008

to 2020 (iv) 40 reduction in greenhouse gas emissions needs to be met by 2020 in

comparison with 1990 while the target no greenhouse gas emission by 2050 is set for

a longer vision

Aiming at these targets the changes in the future DNs are implemented in accor-

dance with the three dimensions in scenarios Table 38 gives a first idea of these future

developments

Table 38 Estimation of evolution of DERs

DER Short-term Long-term

DG 30 of the peak load ge50 of the peak loadEV 20 of total vehicles 50 of total vehiclesAD 20 of total customers 50 of total customersADenergyefficiency ndash 20 reduction of load

30

Chapter 4

Construction of the SimulationToolbox

This chapter focuses on the modelling of DNs and DERs based on the theory stated in

the previous chapter The radial and meshed network models are presented in the first

section where some important parameters are listed in table form The algorithms and

parameters for the DER models are given in the following sections

41 Network Model

In Simulink two different network models are constructed To capture their static

characteristics both generation and loads are constituted as Load blocks in Simulink

ie generation is indicated as the negative input and the consumption as the positive

one To detect the voltage and power flow on each bus Scopes are added with some

sufficient Transformation blocks Other components (eg switches) and functions can

be joined into the model for further study The network parameters are based on the

report [13] (see Chapter 3) and summarized in Section 45 The topologies of these

networks are shown in Fig A5 and Fig A6 respectively In these two networks

the number of individual residential loads are about 3800 (radial) and 1800 (meshed) 1

Detailed data concerning these two networks can be found in Table 41 Fig A7 and

Fig A8 in Appendix

Table 41 Comparison of demonstrative parameters of DN topologies

Topology Voltage Level Buses Loads Generators Referring Network Peak Load

Radial 60 kV10 kV 11 7 3 Bornholm [37] 15MWMeshed 60 kV10 kV 11 8 2 German rural [13] 20MW

1Numbers of loads are defined according to the report [13] corresponding to the real conditions indifferent types of Swedish areas in which about 400 residential loads are connected to each load point

31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]

Figure 41 Wind power production on 24-hour base

A cluster of wind power plants from one wind wind park is modelled as an aggre-

gated generation unit connected to the MV-DNs at the production points Their pro-

duction profiles are depicted on an hourly basis and their volumes are collected from

[41] (source wind production data of Gotland in year 2010) To capture the features

of wind generation the mean values and deviations are calculated and it is assumed

to follow a normal distribution with high deviations [52] An example of production

profiles in four different seasons are shown in Fig 41 The potential production of

wind power is determined by the penetration level of DG microDGpen ie the ratio between

the installed DG capacity and the peak load The aggregated wind generation and their

sizes are presented in Table 42 with respect to the peak load The unit names can be

found in the topologies of network models (See Fig A6 and Fig A5)

Table 42 Location and the penetration level of wind power

Network Radial network Meshed network

Unit Name G1 G2 G3 G1 G2Penetration 2

7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model

431 Algorithm of Modelling

EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1

Figure 42 Characteristics of EVs - Assign characteristics by different classes

There are two types of EVs studied in this paper ie Pure Battery Electric Vehi-

cles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) The total number of EVs

is in proportion to the number of households in the studied area defined by microEVpen It

is assumed that if one EV leaves the geographic area reflecting the studied distribution

networks an EV with the same features will enter

The EV fleet is defined by driving patterns type of vehicles and the availability of

charging facilities of which the statistics of the parameters are described in the section

34 The EV fleet is randomly split into different classes as shown in Fig 42 With the

categorized properties the EVs are performed on the state transitions

Three states for EVs are set rdquoRunning (0)rdquo rdquoCharging (1)rdquo and rdquoParking (2)rdquo

The initial states of all EVs are assumed to be at home and charging In each time

step according to the last state of the EVs different transition procedures are applied

The State of Charge (SOC) power consumption duo to charge remaining trip length

and numbers of completed trips are updated at the beginning of each iteration The

33

43 EV Model

algorithm is described by the flowchart in Fig B2

In the block rdquoPower and SOC updaterdquo (Fig B1 (d)) the following steps are imple-

mented

bull if the EV is charging

PCharging(busit+1 t+ 1) = PCharging(busit t) + Chargingi

SOC(i t+ 1) = SOC(i t) + ηCharging lowast Chargingi middot tCapi

bull if the EV is running

SOC(i t+ 1) = SOC(i t)minus vi middot∆t middot ConsiCapi

RTL(i) = RTL(i)minus vi middot∆t

where

vi is the speed of the EV i

PCharging(busit t) is the equivalent load of charging on busit at time t

Chargingi is the load of EV i according to the type of charging now

SOC(i t) is the State of Charge of EV i at time t Capiand Consi are the Capacity

[kWh] and Consumption [kW km] of EV i respectively

RTL(i) is the remaining trip length [km] of EV i in current trip

In the block rdquoTransition of Statesrdquo different strategies are applied to different original

state (transitions in Fig B1 )

To make the algorithm more easy to follow some boolean indices are set

bull prtl is 1 when RTL(i) is larger than 0

bull psocfull is 1 when SOC(i t) is smaller than 1

bull psoc is 1 when the vehicle can support the remaining length of the trip (if the

vehicle is PHEV psoc = 1)

bull ptime is 1 when the departure time is arriving

In each cycle the state rdquoRunningrdquo is prioritized When prtl psoc and ptime are 1 (ie

when a EV is supposed to start and there is enough power to run the remaining trip)

then the next state of EV i is rdquoRunningrdquo rdquoChargingrdquo will be applied when either of

the aforementioned premises is not satisfied if there is charging facilities available and

psocfull is 1 In cases that EV does not have sufficient power to drive to the destinations

it is either in the state of rdquoChargingrdquo if there is charging facilities available on sites

or in the state of rdquoParkingrdquo if not The charging time is dependent on the remaining

time before the next trip and the type of charging facilities If the EV cannot run or

34

43 EV Model

get charged then the state of EV i will be set as rdquoParkingrdquo For observations states

location and SOC in each time instant for every EV are recorded in matrices together

with the equivalent load matrix

432 Parameter Sets for Simulations

Table 43 Allocation of characteristics of the EV fleet

Network PrVCVSpeed Consumption Trip Length Trip Type[kmh] [kWkm] [km] 1 2 3 4

Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10

Allocation of parameters for different types of EVs

The percentage of PrVCV and PHEVBEV are random numbers following a normal

distribution so as departure time of each trip and the capacity of battery in each vehicle

Trip lengths of all trips follow a log-normal distribution Table 43 and Table 44 shows

characteristics of the EV fleet

Due to the effect of temperature the consumption of the EVs vary Hence the

common seasonal factor is introduced to reflect the effect of temperature to some extent

Possibility of charging and charging type

The availability of the charging facilities determine the possibilities to be charged in

Table 44 Capacity of Battery of each PHEVBEV[kWh]

Type Cap Mean Cap Dev

PHEV 9 3BEV 25 5

the model If the available charging duration is less than 1 hour there is no chance for

charging (ie no fast charging utility) if the available charging duration is less than 4

hours and more than 1 hour the possibility of charging is 03 and the charging type is

standard charging if the available charging duration is even longer or the EV is at home

( the initial place) the charging possibility is 1 and the charging type is slow charging

Commuting patterns and corresponding probabilities (see Table 45)

After studying the customersrsquo behaviour a number of trip types are designed with

different possibilities as shown in Table 43

35

44 AD Model

For PrVs four possibilities are considered

bull Two trips a day One is in the morning and the other one is in the afternoon (go

to work and go back home)

bull Three trips a day Except for those two trips there is one extra trip for leisure

after work

bull Four trips a day Except for those two trips there are two extra trips for leisure

at noon

bull No trip in the whole day

For CVs three possibilities are considered

bull One trip a day running since morning

bull Two trips a day One is in the morning the other one is in the afternoon


Table 45 Trip Types of different types of EV[27]

Type of Vehicles Type of trips Departure time Possibility

PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005

433 Individual Results

By applying the algorithm stated above the simulation result can be derived as shown

in Fig 43 In Fig 43 (b) we can observe that the critical situation appears in the

evening around 8 pm when most of the vehicles drive back home and start to charge

There are two dips of the equivalent load curve at noon since some vehicles drive off

for lunch and cannot connect to the grid

44 AD Model

To evaluate the consequences of the changes on the demand side a general model of

the flexible loads is developed according to the discussion in Chapter 3 [1] with the con-

sequence as a reshaped load profile (see Fig 44) Smart meters control the operation

of the appliances by reading the electricity price on an hourly basis DCAD converters

transmit the power production of PV panels to supply consumption of appliances and

36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

run(0)charge(1)park(2)

Sta

te

(a) The state of a certain EV in the simulation

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]

(b) Equivalent load curve of EVs in Bus4

Figure 43 Individual Results of the model of EVs

Figure 44 Typical structure of a house as a flexible demand - residential applianceswith smart meters PV panels and DCAC Converters [49]

to feed power back to the grid to some extent

The original residential load profiles are given by [12] The structure of the mathe-

matical model of AD is given in Fig B3 Properties such as location (at which bus)

type (apartment or house) are assumed to be random The original load profiles are

reshaped if and only if the three independent factors affect the customersrsquo behaviour

441 Price Sensitivity

AD will change the electricity consumption (eg from heat pumps) with respect

to the electricity price from the day-ahead market with a purpose to reduce energy

bills We assume that consumers are price sensitive and proper metering technology

and market contracts are available The electricity price changes on an hourly basis

which can be read by the metering system The price sensitivity strategy is defined as a

