optimal renewable generation integration to the...

Optimal Renewable Generation Integration to the

Power Distribution Systems

Thesis submitted in partial fulfilment

of the requirements for the degree of

Master of Science by Research

in IT in Power Systems

by

N VENKATA SRINATH

200971001

[email protected]

International Institute of Information Technology, Hyderabad

(Deemed to be University)

Hyderabad – 500 032, INDIA

July 2015

International Institute of Information Technology

Hyderabad, India

CERTIFICATE

It is certified that the work contained in this thesis, titled “Optimal Renewable Generation

Integration to the Power Distribution Systems” by N Venkata Srinath, has been carried out

under my supervision and is not submitted elsewhere for a degree.

Date Advisor: Dr. Amit Jain

Acknowledgments

This thesis wouldn’t have become a success without the people who have provided a

constant support throughout. Foremost, I would like to express my sincere gratitude to my

advisor Prof. Amit Jain for his continuous guidance and support during the entire course of

my research. His knowledge and encouragement helped me a lot in the course of my

research. I am also indebted to Prof. M. Ramamoorty who taught me many concepts and also

helped me in advancing my knowledge through intellectual discussions. I am also thankful to

IIIT Hyderabad for giving me this opportunity to post graduation.

I feel grateful to be a part of Power Systems Research Center. I am very thankful to

Dr. Amit Jain and PSRC for funding my postgraduate education. I extend my thanks to dear

friends at PSRC for their support, especially Ravikanth, Sivaram and Lingamurthy for their

support and encouragement. They always stood beside me during the entire duration of MS.

I take this opportunity to thank my friends from MS 2009 batch for giving me so

many good memories and joyful times during my stay at IIIT Hyderabad.

Last but not the least to mention is my family who are always concerned about my

future and well being. I extend my in-depth gratitude to my father, mother and sisters for their

love, patience and support.

Abstract

The modern day societies are becoming more and more dependent on electrical

energy. It is the most important part of our day to day life whose demand is increasing

continuously. Even with today's advanced power system technologies the losses in the system

are very high which are needed to be reduced for higher efficiencies and better receiving end

voltages. This is experienced even more in distribution system where there is a direct impact

on customer, owing to long radial lines (unique path from any given load bus to source).

Hence, a solution that addresses both the concerns i.e. reduced distribution losses and better

voltage profile in the system is of great interest. This can be achieved by harnessing

renewable resources and integrating them into the system as distributed generation. The

size/installed capacity of renewable generation is also an important factor that cannot be

neglected due to the intermittent nature of availability of many renewable sources like solar

and wind.

Renewable energy sources can be deployed and used in the distribution systems with

more eagerness in present day scenario. They impose a considerable impact on the system

and customer owing to the intermittence nature. Thus, the problem of incorporating

renewable generation into distribution system becomes more complex. This thesis proposes a

methodology for integrating the renewable generation at optimal location with reduced

distribution losses. The total capacity of renewable generation to be installed is calculated for

the given distribution network. The generation capacity is decided so as to maintain all the

bus voltages in acceptable limits based on 24 hour system load.

The approach involves a process of finding bus voltages, line currents and power loss

in the system by distribution load flow. The proposed work uses radial distribution load flow

algorithm for the chosen distribution test networks. This thesis considers integration of

renewable energy sources namely Wind, Solar photovoltaic and Biomass as distributed

generation. Each type of renewable energy source is mathematically modeled and the exact

amount of power output is calculated for the given meteorological conditions.

The proposed methodology is validated by implementing on standard radial 33

and 69 bus test systems. The simulation results are presented for both with and without

inclusion of renewable energies and discussed.

i

Contents

Chapter Page

1 Introduction ............................................................................................................................. 1

1.1 Distribution Generation .................................................................................................... 1

1.2 Radial Distribution Load Flow ......................................................................................... 2

1.3 Optimal Placement and Penetration Capacity .................................................................. 2

1.5 Thesis Organization.......................................................................................................... 3

2 Radial Distribution Load Flow ............................................................................................... 5

2.1 Balanced Radial Distribution Load Flow (RDLF) ........................................................... 8

2.1.1 30 Bus system Analysis ......................................................................................... 10

2.1.2 69 Bus System Analysis ........................................................................................ 11

2.2 Summary ........................................................................................................................ 12

3 Optimal Location and Optimal Generation........................................................................... 13

3.1 Finding Optimal Location and Optimal Capacity .......................................................... 15

3.2 Results ............................................................................................................................ 16

3.2.1 33 Bus Results .............................................................................................. 16

3.2.2 69 Bus Results .............................................................................................. 17

3.3 Summary ........................................................................................................................ 17

4 Integration of Distributed Generation ................................................................................... 18

4.1 Wind ............................................................................................................................... 19

4.1.1 Results for 33 Bus System ........................................................................... 19

4.1.2 Results for 69 Bus System ........................................................................... 22

4.2 Summary ........................................................................................................................ 25

5 Integration of Distribution Generation: Solar ....................................................................... 26

5.1 Solar Integration ............................................................................................................. 26

5.1.1 33 Bus Results .............................................................................................. 27

5.1.2 69 Bus Results .............................................................................................. 29

5.2 Combination of Solar and Wind .................................................................................... 31

5.2.1 33 Bus Results .............................................................................................. 31

5.2.2 69 Bus Results .............................................................................................. 34

5.3 Summary ........................................................................................................................ 36

ii

6. Integration of Distribution Generation: Biomass ................................................................. 37

6.1 33 Bus Results ................................................................................................................ 37

6.1.1 Case 1: Integration of biomass with wind .................................................... 38

6.1.2 Case 2: Integration of biomass with solar .................................................... 41

6.1.3 Case 3: Integration of biomass with wind &solar ........................................ 43

6.2 69 Bus Results ................................................................................................................ 46

6.2.1 Case 1: Integration of Wind and Biomass ................................................... 46

6.2.2 Case 2: Integration of Solar and Biomass .................................................... 49

6.2.3 Case 3: Integration of Wind, Solar and Biomass ......................................... 52

6.3 Summary ........................................................................................................................ 54

7. Summary and Conclusions .................................................................................................. 55

Future Scope ......................................................................................................................... 56

Appendix A: Modeling of Wind, Solar and Biomass .............................................................. 57

A.1 Wind Modeling.............................................................................................................. 57

A.2 Solar Modeling .............................................................................................................. 60

A.2 Biomass Modeling ......................................................................................................... 62

Appendix B: Standard Network Data of 30 Bus, 33Bus and 69 Bus. ..................................... 63

B.1 30 Bus Test System Data ............................................................................................... 63



References ................................................................................................................................ 68

Publications .............................................................................................................................. 70

iii

List of Figures

Figure Page

Figure 2.1 Single-line diagram with two busses ........................................................................ 8

Figure 2.2 Single-line diagram with 6 busses ............................................................................ 8

Figure 2.3 (a) Single-line diagram of 30 bus network ............................................................. 10

Figure 2.3 (b) 12 bus reduced network .................................................................................... 10

Figure 2.4 Single-line diagram of 69 bus network .................................................................. 11

Figure 3.1 Percentage peak load for 24 hrs .............................................................................. 14

Figure 3.2 Single-line diagram of 33 bus network .................................................................. 16

Figure 4.1 24 hrs LP , Lq and Lv curves for 6 days with integration of wind DG on 33 bus

system ...................................................................................................................................... 21


system ...................................................................................................................................... 24

Figure 5.1 24 hrs LP , Lq and Lv curves for 6 days with integration of solar DG on 33 bus

system ...................................................................................................................................... 28

Figure 5.2 24 hrs LP , Lq and Lv curves for 6 days with integration of solar DG in 69 bus

system ...................................................................................................................................... 30

Figure 5.3 24 hrs LP , Lq and Lv curves for 6 days with integration of combination of wind &

solar DG in 33 bus system ....................................................................................................... 33

Figure 5.4 24 hrs LP , Lq and Lv curves for 6 days with integration of combination of wind &

solar DG in 69 bus system ....................................................................................................... 36

Figure 6.1 24 hrs LP, Lq & Lv curves for 6 days with wind & biomass integration in 33 bus

system ...................................................................................................................................... 40

Figure 6.2 24 hrs LP , Lq and Lv curves for 6 days with integration of biomass & solar

generation in 33 bus system ..................................................................................................... 43

Figure 6.3 24 hrs LP , Lq and Lv curves for 6 days with integration of biomass and wind &

solar generation in 33 bus system ............................................................................................ 45

Figure 6.4 24 hrs LP , Lq and Lv curves for 6 days with integration of wind & biomass in 69

bus system ................................................................................................................................ 48

Figure 6.5 24 hrs LP , Lq and Lv curves for 6 days with integration of solar & biomass in 69

bus system ................................................................................................................................ 51

Figure 6.6 24 hrs LP , Lq and Lv curves for 6 days with integration of biomass and wind &

solar generation in 69 bus system ............................................................................................ 54

Figure A.1 Induction machine equivalent circuit .................................................................... 58

iv

Figure A.2 Slip Vs output power for the considered induction motor..................................... 59

Figure A.3 Equivalent circuit of PV cell ................................................................................. 60

Figure A.4 Mathematically generated IV and PV curves of considered PV panel.................. 61

Figure A.5 Biomass gasifier plant ........................................................................................... 62

v

List of Tables

Table Page

Table 2.1 Iteration wise RDLF results for modified 12 bus network ...................................... 11

Table 2.2 Comparison of 69 bus RDLF results with results in [26] ........................................ 12

Table 3.1 Score obtained for critically loaded nodes in 33 bus network ................................. 16

Table 3.2 Score obtained for critically loaded nodes in 69 bus network ................................. 17

Table 4.1 Hourly active & reactive losses and minimum voltage attained with and without

integration of wind generation for days 1 - 3 ........................................................................... 20



Table 4.3 24 hrs losses and minimum voltage with wind integration in 33 bus system .......... 22





Table 4.6 24 hrs losses and minimum voltage with Wind Integration in 69 bus system......... 25


integration of solar generation for days 1 - 3 ........................................................................... 27



Table 5.3 24 hrs losses and minimum voltage with solar integration in 33 bus system .......... 28


integration of solar DG for days 1 - 3 ...................................................................................... 29



Table 5.6 24 hrs losses and minimum voltage with solar integration in 69 bus system .......... 30


integration of combination solar & wind generation for days 1 - 3 ......................................... 32


integration of combination solar & wind generation for days 4 - 6 ......................................... 33

Table 5.9 24 hrs Losses and minimum voltage with combination of solar and wind integration

in 33 bus system ....................................................................................................................... 33


integration of solar and wind generation for days 1 - 3 ........................................................... 34

vi


integration of solar and wind generation for days 4 - 6 ........................................................... 35

Table 5.12 24 hrs losses and minimum voltage with wind and solar integration in 69 bus

system ...................................................................................................................................... 36

Table 6.1 Different strategies of integrating biomass in 33 bus System ................................. 38


integration of wind and biomass generation for days 1 - 3 ...................................................... 39



Table 6.4 hrs losses and minimum voltage with integration of wind and biomass in 33 bus

system ...................................................................................................................................... 40


integration of biomass and solar generation for days 1 - 3 ...................................................... 41


integration of biomass and solar generation for days 4 - 6 ...................................................... 42

Table 6.7 24 hrs losses and minimum voltage with integration of biomass and solar

generation in 33 bus system ..................................................................................................... 43


integration of solar, wind and biomass generation for days 1 - 3 ............................................ 44


integration of solar, wind and biomass generation for days 4 - 6 ............................................ 45

Table 6.10 24 hrs Losses and minimum voltage with biomass and wind & solar integration in

33 bus system ........................................................................................................................... 46

Table 6.11 Different strategies for integrating biomass in 69 bus system ............................... 46





Table 6.14 24 hrs losses and minimum voltage with integration of wind and biomass in 69

bus system ................................................................................................................................ 49


integration of solar and biomass generation for days 1 - 3 ...................................................... 50


integration of solar and biomass generation for days 4 - 6 ...................................................... 51

Table 6.17 24 hrs losses and minimum voltage with integration of solar and biomass in 69

bus system ................................................................................................................................ 51

vii


integration of wind, solar and biomass generation for days 1 - 3 ............................................ 52


integration of wind, solar and biomass generation for days 4 - 6 ............................................ 53

Table 6.20 24 hrs losses and minimum voltage with integration of wind, solar and biomass in

69 bus system ........................................................................................................................... 54

Table A.1 Industrial specifications Vs MATLAB results ....................................................... 61

Table B.1 30 Bus line and load data with base values 100 MVA and 11 kV .......................... 64

Table B.2 33 bus line and load data ......................................................................................... 65

Table B.3 69 Bus line and load data ........................................................................................ 67

viii

List of Abbreviations

APFC Automatic Power Factor Controller

DG Distributed Generation

EHV Extra High Voltage

HV High Voltage

KCL Kirchoff's Current Law

KVAR kilo Volt Ampere Reactive

KVL Kirchoff's Voltage Law

KW kilo Watts

LU Lower Upper

LV Low Voltage

NR Newton - Raphson

O & M Operational and Maintenance

PCC Point of Common Coupling

pu per unit

PV Photo Voltaic

RDLF Radial Distribution Load Flow

Y admittance

Z impedance

ix

Nomenclature

(Lf)n Percentage Peak Load Factor

(PDG)n Power output from Distribution Generation in nth

Hour

(PL)ij Active Power Loss in branch ij

(QL)ij Reactive Power Loss in branch ij

A Rotor Swept Area

Cp Power Coefficient

G Insolation

I0 Diode Current

ICell Photon Current

Iii Load Current at ith

Bus

Iij Current flowing from Bus i to j

Imax Current at Maximum Power

Iout Output Current from PV Module

Isc Short Circuit Current of a PV Module

k Boltzmann constant1.38065*10-23

LP Indices for active power Loss Reduction

LQ Indices for Reactive power Loss Reduction

LV Indices for Minimum Voltage Improvement

m Mass of Air

n Hour

P(i) Active Load Demand at ith Bus

PDG Power output from Distribution Generation

PL Active Power Loss for 24Hrs

Pmax Maximum Power of a PV Module

q Electron charge 1.60217656*10-19C

Q(i) Reactive Load Demand at ith

Bus

QL Reactive Power Loss for 24Hrs

R1 Stator Resistor of Induction Generator

R2 Rotor Resistor of Induction Generator

Rfe Magnetizing Resistor of Induction Generator

x

Rs Series Resistance

s Slip

T Temperature

v Wind Speed

V Voltage for Wind Generator

v0 Down Stream Wind Speed

Vi Voltage in ith

Bus

Vj Voltage in jth

Bus

Vmax Voltage at Maximum Power

Voc Open Circuit Voltage

Vout Output Voltage from PV Module

X1 Stator Reactance of Induction Generator

X2 Rotor Reactance of Induction Generator

Xm Magnetizing Reactance of Induction Generator

Zij Impedance between bus i & j

ρ Air Density

1

Chapter 1

1 Introduction

Electricity, one of the basic needs of modern world is experiencing a consistent rise in

demand. To cater to this ever rising demand of electricity, conventional energies like thermal,

hydro and nuclear power have served us greatly. According to [1] approximately 39% of

world’s electricity generation is contributed by coal, 9% from hydro, 15% from gas, 16%

from nuclear and 10% from oil. But these conventional resources used for production of

power are converging to finish at a faster rate. Even with today's advanced power system

technologies the power utilities have constantly failed in catering to their consumers with

continuous and reliable power at a cheaper cost, which is very much required. Owing to long

radial lines in a country like India, the distribution systems have often experienced poor

quality of power supply impacting the customer directly. The need to meet the demand with

quality while adhering to the safety of the environment and society, is of great importance.

This can be addressed by incorporating power generation from renewables into existing

power system. This thesis makes an attempt to explore and implement such an option.

1.1 Distribution Generation

So far, the continuously varying power demand was supplied by centralized power

generation sourced from conventional fuels. Power flows from centralized generating station

to the consumers via EHV, HV and LV (where the distribution system is operated) networks.

Generation powered by renewable energy sources which are integrated into the existing

distribution network are known as distributed, embedded or dispersed generation. Except

large solar and wind power generation all dispersed generation are connected to the grid at

LV level due to high cost of high-voltage transformer. This, in turn, is an added advantage as

the power transfer is from point to point within the distribution network which reduces the

distribution losses greatly. Since renewable generation cannot completely replace the

conventional generation in near future, the solution to this lies in effective diversion of load

demand onto distributed generation (DG) [2]. As there are many renewable sources in the

market and few are still at research and development level [2] [3] [4] [5], this thesis considers

three renewable sources solar, wind and biomass to the accepted fact of abundantly available

2

in India. These renewable sources are mathematically modelled and the power outputs from

these renewable sources are calculated for the given Indian meteorological conditions.

1.2 Radial Distribution Load Flow

Load flow is a calculation which provides the vital information like voltage and

currents at all the busses and the complex power flowing in the lines for the specified steady

state operating conditions. The additional information such as losses in the lines can be easily

calculated later. Even though traditional solution techniques like Newton Raphson (NR) and

fast decoupled load flows have solved for power systems for a long time, one cannot apply

these techniques to distribution networks which have special features like radial network

structure, high R/X ratio and unbalanced loading resulting in non-convergent solutions. With

respect to the distribution network features, suitable methods that use ladder network

approach and basic network laws like Kirchhoff's Current Law (KCL) and Kirchhoff's

Voltage Law (KVL) [6] are applicable.

The ladder network method consists of two steps: 1. Backward sweep, where node

currents are calculated with assumed bus voltages and 2. Forward sweep, where the voltage

drops from sources are calculated by making use of currents calculated in backward sweep.

These two forward/backward sweep iterations are repeated until convergence criteria is

satisfied.