37

44 AD Model

piecewise function that shifts or sheds consumption of prioritized residential appliances

based on customersrsquo preferences

Stochastic hourly prices are generated based on the electricity price data from Nordic

energy market (Nordpool) for the time period 012009 to 102011 [53] The random

coefficient is set to 001 The electricity price is modelled as a normal distribution and

is dependent on the price of the previous hour These two factors are assumed to have

an equal effect on the price setting

Figure 45 Appliances investigated in the strategy

Different groups of appliances should be shifted or shed when the electricity price

crosses a certain value based on the designed strategy For example when the electricity

price reaches 10 above the average price the tasks of tumble dryers supposed to start

at this particular instant will be postponed for 6 hour when a lower electricity price

can be expected Thus the cost of electricity is reduced A summary of the patterns

of different house appliances is presented in Table 46 Fig 45 illustrate the size of

different appliances

442 Energy Efficiency Actions

Energy efficiency actions aim to save energy by implementing solutions to reduce the

consumption of the households (eg improving the insulation of the house updating

house appliances etc) The efforts of energy efficiency actions can significantly reduce

38

44 AD Model

Table 46 Price sensitivity strategy for appliances [19][54]

AppliancesStrategy

priority control price limit

Dish Washer 1 postpone for 6h 105 ndashTumble Dryer 2 postpone for 6h 110 ndash

Washing Machine 3 postpone for 6h 115 ndashHeating 4 postpone for 1h amp cut 30 off 120 - 500 1h

Kitchen Appliance 5 postpone for 1h 500 1h

the total consumption in households An optimistic study shows that the consumption

can be reduced by 80 [55])

The consumption in the future will increase due to the gain of appliances in house-

holds On the contrary the energy efficient actions will lead to the reduction of con-

sumption Hence in general it is reasonable to eliminate these two factors during the

modelling for the short-term case In the long-term vision by applying a lot of actions

we assume that the size of load is lowered (see Table 38) Additional effects is open for

discussion at the present stage ηeff denotes the improved energy efficiency as defined

as

ηeff =Loadnewres minus PPV

Loadorigres

(41)

where

Loadnewres is the new residential load [kW]

PPV is the PV production [kW]

Loadorigres is the original residential load [kW]

In the project rdquoOne Tonne Liferdquo [55] a family lives in a house with all environmental-

friendly and energy-efficient appliances foods building materials EVs smart tempera-

ture management system and PV panels The footprints of their CO2 emission as well

as energy consumption shows that the efforts of energy efficiency actions decrease the

total consumption by 80 Hence we will simulate this extreme situation to find out

interesting points compared with other cases (eg when the house is almost isolated

from external grid is it still interesting to apply price sensitivy actions)

443 Small Scale Productions

Consumers have their own small production unit at their premises in our case PV

panels It is important to mention that these generators are connected to the low voltage

network (eg 400 V) and are modelled as load reduction rather than as pure production

(as in the case of DG) We assume that all the customers who are price sensitive own

a PV panel as well The main purpose is to decrease the energy cost of the households

39

44 AD Model

The interest in small scale production is increased due to environmental awareness and

potential cost reduction ie (price times reduced load + income from sold energy)minus invest-

ment and running costgt 0 PV energy production is estimated by applying historical

irradiation data from NASA [51] In order to eliminate the fluctuation of PV we as-

sume that a low-pass filter is connected with PV to smooth the wave The production

of electricity is proportional to the area of PV panels in each bus


By applying the strategies stated above three actions are applied on the conventional

residential loads Stochastic result of the loads (one is apartment and the other one is

house) are obtained and the difference between old curves and new ones is shown in

Fig 46 In the figure we can find that different groups of loads are shifted or shed

from day-time to night A portion of the consumption is kept constant due to the fact

that the electricity price does not hit a sufficiently high value or the appliances does not

participate in the strategy

40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]

Fixed (Lighting Audio amp TV Computer Cooking Other)

Dish washer

Tumble dryer

Washing machine

Heating amp water heating

White goods

Total

(a) Difference in hourly load curve (Apartment with two people Workdays)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total

(b) Difference in hourly load curve (House with two people Workdays)

Figure 46 Structure of the difference between original and reshaped hourly load curve

41

45 Summary of the Parameters


The most important modelling parameters are listed in Table 47

Table 47 Modelling Parameters

Parameter Numerical value Description

microDGpen 10 - 80 the penetration level of DGs (ratio between ratedproduction and peak load)

microEVpen 0 - 70 the penetration level of EVs (ratio between numberof EVs and total numbers of vehicles)

microADpen 0 - 100 the penetration level of ADs (ratio between num-ber of flexible loads and total number of residentialloads)

ηeff 0- 80 the proportion of size of load by applying energyefficiency actions

timeh 1 24 hour [h]

n3800 (radial) numbers of residential loads and private vehicles

in the network1800 (meshed)Pwind historical data wind production

P ratewind7 (radial)

rated wind production (installed capacity) [MW]15 (meshed)

Speed Table43 speed of electric vehicles [kmh]Cons Table43 consumption of electric vehicles [kWhh]TripL Table43 the length of one trip [km]TripT Table45 and 43 trip typepPrV 0 1 the index marking CV (0) and PrV (1)pEV 0 1 the index marking BEV (1) and PHEV (0)

TempEV Table 37 temperature coefficientCapEV Table44 the capacity of battery in EVs [kWh]SOC 99 (SOC0) State of Charge of EVs []LoadEV equivalent load of EVs [kW]priceav average price [SEKWh]price historical data hourly market price [SEKWh]phs 0 1 the index marking house (1) and apartment (0)pps 0 1 the index marking price sensitivity (1) and not (0)

Tempap Table 37 temperature coefficient of different appliancesLoadres historical data [46] residential load [kW]Loadind historical data [34] industrial load [MW]Loadpub historical data [34] public and agricultural load [MW]

Loadorigres original residential loadLoadnewres new residential loadPPV historical data PV production

Power factor 09

42

Chapter 5

Results and Analyses

In this chapter the simulations are performed based on the set up of scenarios and

algorithms introduced in the previous chapters In the beginning the whole simulation

process is explained Subsequently the simulation results are presented and discussed

in three phases separately

Generally three key variables are organized for different scenarios as shown in Fig

DG

EV

AD

+ +

point in DER space season network

Figure 51 The organization of scenarios - Different scenarios in different seasonssimulated in different networks

51 The foundation of the Simulink model is a collection of several historical data sets

eg day-ahead Swedish electricity prices typical load profiles in Sweden typical driv-

ing behaviour of vehicle owners in Sweden recorded solar irradiation data in Gotland

and historical wind production in Gotland etc Each scenarios have 8 sub use cases

performed in two different network models and in four different seasons Subsequently

the targets of the simulations the voltage and power losses are obtained and discussed

to forecast the problems in the system operation (see Fig 52) For each scenario the

time horizon is set to 24 hours The time step of EVs is 10 minutes while that of other

dimensions is 1 hour For synchronizing the time step shrinks into 10 minutes for DGs

and ADs in Simulink

43

51 Simulation Process

$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3

Figure 52 The study procedure of simulations - Bottom layer ndash major variables indifferent cases


$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20

Figure 53 The simulation process - Results are drawn in each phases

The whole simulation process is constituted by three different phases as shown in

Fig 53 Data of three dimensions is executed in advance as the basis of simulations

in the network In the second phase three scenarios are implemented consisting of 8

cases within 2 DNs and 4 seasons The reference scenario indicates the condition of the

present DN (ie no AD no EV and limited DGs) Two scenarios with combination

of different penetration levels on 3 dimensions are created on the basis of envision of

the DN structure in the future These cases are quantified as a short-term vision (eg

year 2020) with more penetration of DERs and a long-term vision (eg year 2050) with

much more DER participants In the third phase the effects of an individual dimension

are obtained by sensitivity simulations to indicate the weight of the influential factors

of each dimension To avoid the effect of the other factors the cases are conducted in

the radial network long-term case spring (see Table 51)

44

52 Phase 1 ndash Simulation of Individual Dimensions

Table 51 Simulation Scenarios

Basic Cases Scenario Points in scenario space

4 Seasons2 Networks

Reference microDGpen = 10 microEVpen = microADpen = 0

Short-termmicroDGpen = 30 microEVpen = 20

microADpen = 20

Long-termmicroDGpen = 50 microEVpen = 50

microADpen = 50 ηeff = 20

Sensitive Simulation Dimension Case ID Points in scenario space

SpringRadialLong-term

DG1 microDGpen = 0

2 microDGpen = 80

3 microDGpen = 80 amp Pwind = P ratedwind

EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80


During the simulations all components are modelled as consumption or generation

that changes over time The size and the shape of the power curve change via altering

parameters Since these curves representing the DER components in the toolbox de-

termine the operation condition in the DNs it is important to look upon the results of

individual dimension to discover the underlying causes of the phenomena Fig54 is

an example of the consumption and DG production on an hourly basis In this case the

installed capacity of DG is about half of the peak load in the network and the actual

generation is just around 30 due to the variation of the wind speed The consumption

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production

Figure 54 Allocation of different load profiles - in radial network long term spring

45


peak occurs at around 1300 when the contribution from the EVs is comparably large

The injected power of every bus can be derived from the difference between the DG

production and the consumption as shown in the example Given the injected power in

the system we can calculate the power flow afterwards

Spring Summer Autumn Winter0

1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm

Figure 55 Total wind production in the radial network - Hourly production infour seasons

DG dimension Due to different penetration levels between the cases the wind pro-

duction curves are different in size From Fig 55 we can see that wind production

is largest in the winter time The hourly production is independent from neighbouring

hours In a short period (eg one day) the wind production varies dramatically


500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]

Short Term (Radial) Long Term (Radial) Short Term (Mesh) Long Term (Mesh)

Figure 56 Total consumption of EVs in the network - daily curve in four seasons

EV dimension The equivalent load curve of an random EV fleet in the short-term

cases and the long-term cases are shown in Fig 56 The stress of the EV load on the

power system increases when the numbers of EVs in the studied area increase The

peak load occurs at around 1800 ndash2200

46

53 Phase 2 ndash Estimated Use Cases

AD dimension Based on the historical data of the electricity market prices price

curves for four seasons are produced Electricity prices in the winter are highest through-

out the whole year due to the large demand of heat and less hydro production

As for the EV fleet the total residential load curves in the radial DN are plotted in