1.3 Optimal Placement and Penetration Capacity

The point where the distribution generation is integrated into the network is called

point of common coupling (PCC). Inclusion of distribution generation aids in the reduction of

total system losses, improves the voltages, and also decreases the load on the conventional

system. The choice of PCC plays an important role in the distribution network. Wrong choice

of PCC affects the target of loss reduction and voltage improvement [7]. It may also result in

increased installed distribution generation capacity for the same target. Hence, distribution

generation must be always installed at best PCC, optimal location. In other words optimal

location means the placement of distribution generation that can maximize the effects like

loss reduction and voltage improvement with minimum installed capacity. Selection of

3

distribution generation capacity also known as Optimal capacity is based on desired voltage

improvement and it is the minimum size of distribution generation to be installed at the

optimal location to achieve the pre-desired voltage improvement.

Distribution generation is mainly sourced from renewable energies whose availability

is highly intermittent. On the other hand load is also of continuously varying nature. This is

where the optimal placement of DG has attracted many researchers' attention. Appropriate

techniques which take variable nature of both distributed generation and load must be

considered for the selection of optimal location and optimal capacity for the given network.

This thesis proposes a methodology to determine the optimal location and capacity for

the installation of DG. A distribution load curve which is varying on an hourly basis is

considered and available Indian meteorological conditions are also considered to calculate the

amount of output from distribution generation plants. The proposed method is validated by

implementing it on the standard radial distribution networks of 33 buses and 69 buses. The

simulation results obtained with and without inclusion of distribution generation are

compared and discussed. The proposed work will be providing a simplified way to power

utilities to install optimal renewable energy in their power distribution system. All the

simulations have been carried out using MATLAB.

1.5 Thesis Organization

This thesis is divided into 7 chapters. The current chapter 1 drives the reader to the

topics dealt in this thesis. It mainly deals with the current status of the distribution network,

its characteristics and problems faced. It gives the basic introduction how the distribution

system is analyzed by making use of radial distribution load flow. It also gives a brief

introduction to the solution of the problem and its implementation.

Chapter 2 completely deals with radial distribution load flow (RDLF) like literature

survey on different types of RDLF algorithms and the one that is being used in this thesis.

The results obtained for the considered networks (which will serve as the base case i.e. no

distribution generation in the network) were presented and compared. Chapter 3 provides the

details on implementation of the proposed algorithm in finding the optimal location for the

4

installation of distribution generation and the minimum capacity to be installed in order to

obtain the desired voltage improvement.

Chapter 4 has the implementation details for one renewable source- wind energy,

which is highly intermittent in nature as distribution generation and the details about the

improvement in the system losses and voltage profile are included. In chapter 5,

implementation details for the proposed method are given for another renewable source- solar

PV system, which is little more predictable when compared to wind. It also deals with the

hybrid combination of both wind and solar where the installed DG capacity is divided equally

between these two sources.

Chapter 6 introduces one more renewable energy source, biomass energy, which can

deliver power continuously. As installing a sole biomass plant with high capacity is

practically expensive, a part of the optimal DG capacity to be installed is served by biomass

energy source and the remaining capacity is served either by wind energy or solar energy or

combination of wind and solar energy in three different cases. In chapter 7, observations and

conclusions obtained are presented and discussed.

The mathematical models of wind, solar and biomass are included in Appendix-A

along with data of test networks considered in Appendix-B .

5

Chapter 2

2 Radial Distribution Load Flow

Load flow analysis provides bus voltages, line currents and the complex power

flowing in the lines for the specified operating conditions under steady state. The additional

information such as losses in the lines can be easily calculated. The essential information

required for any load flow calculation are network topology, line parameters, line flows,

switch status, transformer tap settings and line compensations.

This calculation is essential for the continuous evaluation of power system and is used

for the following:

• To check whether all voltages throughout the network are within acceptable limits

• To check whether all lines are loaded within the limits

• To assist in meeting new load demands and planning for additional generation

• Expansion of transmission and distribution network

• Maintenance on power system network

Conventional iterative techniques which are commonly used in transmission power-

flow studies like NR and fast decoupled load flow solution techniques cannot be used for

solving the distribution load flow problem [8]. The distribution system has some special

features like radial network, high R/X ratio etc. which makes the conventional load flow

methodologies unsuitable, inefficient and poor convergence [9]. Iterative techniques

specifically designed for solving radial distribution networks should be used.

Several attempts were made and many algorithms have been developed to solve and

study the distribution system. This section deals with the methods/algorithms followed in

solving radial distribution load flow.

Antonio et al. [10] presented a modified NR algorithm to solve the load flow problem

for radial networks where instead of working with the conventional state variables (complex

bus voltages), it resorts to a pair of branch variables plus a bus voltage variable. This leads to

6

a set of 3N equations, of which 2N are related to power injections & are linear, while the

remaining N equations related to bus voltages are quadratic. The NR method is then applied

to these equations, and the resulting linear system is rearranged so that its block structure

resembles the network tree.

Lin et al. [11] presented a branch-current-based Phase-Decoupled NR method for

distribution power networks. The branch current-based algorithm uses a constant Jacobian

matrix which needs to be factorized only once.

Teng et al. [12] presented another approach for distribution load flow. The

formulation uses an algorithm that systematically builds the Jacobian matrix and utilizes an

improved solution technique to obtain the load flow solution. The proposed method uses

branch voltages as state variables and employs the NR algorithm to solve the load flow

problem.

Das et al. [13] presented a unique lateral, node and branch numbering scheme for

solving radial distribution load flow. It involves simple voltage expressions that are solved

iteratively to converge.

Fukuyama et al. [14] developed a fast distribution power flow using multi-processors.

It uses only active and reactive power injections at main feeder and laterals as state variables.

In this, radial distribution networks are mapped into the tree structure of multi-processor

system for allocating each node to process. A similar type of data structure procedure was

also presented by Venkatesh et al. in [15].

Thakur et al. [16] presented a new approach to load flow solutions for radial

distribution system in which the choice of switches to be opened or closed is based on the

calculation of bus voltages, real and reactive power flowing through lines, real power losses

and voltage deviation.

Augugliaro et al. [17] proposed an iterative algorithm with some special procedures to

increase the convergence speed where the bus voltages are considered as state variables. Each

branch is represented as series inductive impedance and the shunt capacitances are taken into

account by two shunt admittances lumped at the ends of each branch. In the paper, three

different types of loads are used: 1. constant power 2. constant current and 3. constant

impedance. The extended work in [18] proposed an algorithm neglecting the lines' shunt

7

admittances. Later in [19] some more improvements are proposed and concluded that a

proper initial choice of the nodes voltages can help in reducing the number of iterations when

compared to flat start.

Eminoglu et al. [20] proposed a method that takes voltage dependency of static loads

and line charging capacitance into account. The method is based on the forward and

backward voltage calculation by using polynomial voltage equation and Kirchoff’s Law at

each branch. Convergence speed and reliability of this method were also compared with

standard NR, PFLOW and Ratio-Flow method which is based on classical forward-backward

ladder method.

Ou et al. [21] formulated a novel direct and rigid approach for large scale distribution

power flow analysis. This algorithm uses two primary matrices: BI and ZV-BC, which are

built from the topological characteristics of distribution networks and are used to achieve the

power flow solutions. BI matrix is the bus injection to branch current matrix and the ZV-BC

matrix describes the relationship between the bus voltage mismatches and branch currents.

The time-consuming LU decomposition and forward/backward substitution of the Jacobian

matrix or the Y admittance matrix, required in the traditional Z-Matrix building algorithms, is

not needed in this approach. Similar approaches for three phase unbalanced power flow using

Z-matrix were presented in [22] [23] [24].

Wei et al. [25] proposed a three phase unbalanced load flow solution involving

transformers with various winding connections and multi-voltage levels. The proposed

solution uses a sensitivity constant incident matrix between node voltages and injection

currents which was derived based on path matrix. Then, a simple direct algorithm in

impedance form was utilized to resolve the three phase unbalanced power flow. An efficient

layered algorithm was obtained by dividing a large and complicated distribution network into

several small and simple networks (called layers) to overcome the large size problem of

incident matrix for a complicated distribution network as it is multi-voltage level.

This thesis has considers a balanced radial distribution load flow developed based on

ladder network theory which uses Kirchoff's voltage law (KVL) and Kirchoff's current law

(KCL). This method involves evaluation of algebraic expressions which are expressed in

terms of receiving end voltage (Vj) and sending end voltage (Vi). This method is chosen due

to computational efficiency, accuracy and fast converging [26].

8

2.1 Balanced Radial Distribution Load Flow (RDLF)

The present thesis considers that the three phase radial distribution load is operating

under balance-load which can be represented by equivalent single line diagram and analyzed.

The line-to-ground capacitance at the distribution level will be too small to consider and can

be neglected [2] [ 4].

Figure 2.1 Single-line diagram with two busses

From Figure 2.1, the receiving end voltage can be expressed as sending end voltage

less drop in the line connecting these two nodes as shown below:

j i i j i jV V I Z= − 2.1

where Zij = Rij + jXij impedance of the line between the nodes i & j.

Iij is the current flowing from bus i to j

Example: Consider the single line diagram of a three phase radial distribution network

with 6 nodes as shown in Figure 2.2

Figure 2.2 Single-line diagram with 6 busses

then

2 1 1 2 1 2V V I Z= − 2.2

9

3 2 2 3 2 3V V I Z= − 2.3

5 2 2 5 2 5V V I Z= − 2.4

and the load current at any node is given by KCL

1 2 2 3 2 5 2 2I I I I= + + 2.5

23 3 4 3 6 3 4I I I I= + + 2.6

2 5 5 5I I= 2.7

load current at any bus-i can be given as:

( ) ( )

( )*

D D

i i

P i j Q iI

V i

−=

2.8

The iteration procedure starts with backward sweep where node currents are

calculated using equation (2.8) assuming flat voltages (1 pu) at all the nodes. In the next

iterations, updated voltages are used for node-current calculations. Once the node currents are

calculated, the branch currents (solved example equation (2.5-2.6)) are determined. The first

iteration implies that the system is loss less. Therefore, the current calculated is the most

minimal current that the source end provides. Now in the forward sweep all the voltages

except node-1 are updated using equation (2.1). As node-1 is source bus which is a voltage

control bus it is always maintained at 1pu. Now the load/node currents are calculated with

these updated voltages followed by branch current calculations. This backward/forward

iteration continues until the convergence criteria is met. The convergence criteria can be on

bus voltages, line currents, line flows or total power loss in the systems which are basically

inter-dependent on one another so it is a user defined criteria. The convergence criteria here

are the maximum modular difference of voltage magnitudes in two successive iterations and

should be less than 10-6

.

Once the convergence criteria is met, the radial distribution load flow is finished and

then the post load flow calculations like total line losses in the system, line flows, voltage

violations, line flow violations, contingency analysis, network reconfiguration analysis can be

performed.

10

The total loss in the system is the sum of the losses in all branches. Complex Power

loss in any branch ij can be given as:

( ) * *

L L i ij j jiijP jQ V I V I+ = + 2.9

Two networks 30bus and 69bus distribution networks have been used and results are

compared with the existing literature to test the correctness of the above described RDLF.

2.1.1 30 Bus system Analysis

Figure 2.3 (a) shows 30 bus radial distribution network whose data is taken from [27]

and is also presented in Appendix-B. This 30 bus network has modelled into a 12 bus

equivalent system by omitting all laterals as shown in Figure 2.3 (b). The total power load of

each lateral is added to its sending end feeder. Table 2.1 presents the comparison of the

results obtained by the considered methodology in this thesis Vs the results published in [20].

Figure 2.3 (a) Single-line diagram of 30 bus network

Figure 2.3 (b) 12 bus reduced network

11

Node Iterations wise results till the convergence is meet

Results in [20] 1 2 3 4 5 6

1 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

2 0.9895 0.9888 0.9888 0.9888 0.9888 0.9888 0.9888

3 0.9803 0.9791 0.9790 0.9790 0.9790 0.9790 0.9790

4 0.9677 0.9657 0.9656 0.9656 0.9656 0.9656 0.9655

5 0.9568 0.9541 0.9540 0.9540 0.9540 0.9540 0.9539

6 0.9466 0.9432 0.9430 0.9430 0.9430 0.9430 0.9429

7 0.9397 0.9357 0.9355 0.9355 0.9355 0.9355 0.9354

8 0.9360 0.9318 0.9315 0.9315 0.9315 0.9315 0.9315

9 0.9317 0.9271 0.9268 0.9268 0.9268 0.9268 0.9268

10 0.9297 0.9249 0.9246 0.9246 0.9246 0.9246 0.9246

11 0.9289 0.9241 0.9237 0.9237 0.9237 0.9237 0.9237

12 0.9286 0.9238 0.9235 0.9235 0.9235 0.9235 0.9235

Table 2.1 Iteration wise RDLF results for modified 12 bus network

2.1.2 69 Bus System Analysis

Figure 2.4 shows 69 bus balanced loaded radial distribution network with total active

load of 3802.19 KW and reactive load of 2694.6 KVAR supplied by 12.66 KV substation.

The network data is taken from [28] and is also presented in Appendix-B. Table 2.2 presents

the results from the RDLF which are compared with results presented in [26].

Figure 2.4 Single-line diagram of 69 bus network

12

Node RDLF

Sol

Results in

[26] Node

RDLF

Sol

Results in

[26] Node

RDLF

Sol

Results in

[26]

1 1.00000 1 .0000 24 0.95650 0.95660 47 0.99979 0.99979

2 0.99997 0.99997 25 0.95633 0.95643 48 0.99854 0.99854

3 0.99993 0.99993 26 0.95626 0.95636 49 0.99470 0.99470

4 0.99984 0.99984 27 0.95624 0.95634 50 0.99415 0.99415

5 0.99902 0.99902 28 0.99993 0.99993 51 0.97854 0.97854

6 0.99008 0.99009 29 0.99985 0.99985 52 0.97853 0.97853

7 0.98079 0.98079 30 0.99973 0.99973 53 0.97466 0.97466

8 0.97858 0.97858 31 0.99971 0.99971 54 0.97141 0.97142

9 0.97744 0.97745 32 0.99961 0.99961 55 0.96694 0.96694

10 0.97244 0.97245 33 0.99935 0.99935 56 0.96257 0.96257

11 0.97134 0.97135 34 0.99901 0.99901 57 0.94010 0.94010

12 0.96818 0.96819 35 0.99895 0.99895 58 0.92904 0.92904

13 0.96525 0.96526 36 0.99992 0.99992 59 0.92476 0.92476

14 0.96235 0.96237 37 0.99975 0.99975 60 0.91973 0.91974

15 0.95948 0.95950 38 0.99959 0.99959 61 0.91234 0.91234

16 0.95892 0.95897 39 0.99954 0.99954 62 0.91205 0.91205

17 0.95804 0.95809 40 0.99954 0.99954 63 0.91170 0.91166

18 0.95803 0.95808 41 0.99885 0.99884 64 0.90980 0.90976

19 0.95756 0.95761 42 0.99855 0.99855 65 0.90923 0.90919

20 0.95727 0.95731 43 0.99851 0.99851 66 0.97128 0.97129

21 0.95678 0.95683 44 0.99851 0.99850 67 0.97128 0.97129

22 0.95678 0.95683 45 0.99841 0.99841 68 0.96785 0.96786

23 0.95665 0.95675 46 0.99841 0.99840 69 0.96785 0.96786

Table 2.2 Comparison of 69 bus RDLF results with results in [26]

2.2 Summary

This chapter has completely dealt with various RDLF algorithms available in the

literature. It explained RDLF algorithm that has been followed in this thesis. It also presented

the results when the technique is exercised on two networks 30 bus & 69 bus and the results

are matching to that of results published in the literature.

13

Chapter 3

3 Optimal Location and Optimal Generation

The continuously varying electrical load demand is being served by centralized

generations sourced from conventional fuels. Power flows from these centralized generating

stations to the consumers via EHV, HV and LV (where the distribution system is operated)

networks. For a long time it was seen that the distribution network suffers from poor voltage

profile and high loss showing direct effect on consumers. This can be addressed by

harnessing renewable resources and integrating them into the system as distributed

generation. Utilization of renewable energy is also being encouraged by various national

policies aiming to reduce greenhouse gas emissions and ease global warming. Except for the

large capacity based wind and solar power generation (owing to high cost of high-voltage

transformer), all the low rated generating stations are connected to the grid at LV level. This

is an added advantage to DGs as the power transfer is from point to point within the

distribution network, which can reduce the distribution losses. The point in the network

where distribution generation is integrated is called point of common coupling (PCC). As

DGs have direct impact on distribution losses, the choice of PCC is important in this regard.

Wrong choice of PCC affects the target of loss reduction and voltage improvement. It may

also result in increased installed distribution generation capacity for the same target.

Hence, distribution generation must always be installed at the best PCC i.e. at optimal

location. In other words optimal location means the placement for DG that can maximize the

effects like loss reduction and voltage improvement with minimum installed capacity. The

optimal capacity of DG is based on desired voltage improvement and it is the minimum size

of distribution generation to be installed at the optimal location.

Algorithm proposed in [29] has determined the optimal location and sizing of a DG

for the radial distributed network using stochastic optimization technique. It is based on the

movement and intelligence of agents/particles in a swarm, known as Particle Swarm

Optimization. Reduction of line losses in the radial distribution network was the main

objective.

14

Tan et al. in [30] presented a novel multi population-based genetic algorithm for

optimal location and sizing of distributed generation in a radial distribution system. The

objective is to minimize total power losses in the system and improve voltage stability within

the voltage constraints. An analysis similar to that in [31] is carried out on 30 bus system

where primary objective is to minimize the losses,.

Linh et al. in [32] presented a new methodology using artificial bees colony algorithm

for the placement and size of DG in the radial distribution systems.

Siripuram et al. in [33] presented a method which employs Improved Teaching–

Learning Based Optimization (ITLBO) algorithm to determine the optimal placement and

size of DG units in distribution systems. The objective function which can minimize total

power loss has been considered.