Fig 58 It shows that the negative consumption appears in the long-term case due to

the PV energy production The power flow is reversed at noon

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]

Spring Summer Autumn Winter

Figure 57 Electricity price - Electricity price in Sweden from Nordpool [53]


minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term

Figure 58 Total consumption of residential customers (radial network) - byshifting load and installing PV panels on the roof


531 Results of Cases in the Radial Network

By running simulations in Simulink the operation of the whole network is obtained

Fig 59 is an example showing the voltage changes over time in each bus It is observed

that the voltage profile in Bus2 (the radial network) has insignificant voltage drops and

variations while those on Bus5 and Bus32 are of worse condition The reason to this is

a long electrical distance to these points from the external grid The comparison of the

47


81

82

83

84

85

86

87

88

89

9

Bus2 Bus21 Bus22 Bus3 Bus31 Bus32 Bus4 Bus41 Bus42 Bus5

Voltage flu

ctu

ation [kV

]

(a) Voltage distribution [kV] (in radial network long term spring)

(b) Topology

Figure 59 Voltage condition in Radial Network

48


voltage profiles on Bus5 is summed up in Fig 510 Another noticeable phenomenon

is that in the radial network Bus42 has small voltage drops It is because Bus42 is

connected to Bus3 with a cable where the voltage drop can be compensated by reactive

power The nominal values are the mean voltage in the reference cases Power losses in

the network are also obtained from the simulations (see Fig 511)

In Fig 510 the voltage varies more actively when the integration of DERs increase

whereas significant fluctuations appear in the long-term cases There is a notable voltage

drop by 7 in the winter time compared to in other seasons Power losses share the

same trends as the voltage profiles as shown in Fig 511

09

092

094

096

098

1

102

104

106

108

Ref Short Long Ref Short Long Ref Short Long Ref Short Long


Figure 510 Voltage on Bus5 - in radial network different scenarios

0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]


Figure 511 Total power losses in the network - in radial network different scenarios

49


86

87

88

89

9

91

SS1 SS2 SS3 SS4 SS5 SS6 SS7 SS8 SS9 SS10

Voltage flu

ctu

ation [kV

]

(a) Voltage distribution [kV] (in meshed network long term spring)

(b) Topology

Figure 512 Voltage condition in Meshed Network

50


532 Results of Cases in the Meshed Network

In the meshed network the voltage profiles tends to be more heterogeneous due to

the fact that there are more connections among buses comparing with the topology of

the radial network Fig 512 shows the voltage curve of the meshed network during

spring in the long-term case From the figure we can see that Bus SS10 is the most

distant point in the network and sharing the worst experiences of voltage profiles in the

network Thus we choose this bus for the further observation as shown in Fig 513

Power losses in the meshed network are also obtained from the simulations (see Fig

514)

Still the most serious voltage drop appears in the winter time and more DER

components introduce more variations on the voltage profiles However the range is

comparably smaller than in the radial network The value of the power loss in the

meshed network is about 90 higher than in the radial network

097

098

099

1

101

102

103

104

105



Figure 513 Voltage on Bus SS10 - (pu) in the meshed network different scenarios

51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]


Figure 514 Total power losses in the network - in the meshed network differentscenarios

52


533 Analysis

5331 Impacts on voltage fluctuations

In Standard EN50610[20] it is defined that 95 of the 10-minutes average values of

the voltage measured should be within the range of 10 of the nominal voltage under

normal operation The largest voltage fluctuations on Bus5 or Bus SS10 are summarised

in Table 52 below

Scenario SeasonRadial Network Meshed Network

Voltage [pu] Time [Hr] Voltage [pu] Time [Hr]

Reference

Spring 1017 5 0989 18Summer 1040 3 1015 1Autumn 0982 20 0988 17Winter 0912 8 0975 16

Short-term


Long-term


Table 52 Summary of Voltage Fluctuation

Influence of the DN Topology Comparing the results in the meshed network with

those in the radial network we can find that the conditions in different points in the

meshed network are much more homogeneous than in the radial network And the

fluctuations of voltages of all points in Meshed Network are lower than those in Radial

Network The meshed network can provide better voltage quality and power supply

However when observing the power flow in the meshed one the direction changes with

time to balance the consumption and production in every location All the points of the

network are affected by each other The fact makes the network operation and planning

more complicated

In our case more attention should be paid on the radial network as noticed in Table

52 In Long-term and Spring and Short-term and Spring cases the voltage is close to

the limit in a short period of time which may damage operating equipment connected

to Bus32 or Bus5 In the radial network load points are more independent compared to

the loads in the meshed network and usually there is only one way to supply the power

Further large transmitted power and long transmission lines will do harm to the voltage

quality

53


Apart from the network topologies comparing Bus32 with Bus22 in the radial network

the voltage profile in Bus32 is much severe than in Bus22 caused by the different length

of transmission lines to the strong point in the network However the condition of

Bus42 is better than the one of Bus32 since cables instead of overhead lines are installed

along the trace from Bus3 to Bus42 Underground cables have tremendous advantages to

improve appearance and to maintain qualified voltages because in all tests the power

factor of generators and loads is about 09 and all components are inductive which

require reactive power to compensate If cables and overhead lines are managed well

the voltage profiles everywhere in the network are improved

Influence of Season In the project the only concerned parameters of time is season

Four seasons in a year are represented by their typical months (ie April for spring July

for summer October for autumn and January for winter) In Table 37 the seasonal

coefficients of residential loads are defined So are those of DGs and EVs For different

loads in the networks consumption is higher in the winter and lower in the summer

linked to high value of voltage in winter and low in summer In Fig 510 and Fig

513 it is easily found that the lowest voltage occurs in winter and the lowest voltage

will be close to the lower voltage limit during some short periods (ie in 9 am and 8

pm) While the generation is large and consumption is comparably low the voltage

may hit the highest value (ie in Autumn 5 am) From Table 52we can conclude that

special attention must be paid to the period when the mismatch between consumption

and generation is most significant In addition attention must be paid to Spring and

Autumn since the production or consumption of DERs varies a lot compared in Summer

and Winter Large deviations are illustrated from the results If the participation of

DERs increases there might be some critical situations

Since the meshed network is much more robust than the radial network the variation

of voltage is not that serious So the frequency of extreme value situations are not fixed

for the different cases In the radial network the lowest voltage occurs at 8 am in

Case 4 (reference scenario during the winter) and meanwhile at around 8 pm in the

other cases during the winter It is because that the EVs change the shape of total load

and shift the maximal load to 8 pm

Influence of Penetration Levels In our vision of development of future DNs the

penetration levels of three dimensions are increasing and referred as the scenarios rdquoref-

erencerdquo rdquoshort-termrdquo and rdquolong-termrdquo We use the mean values of voltage in reference

cases as the nominal values Comparing subgroups of different scenarios in Fig 510

and Fig 513 it can be seen that the voltage sags in the reference cases are reduced

54


in general by increasing penetration levels of DERs while the fluctuations increase as

well The worst situations in long-term cases are even more severe (see Table 52) since

high penetration levels introduce large variation of power input and output Thus the

voltage curves in long-term looks sharper and hit the worst value comparing to refer-

ence and short-term cases However in the winter the worst case in hour 20 has been

improved in long-term case due to the reason that the consumptions are decreased by

energy efficiency actions

Potential threat to the normal operation To secure the voltage quality invest-

ments must be made in additional regulating devices approximately related to how

frequently the problem occurs Hence the frequency of voltage beyond the range of

5 is accumulated to imply the potential threat of voltage variation (see Table 53)

and how serious it is The winter time is always the hash time when there is a large

possibility that the voltage goes out of the range of 5 It is observed that in long-term

scenarios the stability of voltage is the worst indicating that advanced management of

DERs is necessary Moreover the quality of the voltage profile in winter is most critical

compared with in other seasons even though DERs help bring the voltage up away from

the limit

Scenario Reference Short-term Long-term

Season SP SU FA WI SP SU FA WI SP SU FA WI

Frequency 0 0 0 82 0 0 0 77 8 8 1 36

Table 53 The Extent of Voltage Fluctuations in Radial Network

5332 Impacts on power losses in DNs

The largest power losses in the both networks in different seasons and cases are

summarised in Table 54

Influence of Season From Table 54 Fig 511 and Fig 514 it is easily observed

that seasonal effects are very large and causes high losses in the winter and low losses

in the summer The average losses in the summer is reduced by up to around 50 of

spring and 70 of winter (radial network)

Influence of the DN Topology Power losses in the radial network are lower than

in the meshed network because the larger amount sizes of production and consumption

capacities (see Fig A7 and Fig A8) and the most effective factor on power losses is

the current in the transmission lines Hence the larger capacities cause larger current

55


Scenario SeasonPav [kW] Total Energy Lost [MWh]

Radial Meshed Radial Meshed

Reference

Spring 217 543

2942 5486Summer 118 514Autumn 232 549Winter 658 678

Short-term

Spring 202 512


Long-term

Spring 161 493


Table 54 Summary of Average Power Losses

which leads to larger losses in the network If we can reduce the current or heat over

the lines the loss is efficiently reduced Comparing the results between the networks it

is observed that the seasonal effects has a small significance since the network topology

can help to reduce the heat on each transmission line

Influence of Penetration Levels From Table 54 Fig 511 and Fig 514 we can

see that the average values of power losses in long-term cases are reduced in comparison

to the reference cases It is because that in the long-term scenario penetration levels of

DGs and ADs are increased Thus the total equivalent load in the network is reduced