All the above described methods present solutions on hourly basis which is not very

practical as we cannot vary the PCC location. This thesis presents a heuristic approach for

finding optimal location and optimal generation capacity using bus ranking technique based

on the level of loss reduction per KW DG installed at that bus. The DG penetration capacity

and optimal location are found by maintaining the minimum voltage greater than 0.95 pu and

the maximum voltage less than 1.05 pu for all the 24 hours. For distribution generation,

mainly sourced from renewable energies, availability is highly intermittent in nature. On the

other hand, load is also of constantly varying nature. This is where the optimal placement of

DG has attracted many researchers’ attention. Appropriate techniques which take variable

nature of both distributed generation and load must be considered for the selection of optimal

location and optimal capacity for the given network.

The hourly variation in load change is brought out using a 24 hrs load curve, as shown

in Figure 3.1, by multiplying the corresponding hour's percentage of maximum load with

total system maximum load [34].

Figure 3.1 Percentage peak load for 24 hrs

15

3.1 Finding Optimal Location and Optimal Capacity

Essential data required for this problem are as follows:

1. Network Topology

2. Network parameters

3. Peak load data

4. 24 hrs average load curve (Percentage full load, Lf)

The following algorithm has been implemented for finding optimal capacity of DG to

be installed at optimal location:

Step 1: Obtain the connectivity matrix from the given network topology along with

the line parameters, resistance and reactance.

Step 2: Find the hourly load demand with the help of peak load demand and 24hr load

curve.

Step 3: Perform RDLF with peak load demand to find the buses which are critically

loaded and the same are considered for the analysis to find optimal location for DG

installation.

Step 4: If Lf is the percentage load in nth

hour and PDGi is the amount of DG

generation in nth

hour at ith

bus, then the net load demand at ith

bus in nth

hour is given by:

( ) ( ) ( )* ( )i

n n

D D n D Gi iP j Q P j Q L f P− = − +

3.1

Step 5: The buses are given scores in ascending order based on the reduction in

system losses, by incrementally varying the capacity of DG installed at the critical nodes, for

all 24 hours. The node with least sum of scores is considered as the optimal location for

installing DG.

Step 6: Once the optimal location is found the capacity of DG is varied by

incrementing/decrementing to obtain the desired voltage improvement.

16

3.2 Results

Two distribution networks of 33 and 69 buses are exercised to validate the proposed

methodology and the results were presented. Penetration level of DG has been found to

maintain the minimum voltage greater than 0.95 and the maximum voltage less than 1.05 for

the whole day.

3.2.1 33 Bus Results

Figure 3.2 shows the single-line diagram of a 33 bus network with total load of 3715

KW and 2300 KVAR supplied by 12.66 KV substation. It has found that busses 8-18 & 28-

33 are critically loaded when initial RDLF is carried with peak load demand. Implementation

of the proposed algorithm resulted bus 8 as the optimal location and 1511 KW as the optimal

generation to maintain voltages between 0.95 pu - 1.05 pu for all 24 hrs. Table 3.1 show the

score obtained by all critical nodes when DG capacity of 1511 KW is installed. It is observed

that the total active loss is 1898 KW and reactive loss of 1262 KVAR with minimum voltage

of 0.9183 for 24 hrs.

Figure 3.2 Single-line diagram of 33 bus network

Sl. No Node Score Sl. No Node Score Sl. No Node Score

1 8 40 7 10 168 13 33 316

2 27 43 8 11 192 14 14 332

3 28 72 9 12 216 15 15 360

4 9 88 10 31 240 16 16 384

5 29 118 11 32 264 17 17 408

6 30 143 12 13 288 18 18 432

Table 3.1 Score obtained for critically loaded nodes in 33 bus network

17


Figure 2.4 shows the single-line diagram of a 69bus network with total load of 3802

KW and 2694.6 KVAR supplied by 12.66 KV substation. It is found that 13 nodes 53-65 are

critically loaded when initial RDLF is carried with peak load demand. Implementation of the

proposed algorithm resulted in bus 61 being the optimal location and 1148 KW the optimal

generation to maintain voltages between 0.95pu - 1.05pu for whole day. Table 3.2 show the

score obtained by all critical nodes when DG capacity of 1148 KW is installed.

Sl. No Node Score Sl. No Node Score

1 61 24 8 57 202

2 62 48 9 65 206

3 63 74 10 56 234

4 60 101 11 55 261

5 59 134 12 54 286

6 64 136 13 53 312

7 58 166

Table 3.2 Score obtained for critically loaded nodes in 69 bus network

Ref [35] solves the 69 bus network for optimal capacity, location of DGs and resulted

in 0.5, 2.25 and 1.25MW as required generations (Total: 4.25MW) at 27, 65 and 69 busses

respectively to maintain 1pu at all the bus. When solved with above described methodology it

resulted to install 4.12 MW at node 61 with system voltages maintained between 0.95-1.05

pu.

3.3 Summary

In this chapter the proposed algorithm has been discussed and implemented on 33 and

69 bus systems. The optimal location and optimal generation for the two networks considered

are also found. The proposed method is also validated by comparing with existing literature.

18

Chapter 4

4 Integration of Distributed Generation

The solution obtained in the previous sections for optimal location and optimal

generation capacity is implemented by integrating renewable energies into the system. The

implementation of the methodology is applied on two considered networks. This chapter

deals with the integration of renewable energies and the effects on reduction in active power

loss, reactive power loss and possible improvement in the system voltage profile. There are

many renewable generations available in the market out of which few are still at research and

development level. Many of these renewables may not be suitable to be used as distributed

generation for power distribution network. In this thesis, work is carried out on the

implementation of Wind, Solar and Biomass as renewable resources for distributed

generation. To bring in the effect of seasonal variations for Wind and Solar on the total

considered renewable generation, the analysis is carried out for a total 6 days in two seasons

for three consecutive days in each. The data of wind speed, solar insolation and temperatures

are taken from [36] for the Hyderabad in India.

This thesis outlines the effects of DG on active loss reduction, reactive loss reduction

and voltage improvements in the form of indices known as LP, LQ and LV respectively [34].

Real losses with DG in hour

Real losses without DG in hour

L (k) =1-P

k

k

4.1

Reactive losses with DG in hour

Reactive losses without DG in hour

L (k) =1-Q

k

k

4.2

V

Minimum Voltage with DG in hour

Minimum Voltage without DG in hour

=L (k)k

k

4.3

19

4.1 Wind

In this chapter wind energy, one of the renewable energy sources considered in this

thesis, is taken as distribution generation and integrated into the power distribution system.

So far, approximately 29,536 MW of renewable power capacity have been installed in India,

which includes about 19,933 MW from wind energy. India is the fifth largest wind power

producer in the world. This thesis models a wind turbine of capacity 10 KW mathematically;

Appendix B.1 gives more insight on the mathematical modelling.

As pointed out in the earlier sections the analysis is carried out on two distribution

networks of size 33 buses and 69 buses. The 33 bus system is a healthier system compared to

69 bus system.

The optimal capacity to be installed for 33 bus system is found to be 1511 KW and

that for the 69 bus system is found to be 1148 KW. Therefore a total of 152 and 115 no's of

10 KW wind turbine respectively are required to be installed respectively. The optimal

locations for the installation are found as node 8 for 33-bus system and node 61 for 69-bus

system.

4.1.1 Results for 33 Bus System

Table 4.1 & 4.2 give hour-wise detailed results like DG (KW), active power loss

(KW), reactive power loss (KVAR) and minimum voltage (pu) attained for the analysis with

and without inclusion of DG for 6 days on 33 bus radial distribution system. It is observed

that, in the absence of DG, the total active loss is 1898 KW and reactive loss of 1262 KVAR

with minimum voltage of 0.9183 pu in 24hrs. Figure 4.1 shows the LP, LQ and LV plots for 6

days and the results are summarized in Table 4.3.

Hr Lf Without DG

With 1511 KW wind DG

Day 1 Day 2 Day 3

Vmin PL QL DG Vmin PL QL DG Vmin PL QL DG Vmin PL QL

1 0.5 0.9606 39 26 0 0.9606 39 26 1415.1 0.988 25 19 129.6 0.9632 34 22

2 0.48 0.9623 35 24 129.6 0.9649 31 20 0 0.9623 35 24 0 0.9623 35 24

3 0.49 0.9614 37 25 568.1 0.9728 21 14 0 0.9614 37 25 0 0.9614 37 25

4 0.5 0.9606 39 26 196.4 0.9646 31 21 0 0.9606 39 26 0 0.9606 39 26

5 0.56 0.9557 49 32 0 0.9557 49 32 196.4 0.9597 40 27 0 0.9557 49 32

20

6 0.52 0.9590 42 28 129.6 0.9616 37 24 0 0.959 42 28 0 0.959 42 28

7 0.54 0.9574 45 30 129.6 0.96 40 26 0 0.9574 45 30 0 0.9574 45 30

8 0.57 0.9549 51 34 0 0.9549 51 34 498.1 0.965 32 21 0 0.9549 51 34

9 0.95 0.9227 147 98 239.5 0.9279 128 85 568.1 0.9348 107 71 314.9 0.9295 123 82

10 1 0.9183 164 109 568.1 0.9305 121 80 1104.2 0.9429 91 61 402.8 0.927 132 87

11 0.95 0.9227 147 98 1038.2 0.9446 84 56 940.9 0.9426 88 59 498.1 0.9334 111 73

12 0.96 0.9218 151 100 1145.6 0.95 77 52 683.6 0.9364 103 68 174.4 0.9256 136 91

13 0.9 0.9271 131 87 568.1 0.9391 94 62 940.9 0.9468 77 51 0 0.9271 131 87

14 0.88 0.9288 125 83 314.9 0.9355 103 68 129.6 0.9316 116 77 0 0.9288 125 83

15 0.85 0.9314 116 77 402.8 0.9399 90 60 0 0.9314 116 77 0 0.9314 116 77

16 0.75 0.9399 90 60 0 0.9399 90 60 0 0.9399 90 60 0 0.9399 90 60

17 0.7 0.9441 78 52 0 0.9441 78 52 0 0.9441 78 52 0 0.9441 78 52

18 0.68 0.9458 73 49 0 0.9458 73 49 0 0.9458 73 49 0 0.9458 73 49

19 0.7 0.9441 78 52 568.1 0.9558 51 34 0 0.9441 78 52 0 0.9441 78 52

20 0.7 0.9441 78 52 0 0.9441 78 52 0 0.9441 78 52 116.1 0.9465 71 47

21 0.59 0.9532 54 36 0 0.9532 54 36 129.6 0.9559 48 32 129.6 0.9559 48 32

22 0.6 0.9524 56 37 129.6 0.9551 50 33 0 0.9524 56 37 129.6 0.9551 50 33

23 0.5 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26 129.6 0.9632 34 22

24 0.48 0.9623 35 24 0 0.9623 35 24 0 0.9623 35 24 0 0.9623 35 24

Table 4.1 Hourly active & reactive losses and minimum voltage attained with and

without integration of wind generation for days 1 - 3

Hr Lf Without DG


Day 4 Day 5 Day 6


1 0.5 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26

2 0.48 0.9623 35 24 0 0.9623 35 24 0 0.9623 35 24 0 0.9623 35 24

3 0.49 0.9614 37 25 0 0.9614 37 25 0 0.9614 37 25 0 0.9614 37 25

4 0.5 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26

5 0.56 0.9557 49 32 0 0.9557 49 32 0 0.9557 49 32 0 0.9557 49 32

6 0.52 0.9590 42 28 0 0.959 42 28 0 0.959 42 28 0 0.959 42 28

7 0.54 0.9574 45 30 0 0.9574 45 30 0 0.9574 45 30 0 0.9574 45 30

21

8 0.57 0.9549 51 34 0 0.9549 51 34 129.6 0.9575 45 30 0 0.9549 51 34

9 0.95 0.9227 147 98 129.6 0.9255 137 91 239.5 0.9279 128 85 196.4 0.9269 132 87

10 1 0.9183 164 109 129.6 0.9212 153 102 940.9 0.9383 100 67 196.4 0.9226 148 98

11 0.95 0.9227 147 98 498.1 0.9334 111 73 940.9 0.9426 88 59 402.8 0.9313 117 78

12 0.96 0.9218 151 100 940.9 0.9417 91 60 402.8 0.9305 120 79 239.5 0.927 131 87

13 0.9 0.9271 131 87 683.6 0.9415 88 58 568.1 0.9391 94 62 402.8 0.9356 103 68

14 0.88 0.9288 125 83 1144.2 0.953 66 44 498.1 0.9393 92 61 290.8 0.935 105 69

15 0.85 0.9314 116 77 568.1 0.9433 82 54 402.8 0.9399 90 60 196.4 0.9355 103 68

16 0.75 0.9399 90 60 568.1 0.9516 60 40 129.6 0.9426 82 54 0 0.9399 90 60

17 0.7 0.9441 78 52 402.8 0.9524 57 38 0 0.9441 78 52 0 0.9441 78 52

18 0.68 0.9458 73 49 196.4 0.9498 62 41 129.6 0.9485 66 44 0 0.9458 73 49

19 0.7 0.9441 78 52 0 0.9441 78 52 0 0.9441 78 52 0 0.9441 78 52

20 0.7 0.9441 78 52 239.5 0.9491 64 43 0 0.9441 78 52 0 0.9441 78 52

21 0.59 0.9532 54 36 239.5 0.9581 44 29 0 0.9532 54 36 0 0.9532 54 36

22 0.6 0.9524 56 37 129.6 0.9551 50 33 0 0.9524 56 37 0 0.9524 56 37

23 0.5 0.9606 39 26 239.5 0.9655 30 20 0 0.9606 39 26 0 0.9606 39 26

24 0.48 0.9623 35 24 402.8 0.9703 23 15 0 0.9623 35 24 0 0.9623 35 24




system

22

Sl. No Day DG for 24 hrs

(KW)

Active loss for 24 hrs

(KW)

Reactive loss for

24 hrs (KVAR) Vmin

0 NO DG 0 1898 1262 0.9183

1 Day 1 6328.2 1541.348 1024.054 0.9279

2 Day 2 6366.5 1567.399 1044.049 0.9314

3 Day 3 2024.7 1762.255 1170.506 0.9256

4 Day 4 6532.3 1530.858 1016.469 0.9212

5 Day 5 4381.9 1607.847 1067.495 0.9279

6 Day 6 1925.1 1753.718 1164.639 0.9226

Table 4.3 24 hrs losses and minimum voltage with wind integration in 33 bus system

4.1.2 Results for 69 Bus System

Table 4.4 and 4.5 give hour-wise detailed results like DG (KW), active power loss

(KW), reactive power loss (KVAR) and minimum voltage (pu) attained for the analysis with

and without inclusion of DG for 6 days on 69 bus radial distribution system. In the absence of

DG, it has been observed that the total active loss is 2571 KW and reactive loss of 1170

KVAR with minimum voltage of 0.9092 pu in 24 hrs. Figure 4.2 show the plots of LP, LQ and

LV for all the 6 considered days and the results are summarizes in Table 4.6.

Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9567 52 24 0 0.9567 52 24 1130 0.9861 23 11 98.7 0.9603 45 21

2 0.48 0.9585 47 22 98.7 0.9621 41 19 0 0.9585 47 22 0 0.9585 47 22

3 0.49 0.9576 50 23 432.7 0.9732 27 13 0 0.9576 49 23 0 0.9576 49 23

4 0.5 0.9567 52 24 149.6 0.9622 42 19 0 0.9567 52 24 0 0.9567 52 24

5 0.56 0.9512 65 30 0 0.9512 65 30 149.6 0.9568 54 25 0 0.9512 65 30

6 0.52 0.9549 56 26 98.7 0.9585 49 23 0 0.9549 56 26 0 0.9549 56 26

7 0.54 0.9531 61 28 98.7 0.9567 53 25 0 0.9531 61 28 0 0.9531 61 28

8 0.57 0.9503 68 31 0 0.9503 68 31 379.3 0.9642 43 20 0 0.9503 68 31

9 0.95 0.9142 201 91 182.4 0.9214 175 80 432.7 0.9312 145 67 239.9 0.9237 167 77

10 1 0.9092 225 102 432.7 0.9264 164 76 886.6 0.9437 120 57 306.8 0.9215 180 83

23

11 0.95 0.9142 201 91 790.7 0.9447 111 53 716.5 0.942 117 56 379.3 0.9291 151 70

12 0.96 0.9132 206 94 1024.8 0.9524 98 48 520.6 0.9336 139 65 132.8 0.9185 186 85

13 0.9 0.9191 179 81 432.7 0.9359 127 59 716.5 0.9466 102 48 0 0.9191 179 81

14 0.88 0.9211 171 78 239.9 0.9304 140 65 98.7 0.925 157 72 0 0.9211 170 78

15 0.85 0.924 158 72 306.8 0.9359 122 57 0 0.924 158 72 0 0.924 158 72

16 0.75 0.9336 121 55 0 0.9336 121 55 0 0.9336 121 55 0 0.9336 121 55

17 0.7 0.9383 105 48 0 0.9383 105 48 0 0.9383 105 48 0 0.9383 105 48

18 0.68 0.9402 98 45 0 0.9402 98 45 0 0.9402 98 45 0 0.9402 98 45

19 0.7 0.9383 105 48 432.7 0.9545 68 32 0 0.9383 105 48 0 0.9383 105 48

20 0.7 0.9383 105 48 0 0.9383 105 48 0 0.9383 105 48 88.4 0.9416 96 44

21 0.59 0.9485 73 33 0 0.9485 73 33 98.7 0.9522 65 30 98.7 0.9522 65 30

22 0.6 0.9476 76 34 98.7 0.9513 67 31 0 0.9476 76 34 98.7 0.9513 67 31

23 0.5 0.9567 52 24 0 0.9567 52 24 0 0.9567 52 24 98.7 0.9603 45 21

24 0.48 0.9585 47 22 0 0.9585 47 22 0 0.9585 47 22 0 0.9585 47 22



Hr Lf Without DG


Day 4 Day 5 Day 6


1 0.5 0.9567 51.6 23.6 0 0.9567 52 24 0 0.9567 52 24 0 0.9567 52 24

2 0.48 0.9585 47.4 21.6 0 0.9585 47 22 0 0.9585 47 22 0 0.9585 47 22

3 0.49 0.9576 49.5 22.6 0 0.9576 49 23 0 0.9576 49 23 0 0.9576 49 23

4 0.5 0.9567 51.6 23.6 0 0.9567 52 24 0 0.9567 52 24 0 0.9567 52 24

5 0.56 0.9512 65.4 29.8 0 0.9512 65 30 0 0.9512 65 30 0 0.9512 65 30

6 0.52 0.9549 56.0 25.6 0 0.9549 56 26 0 0.9549 56 26 0 0.9549 56 26

7 0.54 0.9531 60.6 27.6 0 0.9531 61 28 0 0.9531 61 28 0 0.9531 61 28

8 0.57 0.9503 67.8 30.9 0 0.9503 68 31 98.68 0.954 60 28 0 0.9503 68 31

9 0.95 0.9142 201.2 91.4 98.679 0.9181 187 85 182.39 0.9214 175 80 149.58 0.9202 179 82