However since the introduction of DERs leads to larger variation of power input and

output in the network the variation of power loss in different hours become larger and

the worst situations always appear in long-term cases In Table 54 the red numbers

show that power losses increase while DERs increase (for the radial network in the

summer) Identical results for the winter time (green figures) The reason is that during

summer wind power produces less energy than in the other seasons With the growing

consumption of EVs the difference between production and consumption become higher

which cannot be covered by the increased penetration of wind power

After summarizing all the results we can conclude that the worst condition is the case

of Short-term winter in the radial network In that case the shortage of wind power

and the increased numbers of EVs lead to a larger power loss in the network than in

other cases which is very interesting The noticeable reverse of the trend verified in the

simulation gives the preliminary idea concerning the planning of integration of DERs

56

54 Phase 3 ndash Sensitive Analysis and Extreme Cases


541 DG Dimension

0

2

4

6

8

10

12

pen = 50 pen = 0 pen = 80 pen = 80 Rated P

Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P

Figure 515 Total wind production in the network - in the radial network Long-term spring

096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]

Figure 516 Voltage on Bus5 - in the radial network Long-term spring

The first three cases with changes of parameter values in the DG dimension show

the effects of penetration of wind production in the network The last two cases indicate

the impacts of wind speed ie two extreme cases as no wind production and the

production of wind power keep producing at its maximal capacity Fig 515 shows

the wind production of these cases Fig 516 indicates the results in Simulink The

voltage drop is very serious when little wind power is produced in the network In case

2 and the normal case of long-term the introduction of wind power improve the voltage

levels in most of the hours but also increase the fluctuation of the voltage The light

green curve shows the extreme situation (referred as Case 3) We can find the voltage

rises significantly with about 10 of that in the normal case of long-term It hits the

higher feasible limit of the voltage value in almost all hours with small fluctuation Power

losses of this case are lower due to the large power production Since wind production

is kept constant at its maximal value there is very litter variation of voltage and power

losses caused by DG dimension in Case 3

57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50

pen = 0pen = 50 amp SOC

0 = 50

pen = 70

Figure 517 Equivalent load curve of EVs in the network - in the radial networkLong-term spring

094

096

098

1

102

104

106

pen = 50 pen = 0 pen = 50 amp SOC_0 = 50pen = 70

Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06

pen = 50 pen = 0 pen = 50 amp SOC_0 = 50 pen = 70

Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

Figure 518 The Voltage on Bus5 and power losses in the network - in the radialnetwork Long-term spring

Cases No4 to No6 imply changes in the EV dimension which will lead to al-

ternation of the EV behaviour Case 6 is an example showing the impacts of different

charging patterns Since the size of power of EVs are smaller comparing the size of wind

production the changes of voltage and power flow are comparably smaller than in cases

1 to 3 Fig 517 Fig 518 show the results of these cases

In Fig 517 we can indicate that the equivalent load of EVs are increasing with the

growing numbers of EVs in the network From the light green curve we can find that

the initial SOC is an important parameter which can alter the shape of curve dramat-

ically especially in the early hours of a day However Case 6 does minor harm to the

general system operation because there is not much consumption in the early morning

In Fig 518 it is observed that the penetration level of EVs is correlated with the

equivalent load of EV dimension to some extent The trend of growing numbers of EVs

in the network lead to larger voltage sags larger voltage fluctuations and larger power

losses in the network Subsequently to keep a healthy operation of the power system

especially in the evening some corresponding solutions are necessary

58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50

Figure 519 Total consumption of the ADs in the network - in the radial networkLong-term spring

05

1

15

2

25

3

35

pen = 50 pen = 0 pen = 100 pen = 50 eff = 0 pen = 50 eff = 80

Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]

Figure 520 Consumption of all kinds of loads in the network - in the radialnetwork Long-term spring

The impact of parameters in AD dimension are simulated by introducing cases

No7 to No 10 In Fig 519 the alternated power curves of the ADs are obtained From

Fig 519 we can find that in some situations the direction of power flow is reversed as

in case No10 By shifting and shedding according to the electricity price the profits of

the retailer increases but might amplify the stress on the transmission lines especially

in low-price hours The problems connected to the demand side actions occur especially

in the Hr20 - Hr21 In Fig 520 and Fig 521 we can illustrate that in Case 7 8 and 9

there is little influence on the network of the variation of parameters due to the reason

that shifted residential loads in the network are not large enough compared to DGs and

EVs However the flexible changes are enough to be applied to smooth the peak and

reduce the stress on the transmission lines The energy efficiency actions if successfully

obtained can obviously affect both the mean and deviation of voltage levels and power

59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

Figure 521 Voltage on Bus5 and power losses in the network - in radial networkLong-term spring

lossesWhen the prices are set 10 higher the red crosses show the impacts by AD

actions are larger The reduction of losses is dependent on the numbers of households

involved in AD actions However energy efficiency actions decrease the impacts since

they will reduce the size of residential loads directly

60

Chapter 6

Discussion and Future Work

61 Discussion

The simulation results presented in Chapter 5 show the potential changes brought by

large scale deployment of DERs in the DN models However the models are adapted

to a simplified and essential condition and are based on the assumptions that only

external factors will affect the deployment of DERs eg no risk of customersrsquo attitude

no improper subsidy Meanwhile the whole DNs including existing DERs are totally

without advanced management Some difficulties during the study are drawn in this

chapter The potential barriers to introduce DERs in a large scale are presented here

as well

Lack of sources for data When constructing models the most serious problems are

to find proper sources to support modelling and to provide reliable data sets Until now

there are few research studies concerning all kinds of DERs in the project So it is hard

to find the validated data from a single source More time is spent on looking for proper

sources and deciding the parameters based on the data and description Moreover due

to the multiple sources it is meaningless to indicate the exact threshold values for when

and where the critical phenomena occur

Barriers on simulation software Simulink Matlab is a good software tool for sim-

ulations in different areas However it cannot handle simulations of a normal sized DN

(ie with 40-100 buses) since the pre-defined models in the library are complicated and

call more calculations on variables which are not that interesting Meanwhile some

unnecessary parameters in the models need to be set to keep the simulation running in

a normal condition

Cost on investment One of the prompts to develop DERs is to reduce the power

transmission in the DNs and consequently reduce the cost for power losses building

new transmission lines install additional shunt capacitors ancillary services etc How-

ever the results present a not that optimistic vision on developing DERs Thus the

61

62 Future Work

investment on metering devices contracts with customers advanced management may

be even higher than that on conventional DNs These messages will definitely influence

on the decision of both the DSO and the customers in the DNs The information about

this part is not considered in the thesis work

Technical problems Since all DERs are integrated at MV-level detailed information

of the DERs is hard to be interpreted such as the reliability of production the dynamic

behaviours and some parameters concerning the regulation (eg power factor power

reserve etc)

62 Future Work

In the master thesis project most of the questions in Section 121 are answered

by conducting DER toolbox simulations and analyses Some potential operation prob-

lems are extrapolated from the results (eg the worst case of short-term scenario in

radial winter condition) To solve these problems and to ensure the quality and safety

of power supply some study of aggregation approaches (such as [21][56][23]) could be

accomplished based on the analyses in future studies

Wind production as the main DER sharing a large response of the operation is

intermittent and introduces large voltage fluctuations As the development of communi-

cation technology in the power system as well as the increasing interest of rdquoAggregatorrdquo

concept the contribution of DERs could be larger by applying proper aggregation ap-

proaches Flexible demand-side management to cover the mismatch between power

production and consumption in the DN could contribute to the steady operation of

power system Another interesting aggregator could be executed in the EV dimension

by managing the charging behaviours of EVs to change the shape of load curve when

there is a large penetration of EVs in the DN

These aggregators can be modelled in the program as well afterwards Results will

subsequently feedback to the study of aggregators Thus business concepts and techni-

cal issues will not be isolated any more

Another aspect of efforts based on the thesis work could be connecting the whole

program with advanced communication system eg real-time simulator to get more

accurate results And dynamic performance of DNs might provide more interesting

problems to study In the toolbox although at the first time some dynamic models of

generators are implemented DERs are simplified as changeable inputs or outputs in the

end Dynamic models and more functions could be added into the existing program

Finally in the thesis work almost no historical data is from the same source There-

fore some large error may appear in the results A pilot network is necessary to get

more adequate results Some parameters in the program are considered independently

however in fact there might be some underlying correlations which could be found

when data come from the same source

62

Chapter 7

Conclusion

This paper presents quantitative analysis of the potential changes brought by large

scale deployment of DERs in European distribution networks The contribution are

two folded First of all we created models for DG EV and AD for the condition and

prerequisites of Sweden Secondly we performed quantitative simulations of different

DER deployment in two different network models The output of the simulations re-

flected the voltage variations and power losses incurred by the DERs In general the

introduction of DER could reduce the power losses and voltage drop by limiting power

transferred from the centralized and remote sites However it must be noted that the

opposite results can happen due to the uncertain characteristics of the DERs The im-

pacts of the influential parameters in the models are evaluated in the sensitive analysis

It is tempting to apply our models and methodology to study other DER development

scenarios in the future The research output could potentially be used to foresee the

potential benefits and challenges for the upcoming smart grid business cases (eg the