10 1 0.9092 225.0 102.2 98.679 0.9132 209 96 716.55 0.9373 134 63 149.58 0.9152 202 92

11 0.95 0.9142 201.2 91.4 379.319 0.9291 151 70 716.55 0.942 117 56 306.76 0.9263 159 74

12 0.96 0.9132 205.8 93.5 716.542 0.941 121 57 306.76 0.9254 163 75 182.39 0.9205 179 82

13 0.9 0.9191 178.9 81.3 520.59 0.9393 118 55 432.68 0.9359 127 59 306.71 0.9311 140 65

24

14 0.88 0.9211 170.5 77.5 886.61 0.9547 85 41 379.32 0.9358 125 58 221.44 0.9297 142 66

15 0.85 0.924 158.2 71.9 432.683 0.9406 110 51 306.76 0.9359 122 57 149.57 0.9298 139 64

16 0.75 0.9336 121.0 55.1 432.68 0.9499 81 38 98.68 0.9373 110 51 0 0.9336 121 55

17 0.7 0.9383 104.5 47.6 306.7651 0.9498 77 36 0 0.9383 105 48 0 0.9383 105 48

18 0.68 0.9402 98.3 44.8 149.5772 0.9458 84 39 98.68 0.9439 89 41 0 0.9402 98 45

19 0.7 0.9383 104.5 47.6 0 0.9383 105 48 0 0.9383 105 48 0 0.9383 105 48

20 0.7 0.9383 104.5 47.6 182.3849 0.9452 87 40 0 0.9383 105 48 0 0.9383 105 48

21 0.59 0.9485 72.9 33.2 182.3849 0.9553 59 27 0 0.9485 73 33 0 0.9485 73 33

22 0.6 0.9476 75.5 34.4 98.6793 0.9513 67 31 0 0.9476 76 34 0 0.9476 76 34

23 0.5 0.9567 51.6 23.6 182.3849 0.9633 40 19 0 0.9567 52 24 0 0.9567 52 24

24 0.48 0.9585 47.4 21.6 306.7651 0.9696 31 15 0 0.9585 47 22 0 0.9585 47 22




system

25

Sl. No Day

Distribution

Generation of 24

hrs

Active loss in 24 hrs

in KW

Reactive loss in

24 hrs in KVAR Vmin

0 No DG 0 2571 1170 0.9092

1 Day 1 4820.0 2071 957 0.9214

2 Day 2 5129 2096 967 0.924

3 Day 3 1542 2384 1091 0.9185

4 Day 4 4975 2060 952 0.9132

5 Day 5 3337 2166 998 0.921

6 Day 6 1466 2372 1085 0.9152

Table 4.6 24 hrs losses and minimum voltage with Wind Integration in 69 bus system

4.2 Summary

In this chapter wind renewable distributed generation has been integrated into

considered 33 and 69 bus distribution systems and various results like loss reduction and

minimum voltage improvement are tabulated. It is observed that the intermittent nature of

wind availability has clearly resulted in intermittent improvement. It is seen that for 33 bus

system the average voltage has raised from 0.9182 to 0.9261 and for 69 bus system 0.9092 to

0.9188 for the analysed 6-days, thus giving scope for more improvement.

26

Chapter 5

5 Integration of Distribution Generation: Solar

As we have seen the intermittent nature of wind and its discontinued nature in

improvement of loss reduction and voltage profile, this makes wind energy a less reliable

source for DG. This chapter deals with integration of solar generation which is more

predictable when compared to wind energy. India being a tropical region solar energy is

available almost 300-330 days of a calendar year. India has approximately 2079 MW of solar

installed capacity at present. Solar has many advantages like low risk, less construction time,

zero running/fuel cost etc. There are many technologies available for harnessing solar energy

like non-concentrating or flat plate type, concentrating or solar thermal etc. In this thesis,

solar power from solar photovoltaic (PV) energy generation is considered and 60 W solar-

panel is modelled based on [37] - [38]. The details of this model can be found in Appendix

A.2.

5.1 Solar Integration

The previously considered optimal capacities of wind generation are now replaced

with solar generation at the same locations in both networks. As solar energy is available only

during 6-18 hours, the results are not presented for the remaining hours. However the total

losses in 24 hrs are tabulated at the end. Tables 5.1-5.2 and Tables 5.4 -5.5 show the results

obtained due to the integration of solar PV system for 33 and 69 bus systems respectively.

Figures 5.1 - 5.3 and Figures 5.1 & 5.2 shows the LP, LQ and LV curves for 33 and 69 bus

systems respectively. It is observed that the total active losses are 1898 KW & 2571 KW and

reactive losses 1262 KVAR & 1170 KVAR with minimum voltage of 0.9183 & 0.9092 pu

for 33 and 69 bus systems respectively, for 24 hrs. Tables 5.3 & 5.6 present the cumulative

results for the whole of 24hours.

27


Hr Lf Without DG

With 1511 KW solar DG

Day 1 Day 2 Day 3


7 0.54 0.9574 45 30 152.5 0.9605 39 26 159.6 0.9606 39 26 72.7 0.9588 42 28

8 0.57 0.9549 51 34 399.4 0.963 35 23 528.4 0.9656 31 21 357.4 0.9621 36 24

9 0.95 0.9227 147 98 922.5 0.9422 89 59 919.9 0.9421 89 59 737.4 0.9384 98 65

10 1 0.9183 164 109 1292.3 0.9455 87 58 1248 0.9446 88 59 1097.2 0.9416 94 62

11 0.95 0.9227 147 98 1483.7 0.9536 72 49 1461.4 0.9531 72 49 1356.9 0.951 75 50

12 0.96 0.9218 151 100 1509.4 0.9554 72 49 1450.8 0.9554 72 49 1406.2 0.9532 73 50

13 0.9 0.9271 131 87 1500.1 0.9603 61 42 1442.7 0.9606 61 42 1450.1 0.9603 61 42

14 0.88 0.9288 125 83 1403.1 0.9577 61 41 1458.6 0.9608 59 41 1480.4 0.9612 59 41

15 0.85 0.9314 116 77 1337 0.9589 57 39 1338 0.9589 57 39 1445.6 0.961 55 38

16 0.75 0.9399 90 60 877.8 0.9579 50 33 1027.5 0.9609 47 31 1218.6 0.9647 43 30

17 0.7 0.9441 78 52 598.7 0.9564 50 33 614.9 0.9567 49 33 594.6 0.9563 50 33

18 0.68 0.9458 73 49 328.9 0.9526 56 37 265.7 0.9513 59 39 425.5 0.9545 52 35


without integration of solar generation for days 1 - 3

Hr Lf Without DG


Day 4 Day 5 Day 6


7 0.54 0.9574 45 30 3 0.9574 45 30 0 0.9574 45 30 13.2 0.9576 45 30

8 0.57 0.9549 51 34 172 0.9584 43 29 74.3 0.9564 47 31 234 0.9597 41 27

9 0.95 0.9227 147 98 601.4 0.9355 105 69 265.8 0.9284 126 84 580.2 0.9351 106 70

10 1 0.9183 164 109 894.2 0.9374 103 68 397.8 0.9269 132 88 889.1 0.9373 103 68

11 0.95 0.9227 147 98 1198.5 0.9478 79 53 811.6 0.9399 94 62 1096.5 0.9458 82 55

12 0.96 0.9218 151 100 1195.9 0.9469 81 54 667.1 0.936 104 69 1220.5 0.9474 80 54

13 0.9 0.9271 131 87 926.6 0.9465 77 51 883.2 0.9456 79 53 1135.6 0.9507 70 47

28

14 0.88 0.9288 125 83 877.7 0.9471 75 50 784.6 0.9452 79 52 893.3 0.9475 74 49

15 0.85 0.9314 116 77 517.2 0.9422 84 56 531.1 0.9425 83 55 698 0.946 76 50

16 0.75 0.9399 90 60 388.3 0.948 68 45 303.8 0.9462 72 48 288.2 0.9459 73 48

17 0.7 0.9441 78 52 445.1 0.9549 67 32 457.2 0.9554 66 31 442.1 0.9548 67 32

18 0.68 0.9458 73 49 244.6 0.9493 76 35 197.5 0.9476 80 37 316.4 0.952 71 33



Figure 5.1 24 hrs LP , Lq and Lv curves for 6 days with integration of solar DG on 33 bus

system

Sl. No Day Distribution Generation

of 24 hrs

Active loss in 24-Hrs

in KW

Reactive loss in

24- hrs in KVAR Vmin

1 Day 1 11814 1308.0308 875.8878 0.9422

2 Day 2 11954 1301.0054 871.8573 0.9421

3 Day 3 11672 1317.1495 882.174 0.9384

4 Day 4 6894.1 1483.3672 986.1327 0.9355

5 Day 5 4924 1580.761 1049.4146 0.9269

6 Day 6 7144.6 1474.7913 980.7241 0.9351

Table 5.3 24 hrs losses and minimum voltage with solar integration in 33 bus system

29


Hr Lf Without DG


Day 1 Day 2 Day 3


7 0.54 0.9531 60.6 27.6 113.4 0.9572 52 24 118.7 0.9574 52 24 54.1 0.9551 57 26

8 0.57 0.9503 67.8 30.9 296.9 0.9612 47 22 392.9 0.9647 42 20 265.7 0.9601 49 23

9 0.95 0.9142 201.2 91.4 685.8 0.9408 120 57 683.9 0.9407 120 57 548.3 0.9356 133 62

10 1 0.9092 225.0 102.2 960.8 0.9464 114 55 927.9 0.9452 117 56 815.8 0.941 125 60

11 0.95 0.9142 201.2 91.4 1103.1 0.9562 92 44 1086.5 0.9556 92 45 1008.8 0.9528 97 47

12 0.96 0.9132 205.8 93.5 1104 0.959 89 43 1102.1 0.9589 89 43 1019.8 0.9559 93 45

13 0.9 0.9191 178.9 81.3 1101.5 0.9642 75 37 1121.4 0.965 75 36 1104.5 0.9644 75 37

14 0.88 0.9211 170.5 77.5 1043.2 0.9604 77 37 1058.8 0.9645 72 35 1075 0.9651 72 35

15 0.85 0.924 158.2 71.9 994 0.9613 72 35 994.8 0.9613 72 35 1074.8 0.9642 69 34

16 0.75 0.9336 121.0 55.1 652.6 0.958 66 32 763.9 0.962 61 29 906 0.9671 55 27

17 0.7 0.9383 104.5 47.6 445.1 0.9549 67 32 457.2 0.9554 66 31 442.1 0.9548 67 32

18 0.68 0.9402 98.3 44.8 244.6 0.9493 76 35 197.5 0.9476 80 37 316.4 0.952 71 33


without integration of solar DG for days 1 - 3

Hr Lf Without DG

Day 4 Day 5 Day 6


7 0.54 0.9531 60.6 27.6 2.20 0.9532 60 28 0 0.9531 61 28 9.84 0.9534 60 27

8 0.57 0.9503 67.8 30.9 127.91 0.9551 58 27 55.25 0.9524 63 29 173.94 0.9568 55 25

9 0.95 0.9142 201.2 91.4 447.16 0.9317 143 67 197.61 0.922 173 79 431.4 0.9311 145 67

10 1 0.9092 225.0 102.2 664.83 0.9353 139 65 295.79 0.921 181 84 661.07 0.9352 139 66

11 0.95 0.9142 201.2 91.4 891.05 0.9485 104 50 603.45 0.9377 127 60 815.23 0.9456 109 52

12 0.96 0.9132 205.8 93.5 889.12 0.9475 107 51 495.96 0.9327 142 66 907.47 0.9481 106 51

13 0.9 0.9191 178.9 81.3 688.91 0.9456 104 49 656.63 0.9444 106 50 844.29 0.9513 93 45

14 0.88 0.9211 170.5 77.5 652.56 0.9461 101 48 583.32 0.9435 106 50 664.14 0.9465 100 47

15 0.85 0.924 158.2 71.9 384.51 0.9388 114 53 394.9 0.9392 113 53 518.94 0.9439 102 48

30

16 0.75 0.9336 121.0 55.1 288.68 0.9445 92 43 225.86 0.9422 98 45 214.31 0.9417 99 46

17 0.7 0.9383 104.5 47.6 86.59 0.9416 96 44 140.2 0.9436 91 42 64.48 0.9407 98 45

18 0.68 0.9402 98.3 44.8 2.12 0.9402 98 45 12.13 0.9406 97 44 6.8821 0.9404 98 44



Figure 5.2 24 hrs LP , Lq and Lv curves for 6 days with integration of solar DG in 69 bus

system


of 24 hrs

Active loss in 24-Hrs in

KW

Reactive loss in

24- hrs in KVAR

Vmin

1 Day 1 8951.4 1726.7565 808.3309 0.9383

2 Day 2 9234 1714.4679 802.9424 0.9383

3 Day 3 8952.8 1738.5916 813.3387 0.9356

4 Day 4 5126 1994.9659 924.2751 0.932

5 Day 5 3661.079 2136.8963 985.2109 0.921

6 Day 6 5311.981 1981.9449 918.6492 0.9311

Table 5.6 24 hrs losses and minimum voltage with solar integration in 69 bus system

31

5.2 Combination of Solar and Wind

Renewable generation from solar in the above case is limited to only 10-12 hours of

availability. This leads to zero power generation for 12 hours from 6PM - 6AM, which

encourages to explore new combinations of energy sources during this solar off hours. This

sub-section deals with the combination of solar and wind energies. The total required

installation capacity is divided equally among both the sources for distribution generation.

These two sources are integrated at the same optimal location. Tables 5.7 -5.8 and 5.10 - 5.11

show without and with distribution generation the active & reactive losses and minimum

voltage for all 24 hours of 6 days for 33bus and 69 bus systems and are summarized in Tables

5.9 & 5.12 respectively. It is observed that the total active loss is 1898 KW & 2571 KW and

reactive loss of 1262 KVAR & 1170 KVAR with minimum voltage of 0.9183 & 0.9092 in 24

hrs for 33 bus and 69 bus systems respectively, in absence of DG. Figures 5.3 & 5.4 shows

the curves of LP, LQ & LV indices for both the 33 and 69 bus networks respectively.