Aggregator role [21][56]) related to the deployment of DERs

63

References

[1] C Sandels ldquoWork document Generating scenarios for the future distribution net-

worksrdquo 2011 1 2 26 36

[2] ldquoThe EU rdquo20-20-20rdquo targetsrdquo httpeceuropaeuclimapoliciespackage

index_enhtm 1 30

[3] ldquoDatabase of State Incentives for Renewables amp Efficiencyrdquo httpwww

dsireusaorg 1

[4] ldquoThe Energy Status And Policy of Chinardquo httpwwwgovcnzwgk2007-12

26content_844159htm 1

[5] ldquoUk government feed-in-tariffsrdquo httpwwwdeccgovukencontentcms

meeting_energyRenewable_enerfeedin_tarifffeedin_tariffaspx 2012

1

[6] ldquoEnergy roadmap 2050rdquo tech rep European Commision 2011 1

[7] C of the European Communities ldquoAction plan for energy efficiency Real-

ising the potentialrdquo httpeur-lexeuropaeuLexUriServLexUriServdo

url=COM20060545FINENPDF 2006 1 26

[8] Integration of Distributed Generation in the Power System Wiley-IEEE Press

2011 httpeuwileycomWileyCDAWileyTitleproductCd-0470643374

html 2 3 6 9

[9] P S Georgilakis ldquoTechnical challenges associated with the integration of wind

power into power systemsrdquo Renewable and Sustainable Energy Reviews vol 12

pp 852ndash863 2008 2

[10] K Clement-Nyns and e a E Haesen ldquoThe impact of charging plug-in hybrid elec-

tric vehicles on a residential distribution gridrdquo Power Systems IEEE Transactions

vol 25 pp 371ndash380 2010 2

[11] M Braun ldquoTechnological control capability of der to provide future ancillary ser-

vicesrdquo International Journal of Distributed Energy Resources vol 3 no 3 2007

2 5

64

REFERENCES

[12] ldquoEuropean distributed energy resources projectsrdquo tech rep European Communi-

ties - Directorate-General for Research Unit J2 2004 httpwwwfp7orgtr

tubitak_content_files271docsProje_Kataloglaridis_energy_enpdf

2

[13] e Christine Schwaegerl ldquoDevelopment of future scenarios and eu network data

collectionrdquo tech rep More Microgrid Group 2008 httpwwwmicrogridseu

indexphppage=presentationsamplimit=9999 2 9 12 16 17 18 31

[14] H Zareipour K Bhattacharya and C Canizares ldquoDistributed generation Current

status and challengesrdquo in Proc 36th Annual North American Power Symposium

(NAPS) pp 9ndash10 2004 2

[15] e Regine BELHOMME Ramon CERERO REAL DE ASUA ldquoADDRESS AC-

TIVE DEMAND FOR THE SMART GRIDS OF THE FUTURE booktitle

= CIRED Seminar 2008 SmartGrids for Distribution year = 2008 number

= 80 note = httpieeexploreieeeorgstampstampjsptp=amparnumber=

4591822rdquo 2 3

[16] Renewable and efficient electric power systems NY USA Wiley Online Library

2004 2 3

[17] K Schneider et al ldquoDistribution system analysis to support the smart gridrdquo in

Power and Energy Society General Meeting pp 1ndash8 2010 2 5

[18] J Driesen and R Belmans ldquoDistributed generation challenges and possible so-

lutionsrdquo in Power Engineering Society General Meeting 2006 IEEE pp 8ndashpp

IEEE 2006 2

[19] T N Emilia Bierlestam ldquoPeak management final reportrdquo tech rep Vattenfall

2011 2 3 27 39

[20] ldquoStandard en 50160 -voltage characteristics in public distribution sys-

temsrdquo httpwwwcopperinfocoukpower-qualitydownloadspqug

542-standard-en-50160-voltage-characteristics-inpdf 2 14 53

[21] C Sandels and U Franke ldquoVehicle to grid monte carlo simulations for optimal

aggregator strategiesrdquo in International Conference on Power System Technology

IEEE 3 62 63

[22] ldquoEnergy plan for the municipality of gotland year 2007 ndash 2010rdquo tech rep Munic-

ipality of Gotland Executive office 2006 3

[23] Q Lambert ldquoBusiness models for an aggregator Is an aggregator economically

sustainable on gotlandrdquo Masterrsquos thesis KTH - Royal Institute of Technology

2012 3 62

65

REFERENCES

[24] T Ackermann G Andersson and L Soder ldquoDistributed generation a def-

initionrdquo Electric Power Systems Research vol 57 no 3 pp 195ndash204 2001

httppdnsciencedirectcomscience_ob=MiamiImageURLamp_cid=271091amp_

user=4478132amp_pii=S0378779601001018amp_check=yamp_origin=articleamp_

zone=toolbaramp_coverDate=20-Apr-2001ampview=camporiginContentFamily=

serialampwchp=dGLbVlV-zSkzSampmd5=31179d05fabaad1d8589524657ac84c81-s2

0-S0378779601001018-mainpdf 3 6

[25] S Yee J Milanovic and F Hughes ldquoOverview and comparative analysis of gas

turbine models for system stability studiesrdquo Power Systems IEEE Transactions

on Power System vol 23 pp 108ndash118 Feb 2008 httpieeexploreieeeorg

stampstampjsptp=amparnumber=4374139 3

[26] P Georgilakis ldquoTechnical challenges associated with the integration of wind power

into power systemsrdquo Renewable and Sustainable Energy Reviews vol 12 no 3

pp 852ndash863 2008 httpusersntuagrpgeorgilFilesJ23pdf 3 19

[27] Grid for Vehicles Parameter Manual httpwwwg4veudatasParameter_

Manual_WP1_3_RWTH_101216pdf 6 22 23 36

[28] ldquoRes 2005-2006 the national travel surveyrdquo tech rep SwedishInstitiute for Trans-

port and Communications Analysis SIKA 2007 6 8 21 22

[29] S S centralbyra ldquoStatistical yearbook of sweden 2011rdquo tech rep

Sveriges Statistiska centralbyra 2011 httpwwwscbsePages

PublishingCalendarViewInfo____259924aspxpublobjid=17399 8 21

22 25

[30] M Rousselle ldquoImpact of the electric vehicle on the electric systemrdquo Masterrsquos the-

sis Kungliga Tekniska Hogskolan 2009 httpseeweb01eekthseupload

publicationsreports2009XR-EE-ES_2009_018pdf 9

[31] B W Lennart Rade Beta Mathematics Handbook CRC-Press 1997 9

[32] M H Kalos and P A Whitlock Monte Carlo Methods WILEY-VCH 2 ed


descCd-descriptionhtml 10

[33] Electrical Power Engineering Reference amp Applications India Taylor amp Francis

Group 2007 httpwwwelectricalengineering-bookcom 11

[34] M U Oskar Engblom ldquoRepresentativa testnat for svenska eldistri-

butionsnatrdquo tech rep ELFORSK AB 2008 httpwwwelforsk

seProgramomradenAnvandningMarketDesignPublications2008

0842-Representativa-testnat-for-svenska-eldistributionsnat 11

12 14 16 25 26 42

66

REFERENCES

[35] R Brown ldquoUnderground vs overhead distribution wires Issues to considerrdquo tech

rep InfraSource Technology 2007 httpwarringtonufledupurcdocs

initiatives_UndergroundingAssessmentpdf 12 13

[36] G McPherson and R Laramore An introduction to electrical machines and

transformers Wiley 1990 httpeuwileycomWileyCDAWileyTitle

productCd-0471635294html 13

[37] J Nielsen and J Oslashstergaard ldquoThe bornholm power system-an overviewrdquo

Centre for Electric Technology Technical University of Denmark 2008

httpwwwpowerlabdkuploadsitespowerlabdkmediathe_bornholm_

power_system_an_overview_v2pdf 16 31

[38] S Babaei D Steen O Carlson L Bertling et al ldquoEffects of plug-in electric

vehicles on distribution systems A real case of gothenburgrdquo in Innovative Smart

Grid Technologies Conference Europe (ISGT Europe) 2010 IEEE PES pp 1ndash8

IEEE 2010 16

[39] J Cardell and M Ilic ldquoMaintaining stability with distributed generation in a re-

structured industryrdquo in Power Engineering Society General Meeting 2004 IEEE

pp 2142ndash2149 IEEE 2004 16

[40] ldquoIEEE testfeederrdquo httpewhieeeorgsocpesdsacomtestfeeders

indexhtml 16 18

[41] ldquoGEABrdquo httpwwwgotlandsenergise 17 21 32

[42] N W Miller W W Price and J J Sanchez-gasca ldquoDynamic modeling of ge 15

and 36 wind turbine-generatorsrdquo tech rep GE-power Systems Energy Consulting

2003 19

[43] T M Inc ldquoSimulink help in matlab 2010ardquo httpwwwmathworkssehelp

toolboxsimulink 20

[44] ldquoVastas product brochuresrdquo httpwwwvestascomenmediabrochures

aspx 20

[45] Energimyndigheten ldquoEnergy in sweden 2010rdquo Tech Rep 167 Energimyn-

digheten 2010 httpwebbshopcmseSystemDownloadResource

ashxp=Energimyndighetenamprl=defaultResourcesPermanentStatic

e0a2619a83294099a16519a0b5edd26fET2010_46pdf 21 27 30

[46] E Swedish Energy Agency ldquoEnd-use metering campaign in 400 households

in sweden and assessment of the potential electricity savingsrdquo tech rep

Swedish Energy Agency Enertech 2009 httpwwwenergimyndighetenseen

Facts-and-figures1Improved-energy-statistics-in-buildings 24 25 42

67

REFERENCES

[47] A A Jalia ldquoOrganization and control of a local market based concept towards

onsite distributed energy resources based electricity supply in urban swedish res-

idential buildingsrdquo Masterrsquos thesis KTH - Royal Institute of Technology 2011

25

[48] M Buresch Photovoltaic Energy Systems Design and Installation New York

McGraw-Hill Book Co 1983 httpadsabsharvardeduabs1983mgh

bookB 27 28

[49] E S Trust ldquoSolar calculator sizingrdquo httpwwwsolarenergynetArticles

solar-calculatoraspx 27 37

[50] I H Altas and aM Sharaf ldquoA photovoltaic array simulation model for matlab-

simulink gui environmentrdquo 2007 International Conference on Clean Electri-

cal Power pp 341ndash345 May 2007 httpwwwihaltascomdownloads

publicationspapers_eng082_ICCEP_07_Capri_Italy_altas_sharafpdf 29

[51] ldquoRETScreen atmospheric science data centerrdquo httpeosweblarcnasagov

sseRETScreen 29 40

[52] J S Rohatgi and V Nelson Wind Characteristics An Analysis for the Generation

of Wind Power Alternate Energy Institute West Texas Aamp M University 1994

32

[53] ldquoNordpoolrdquo httpwwwnordpoolspotcom 38 47

[54] J Paatero and P Lund ldquoA model for generating household electricity load profilesrdquo

International journal of energy research vol 30 no 5 pp 273ndash290 2006 39

[55] ldquoOne tonne life projectrdquo httponetonnelifecom 2011 39

[56] E Peeters R Belhomme C Batlle F Bouffard S Karkkainen D Six and

M Hommelberg ldquoAddress scenarios and architecture for active demand devel-

opment in the smart grids of the futurerdquo in Electricity Distribution-Part 1 2009

CIRED 2009 20th International Conference and Exhibition on pp 1ndash4 IET 2009

62 63

68

Appendix A

Topologies and Description ofTest Networks

Figure A1 Topology of the Rural Bornholm MV Feeder

69

Figure A2 Topology of the IEEE Test Feeder

70

Figure A3 Topology of the Rural network from the Swedish reliability report

71

Figure A4 Topology of the Urban network from the Swedish reliability report

72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2

Transformer60 kV11 kV

External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7

Figure A5 Topology of radial network

B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9

Figure A6 Topology of meshed network

73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(

T( Q48-( gtU( SR( SR2(

U( Q48-( gtV( SR2( SRR(

( Q48-( 2gtU( SF( SF2(

E( Q48-( 2gtR( SF2( SFR(

V( L( 2gtU( ST( ST2(

D( L( 2gtR( ST2( STR(

$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((

9IP6( W(X+YZ( [(X+YZ( H14(A$4( Slt(

)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((

Q48-(a( gtF( 2gtFIbF( 2gtIbV(

Q48-(( gtER( Ugt2IbF( 2gtIbE(

L(a( gt2( FgtRIbF( FgtIb(

L(( gt2( RgtVEIbF( UgtIb( `c( M$140(964HG5(

456A4=4( Hc(dampbb922( (0( 20e2gt2( =c( 22b2b2(

P$-(W14( P6(]HgtCgt^( fA(]HgtCgt^( Wg66( Ng6(]HgtCgt^( I-$4c( _5B(NC(

U+3( gt2( gtR(RU(0Y( gt((( 465( (

Figure A7 Network Description of the Radial Network

74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(

Q( O86-( =D( GGC( GGQ(

R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(

UHN5( V(W+XY( Z(W+XY( 18(lt$8( [G(

)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(

JA(a( =2( P=CHbP( P=Hb(

JA(( =2( C=DSHbP( R=Hb( `c( K$180(U5lt8E7(

gt875848( gtFc(dampbbU22( (0( 20e2=2( gt4c( 22b22bCP(

N$-(V18( N5(]=B=^( f(]=B=^( Vg55( Lg5lt(]=B=^( H-$8c( _7(LB(

R+3( =2( =C(CR(0X( =(((857( (

Figure A8 Network Description of the Mesh Network

75

Appendix B

Flow charts of models

Running Parking

Charging

remaining trip lengthgt0SOCgt0

(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking

SOClt1 ampt_availabe --gt

chance to charge (p_c)

t_availabe --gt fail to charge (p_p=1-p_c)

Remaining trips-1

Remainning trips-1Update

Update

Update

Update

Update


(p_r)


(p_r)



SOCgt=1 or t_availabe --gt fail to charge

(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB

amp((2-016lt1345C8DDE

$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=

I2H6lt6lt1(06FJ2lt1(6()I2H6lt6lt1(06FJ2lt1(6(78)7(06F

No

Yes

Yes

No

(a)

(b)

(c)

(d)

Figure B1 Detailed description of blocks in the flowchart - (a) rdquoRunningrdquo (b)rdquoChargingrdquo (c) rdquoParkingrdquo (d) rdquoflowchart of Update blockrdquo pp pr and pc represent theprobability of parking running and charging

76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt

7112(gt4amp(71(gt$A762amp1((B$gtC

$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No

Figure B2 Flowchart of EVrsquos algorithm

77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C

Damp$E60=gtampgt60gtamp2

F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes

Figure B3 Flowchart of load generation procedure

78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567

ampampampampampampampampampampampampampampampampampampampampampampampamp

896lt

+

(

)

(

(

(+

-

+

+(

+

++

ampampampamp-()()amp((

=29gt

A-+AA++A+Aamp+A((A(A-))A(

))(-A)(A+()A(A)(A-

(-)(AA++A-(A+AA+A)A-

03BC4$

ampA)amp)A

amp)amp(Aamp(Aamp)Aamp)A

ampAampA-

(

(A+)A+A(-A(A(()A(Aamp)Aamp)A())A()A)A(

0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5

amp+-amp+amp+amp+amp+amp((amp(amp(-ampamp+amp+amp+ampamp+amp+(amp++ampampamp-amp)amp-amp

F95GH54$

amp)A

)A

A

A

A

Aamp

AampampA+ampA-ampA-amp+AampampA)ampAampAamp)Aamp

amp

ampamp-A+)ampAA-)ampA)A(

+

+

3I142$J4$

AAampampA

+A+

(Aamp

(AampAA+A-)Aampamp+A)ampAampA(ampA-AampampA(A)(A+)(A+)AA+A(A)A+

KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp

A(ampAA(-Aamp(ampA--A))A+A)Aampamp)A+amp-A

amp)A-Aampamp(AampA(

-A)

)A+

4H6lt

amp(ampamp)-+amp)amp)-ampamp)amp-(amp+-ampamp)-amp(amp(ampampamp-ampampamp+-ampamp-+amp()(amp((amp(ampamp)amp)+amp+)amp+-

-01231amp()+amp

012234567


896lt

ampAA+ampampAamp+A(ampampAA+-+AA+--A+)A+-

A+amp(A))ampAamp)A+)A++A--+Aamp)A--)+A-ampAamp

=29gt

ampA

A)

)(ampA+A)amp+A-amp)A)amp(Aamp)A)

-A)A-(A)))A)-A-A)ampA)amp-A)amp-A+amp(A)A-

03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+

)ampamp-A)amp-amp)A)

KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)

Figure C1 Load profiles of apartments and houses

80

Appendix D

Pre-study on impacts of DERs

Figure D1 Impacts of DGs and EVs on DNs

81


82

Appendix E

Matlab GUI

Figure E1 GUI application

83

xue fron pagepdf

Final Report_lastversion

List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data






36 Estimation of the Development of DERs and the Changes of Activities in DNs

4 Construction of the Simulation Toolbox

41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model






5 Results and Analyses






533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension

6 Discussion and Future Work

61 Discussion

62 Future Work

7 Conclusion

References

A Topologies and Description of Test Networks

B Flow charts of models

C Load Profile of AD

D Pre-study on impacts of DERs

E Matlab GUI

Abstract

There has been a large body of statements claiming that the large scale

deployment of Distributed Energy Resources (DERs) will eventually reshape

the future distribution grid operation in numerous ways However there is

a lack of evidence specifying to what extent the power system operation will

be alternated In this project quantitative results in terms of how the future

distribution grid will be changed by the deployment of distributed genera-

tion active demand and electric vehicles are presented The quantitative

analysis is based on the conditions for both a radial and a meshed distri-

bution network The input parameters are on the basis of the current and

envisioned DER deployment scenarios proposed for Sweden

The simulation results indicate that the deployment of DERs can signif-

icantly reduce the power losses and voltage drops by compensating power

from the local energy resources and limiting the power transmitted from the

external grid However it is notable that the opposite results (eg severe

voltage fluctuations larger power losses) can be obtained due to the inter-

mittent characteristics of DERs and the irrational management of different

types of DERs in the DNs Subsequently this will lead to challenges for the

Distribution System Operator (DSO)