Hr Lf Without DG

1511 KW installed capacity with solar PV (761 KW) and wind (750 KW)

Day 1 Day 2 Day 3


1 0.5 0.9606 39 26 0 0.9606 39 26 802.2 0.9766 19 13 64.4 0.9619 36 24

2 0.48 0.9623 35 24 64.4 0.9636 33 22 0 0.9623 35 24 0 0.9623 35 24

3 0.49 0.9614 37 25 282.2 0.9671 27 18 0 0.9614 37 25 0 0.9614 37 25

4 0.5 0.9606 39 26 97.6 0.9626 35 23 0 0.9606 39 26 0 0.9606 39 26

5 0.56 0.9557 49 32 0 0.9557 49 32 97.6 0.9577 44 29 0 0.9557 49 32

6 0.52 0.9590 42 28 67.4 0.9604 39 26 1.4 0.959 42 28 0 0.959 42 28

7 0.54 0.9574 45 30 141.1 0.9602 39 26 80.3 0.959 42 28 36.6 0.9581 44 29

8 0.57 0.9549 51 34 200.9 0.959 42 28 513.2 0.9653 32 21 179.8 0.9586 43 28

9 0.95 0.9227 147 98 583 0.9351 106 70 745 0.9385 97 64 527.4 0.934 109 72

10 1 0.9183 164 109 932.3 0.9382 101 67 1206.1 0.9438 90 60 752.1 0.9344 110 73

11 0.95 0.9227 147 98 1262.1 0.9491 77 52 1202.5 0.9479 79 53 930 0.9423 89 59

12 0.96 0.9218 151 100 1483 0.9527 74 50 1152.9 0.9461 83 55 844.4 0.9397 95 63

13 0.9 0.9271 131 87 1095.2 0.9499 72 48 1293.8 0.9539 66 45 815.1 0.9442 82 54

14 0.88 0.9288 125 83 862.3 0.9468 75 50 848.5 0.9466 76 51 795.1 0.9455 78 52

32

15 0.85 0.9314 116 77 872.7 0.9495 69 46 673.1 0.9455 77 51 727.3 0.9466 74 49

16 0.75 0.9399 90 60 441.6 0.9491 65 43 516.9 0.9506 62 41 613.1 0.9526 58 39

17 0.7 0.9441 78 52 301.2 0.9503 62 41 309.3 0.9505 61 40 299.2 0.9503 62 41

18 0.68 0.9458 73 49 165.5 0.9492 64 42 133.6 0.9485 66 44 214.1 0.9502 62 41

19 0.7 0.9441 78 52 283.5 0.95 62 41 17.8 0.9445 76 51 14.6 0.9444 77 51

20 0.7 0.9441 78 52 0 0.9441 78 52 0 0.9441 78 52 57.7 0.9453 74 49

21 0.59 0.9532 54 36 0 0.9532 54 36 64.4 0.9546 51 34 64.4 0.9546 51 34

22 0.6 0.9524 56 37 64.4 0.9537 53 35 0 0.9524 56 37 64.4 0.9537 53 35

23 0.5 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26 64.4 0.9619 36 24

24 0.48 0.9623 35 24 0 0.9623 35 24 0 0.9623 35 24 0 0.9623 35 24


without integration of combination solar & wind generation for days 1 - 3

Hr Lf Without DG


Day 4 Day 5 Day 6


1 0.5 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26

2 0.48 0.9623 35 24 0 0.9623 35 24 0 0.9623 35 24 0 0.9623 35 24

3 0.49 0.9614 37 25 0 0.9614 37 25 0 0.9614 37 25 0 0.9614 37 25

4 0.5 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26 0 0.9606 39 26

5 0.56 0.9557 49 32 0 0.9557 49 32 0 0.9557 49 32 0 0.9557 49 32

6 0.52 0.9590 42 28 0 0.959 42 28 0 0.959 42 28 0 0.959 42 28

7 0.54 0.9574 45 30 1.5 0.9574 45 30 0 0.9574 45 30 6.7 0.9575 45 30

8 0.57 0.9549 51 34 86.6 0.9567 47 31 101.7 0.957 46 30 117.7 0.9573 45 30

9 0.95 0.9227 147 98 366.9 0.9306 119 79 252.7 0.9282 127 85 389.5 0.9311 118 78

10 1 0.9183 164 109 514.2 0.9294 124 82 667.5 0.9326 115 76 544.9 0.9301 122 81

11 0.95 0.9227 147 98 850.3 0.9407 92 61 875.6 0.9412 91 60 751.7 0.9387 97 64

12 0.96 0.9218 151 100 1069 0.9444 85 57 535.7 0.9333 111 74 733 0.9374 100 66

13 0.9 0.9271 131 87 805.7 0.944 82 55 726.5 0.9424 86 57 771.4 0.9433 84 56

14 0.88 0.9288 125 83 1019.8 0.95 70 47 642.1 0.9423 85 56 593.8 0.9413 87 58

15 0.85 0.9314 116 77 542.4 0.9428 83 55 467.3 0.9412 87 57 448.7 0.9408 88 58

16 0.75 0.9399 90 60 477.5 0.9498 64 42 217.2 0.9444 77 51 145 0.9429 81 53

33

17 0.7 0.9441 78 52 258.7 0.9495 64 42 94.9 0.9461 72 48 43.6 0.945 75 50

18 0.68 0.9458 73 49 99 0.9478 67 45 72.6 0.9473 69 46 4.7 0.9459 73 48

19 0.7 0.9441 78 52 0 0.9441 78 52 0 0.9441 78 52 0 0.9441 78 52

20 0.7 0.9441 78 52 118.9 0.9466 71 47 0 0.9441 78 52 0 0.9441 78 52

21 0.59 0.9532 54 36 118.9 0.9557 49 32 0 0.9532 54 36 0 0.9532 54 36

22 0.6 0.9524 56 37 64.4 0.9537 53 35 0 0.9524 56 37 0 0.9524 56 37

23 0.5 0.9606 39 26 118.9 0.963 34 23 0 0.9606 39 26 0 0.9606 39 26

24 0.48 0.9623 35 24 200.1 0.9663 28 19 0 0.9623 35 24 0 0.9623 35 24


without integration of combination solar & wind generation for days 4 - 6

Figure 5.3 24 hrs LP , Lq and Lv curves for 6 days with integration of combination of

wind & solar DG in 33 bus system

Sl. No Day Distribution Generation of

24 hrs

Active loss in 24-Hrs in

KW

Reactive loss in


1 Day 1 9200.4 1388.5273 923.6896 0.9351

2 Day 2 9658.6 1381.6499 919.7073 0.9385

3 Day 3 7064 1469.1564 975.0784 0.934

4 Day 4 6712.8 1495.5608 992.8978 0.9294

5 Day 5 4654 1590.5179 1055.7008 0.9282

6 Day 6 4551 1594.414 1058.2992 0.9301

Table 5.9 24 hrs Losses and minimum voltage with combination of solar and wind

integration in 33 bus system

34


Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9567 52 24 0 0.9567 52 24 609.7 0.9785 24 12 48.9 0.9585 48 22

2 0.48 0.9585 47 22 48.9 0.9603 44 20 0 0.9585 47 22 0 0.9585 47 22

3 0.49 0.9576 50 23 214.5 0.9654 37 17 0 0.9576 49 23 0 0.9576 49 23

4 0.5 0.9567 52 24 74.1 0.9594 47 21 0 0.9567 52 24 0 0.9567 52 24

5 0.56 0.9512 65 30 0 0.9512 65 30 74.1 0.954 60 27 0 0.9512 65 30

6 0.52 0.9549 56 26 51.2 0.9568 52 24 1.1 0.9549 56 26 0 0.9549 56 26

7 0.54 0.9531 61 28 107.3 0.957 53 24 61.1 0.9553 56 26 27.9 0.9541 58 27

8 0.57 0.9503 68 31 152.9 0.956 56 26 390.3 0.9646 42 20 136.9 0.9554 57 27

9 0.95 0.9142 201 91 443.6 0.9316 143 67 566.7 0.9363 131 61 401.2 0.93 148 69

10 1 0.9092 225 102 709.3 0.937 135 64 917.3 0.9448 117 56 572.2 0.9318 148 70

11 0.95 0.9142 201 91 960 0.951 100 48 914.7 0.9493 102 49 707.6 0.9416 118 56

12 0.96 0.9132 206 94 1128 0.9562 93 45 877.1 0.947 108 52 642.5 0.9382 127 60

13 0.9 0.9191 179 81 833.2 0.9509 94 45 984.2 0.9564 85 41 620.3 0.943 109 52

14 0.88 0.9211 171 78 656.2 0.9462 100 48 645.7 0.9458 101 48 605.1 0.9443 104 49

15 0.85 0.924 158 72 664 0.9493 91 43 512.3 0.9436 103 48 553.5 0.9452 100 47

16 0.75 0.9336 121 55 336.1 0.9463 88 41 393.4 0.9484 84 39 466.6 0.9511 78 37

17 0.7 0.9383 105 48 229.2 0.9469 83 38 235.4 0.9472 83 38 227.7 0.9469 83 39

18 0.68 0.9402 98 45 125.9 0.9449 86 40 101.7 0.944 89 41 162.9 0.9463 83 38

19 0.7 0.9383 105 48 215.5 0.9464 84 39 13.6 0.9388 103 47 11.1 0.9387 103 47

20 0.7 0.9383 105 48 0 0.9383 105 48 0 0.9383 105 48 43.8 0.9399 100 46

21 0.59 0.9485 73 33 0 0.9485 73 33 48.9 0.9503 69 32 48.9 0.9503 69 32

22 0.6 0.9476 76 34 48.9 0.9494 71 33 0 0.9476 76 34 48.9 0.9494 71 33

23 0.5 0.9567 52 24 0 0.9567 52 24 0 0.9567 52 24 48.9 0.9585 48 22

24 0.48 0.9585 47 22 0 0.9585 47 22 0 0.9585 47 22 0 0.9585 47 22


without integration of solar and wind generation for days 1 - 3

35

Hr Lf Without DG


Day 4 Day 5 Day 6


1 0.5 0.9567 52 24 0 0.9567 52 24 0 0.9567 52 24 0 0.9567 52 24

2 0.48 0.9585 47 22 0 0.9585 47 22 0 0.9585 47 22 0 0.9585 47 22

3 0.49 0.9576 50 23 0 0.9576 49 23 0 0.9576 49 23 0 0.9576 49 23

4 0.5 0.9567 52 24 0 0.9567 52 24 0 0.9567 52 24 0 0.9567 52 24

5 0.56 0.9512 65 30 0 0.9512 65 30 0 0.9512 65 30 0 0.9512 65 30

6 0.52 0.9549 56 26 0 0.9549 56 26 0 0.9549 56 26 0 0.9549 56 26

7 0.54 0.9531 61 28 1.1331 0.9531 60 28 0 0.9531 61 28 5.0656 0.9533 60 27

8 0.57 0.9503 68 31 65.8742 0.9528 63 29 77.3641 0.9532 62 28 89.5812 0.9537 61 28

9 0.95 0.9142 201 91 279.1973 0.9252 163 75 192.1664 0.9218 174 80 296.3045 0.9259 160 74

10 1 0.9092 225 102 391.2985 0.9248 169 78 507.4873 0.9293 155 73 414.5871 0.9257 166 77

11 0.95 0.9142 201 91 646.9013 0.9393 123 58 665.9321 0.9401 122 57 571.8915 0.9365 130 61

12 0.96 0.9132 206 94 813.0552 0.9446 113 54 407.4686 0.9293 151 70 557.7474 0.935 135 63

13 0.9 0.9191 179 81 612.8182 0.9427 110 52 552.6243 0.9405 115 54 586.8603 0.9418 112 53

14 0.88 0.9211 171 78 775.5209 0.9506 92 44 488.4197 0.9399 115 54 451.7915 0.9385 118 55

15 0.85 0.924 158 72 412.4813 0.9399 112 52 355.4207 0.9377 117 55 341.3906 0.9372 119 55

16 0.75 0.9336 121 55 363.1304 0.9473 86 40 165.2285 0.9399 104 48 110.3675 0.9378 109 50

17 0.7 0.9383 105 48 196.6446 0.9457 86 40 72.1978 0.941 97 45 33.208 0.9395 101 46

18 0.68 0.9402 98 45 75.2276 0.943 91 42 55.1576 0.9422 93 42 3.5443 0.9403 98 45

19 0.7 0.9383 105 48 0 0.9383 105 48 0 0.9383 105 48 0 0.9383 105 48

20 0.7 0.9383 105 48 90.3995 0.9417 95 44 0 0.9383 105 48 0 0.9383 105 48

21 0.59 0.9485 73 33 90.3995 0.9519 66 30 0 0.9485 73 33 0 0.9485 73 33

22 0.6 0.9476 76 34 48.9106 0.9494 71 33 0 0.9476 76 34 0 0.9476 76 34

23 0.5 0.9567 52 24 90.3995 0.96 46 21 0 0.9567 52 24 0 0.9567 52 24

24 0.48 0.9585 47 22 152.0488 0.964 38 18 0 0.9585 47 22 0 0.9585 47 22


without integration of solar and wind generation for days 4 - 6

36

Figure 5.4 24 hrs LP , Lq and Lv curves for 6 days with integration of combination of

wind & solar DG in 69 bus system


of 24 hrs


in KW

Reactive loss in

24 hrs in KVAR Vmin

1 Day 1 6999 1851.5933 862.8422 0.9316

2 Day 2 7347 1839.7647 857.6534 0.9363

3 Day 3 5375 1972.8632 915.1612 0.93

4 Day 4 5105.441 2009.464 930.7326 0.9248

5 Day 5 3539.467 2143.0077 987.8574 0.9218

6 Day 6 3462.34 2148.0753 990.0215 0.9257

Table 5.12 24 hrs losses and minimum voltage with wind and solar integration in 69 bus

system

5.3 Summary

In this chapter implementation of integration of solar PV as renewable energy source

has been presented. An important difference between wind and solar PV based energy

systems is that the solar PV system is off during hours 6PM-6AM apart from its intermittent

nature whereas wind is available during 0-24 hours though highly intermittent. It has been

observed that voltage has improved more in the case when only solar PV system is used when

compared to the case with wind or the combination of wind and solar. Analysis shows that

the system with solar alone has good improvement in voltage but still less than 0.95.

37

Chapter 6

6. Integration of Distribution Generation: Biomass

In the previous chapters implementation of wind, solar and combination of wind &

solar is seen. It was noticed that despite adding optimal generation capacity not all voltages

are greater than 0.95 pu. This is due to the intermittence nature of availability of renewable

sources. The solution to this problem can be better achieved by introducing a renewable

source which can deliver power continuously. One such source is biomass. Biomass is a

resource which is derived from the carbonaceous waste of various human and natural

activities. Biomass, a renewable energy source, is biological material derived from living, or

recently living organisms, such as wood waste. Biomass is used as a fuel in power generation

which replaces the use of fossil fuel to some extent. Biomass can be converted to other

useable forms of energy like producer gas, methane gas or transportation fuels such as

ethanol and biodiesel. After China, India is the second largest in farm output and so far it has

an installed capacity of 3776 MW from bio energies.

It is a known fact that installation of biomass plant with bigger capacity is very

difficult in terms of fuel availability. Hence in this thesis, considering the practical field

situation only a portion of required capacity to be installed is shifted to Biomass. For 33 bus

system and for 69 bus system, a portion of optimal capacity to be installed is substituted by

integration of biomass plants and the remaining portion of capacity is supplied by wind, solar

and combination of wind & solar energies. Each is presented as a case study in three different

sections. The amount of input required for their generation is mentioned in Appendix A.3.

6.1 33 Bus Results

The total capacity to be installed as distribution generation is 1511 KW. Out of which

300 KW is substituted with biomass generation. The remaining 1211 KW is supplied by wind

and solar energies. The analysis is done for the three different cases as described in Table 6.1.

38

Case

No Case Strategy Results Table

Summary of

results

LP, LQ and LV

graphs

1 1511 KW installed capacity with wind (1210

KW) and biomass (300 KW) 6.2 - 6.3 6.4 6.1

2 1511 KW installed capacity with solar PV

(1211 KW) and biomass (300 KW) 6.5 - 6.6 6.7 6.2

3 1511 KW installed capacity with solar PV (611

KW), wind (600 KW) and biomass (300 KW) 6.8 - 6.9 6.10 6.3

Table 6.1 Different strategies of integrating biomass in 33 bus System

6.1.1 Case 1: Integration of biomass with wind

Hr Lf Without DG

1511 KW installed capacity with wind (1211 KW) and biomass (300 KW)

Day 1 Day 2 Day 3


1 0.5 0.9606 39 26 300 0.9667 28 19 1494.2 0.9877 25 19 403.8 0.9687 25 17

2 0.48 0.9623 35 24 403.8 0.9703 23 15 300 0.9683 26 17 300 0.9683 26 17

3 0.49 0.9614 37 25 755.3 0.9764 19 13 300 0.9675 27 18 300 0.9675 27 18

4 0.5 0.9606 39 26 457.4 0.9698 24 16 300 0.9667 28 19 300 0.9667 28 19

5 0.56 0.9557 49 32 300 0.9618 37 24 457.4 0.965 32 21 300 0.9618 37 24

6 0.52 0.9590 42 28 403.8 0.9671 28 19 300 0.965 31 20 300 0.965 31 20

7 0.54 0.9574 45 30 403.8 0.9655 31 20 300 0.9634 34 22 300 0.9634 34 22

8 0.57 0.9549 51 34 300 0.961 38 25 699.1 0.969 28 19 300 0.961 38 25

9 0.95 0.9227 147 98 491.9 0.9332 111 74 755.3 0.9387 97 64 552.4 0.9345 108 71

10 1 0.9183 164 109 755.3 0.9345 110 73 1232.9 0.9443 89 59 622.8 0.9317 117 78

11 0.95 0.9227 147 98 1132 0.9465 81 54 1053.9 0.9449 84 56 699.1 0.9376 99 66

12 0.96 0.9218 151 100 1378.2 0.9506 76 51 847.8 0.9398 95 63 439.7 0.9313 117 78

13 0.9 0.9271 131 87 755.3 0.943 84 56 1053.9 0.9491 73 49 300 0.9335 110 73

14 0.88 0.9288 125 83 552.4 0.9404 89 59 403.8 0.9373 98 65 300 0.9352 104 69

15 0.85 0.9314 116 77 622.8 0.9444 79 52 300 0.9377 96 64 300 0.9377 96 64

16 0.75 0.9399 90 60 300 0.9461 72 48 300 0.9461 72 48 300 0.9461 72 48

17 0.7 0.9441 78 52 300 0.9503 62 41 300 0.9503 62 41 300 0.9503 62 41

18 0.68 0.9458 73 49 300 0.952 58 38 300 0.952 58 38 300 0.952 58 38

19 0.7 0.9441 78 52 755.3 0.9595 45 30 300 0.9503 62 41 300 0.9503 62 41

39

20 0.7 0.9441 78 52 300 0.9503 62 41 300 0.9503 62 41 393 0.9522 57 38

21 0.59 0.9532 54 36 300 0.9594 41 27 403.8 0.9615 38 25 403.8 0.9615 38 25

22 0.6 0.9524 56 37 403.8 0.9606 39 26 300 0.9585 43 29 403.8 0.9606 39 26

23 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 403.8 0.9687 25 17

24 0.48 0.9623 35 24 300 0.9683 26 17 300 0.9683 26 17 300 0.9683 26 17


without integration of wind and biomass generation for days 1 - 3

Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

2 0.48 0.9623 35 24 300 0.9683 26 17 300 0.9683 26 17 300 0.9683 26 17

3 0.49 0.9614 37 25 300 0.9675 27 18 300 0.9675 27 18 300 0.9675 27 18

4 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

5 0.56 0.9557 49 32 300 0.9618 37 24 300 0.9618 37 24 300 0.9618 37 24

6 0.52 0.9590 42 28 300 0.965 31 20 300 0.965 31 20 300 0.965 31 20

7 0.54 0.9574 45 30 300 0.9634 34 22 300 0.9634 34 22 300 0.9634 34 22

8 0.57 0.9549 51 34 300 0.961 38 25 403.8 0.9631 35 23 300 0.961 38 25

9 0.95 0.9227 147 98 403.8 0.9314 117 77 491.9 0.9332 111 74 457.4 0.9325 113 75

10 1 0.9183 164 109 403.8 0.9271 132 87 1053.9 0.9407 95 64 457.4 0.9282 128 85

11 0.95 0.9227 147 98 699.1 0.9376 99 66 1053.9 0.9449 84 56 622.8 0.936 104 69

12 0.96 0.9218 151 100 1053.9 0.944 86 57 622.8 0.9351 106 70 491.9 0.9324 114 76