Keywords Distribution Network Distributed Generation Electric Vehi-

cle Active Demand Power Losses Voltage Profile

Acknowledgements












ronment







advice





research

Xue Han

Stockholm March

Contents

List of Figures iv

List of Tables vi

Abbreviation vii

1 Introduction 1

11 Background 1




123 Delimitation 4



2 Method 8

21 Study Approach 8


3 Theory 11


311 DN 11

312 Components 12










i

CONTENTS





DNs 30


41 Network Model 31

42 DG Model 32

421 Wind Power 32

43 EV Model 33




44 AD Model 36












533 Analysis 53


541 DG Dimension 57

542 EV Dimension 58

543 AD Dimension 59


61 Discussion 61

62 Future Work 62

7 Conclusion 63

References 64

ii

CONTENTS





E Matlab GUI 83

iii

List of Figures



























iv

LIST OF FIGURES



































v

List of Tables




















vi

Abbreviations


vii

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

Acknowledgements












ronment







advice





research

Xue Han

Stockholm March

Contents

List of Figures iv

List of Tables vi

Abbreviation vii

1 Introduction 1

11 Background 1




123 Delimitation 4



2 Method 8

21 Study Approach 8


3 Theory 11


311 DN 11

312 Components 12










i

CONTENTS





DNs 30


41 Network Model 31

42 DG Model 32

421 Wind Power 32

43 EV Model 33




44 AD Model 36












533 Analysis 53


541 DG Dimension 57

542 EV Dimension 58

543 AD Dimension 59


61 Discussion 61

62 Future Work 62

7 Conclusion 63

References 64

ii

CONTENTS





E Matlab GUI 83

iii

List of Figures



























iv

LIST OF FIGURES



































v

List of Tables




















vi

Abbreviations


vii

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

Contents

List of Figures iv

List of Tables vi

Abbreviation vii

1 Introduction 1

11 Background 1




123 Delimitation 4



2 Method 8

21 Study Approach 8


3 Theory 11


311 DN 11

312 Components 12










i

CONTENTS





DNs 30


41 Network Model 31

42 DG Model 32

421 Wind Power 32

43 EV Model 33




44 AD Model 36












533 Analysis 53


541 DG Dimension 57

542 EV Dimension 58

543 AD Dimension 59


61 Discussion 61

62 Future Work 62

7 Conclusion 63

References 64

ii

CONTENTS





E Matlab GUI 83

iii

List of Figures



























iv

LIST OF FIGURES



































v

List of Tables




















vi

Abbreviations


vii

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

CONTENTS





DNs 30


41 Network Model 31

42 DG Model 32

421 Wind Power 32

43 EV Model 33




44 AD Model 36












533 Analysis 53


541 DG Dimension 57

542 EV Dimension 58

543 AD Dimension 59


61 Discussion 61

62 Future Work 62

7 Conclusion 63

References 64

ii

CONTENTS





E Matlab GUI 83

iii

List of Figures



























iv

LIST OF FIGURES



































v

List of Tables




















vi

Abbreviations


vii

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

CONTENTS





E Matlab GUI 83

iii

List of Figures



























iv

LIST OF FIGURES



































v

List of Tables




















vi

Abbreviations


vii

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

List of Figures



























iv

LIST OF FIGURES



































v

List of Tables




















vi

Abbreviations


vii

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

LIST OF FIGURES



































v

List of Tables




















vi

Abbreviations


vii

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

List of Tables




















vi

Abbreviations


vii

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


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+

(

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(

(+

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+

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))(-A)(A+()A(A)(A-


03BC4$

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ampAampA-

(


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+

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amp

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+

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(+

+

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(+

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+

(+

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+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

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(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

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+

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amp

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ampA+

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amp+A-+-A)A)

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amp

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)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

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(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

Abbreviations


vii

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

Chapter 1

Introduction




11 Background













1

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

11 Background



above [1]































[18]


2




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI




































3















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI















123 Delimitation






to some extent














4










1242 Scenario


















5












1244 Active Demand



















6







DG

AD EV

















7

Chapter 2

Method



21 Study Approach














8























f



as following

f(x) =1

σradic


(2σ2) (21)




2

9



F (x) =


(22)


distribution if

P (X = m) =

(nm



f(xλ κ) =


λ)κ x ge 0

(24)











as

E(Y ) =1

N

Nsumi

Yi (25)



ables x12M

10

Chapter 3

Theory






311 DN















11






312 Components








Rs Xs

Bsh

2

Bsh

2





Zs = Rs + jXs (31)

Ysh = jBsh (32)

where


12






in feeders [35]

3122 Transformer












3131 Power flow





equations



13


0 = minusQi +

Nsumk=1



3132 Power losses







Ploss = I2R (35)

I =S

U(36)

where





3133 Power factor





S=

PradicP 2 +Q2

(37)










14




voltage variations


∆U


U2middot 100 (38)






U(39)

rArr

PGmax =∆Umax








the network





micropen = 100

(sumPnomG

P peakL

)(311)







15





















the topology)
















16
























the network [13]







17






























the real condition




in Simulink

18






















Pm = Cp(λ β)ρA

2v3w (312)

Tm =Pmωel

(313)

19


where






vw is wind speed ms



Cp(λ β) = c1

(c2


)e


in which1

λi=

1

λ+ 008βminus 0035

β3 + 1 (315)






332 Historical Data







20




0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

60

70

80

90

Day

Tota

l W

ind P

roduction [M

W]





















behave in Sweden



21




No of Vehicles 3 446 517 1 376 358 526 441 1 869 683


No of Cars 3 479 607 606 570 190 248 815













22













charging places







23




24
































agricultural)

25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

100

150

200

Hour

Consum

ption [kW

]

Industrial

Public

Agriculture














fine grained below














26














micro CHPs)












27







VC = CV

(AkTref

eln

(Iph + Io minus IC

Io

)minusRsIC

)(316)




where














28






coefficients as





CSV = 1minus 1


where




level









29


1983 1987 1991 1995 1999 20030

01

02

03

04

05

06

07

Day (19830701 ~ 20050630)

Cle

arn

ess I

nd

ex















a longer vision



developments




30

Chapter 4






41 Network Model











Fig A8 in Appendix





31

42 DG Model

42 DG Model

421 Wind Power

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sprin

g

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Sum

mer

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Autu

mn

Time [Hr]

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223240

20

40

60

80

Win

ter

Time [Hr]
















7microDGpen

37micro

DGpen

27micro

DGpen

13micro

DGpen

23micro

DGpen

32

43 EV Model

43 EV Model


EV Fleet

Urban Rural

Private Vehicles

Commercial Vehicles

Private Vehicles

Commercial Vehicles

BEV PHEV

SOCTrip Type

Battery Size

70 30 90 10

50 50

1

1
















33

43 EV Model



mented







where























34

43 EV Model







Urban

PrV40 012

20(std)02 03 04 01

(70) plusmn1CV

25 018100 (std)

05 045 005(30) plusmn5

Rural

PrV60 018

28(std)02 03 04 01

(90) plusmn15CV

30 022100 (std)

05 045 005(10) plusmn10












PHEV 9 3BEV 25 5









35

44 AD Model





after work


at noon








PrV

1 9 12 13 17 022 9 17 24 24 033 9 17 19 24 044 24 24 24 24 01

CV

1 9 24 24 24 052 9 13 24 24 0453 24 24 24 24 005







44 AD Model






36

44 AD Model

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241

2

3

4

5

6

7

8B

us

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

010203040506070809

1

SO

C [

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2


Sta

te


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

80

Eq

uiv

ale

nt

loa

d p

rofile

of

EV

s [

kW

]

Time [Hr]















37

44 AD Model




















38

44 AD Model


AppliancesStrategy













as


Loadorigres

(41)

where

















39

44 AD Model















40

44 AD Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus04

minus03

minus02

minus01

0

01

02

03

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine


White goods

Total


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24minus3

minus2

minus1

0

1

2

3

4

5

Hour

Consum

ption o

f diffe

rent applia

nces [kW

hh

]


Dish washer

Tumble dryer

Washing machine

White goods

Total



41










timeh 1 24 hour [h]









Power factor 09

42

Chapter 5







DG

EV

AD

+ +












and ADs in Simulink

43


$amp()+)-(

(01($)+0

2345(amp1)6731

8 9 8lt+(

=34

gt61(

gtamp31

amp0amp

A(031

lt3B313$3

gt410C031

DC031

80)30E33010()

6$3F30G1H)06-

gt3

03 EG31)D3



$amp()$+

-0-(1

$amp(23$amp-(()

4014amp5(367((8(9-01

0amp(30lt=+$gt-(301

-0amp(3-$8$amp$1

-gt-$8((amp1

A-3(amp-(1

8amp3--ampB3-

C($3(D801

EF3(amp-(1

G-gt$8amp8($()F+$39-91

0amp-$amp$001

H)3$01

I+(3ampJamp3$01

C(9Jamp3$01

I0-amp-gtI-8$amp-(1

K-0-(1

L-0-(1

F-0-(1

20














44




4 Seasons2 Networks



microADpen = 20





DG1 microDGpen = 0

2 microDGpen = 80


EV4 microEVpen = 20

5 microEVpen = 70

6 Average SOC0 = 50

AD

7 microADpen = 0

8 microADpen = 100

9 ηeff = 010 ηeff = 80










1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

6

Hour

Pow

er

Cu

rve [M

W]

ADs

plus EVs

plus Other loads

Wind Production


45







1

2

3

4

5

Tota

l w

ind p

roduction [M

W]

Reference

Longminusterm

Shortminusterm







500

1000

1500

2000

Time

To

tal L

oa

d C

urv

e o

f E

Vs [

kW

]







46








1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200

300

400

500

600

Time [Hr]

Ele

ctr

icity p

rice [S

EK

MW

h]




minus4000

minus2000

0

2000

4000

6000

Time

Tota

l R

esid

ential Load [kW

]

Original

Short Term

Long Term









47


81

82

83

84

85

86

87

88

89

9


Voltage flu

ctu

ation [kV

]


(b) Topology


48











09

092

094

096

098

1

102

104

106

108




0

01

02

03

04

05

06

07

08

09


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]



49


86

87

88

89

9

91


Voltage flu

ctu

ation [kV

]


(b) Topology


50










514)





097

098

099

1

101

102

103

104

105




51


045

05

055

06

065

07

075

08


Pow

er

Losses in M

esh N

etw

ork

[M

W]



52


533 Analysis





in Table 52 below



Reference


Short-term


Long-term











more complicated







quality

53





































54


















the limit



Frequency 0 0 0 82 0 0 0 77 8 8 1 36













55




Reference

Spring 217 543


Short-term

Spring 202 512


Long-term

Spring 161 493
























56



541 DG Dimension

0

2

4

6

8

10

12


Win

d p

ow

er

pro

du

ctio

n in

Ra

dia

l N

etw

ork

[M

W]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2

4

6

8

10

12

Hour

pen = 50

pen = 0

pen = 80

pen = 80 Rated P


096

098

1

102

104

106

108

11

112

114


Vo

lta

ge

on

Bu

s5

in

diffe

ren

t ca

se

s [

pu

]

005

01

015

02

025

03

035

04


Po

we

r L

osse

s in

Ra

dia

l N

etw

ork

[M

W]
















57


542 EV Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Hour

Lo

ad

Cu

rve

of

EV

s in

Ra

dia

l N

etw

ork

[M

W]

pen = 50


0 = 50

pen = 70


094

096

098

1

102

104

106


Voltage o

n B

us5 in d

iffe

rent cases [pu

]

01

02

03

04

05

06


Pow

er

Losses in R

adia

l N

etw

ork

[M

W]

















58


543 AD Dimension

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1000

2000

3000

4000

5000

Time [hr]