13 0.9 0.9271 131 87 847.8 0.9449 80 53 755.3 0.943 84 56 622.8 0.9402 91 60

14 0.88 0.9288 125 83 1232.9 0.9543 64 43 699.1 0.9435 82 55 533 0.94 91 60

15 0.85 0.9314 116 77 755.3 0.9471 73 49 622.8 0.9444 79 52 457.4 0.941 87 58

16 0.75 0.9399 90 60 755.3 0.9554 53 36 403.8 0.9483 67 44 300 0.9461 72 48

17 0.7 0.9441 78 52 622.8 0.9569 49 32 300 0.9503 62 41 300 0.9503 62 41

18 0.68 0.9458 73 49 457.4 0.9552 51 34 403.8 0.9541 53 35 300 0.952 58 38

19 0.7 0.9441 78 52 300 0.9503 62 41 300 0.9503 62 41 300 0.9503 62 41

20 0.7 0.9441 78 52 491.9 0.9542 53 35 300 0.9503 62 41 300 0.9503 62 41

21 0.59 0.9532 54 36 491.9 0.9632 35 23 300 0.9594 41 27 300 0.9594 41 27

22 0.6 0.9524 56 37 403.8 0.9606 39 26 300 0.9585 43 29 300 0.9585 43 29

40

23 0.5 0.9606 39 26 491.9 0.9705 23 16 300 0.9667 28 19 300 0.9667 28 19

24 0.48 0.9623 35 24 622.8 0.9747 19 13 300 0.9683 26 17 300 0.9683 26 17



Figure 6.1 24 hrs LP, Lq & Lv curves for 6 days with wind & biomass integration in 33

bus system

Sl. No Day Distribution Generation of 24 hrs Active loss in 24-Hrs in KW Reactive loss in


1 Day 1 12271 1291 857 0.9332

2 Day 2 12602 1309 871 0.9373

3 Day 3 8822 1436 951 0.9313

4 Day 4 12434 1286 854 0.9271

5 Day 5 10711 1331 882 0.9332

6 Day 6 8743 1429 946 0.9282

Table 6.4 hrs losses and minimum voltage with integration of wind and biomass in 33

bus system

41

6.1.2 Case 2: Integration of biomass with solar

Hr Lf Without DG

1511 KW installed capacity with solar PV (1211 KW) and biomass (300 KW)

Day 1 Day 2 Day 3


1 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

2 0.48 0.9623 35 24 300 0.9683 26 17 300 0.9683 26 17 300 0.9683 26 17

3 0.49 0.9614 37 25 300 0.9675 27 18 300 0.9675 27 18 300 0.9675 27 18

4 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

5 0.56 0.9557 49 32 300 0.9618 37 24 300 0.9618 37 24 300 0.9618 37 24

6 0.52 0.9590 42 28 304.8 0.9651 31 20 302.2 0.9651 31 20 300 0.965 31 20

7 0.54 0.9574 45 30 422.1 0.9659 30 20 427.8 0.966 30 20 358.2 0.9646 32 21

8 0.57 0.9549 51 34 619.7 0.9674 29 20 723 0.9694 27 18 586.1 0.9667 30 20

9 0.95 0.9227 147 98 1038.4 0.9446 84 56 1036.4 0.9445 84 56 890.3 0.9415 90 60

10 1 0.9183 164 109 1334.4 0.9464 86 58 1299 0.9457 87 58 1178.3 0.9432 91 61

11 0.95 0.9227 147 98 1487.7 0.9536 72 49 1469.8 0.9533 72 49 1386.2 0.9516 74 50

12 0.96 0.9218 151 100 1496.3 0.955 72 49 1494.2 0.9549 72 49 1505.7 0.9532 73 50

13 0.9 0.9271 131 87 1493.6 0.9599 62 43 1500 0.9603 62 42 1496.9 0.9599 62 43

14 0.88 0.9288 125 83 1423.2 0.9581 61 41 1447.7 0.9606 59 41 1465.1 0.9609 59 41

15 0.85 0.9314 116 77 1370.2 0.9595 56 38 1371 0.9595 56 38 1457.2 0.9613 55 38

16 0.75 0.9399 90 60 1002.7 0.9604 47 32 1122.5 0.9628 45 30 1275.5 0.9658 43 29

17 0.7 0.9441 78 52 779.3 0.96 44 30 792.2 0.9603 44 29 776 0.96 44 30

18 0.68 0.9458 73 49 563.3 0.9573 47 31 512.7 0.9563 49 33 640.6 0.9589 45 30

19 0.7 0.9441 78 52 302.1 0.9503 61 41 328.3 0.9509 60 40 323.2 0.9508 61 40

20 0.7 0.9441 78 52 300 0.9503 62 41 300 0.9503 62 41 300 0.9503 62 41

21 0.59 0.9532 54 36 300 0.9594 41 27 300 0.9594 41 27 300 0.9594 41 27

22 0.6 0.9524 56 37 300 0.9585 43 29 300 0.9585 43 29 300 0.9585 43 29

23 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

24 0.48 0.9623 35 24 300 0.9683 26 17 300 0.9683 26 17 300 0.9683 26 17


without integration of biomass and solar generation for days 1 - 3

42

Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

2 0.48 0.9623 35 24 300 0.9683 26 17 300 0.9683 26 17 300 0.9683 26 17

3 0.49 0.9614 37 25 300 0.9675 27 18 300 0.9675 27 18 300 0.9675 27 18

4 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

5 0.56 0.9557 49 32 300 0.9618 37 24 300 0.9618 37 24 300 0.9618 37 24

6 0.52 0.9590 42 28 300 0.965 31 20 300 0.965 31 20 300 0.965 31 20

7 0.54 0.9574 45 30 302.4 0.9635 34 22 300 0.9634 34 22 310.6 0.9636 33 22

8 0.57 0.9549 51 34 437.7 0.9638 34 22 359.5 0.9622 36 24 487.3 0.9647 33 22

9 0.95 0.9227 147 98 781.4 0.9393 95 63 512.8 0.9337 110 73 764.5 0.9389 96 64

10 1 0.9183 164 109 1015.8 0.9399 97 65 618.5 0.9316 118 78 1011.7 0.9398 97 65

11 0.95 0.9227 147 98 1259.4 0.9491 77 52 949.7 0.9428 88 58 1177.7 0.9474 80 53

12 0.96 0.9218 151 100 1257.3 0.9482 79 53 834 0.9395 95 63 1277 0.9486 79 53

13 0.9 0.9271 131 87 1041.7 0.9488 73 49 1007 0.9481 75 50 1209 0.9522 68 46

14 0.88 0.9288 125 83 1002.6 0.9497 71 47 928 0.9482 73 49 1015.1 0.9499 70 47

15 0.85 0.9314 116 77 714 0.9463 75 50 725.2 0.9465 74 49 858.7 0.9493 69 46

16 0.75 0.9399 90 60 610.8 0.9525 58 39 543.2 0.9511 61 40 530.7 0.9509 62 41

17 0.7 0.9441 78 52 393.2 0.9522 57 38 450.9 0.9534 55 36 369.4 0.9517 58 39

18 0.68 0.9458 73 49 302.3 0.952 58 38 313.1 0.9522 57 38 307.4 0.9521 57 38

19 0.7 0.9441 78 52 300 0.9503 62 41 300 0.9503 62 41 300 0.9503 62 41

20 0.7 0.9441 78 52 300 0.9503 62 41 300 0.9503 62 41 300 0.9503 62 41

21 0.59 0.9532 54 36 300 0.9594 41 27 300 0.9594 41 27 300 0.9594 41 27

22 0.6 0.9524 56 37 300 0.9585 43 29 300 0.9585 43 29 300 0.9585 43 29

23 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

24 0.48 0.9623 35 24 300 0.9683 26 17 300 0.9683 26 17 300 0.9683 26 17


without integration of biomass and solar generation for days 4 - 6

43

Figure 6.2 24 hrs LP , Lq and Lv curves for 6 days with integration of biomass & solar

generation in 33 bus system


of 24 hrs


in KW

Reactive loss in

24 hrs in KVAR Vmin

1 Day 1 16638 1128 756 0.9446

2 Day 2 16827 1124 754 0.9445

3 Day 3 16639 1135 761 0.9415

4 Day 4 12719 1246 828 0.9393

5 Day 5 11142 1314 871 0.9316

6 Day 6 12919 1241 824 0.9389

Table 6.7 24 hrs losses and minimum voltage with integration of biomass and solar

generation in 33 bus system

6.1.3 Case 3: Integration of biomass with wind &solar

Hr Lf Without DG

1511 KW installed capacity with solar PV (611 KW), wind (600 KW) and biomass (300 KW)

Day 1 Day 2 Day 3


1 0.5 0.9606 39 26 300 0.9667 28 19 941.7 0.979 18 13 351.5 0.9677 27 18

2 0.48 0.9623 35 24 351.5 0.9693 24 16 300 0.9683 26 17 300 0.9683 26 17

3 0.49 0.9614 37 25 525.7 0.9719 22 14 300 0.9675 27 18 300 0.9675 27 18

4 0.5 0.9606 39 26 378 0.9682 26 17 300 0.9667 28 19 300 0.9667 28 19

5 0.56 0.9557 49 32 300 0.9618 37 24 378 0.9634 34 23 300 0.9618 37 24

6 0.52 0.9590 42 28 353.9 0.9661 29 19 301.1 0.9651 31 20 300 0.965 31 20

44

7 0.54 0.9574 45 30 413.1 0.9657 30 20 364.5 0.9647 32 21 329.4 0.964 33 22

8 0.57 0.9549 51 34 461.3 0.9642 33 22 711.4 0.9692 28 18 444.4 0.9639 34 22

9 0.95 0.9227 147 98 767.8 0.939 96 64 897.4 0.9417 90 60 723 0.9381 98 65

10 1 0.9183 164 109 1047.8 0.9405 96 64 1266.7 0.945 88 59 903.3 0.9376 102 68

11 0.95 0.9227 147 98 1311.9 0.9501 76 51 1264.2 0.9492 77 52 1046 0.9447 84 56

12 0.96 0.9218 151 100 1488.8 0.9528 74 50 1224.7 0.9475 80 54 977.7 0.9425 89 59

13 0.9 0.9271 131 87 1178.6 0.9516 69 47 1337.5 0.9548 66 44 954.5 0.947 76 51

14 0.88 0.9288 125 83 992 0.9495 71 47 981.1 0.9493 71 47 938.4 0.9484 73 48

15 0.85 0.9314 116 77 1000.1 0.9521 65 43 840.5 0.9489 70 46 884 0.9498 68 46

16 0.75 0.9399 90 60 654.6 0.9534 57 38 715.1 0.9546 55 36 792.3 0.9562 52 35

17 0.7 0.9441 78 52 541.9 0.9552 52 34 548.4 0.9554 51 34 540.2 0.9552 52 34

18 0.68 0.9458 73 49 432.9 0.9547 52 34 407.3 0.9542 53 35 471.9 0.9555 51 34

19 0.7 0.9441 78 52 526.8 0.9549 52 35 314.3 0.9506 61 40 311.7 0.9505 61 40

20 0.7 0.9441 78 52 300 0.9503 62 41 300 0.9503 62 41 346.1 0.9513 59 39

21 0.59 0.9532 54 36 300 0.9594 41 27 351.5 0.9604 40 26 351.5 0.9604 40 26

22 0.6 0.9524 56 37 351.5 0.9596 41 27 300 0.9585 43 29 351.5 0.9596 41 27

23 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 351.5 0.9677 27 18

24 0.48 0.9623 35 24 300 0.9683 26 17 300 0.9683 26 17 300 0.9683 26 17


without integration of solar, wind and biomass generation for days 1 - 3

Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

2 0.48 0.9623 35 24 300 0.9683 26 17 300 0.9683 26 17 300 0.9683 26 17

3 0.49 0.9614 37 25 300 0.9675 27 18 300 0.9675 27 18 300 0.9675 27 18

4 0.5 0.9606 39 26 300 0.9667 28 19 300 0.9667 28 19 300 0.9667 28 19

5 0.56 0.9557 49 32 300 0.9618 37 24 300 0.9618 37 24 300 0.9618 37 24

6 0.52 0.9590 42 28 300 0.965 31 20 300 0.965 31 20 300 0.965 31 20

7 0.54 0.9574 45 30 301.2 0.9635 34 22 300 0.9634 34 22 305.3 0.9635 34 22

45

8 0.57 0.9549 51 34 369.5 0.9624 36 24 381.5 0.9626 36 24 394.5 0.9629 35 23

9 0.95 0.9227 147 98 594.4 0.9354 105 70 502.5 0.9334 111 73 612.4 0.9358 104 69

10 1 0.9183 164 109 712.7 0.9336 112 74 834.6 0.9361 106 70 737.2 0.9341 111 73

11 0.95 0.9227 147 98 982 0.9434 86 58 1001.7 0.9438 86 57 903 0.9418 90 60

12 0.96 0.9218 151 100 1156.9 0.9461 82 55 729.5 0.9374 100 67 888.2 0.9406 93 62

13 0.9 0.9271 131 87 945.9 0.9469 77 51 882.5 0.9456 79 53 918.8 0.9463 78 52

14 0.88 0.9288 125 83 1117.1 0.952 67 45 814.8 0.9459 77 51 776.4 0.9451 79 52

15 0.85 0.9314 116 77 734.7 0.9467 74 49 674.6 0.9455 77 51 660 0.9452 77 51

16 0.75 0.9399 90 60 682.6 0.954 56 37 474.2 0.9497 64 42 416.4 0.9485 67 44

17 0.7 0.9441 78 52 507.1 0.9545 53 35 376.2 0.9519 58 39 335 0.951 60 40

18 0.68 0.9458 73 49 379.2 0.9536 54 36 358.1 0.9531 55 36 303.7 0.952 57 38

19 0.7 0.9441 78 52 300 0.9503 62 41 300 0.9503 62 41 300 0.9503 62 41

20 0.7 0.9441 78 52 395.2 0.9523 57 38 300 0.9503 62 41 300 0.9503 62 41

21 0.59 0.9532 54 36 395.2 0.9613 38 25 300 0.9594 41 27 300 0.9594 41 27

22 0.6 0.9524 56 37 351.5 0.9596 41 27 300 0.9585 43 29 300 0.9585 43 29

23 0.5 0.9606 39 26 395.2 0.9686 26 17 300 0.9667 28 19 300 0.9667 28 19

24 0.48 0.9623 35 24 460.1 0.9714 22 14 300 0.9683 26 17 300 0.9683 26 17


without integration of solar, wind and biomass generation for days 4 - 6

Figure 6.3 24 hrs LP , Lq and Lv curves for 6 days with integration of biomass and wind

& solar generation in 33 bus system

46


24 hrs

Active loss in 24 hrs in

KW

Reactive loss in

24 hrs in KVAR Vmin

1 Day 1 14578 1186 790 0.939

2 Day 2 1186.462 1183 788 0.9417

3 Day 3 14945 1241 823 0.9376

4 Day 4 12869 1259 835 0.9336

5 Day 5 12581 1320 875 0.9334

6 Day 6 10851 1322 876 0.9341

Table 6.10 24 hrs Losses and minimum voltage with biomass and wind & solar

integration in 33 bus system

6.2 69 Bus Results

The total DG capacity to be installed is 1148 KW. Out of which 200 KW is

substituted with biomass generation. The remaining 948 KW is supplied by wind and solar

energies. The analysis is done for the three different cases as described in Table 6.11

Case

No

Case Strategy Results

Table

Summary of

results

LP, LQ and LV

graphs

1 1150 KW installed capacity with wind (950 KW) and

biomass (200 KW)

6.12 - 6.13 6.14 6.4

2 1148 KW installed capacity with solar PV (948 KW)

and biomass (200 KW)

6.15 - 6.16 6.17 6.5

3 1148 KW installed capacity with solar PV (478 KW),

wind (470 KW) and biomass (200 KW)

6.18 - 6.19 6.20 6.6

Table 6.11 Different strategies for integrating biomass in 69 bus system

6.2.1 Case 1: Integration of Wind and Biomass

Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9567 52 24 200 0.964 39 18 1216.1 0.986 23 11 281.5 0.9669 35 17

2 0.48 0.9585 47 22 281.5 0.9687 32 15 200 0.9658 36 17 200 0.9658 36 17

3 0.49 0.9576 50 23 557.4 0.9776 24 12 200 0.9649 37 17 200 0.9649 37 17

47

4 0.5 0.9567 52 24 323.6 0.9684 33 16 200 0.964 39 18 200 0.964 39 18

5 0.56 0.9512 65 30 200 0.9586 51 24 323.6 0.9631 44 21 200 0.9586 51 24

6 0.52 0.9549 56 26 281.5 0.9651 39 18 200 0.9622 43 20 200 0.9622 43 20

7 0.54 0.9531 61 28 281.5 0.9634 42 20 200 0.9604 47 22 200 0.9604 47 22

8 0.57 0.9503 68 31 200 0.9577 53 25 513.4 0.969 37 18 200 0.9577 53 25

9 0.95 0.9142 201 91 350.7 0.928 154 71 557.4 0.936 132 62 398.1 0.9299 148 69

10 1 0.9092 225 102 557.4 0.9312 150 70 932.4 0.9454 116 56 453.4 0.9272 162 75

11 0.95 0.9142 201 91 853.2 0.9471 107 51 791.9 0.9448 111 53 513.4 0.9343 136 64

12 0.96 0.9132 206 94 1046.5 0.9532 97 47 630.1 0.9378 128 60 309.7 0.9255 163 75

13 0.9 0.9191 179 81 557.4 0.9407 115 54 791.9 0.9494 96 46 200 0.927 152 70

14 0.88 0.9211 171 78 398.1 0.9365 123 57 281.5 0.932 135 63 200 0.9289 145 67

15 0.85 0.924 158 72 453.4 0.9414 108 51 200 0.9318 134 62 200 0.9318 134 62

16 0.75 0.9336 121 55 200 0.9412 100 46 200 0.9412 100 46 200 0.9412 100 46

17 0.7 0.9383 105 48 200 0.9458 86 40 200 0.9458 86 40 200 0.9458 86 40

18 0.68 0.9402 98 45 200 0.9477 80 37 200 0.9477 80 37 200 0.9477 80 37

19 0.7 0.9383 105 48 557.4 0.959 60 29 200 0.9458 86 40 200 0.9458 86 40

20 0.7 0.9383 105 48 200 0.9458 86 40 200 0.9458 86 40 273 0.9486 79 37

21 0.59 0.9485 73 33 200 0.9559 58 27 281.5 0.9589 52 25 281.5 0.9589 52 25

22 0.6 0.9476 76 34 281.5 0.958 55 25 200 0.955 60 28 281.5 0.958 55 25

23 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 281.5 0.9669 35 17

24 0.48 0.9585 47 22 200 0.9658 36 17 200 0.9658 36 17 200 0.9658 36 17



Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

2 0.48 0.9585 47 22 200 0.9658 36 17 200 0.9658 36 17 200 0.9658 36 17

3 0.49 0.9576 50 23 200 0.9649 37 17 200 0.9649 37 17 200 0.9649 37 17

4 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

5 0.56 0.9512 65 30 200 0.9586 51 24 200 0.9586 51 24 200 0.9586 51 24

6 0.52 0.9549 56 26 200 0.9622 43 20 200 0.9622 43 20 200 0.9622 43 20

48

7 0.54 0.9531 61 28 200 0.9604 47 22 200 0.9604 47 22 200 0.9604 47 22

8 0.57 0.9503 68 31 200 0.9577 53 25 281.5177 0.9607 48 23 200 0.9577 53 25

9 0.95 0.9142 201 91 281.5177 0.9253 162 75 350.6658 0.928 154 71 323.5637 0.927 157 73

10 1 0.9092 225 102 281.5177 0.9205 183 84 791.9264 0.9401 127 61 323.5637 0.9221 178 82

11 0.95 0.9142 201 91 513.3506 0.9343 136 64 791.9264 0.9448 111 53 453.4147 0.932 142 66

12 0.96 0.9132 206 94 791.9264 0.9439 114 54 453.4147 0.931 146 68 350.6658 0.9271 158 73

13 0.9 0.9191 179 81 630.0526 0.9434 109 51 557.4342 0.9407 115 54 453.4147 0.9367 125 58

14 0.88 0.9211 171 78 932.4187 0.9564 83 40 513.3506 0.9409 112 53 382.9289 0.9359 125 58

15 0.85 0.924 158 72 557.4342 0.9453 99 47 453.4147 0.9414 108 51 323.5637 0.9365 120 56

16 0.75 0.9336 121 55 557.4342 0.9545 72 34 281.5177 0.9443 93 43 200 0.9412 100 46

17 0.7 0.9383 105 48 453.4147 0.9552 67 31 200 0.9458 86 40 200 0.9458 86 40

18 0.68 0.9402 98 45 323.5637 0.9523 70 33 281.5177 0.9507 74 34 200 0.9477 80 37

19 0.7 0.9383 105 48 200 0.9458 86 40 200 0.9458 86 40 200 0.9458 86 40

20 0.7 0.9383 105 48 350.6658 0.9514 74 34 200 0.9458 86 40 200 0.9458 86 40

21 0.59 0.9485 73 33 350.6658 0.9614 48 23 200 0.9559 58 27 200 0.9559 58 27

22 0.6 0.9476 76 34 281.5177 0.958 55 25 200 0.955 60 28 200 0.955 60 28

23 0.5 0.9567 52 24 350.6658 0.9694 32 15 200 0.964 39 18 200 0.964 39 18

24 0.48 0.9585 47 22 453.4147 0.9748 25 12 200 0.9658 36 17 200 0.9658 36 17



Figure 6.4 24 hrs LP , Lq and Lv curves for 6 days with integration of wind & biomass in

69 bus system

49


of 24 hrs


KW

Reactive loss in

24 hrs in KVAR Vmin

1 Day 1 8781.1 1765 826 0.928

2 Day 2 9119.8 1783 834 0.9318

3 Day 3 6074 1990 923 0.9255

4 Day 4 8909.56 1760 824 0.9205

5 Day 5 7556.686 1830 854 0.928

6 Day 6 6011.115 1979 918 0.9221

Table 6.14 24 hrs losses and minimum voltage with integration of wind and biomass in

69 bus system

6.2.2 Case 2: Integration of Solar and Biomass

Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

2 0.48 0.9585 47 22 200 0.9658 36 17 200 0.9658 36 17 200 0.9658 36 17

3 0.49 0.9576 50 23 200 0.9649 37 17 200 0.9649 37 17 200 0.9649 37 17

4 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

5 0.56 0.9512 65 30 200 0.9586 51 24 200 0.9586 51 24 200 0.9586 51 24

6 0.52 0.9549 56 26 203.8 0.9623 43 20 201.8 0.9623 43 20 200 0.9622 43 20

7 0.54 0.9531 61 28 295.6 0.9639 42 20 300.1 0.964 41 19 245.6 0.9621 44 21

8 0.57 0.9503 68 31 450.4 0.9668 40 19 531.3 0.9697 36 17 424.1 0.9658 41 19

9 0.95 0.9142 201 91 778.4 0.9443 112 53 776.8 0.9442 112 53 662.4 0.9399 122 58

10 1 0.9092 225 102 1010.3 0.9482 111 53 982.5 0.9472 113 54 888 0.9437 120 57

11 0.95 0.9142 201 91 1130.3 0.9572 90 44 1116.3 0.9567 91 44 1050.8 0.9543 94 46

12 0.96 0.9132 206 94 1115.4 0.9594 89 43 1133.7 0.9593 89 43 1144.4 0.9568 92 45

13 0.9 0.9191 179 81 1113.3 0.9647 75 37 1140 0.9653 74 36 1145.8 0.9648 75 37

14 0.88 0.9211 171 78 1079.8 0.9617 76 37 1177.3 0.9652 72 35 1150 0.9657 71 35

15 0.85 0.924 158 72 1038.3 0.9629 70 34 1038.9 0.9629 70 34 1106.4 0.9653 68 33

16 0.75 0.9336 121 55 750.4 0.9615 61 30 844.3 0.9649 57 28 964.1 0.9691 53 26

17 0.7 0.9383 105 48 575.4 0.9597 59 28 585.5 0.96 59 28 572.8 0.9596 59 28

18 0.68 0.9402 98 45 406.2 0.9553 65 30 366.6 0.9539 67 32 466.8 0.9575 61 29

50

19 0.7 0.9383 105 48 201.6 0.9459 85 39 222.2 0.9467 84 39 218.1 0.9465 84 39

20 0.7 0.9383 105 48 200 0.9458 86 40 200 0.9458 86 40 200 0.9458 86 40

21 0.59 0.9485 73 33 200 0.9559 58 27 200 0.9559 58 27 200 0.9559 58 27

22 0.6 0.9476 76 34 200 0.955 60 28 200 0.955 60 28 200 0.955 60 28

23 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

24 0.48 0.9585 47 22 200 0.9658 36 17 200 0.9658 36 17 200 0.9658 36 17


without integration of solar and biomass generation for days 1 - 3

Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

2 0.48 0.9585 47 22 200 0.9658 36 17 200 0.9658 36 17 200 0.9658 36 17

3 0.49 0.9576 50 23 200 0.9649 37 17 200 0.9649 37 17 200 0.9649 37 17

4 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

5 0.56 0.9512 65 30 200 0.9586 51 24 200 0.9586 51 24 200 0.9586 51 24

6 0.52 0.9549 56 26 200 0.9622 43 20 200 0.9622 43 20 200 0.9622 43 20

7 0.54 0.9531 61 28 201.8555 0.9605 47 22 200 0.9604 47 22 208.2951 0.9607 46 22

8 0.57 0.9503 68 31 307.8717 0.9616 47 22 246.5938 0.9594 50 23 346.6928 0.963 45 21

9 0.95 0.9142 201 91 577.1037 0.9367 130 61 366.6475 0.9286 152 70 563.8062 0.9362 131 62

10 1 0.9092 225 102 760.674 0.9389 130 62 449.4489 0.927 162 75 757.4988 0.9388 130 62

11 0.95 0.9142 201 91 951.4525 0.9507 100 48 708.9086 0.9417 118 56 887.5093 0.9483 104 50

12 0.96 0.9132 206 94 949.8286 0.9497 103 50 618.2603 0.9373 129 61 965.3011 0.9503 102 49

13 0.9 0.9191 179 81 780.9774 0.949 97 46 753.7569 0.948 99 47 912.0214 0.9538 89 43

14 0.88 0.9211 171 78 750.3277 0.9497 94 45 691.9324 0.9475 98 46 760.0947 0.9501 93 44

15 0.85 0.924 158 72 524.267 0.9441 102 48 533.0297 0.9444 101 48 637.6365 0.9483 93 44

16 0.75 0.9336 121 55 443.453 0.9503 80 38 390.4753 0.9483 84 39 380.7313 0.948 85 40

17 0.7 0.9383 105 48 273.0275 0.9486 79 37 318.2268 0.9502 76 35 254.3795 0.9479 81 38

18 0.68 0.9402 98 45 201.7839 0.9477 80 37 210.2296 0.9481 79 37 205.8039 0.9479 80 37

19 0.7 0.9383 105 48 200 0.9458 86 40 200 0.9458 86 40 200 0.9458 86 40

20 0.7 0.9383 105 48 200 0.9458 86 40 200 0.9458 86 40 200 0.9458 86 40

51

21 0.59 0.9485 73 33 200 0.9559 58 27 200 0.9559 58 27 200 0.9559 58 27

22 0.6 0.9476 76 34 200 0.955 60 28 200 0.955 60 28 200 0.955 60 28

23 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

24 0.48 0.9585 47 22 200 0.9658 36 17 200 0.9658 36 17 200 0.9658 36 17


without integration of solar and biomass generation for days 4 - 6

Figure 6.5 24 hrs LP , Lq and Lv curves for 6 days with integration of solar & biomass in

69 bus system

Sl.

No Day

Distribution Generation

of 24 hrs


in KW

Reactive loss in

24-hrs in KVAR Vmin

1 Day 1 12149 1498 711 0.9443

2 Day 2 12377 1489 707 0.9442

3 Day 3 12239 1508 715 0.9399

4 Day 4 9122 1698 797 0.9367

5 Day 5 7887 1805 843 0.927

6 Day 6 9279 1688 793 0.9362

Table 6.17 24 hrs losses and minimum voltage with integration of solar and biomass in

69 bus system

52

6.2.3 Case 3: Integration of Wind, Solar and Biomass

Hr Lf Without DG


Day 1 Day 2 Day 3


1 0.5 0.9567 52 24 200 0.964 39 18 702.7 0.9818 22 11 240.3 0.9654 37 17

2 0.48 0.9585 47 22 240.3 0.9672 34 16 200 0.9658 36 17 200 0.9658 36 17

3 0.49 0.9576 50 23 376.8 0.9712 29 14 200 0.9649 37 17 200 0.9649 37 17

4 0.5 0.9567 52 24 261.1 0.9662 36 17 200 0.964 39 18 200 0.964 39 18

5 0.56 0.9512 65 30 200 0.9586 51 24 261.1 0.9608 47 22 200 0.9586 51 24

6 0.52 0.9549 56 26 242.2 0.9637 41 19 200.9 0.9622 43 20 200 0.9622 43 20

7 0.54 0.9531 61 28 288.5 0.9636 42 20 250.4 0.9622 44 21 223 0.9612 46 21

8 0.57 0.9503 68 31 326.2 0.9623 46 22 522 0.9693 37 18 312.9 0.9618 46 22

9 0.95 0.9142 201 91 566 0.9363 131 61 667.5 0.9401 121 57 531 0.935 134 63

10 1 0.9092 225 102 785.2 0.9399 128 61 956.7 0.9463 115 55 672.1 0.9356 138 65

11 0.95 0.9142 201 91 992 0.9522 98 47 954.6 0.9508 100 48 783.8 0.9445 112 53

12 0.96 0.9132 206 94 1130.5 0.9563 93 45 923.6 0.9487 105 50 730.2 0.9415 119 57

13 0.9 0.9191 179 81 887.5 0.9529 90 43 1011.9 0.9574 83 40 711.9 0.9464 102 49

14 0.88 0.9211 171 78 741.4 0.9494 94 45 732.8 0.9491 95 45 699.4 0.9478 97 46

15 0.85 0.924 158 72 747.8 0.9524 86 41 622.8 0.9477 94 45 656.8 0.949 92 44

16 0.75 0.9336 121 55 477.4 0.9515 77 36 524.7 0.9533 74 35 585.1 0.9555 70 33

17 0.7 0.9383 105 48 389.2 0.9529 71 33 394.3 0.953 70 33 387.9 0.9528 71 33

18 0.68 0.9402 98 45 303.9 0.9515 72 34 283.9 0.9508 73 34 334.5 0.9527 70 33

19 0.7 0.9383 105 48 377.7 0.9524 72 34 211.2 0.9463 85 39 209.1 0.9462 85 39

20 0.7 0.9383 105 48 200 0.9458 86 40 200 0.9458 86 40 236.1 0.9472 82 38

21 0.59 0.9485 73 33 200 0.9559 58 27 240.3 0.9574 55 26 240.3 0.9574 55 26

22 0.6 0.9476 76 34 240.3 0.9565 57 27 200 0.955 60 28 240.3 0.9565 57 27

23 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 240.3 0.9654 37 17

24 0.48 0.9585 47 22 200 0.9658 36 17 200 0.9658 36 17 200 0.9658 36 17


without integration of wind, solar and biomass generation for days 1 - 3

Hr Lf Without DG 1148 KW installed capacity with solar PV (478 KW), wind (470 KW) and biomass (200 KW)

Day 4 Day 5 Day 6

53


1 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

2 0.48 0.9585 47 22 200 0.9658 36 17 200 0.9658 36 17 200 0.9658 36 17

3 0.49 0.9576 50 23 200 0.9649 37 17 200 0.9649 37 17 200 0.9649 37 17

4 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

5 0.56 0.9512 65 30 200 0.9586 51 24 200 0.9586 51 24 200 0.9586 51 24

6 0.52 0.9549 56 26 200 0.9622 43 20 200 0.9622 43 20 200 0.9622 43 20

7 0.54 0.9531 61 28 201.8555 0.9605 47 22 200 0.9604 47 22 208.2951 0.9607 46 22

8 0.57 0.9503 68 31 307.8717 0.9616 47 22 246.5938 0.9594 50 23 346.6928 0.963 45 21

9 0.95 0.9142 201 91 577.1037 0.9367 130 61 366.6475 0.9286 152 70 563.8062 0.9362 131 62

10 1 0.9092 225 102 760.674 0.9389 130 62 449.4489 0.927 162 75 757.4988 0.9388 130 62

11 0.95 0.9142 201 91 951.4525 0.9507 100 48 708.9086 0.9417 118 56 887.5093 0.9483 104 50

12 0.96 0.9132 206 94 949.8286 0.9497 103 50 618.2603 0.9373 129 61 965.3011 0.9503 102 49

13 0.9 0.9191 179 81 780.9774 0.949 97 46 753.7569 0.948 99 47 912.0214 0.9538 89 43

14 0.88 0.9211 171 78 750.3277 0.9497 94 45 691.9324 0.9475 98 46 760.0947 0.9501 93 44

15 0.85 0.924 158 72 524.267 0.9441 102 48 533.0297 0.9444 101 48 637.6365 0.9483 93 44

16 0.75 0.9336 121 55 443.453 0.9503 80 38 390.4753 0.9483 84 39 380.7313 0.948 85 40

17 0.7 0.9383 105 48 273.0275 0.9486 79 37 318.2268 0.9502 76 35 254.3795 0.9479 81 38

18 0.68 0.9402 98 45 201.7839 0.9477 80 37 210.2296 0.9481 79 37 205.8039 0.9479 80 37

19 0.7 0.9383 105 48 200 0.9458 86 40 200 0.9458 86 40 200 0.9458 86 40

20 0.7 0.9383 105 48 200 0.9458 86 40 200 0.9458 86 40 200 0.9458 86 40

21 0.59 0.9485 73 33 200 0.9559 58 27 200 0.9559 58 27 200 0.9559 58 27

22 0.6 0.9476 76 34 200 0.955 60 28 200 0.955 60 28 200 0.955 60 28

23 0.5 0.9567 52 24 200 0.964 39 18 200 0.964 39 18 200 0.964 39 18

24 0.48 0.9585 47 22 200 0.9658 36 17 200 0.9658 36 17 200 0.9658 36 17


without integration of wind, solar and biomass generation for days 4 - 6

54

Figure 6.6 24 hrs LP , Lq and Lv curves for 6 days with integration of biomass and wind

& solar generation in 69 bus system


24hrs


KW

Reactive loss in

24-hrs in KVAR Vmin

1 Day 1 10574 1604 757 0.9363

2 Day 2 10861 1596 754 0.9401

3 Day 3 9235 1693 796 0.935

4 Day 4 9011.548 1720 807 0.9299

5 Day 5 7719.792 1814 848 0.928

6 Day 6 7656.775 1818 849 0.9306

Table 6.20 24 hrs losses and minimum voltage with integration of wind, solar and

biomass in 69 bus system

6.3 Summary

This chapter has presented the integration of biomass energy sources in power

distribution system. Simulation studies have been presented for two distribution networks of

33 bus and 69 buses integrated with Biomass along with wind, solar and combination of wind

& solar. It has been observed that the combination of solar PV and biomass provides the best

performance among the renewable systems considered for integration.