Lo

ad

Cu

rve

of

AD

s in

Ra

dia

l N

etw

ork

(L

on

gminus

term

) [k

W]

penAD

= 0 penEV

= 100 Eff = 0 Eff = 80 penEV

= 50


05

1

15

2

25

3

35


Fle

xib

le R

esid

en

tia

l L

oa

d [

MW

]














59


095

0965

098

0995

101

1025

104

1055

107

1085

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80

0

005

01

015

02

025

03

035

04

045

05

pen=

0 e

ff=20

pen=

50 e

ff=20

pen=

100

eff=

20

pen=

50 e

ff=0

pen=

50 e

ff=80






60

Chapter 6


61 Discussion








as well













a normal condition





61

62 Future Work








reserve etc)

62 Future Work






























62

Chapter 7

Conclusion















63

References




index_enhtm 1 30


dsireusaorg 1





1







html 2 3 6 9



pp 852ndash863 2008 2



vol 25 pp 371ndash380 2010 2



2 5

64

REFERENCES




2











4591822rdquo 2 3


2004 2 3





IEEE 2006 2


2011 2 3 27 39






IEEE 3 62 63





2012 3 62

65

REFERENCES







0-S0378779601001018-mainpdf 3 6















22 25














12 14 16 25 26 42

66

REFERENCES














IEEE 2010 16



pp 2142ndash2149 IEEE 2004 16


indexhtml 16 18




2003 19


toolboxsimulink 20


aspx 20









67

REFERENCES




25



bookB 27 28








sseRETScreen 29 40



32









62 63

68

Appendix A



69


70


71


72

B3

B31B32 B4

B41B42 B5

Line4

B1

B2


External grid

Line1

Line2

Line6

Line7

Line8

Line9 Line3

L3L4

L5

L6

B21B22

L1L2

Line5G1

G2

G3

L7


B1

SS1


External grid

Line9

Line3

SS8

G1

Line5L1

Line1SS3

SS2

Line2

L2

Line6

L3

SS4

Line4

Line8Line7

L4

SS5

L5

SS6

L6

SS7

G2

SS10

L8

Line10

Line11 Line12

L7

SS9


73

$amp()(+-( 0( $amp()

)1( 220(

3(4-(5$140(6(474-($($8(

9568(5$140(amp5((3468-(

lt=gt(8(5$140(566$6(A(

$1($amp(6B(22(C66B(D(

$456=665(56B(E(-6(5-(F(

-6$4C$-(amp54G56gt(8(

H4=$46(5-(-$6(A($8(

5$140(6(-$4=5-(H4$(

4H465G5amp(5(A-4(5(=--(

$amp((A($8(C45(amp4-(5(

lt1-5gt(

IJ$45(K4-(

LHgt( 2+3( (( ((

M=gt($gt( 0(

NOP(PG( 2(

)5(( H( )5amp$8( lt$4$( I5-(

2( Q48-( R( SR( SF(

R( L( R( SF( ST(

F( Q48-( T( ST( SU(



( Q48-( 2gtU( SF( SF2(




$gt()5amp$8( Q48-( L( 34amp( ((

2TgtE( 2( TgtE(

2gtFFFFFF

FF(((


)2( Rgt( 2gtR(

gtVDDVDT

FV( SR2(

)R( gt( gtRD(

gtDFTVD

UF( SRR(

)F( gt( gtF(

gtVDTTRE2

D2( SF2(

)T( 2gtV( gtD2(

gtVDRTFUR

E( SFR(

)U( gt( gtRD(

gtDFTVD

UF( ST2(

)( 2( gtTV(

gtD2URF

UE( STR(

)E( 2( gtTV(

gtD2URF

UE( SU(

K2( R( 2( SR(

KR( F( 2( SF(

KF( R((( 2( ST(

$gt()-( VgtR( Tgt2(

gtVDVFFF

RU(

W4=$46( P]Q8=6O0=^( )]_O0=^( L]`O0=^( ((







U+3( gt2( gtR(RU(0Y( gt((( 465( (


74

$amp()(+-( 0( $amp()

)1( 220(

3(456(7$180(5(898-($($6(847(

7$180(7(88$(48(4lt8amp8-5=(gt6(

7$180(lt755$5(($1($amp(5(22(

AB55(2C($8754557(75(D(-5((7-(C(

-5$8AB$-(amp78E75=(gt6(84$85(

7-(-$5(($6(7$180(5(-$847-(

8$F(8857E7amp($6(8B8(amp8-(lt7-E7(

7(G1-7=(

HI$87(8-(

J=( 2+3( (( ((

K4=($=( 0(

LMN(NE( 2(

)7(( gtF( )7amp$6( G$8$( H7-(

2( O86-( =2( GG2( GGC(

C( O86-( =2( GG2( GGP(

P( O86-( C=2( GGC( GGD(


R( O86-( 2=Q( GGP( GGS(

( O86-( =P( GGQ( GGR(

S( O86-( =R( GGR( GG(

D( O86-( =P( GGS( GG(

T( O86-( 2( GGD( GG(

2( O86-( 2( GGT( GGS(

22( O86-( C=Q( GGD( GG2(

2C( O86-( 2=T( GG2( GGT(

gt$=()7amp$6( O86-( JA( 38amp( ((

22=T( 22=T( (

2=PCCCCC

CCC(


)2( Q=D( C=P(

=DTTRQT

SC2( GGC(

)C( P=Q( 2=QS(

=TCS2

RD( GGP(

)P( =Q( =2T(

=TPCSS

RQ( GGQ(

)Q( =P( =2R(

=DTQQCS

2T2( GGR(

)R( Q=C( C=(

=TC

2PS( GG(

)( C=TT( 2=QR(

=DTTSSD

P( GGS(

)S( =Q( =2T(

=TPCSS

RQ( GGT(

)D( C=C( 2=(

=TDDP

SR( GG2(

2( 2( GGD(

C( R( GG(

gt$=()-( 2D=QR( D=TP(

=T2T

DDP(

V84$85( N]O645M04^()]_M04^( J]`M04^( ((

O86-(a( =P( 2=PHbP( 2=HbD(

O86-(( =SC( R=2HbP( 2=HbS(





R+3( =2( =C(CR(0X( =(((857( (


75

Appendix B


Running Parking

Charging


(p_r)

Update

Running Parking

Charging

if SOCgt=1p_p=1

initial State=

Running

highest priority

if SOClt1p_c=1

initial State=

Charging

Running Parking

Charging

initial State=

Parking




Remaining trips-1


Update

Update

Update

Update


(p_r)


(p_r)




(p_p=(1-p_c)(1-p_r))

$amp()$amp()+-0123456()3456(78)+-01292-62lt-=gt

$((20=

lt

3(0(68AltB


$amp()$amp()

amp((20ltlt6lt1

3456()3456(78)7(06F9-GltampHF(6Gltgt$((20=


No

Yes

Yes

No

(a)

(b)

(c)

(d)


76

$

$amp($)+$-(amp01(2$23(421(

54$6768)(amp01(2$23(421(

8$8)(amp01(2$23(421(

9(7($amp($)amp(2401(2$23(421(

$(($amp01(2$23(421(

lt71=amp7gtgt


$(776$1(764gt$1(764

07B

54$6765D$E(7(24

FD

ampG($HI5DB(

8$7amp7amp(

gtJKLgt7M03N

Yes

NogtJKLgt8N

Yes

No


77

$

amp()amp+

-amp01$1$2

1 hellip n_AD

31$040560(406171$3amp0$0892

9$(60(401$(

lt11(60$amp430=gt$2

+A+ampBlt-C


F$gt02amp2G6

3((02gt(01$49gtamp

Damp4

9$(60D(H1$gt

9$(601$49gtamp

No

Yes


78

Appendix C

Load Profile of AD

79

$

amp

(

)

+

-

amp

ampamp

amp

amp(

amp)

amp

amp+

amp

amp-

amp

amp

(

)

$amp()+amp

012234567


896lt

+

(

)

(

(

(+

-

+

+(

+

++


=29gt


))(-A)(A+()A(A)(A-


03BC4$

ampA)amp)A


ampAampA-

(


0D9lt

+

+

-

amp

(

(

)

)(

((

(

ampampamp-ampamp-

+)

(

amp

ampamp

EC4$5


F95GH54$

amp)A

)A

A

A

A

Aamp


amp


+

+

3I142$J4$

AAampampA

+A+

(Aamp


KH59lt3

H79lt4

A

A(

A(

A(

A+

ampAamp



-A)

)A+

4H6lt


-01231amp()+amp

012234567


896lt



=29gt

ampA

A)



03BC4$

amp

amp

)

)

)

)

(+

(+

+

-)

(+

-)

(+

+

-)

)-

(+

0D9lt

+

(+

-)amp

+

amp-

amp

+

)

)-

amp

)amp(++

)-

+

amp

A)

(A+

EC4$5

)A-

+ampA+A-

amp-

ampampA+

(A+amp(A

)A-

amp-A-

A

A+

A+

-A)

F95GH54$

+

+

ampA

A+AAampA

A

)A-

+

-A)ampAA+(A++A)ampA

amp

A

A

3I142$J4$

ampA+

)A-

amp+A-+-A)A)

-

-ampA+

)

)

-A+


KH59lt3

H79lt4

amp

)A-

)A-amp)A)A+((A+

(((A+((A+(ampA(A)((A+

((A)

+

-)

)

(+

)


80

Appendix D



81


82

Appendix E

Matlab GUI


83

xue fron pagepdf


List of Figures

List of Tables

Abbreviation

1 Introduction

11 Background




123 Delimitation



2 Method

21 Study Approach


3 Theory


311 DN

312 Components







332 Historical Data








41 Network Model

42 DG Model

421 Wind Power

43 EV Model




44 AD Model












533 Analysis


541 DG Dimension

542 EV Dimension

543 AD Dimension


61 Discussion

62 Future Work

7 Conclusion

References





E Matlab GUI

quantitative analysis of distributed energy resources in future distribution networks

Documents