55

Chapter 7

7. Summary and Conclusions

This chapter provides summary of the work presented in this thesis along with

discussions and conclusions on the results obtained.

Renewable energy sources can be deployed and used into power distribution systems

with more eagerness in present day scenario where thrust is to utilize technologies which help

in reducing the carbon emission level. They impose a considerable impact on the system and

customer owing to their intermittent nature. Thus, the problem of incorporating renewable

generation into power distribution system becomes more complex. This thesis proposes a

methodology for integrating the renewable generation into power distribution systems at an

optimal location with minimum distribution losses. The total capacity of renewable

generation to be installed is calculated for the given distribution network. The generation

capacity is decided so as to maintain all the bus voltages in acceptable limits based on 24

hour system load. The proposed method is benchmarked through implementation on 33 bus

and 69 bus systems.

The approach followed in the thesis works well on a power system that has renewable

distributed generation. It considers the 24 hour variation in resource availability while also

considering the impact of their inclusion with respect to the changing 24 hours system load.

The approach has been implemented on standard 33 and 69 bus systems being used in various

papers available in literature. The analysis is carried out for the different combinations of

renewable sources like wind, solar, wind & solar combined and also introducing biomass as

additional source into these 3 cases. The results obtained in each and every case for both 33

and 69 bus networks are presented.

The thesis initially went on with the integration of wind generation alone and has seen

an improvement of approximately 1%. Since wind has got high intermittence nature it was

replaced by solar and it has been observed that the minimum voltages have increased to 2%.

With the intentions of improving voltage for all 24hrs, the combinations of wind and solar is

implemented. As the solar availability is restricted to day time only, it was thought that

availability of wind in night time will have better improvement on overall system

56

performance but not much improvement has been observed. This could be due to the two

reasons:

1. Reduced size of solar capacity has decreased solar generation during day time where

peak demand occurs and not much wind was available in that duration to provide the

competitive generation which could have been provided by solar of the same capacity

2. Increase in size of wind installation capacity whose availability is highly intermittent

in comparison to solar and not much of the generation was contributed.

In order to overcome this, renewable source which can deliver power continuously

(biomass) has been introduced. Since installing biomass plant with higher capacity in practice

is very expensive in-terms of fuel availability, therefore, biomass of a smaller capacity has

been chosen. Thus, to reduce the effect of intermittence of solar and wind energy systems in

the power distribution system, small biomass systems were incorporated with 33bus and

69bus system networks along with wind, solar and combination of wind & solar. It has been

observed that the case that has Biomass with solar for 33bus and 69bus respectively is

performing the best with respect to the reduced losses and improvement in the minimum

voltage. For the 33bus system the average loss has reduced by 36.8% and minimum voltage

has improved by 3%. For 69bus system the average loss has reduced by 37.2% and average

minimum voltage has increased by 3.2% respectively. It is noteworthy that for the

implementation of the proposed method planners need to study the availability of renewable

potential and system load demand for the geographic area for having the best performance of

the renewable integration to the power distribution systems.

Future Scope

The present work provides a methodology for optimal renewable generation

integration to the three phase radial distribution that is operating under balance load which

can be represented by their equivalent single line diagram and analyzed. But in practice there

are many situations when the distribution system operates under unbalanced loading

conditions. The present work can be extended with unbalanced loading condition to provide

solutions that cover broader behaviour of power distribution systems.

57

Appendix A: Modeling of Wind, Solar and Biomass

A.1 Wind Modeling

Wind is the flow of air or other gases that compose an atmosphere. Wind electric

generator converts kinetic energy available in wind to electrical energy by using rotor,

gearbox and generator. The wind turns the blades of a windmill-like machine. The rotating

blades turn the shaft to which they are attached. The turning shaft typically can either power a

pump or turn a generator, which produces electricity [37].

21

2Kinetic Energy m v joules= × × A.1

The power in moving air is the flow rate of kinetic energy per second. Therefore:

( ) 21

sec

2

Power mass flow rate per ond v joules= × × A.2

If PW = mechanical power in moving air

ρ = air density in kg/m3

A = rotor swept area in m2

v = wind speed in m/s

then the volumetric power can be given as

( ) 321

2

1

2P o w e r A v v A v w a t t sρ ρ== × ×

A.3

A.3 gives the total power available on the rotor blades. But, actual power extracted by

the rotor blades is the difference between the upstream and the downstream wind powers.

That is,

58

( ) ( )2 2

0

1s e c

2P o w e r m a s s f l o w r a te p e r v v= × × −

A.4

Since wind speed is discontinuous on the rotor blade, mass flow rate is given by

0

2

v vm a s s f l o w r a t e Aρ

+= × ×

A.5

on substituting A.5 in A.4

3

* *1

* *2

pA vP o w e r Cρ= A.6

where power coefficient, ( ) ( )

2

0 01 1

2p

v v

v vC

+ −

=

A.7

The theoretical maximum value of Cp is 0.59. In practical designs, the maximum

achievable Cp is below 0.5 for high-speed, two-blade turbines, and between 0.2 and 0.4 for

slow speed turbines with more blades. Wind modeling in this thesis assumes Cp = 0.4.

Mathematical modeling of wind turbine in this thesis is based on the equations used in

representing equivalent circuit of Induction motor, Figure: A.1

Figure A.1 Induction machine equivalent circuit

22 2

RZ jX

s= + A.8

59

2 0

2 0

*P

Z ZZ

Z Z=

+

A.9

1in PZ Z Z= + A.10

1

i n

VI

Z=

A.10

*

13S V I= A.11

S is the power delivered into the network from each wind turbine. For a given wind

speed and terminal voltage, the generator rotor speed is determined by equating the

mechanical power input and the developed electrical power. Once the generator rotor speed is

determined, the electrical power output can be computed as described above steps.

This thesis has used 10 KW wind turbine with specification given below and its Slip

Vs power graph is shown in Figure: A.2

R1 = 1.376Ω; R2 = 0.588 Ω; X1 = 1.982 Ω; X2 = 0.896 Ω; Xm = 50.4 Ω; Rfe = 317 Ω; V

= 240; Blade diameter = 8m; Start up wind speed 3m/s; Rated wind speed = 10m/s.

Figure A.2 Slip Vs output power for the considered induction motor

60

A.2 Solar Modeling

The electrical equivalent circuit of the PV cell can be described as a current source in

parallel with a diode & leakage resistor (Rp) which are in series with resistor (Rs) where for

simplicity Rp is neglected as shown in Figure: A.1. The output of the current source is

proportional to the photons falling on the PV cell.

Figure A.3 Equivalent circuit of PV cell

The I-V characteristics of a PV cell can be mathematically modeled by Shockley

diode equation and the two parameters VOC & ISC, are used to describe PV cell.

The mathematical modeling of a PV cell:

( )( )/

01O u t O u t Sq n kT

O u t C ell

V I RI I I e

+= − −

A.12

( ) ( )( )1 0 11

TC e l l C e l lI I k T T= + − A.13

( )

( )

( )

1

1

,S C

C e l l

T n o m

T

n o m

II G

G= ∗

A.14

( ) ( )( )( )

2 1

0

2 1

S C T S C TI I

kT T

−=

−

A.15

( )1

1

31 1

0 0

1

gqV

nknT T

T

TI I e

T

−

−

= ∗ ∗

A.16

61

( )

( )

( )

1

1 1

1

0

1

o c T

S C T

T q V

n k T

II

e

=

−

A.17

1

O C

O u tS

V V

d VR

d I X= − −

A.18

( ) ( )0

1

1 1

1

*

*

VOC T

T qV

nkT

qX I

nkT e

= A.19

To analyze the described mathematical model, Solarex manufactured Photovoltaic

Cell of type MSX60 and 60W capacity is considered. The PV Cell specifications [39] and

results obtained at a industrial conditions of temperature of 250 C and illumination of 1 Sun

are shown in Table A.1. The I-V curve and P-V curve obtained are shown in Figure A.4.

MSX60 60W PV-Cell Industrial Specification Matlab Results

PMax 60W 60.47W

VMax 17.1V 17.08V

IMax 3.5A 3.5405A

ISc 3.8A 3.8A

VOC 21.1V 21.06V

Table A.1 Industrial specifications Vs MATLAB results

Figure A.4 Mathematically generated IV and PV curves of considered PV panel

62

A.2 Biomass Modeling

Biomass is a vast renewable energy which still remains relatively untapped. Through

energy conversion techniques like combustion and gasification, biomass can be converted to

heat, fuel and electrical power. A gasifier is a reactor that converts biomass into clean

gaseous fuel called producer gas having calorific value of the order of 1000–1200

kilocalories per normalized cubic metre (cu.m) [ 4]. Biomass gasifier system shown in Figure

A.4 optimally utilizes biomass for power generation and ensures long uninterrupted dual fuel

operation [40].

Figure A.5 Biomass gasifier plant

The Gasifier design capacity depends upon the amount of agro waste available for the

conversion and the electric demand. The agro waste produced per acre of cultivated land of

paddy was found to be around 1.5 metric tonnes. It can be referred from reliable statistics

obtained from My Home Power Ltd., which owns a biomass plant in the vicinity of

Hyderabad, India, that 1.5 metric ton is capable of yielding 1 Megawatt-hour (MWh) of

energy. Then for a plant of capacity 200 KWh and 300 KWh it would require 30 kg and 45

kg every hour respectively.

63

Appendix B: Standard Network Data of 30 Bus,

33Bus and 69 Bus.

B.1 30 Bus Test System Data

Line No. From Bus To Bus R (pu) X (pu)

Load on To Bus

PL (pu) QL (pu)

1 1 2 0.0967 0.0397 0.0042 0.0026

2 2 3 0.0886 0.0364 0 0

3 3 4 0.1359 0.0377 0.0042 0.0026

4 4 5 0.1236 0.0343 0.0042 0.0026

5 5 6 0.1236 0.0343 0 0

6 6 7 0.2598 0.0446 0 0

7 7 8 0.1732 0.0298 0.0042 0.0026

8 8 9 0.2598 0.0446 0.0042 0.0026

9 9 10 0.1732 0.0298 0.0041 0.0025

10 10 11 0.1083 0.0186 0.0042 0.0026

11 11 12 0.0866 0.0149 0.0025 0.0015

12 3 13 0.1299 0.0223 0.0011 0.0007

13 13 14 0.1732 0.0298 0.0011 0.0007

14 14 15 0.0866 0.0149 0.0011 0.0007

15 15 16 0.0433 0.0074 0.0002 0.0001

16 6 17 0.1483 0.0412 0.0044 0.0027

17 17 18 0.1359 0.0377 0.0044 0.0027

18 18 19 0.1718 0.0391 0.0044 0.0027

19 19 20 0.1562 0.0355 0.0044 0.0027

20 20 21 0.1562 0.0355 0.0044 0.0027

21 21 22 0.2165 0.0372 0.0044 0.0027

22 22 23 0.2165 0.0372 0.0044 0.0027

64

23 23 24 0.2598 0.0446 0.0044 0.0027

24 24 25 0.1732 0.0298 0.0044 0.0027

25 25 26 0.1083 0.0186 0.0044 0.0027

26 26 27 0.0866 0.0149 0.0026 0.0016

27 7 28 0.1299 0.0223 0.0017 0.0011

28 28 29 0.1299 0.0223 0.0017 0.0011

29 29 30 0.1299 0.0223 0.0017 0.0011

Table B.1 30 Bus line and load data with base values 100 MVA and 11 kV


Line No. From Bus To Bus R (ohm) X(ohm)

Load on To Bus

PL ( KW) QL (KVAR)

1 1 2 0.0922 0.047 100 60

2 2 3 0.493 0.2512 90 40

3 3 4 0.3661 0.1864 120 80

4 4 5 0.3811 0.1941 60 30

5 5 6 0.819 0.707 60 20

6 6 7 0.1872 0.6188 200 100

7 7 8 0.7115 0.2351 200 100

8 8 9 1.0299 0.74 60 20

9 9 10 1.044 0.74 60 20

10 10 11 0.1967 0.0651 45 30

11 11 12 0.3744 0.1298 60 35

12 12 13 1.468 1.1549 60 35

13 13 14 0.5416 0.7129 120 80

14 14 15 0.5909 0.526 60 10

15 15 16 0.7462 0.5449 60 20

16 16 17 1.2889 1.721 60 20

17 17 18 0.732 0.5739 90 40

18 2 19 0.164 0.1565 90 40

65

19 19 20 1.5042 1.3555 90 40

20 20 21 0.4095 0.4784 90 40

21 21 22 0.7089 0.9373 90 40

22 3 23 0.4512 0.3084 90 50

23 23 24 0.898 0.7091 420 200

24 24 25 0.8959 0.7071 420 200

25 6 26 0.2031 0.1034 60 25

26 26 27 0.2842 0.1447 60 25

27 27 28 1.0589 0.9338 60 20

28 28 29 0.8043 0.7006 120 70

29 29 30 0.5074 0.2585 200 100

30 30 31 0.9745 0.9629 150 70

31 31 32 0.3105 0.3619 210 100

32 32 33 0.3411 0.5302 60 40

Table B.2 33 bus line and load data


Line No. From Bus To Bus R (ohm) X (ohm)

Load on To Bus

PL ( KW) QL (KVAR)

1 1 2 0.0005 0.0012 0 0

2 2 3 0.0005 0.0012 0 0

3 3 4 0.0015 0.0036 0 0

4 4 5 0.0251 0.0294 0 0

5 5 6 0.366 0.1864 2.6 2.2

6 6 7 0.3811 0.1941 40.4 30

7 7 8 0.0922 0.047 75 54

8 8 9 0.0493 0.0251 30 22

9 9 10 0.819 0.2707 28 19

10 10 11 0.1872 0.0619 145 104

66

11 11 12 0.7114 0.2351 145 104

12 12 13 1.03 0.34 8 5.5

13 13 14 1.044 0.345 8 5.5

14 14 15 1.058 0.3496 0 0

15 15 16 0.1966 0.086 45.5 30

16 16 17 0.3744 0.1238 60 35

17 17 18 0.0047 0.0016 60 35

18 18 19 0.3276 0.1083 0 0

19 19 20 0.2106 0.0696 1 0.6

20 20 21 0.3416 0.1129 114 81

21 21 22 0.014 0.0046 5.3 3.5

22 22 23 0.1591 0.2526 0 0

23 23 24 0.3463 0.1145 28 20

24 24 25 0.7488 0.2475 0 0

25 25 26 0.3089 0.1021 14 10

26 26 27 0.1732 0.0572 14 10

27 3 28 0.0044 0.0108 26 18.6

28 28 29 0.064 0.1565 26 18.6

29 29 30 0.3978 0.1315 0 0

30 30 31 0.0702 0.0232 0 0

31 31 32 0.351 0.116 0 0

32 32 33 0.839 0.2816 14 10

33 33 34 1.708 0.5646 19.5 14

34 34 35 1.474 0.4873 6 4

35 3 36 0.0044 0.0108 26 18.55

36 36 37 0.064 0.1565 26 18.55

37 37 38 0.1053 0.123 0 0

38 38 39 0.0304 0.0355 24 17

39 39 40 0.0018 0.0021 24 17

40 40 41 0.7238 0.8509 1.2 1

41 41 42 0.31 0.3626 0 0

42 42 43 0.041 0.0478 6 4.3

67

43 43 44 0.0092 0.0116 0 0

44 44 45 0.1089 0.1373 39.22 26.3

45 45 46 0.0009 0.0012 39.22 26.3

46 4 47 0.0034 0.0084 0 0

47 47 48 0.0851 0.2083 79 56.4

48 48 49 0.2898 0.7091 384.7 274.5

49 49 50 0.0822 0.2011 384.7 274.5

50 8 51 0.0928 0.0473 40.5 28.3

51 51 52 0.3319 0.1114 3.6 2.7

52 9 53 0.174 0.0886 4.35 3.5

53 53 54 0.203 0.1034 26.4 19

54 54 55 0.2842 0.1447 24 17.2

55 55 56 0.2813 0.1433 0 0

56 56 57 1.59 0.5337 0 0

57 57 58 0.7837 0.263 0 0

58 58 59 0.3042 0.1006 100 72

59 59 60 0.3861 0.1172 0 0

60 60 61 0.5075 0.2585 1244 888

61 61 62 0.0974 0.0496 32 23

62 62 63 0.145 0.0438 0 0

63 63 64 0.7105 0.3619 227 162

64 64 65 1.041 0.5302 59 42

65 11 66 0.2012 0.0611 18 13

66 66 67 0.0047 0.0014 18 13

67 12 68 0.7394 0.2444 28 20

68 68 69 0.0047 0.0016 28 20

Table B.3 69 Bus line and load data

68

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70

Publications

1. "An approach to determine the optimal location and capacity for Solar PV

Distributed Generation system", CD Proceedings of IEEE International Conference

on Control, Instrumentation, Energy & Communication, Kolkata, India, 31 January – 2

February, 2014.

2. “A Simplified Methodology for Determining Optimal Location and Capacity of Solar

PV Distributed Generation to Reduce Losses", The Journal of CPRI, Vol. 10, No. 1,

March 2014.

3. "Sustainable Energy Plan for an Indian Village", Proceedings of the IEEE PowerCon

2010- Technological Innovations Making Power Grid Smarter, Hangzhou, China,

October 24-28, 2010.

4. "Wind Speed Forecasting: Present Status", Proceedings of the IEEE PowerCon 2010-

Technological Innovations Making Power Grid Smarter, Hangzhou, China, October

24-28, 2010.

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