urban dc microgrids - saaei - manuela sechilariu.pdf · public grid s grid data super capacitors ....

UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE

Research Unit AVENUES EA 7284

Urban DC Microgrids Modeling, Optimization and Real-Time Control

Prof. Manuela SECHILARIU

[email protected]


Consortium

2

Urban DC microgrids: Modeling, Optimization and Real-Time Control

Compiègne

School of Engineering • IT Engineer

• Bio-mechanic Engineer

• Mechanic Engineer

• Urban Systems Engineer

• Industrial Process Engineer


Consortium/Alliance

3


https://www.inserm.fr/


Research unit AVENUES EA 7284

Interdisciplinary research on urban systems

Multiscale urban systems modeling

4



Research unit AVENUES EA 7284

Interdisciplinary research on urban systems

Energy management and microgrids

Team: 2 permanent researchers, 1researcher (under project contract)

PhD students, Master students

PhD thesis in microgrids field

2012-2018: 7 PhD defended thesis

2018: 3 PhD thesis on going

Two technological platforms

Building integrated microgrid

Electric vehicles charging station microgrid based

Leader of French research network on Microgrids

5


GDR SEEDS 2994 – CNRS GROUP

SEEDS: Electrical Energy Systems in their Societal Dimensions

SEEDS : national research group supported and funded by CNRS

GT Microgrids: working group

French research network: 20 laboratoires, 1 ITE, 55 researchers

6


URBAN DC MICROGRIDS

Urban DC microgrids: Modeling, Optimization and

Real-Time Control

Outline

1. Context and motivation

2. Urban microgrids

Smartgrid and urban microgrids

Power management interface

Urban energy management strategies

3. Microgrids modeling

4. Microgrids optimization

Building-integrated DC microgrid

Supervisory principle

5. Microgrids real time control

Results

6. Conclusion


7

URBAN DC MICROGRIDS


Real-Time Control

Outline


2. Urban microgrids









Results

6. Conclusion


8

1. CONTEXT AND MOTIVATION

Major preoccupations in urban areas

Buildings energy performances

Charging stations for plug-in electric vehicles

Emerging projects

Smart grid combined with microgrids

Positive-energy buildings increasing

Photovoltaic (PV) arrays most common used renewable sources in urban

area

Local microgrid based on PV sources

Urban microgrids for advanced local energy management

Smart grid communication

Self-consumption


9



10

Production

Source : Commission de

Régulation de l’Energie

Photovoltaic

small sites

Photovoltaic and wind

turbine farms

Nuclear, hydraulic, gas

turbine plants

Photovoltaic and wind

turbine farms

Electricity

transport and

distribution

Consumption / Production

Electricity injection

Electricity supply

Electricity power flow

Data communication

data transmission for the smart

grid and for the end-user

Buildings

Shopping centers

Residential area

Rail network

Remote area:

houses or farms

Factory

Rail network

Control center for

electricity network

operators

High

voltage

Medium

voltage

Low

voltage

SMART GRID

COMMUNICATION

Grid interaction

Optimization

LOCAL

INFORMATIONS

END-USER

DEMAND

Production / Consumption

Distributed electricity production

Power balancing in context of renewable energy integration

Centralized regulation? or local regulation? or both?

Smart grid microgrids (losses diminution, local regulation and optimization…)



11

Adopted microgrid definition (U.S. Energy department)

Microgrid is defined as a group of

interconnected loads and distributed energy resources

renewable energies, storages, and traditional energies (gas, fuel …)

with clearly defined electrical boundaries

that acts as a single controllable entity

with respect to the grid and the end-user

and can connect and disconnect from the grid

to enable it to operate in both grid connected or island mode

Source www.eaton.com

URBAN DC MICROGRIDS


Real-Time Control

Outline


2. Urban microgrids









Results

6. Conclusion


12

2. URBAN MICROGRIDS

Smart grid and urban microgrids

Public grid and communication network

Control interfaces


13

LARGE SCALE

PUBLIC GRID

Static

switch

Point of

common

coupling

Microgrid

controller

MICROGRID

PCC

MIDDLE SCALE PUBLIC GRID

URBAN AREAS

Communication bus Router

Energy

manager and

public control Static

switch

MICROGRID Microgrid

controller Static

switch

2. URBAN MICROGRIDS

Research interests in microgrid field

Techno-economic optimization of microgrid

Real-time power management at the local level


14

Power system

state

Load Control Sources

Control

AC bus or DC Bus or AC-DC Buses

Storage Diesel Generator

AC or DC Load

PV Sources

Control of microgrid

Real time power management

for sources and load END-USER DATA & WEATHER DATA

Public grid

SMART GRID DATA

Super

Capacitors

2. URBAN MICROGRIDS



15

Microgrid interface

and local energy

management

Cost optimization

Monitoring and

End-user management

Weather forecasting

Dynamic

pricing

Grid power injection

prediction

Power peak

shaving

Grid power supply

prediction

Power demand forecasting

Supervision

Smart metering

Photovoltaic

power

0h00 6h00 12h00 18h00

Building tertiary

needed power

Power

Public grid

power

supply

Active consumers

Energy sources

- Photovoltaic

- Wind turbine

- Storage

- Fuel-cell

- Micro-turbine

- (Bio)Diesel generator

Self-consumption

Injection

Public grid

Smart grid

communication

network

2. URBAN MICROGRIDS


Applications

Zero-energy or positive-energy buildings

Prosumer (producer-consumer) building

Self-consumption

Charging stations and infrastructures for electric vehicles


16

V2H

V2G V2H: Vehicle to Home

V2G: Vehicle to Grid

I2H: Infrastructure to Home

I2H

2. URBAN MICROGRIDS

Experimental platforms


17

Platform PLER

16 PV Fabrik-Solar: 2kW STC

Wind Turbine 1kVA

Storage Li-ion, Lead-acide

Power Grid Emulator

Load Emulator

Storage

lead-acide

baterries

Building

Emulator

Voltage

sensors card

Recorder

Real-time

system

Current

sensors card

Interface card

Drivers

IGBT

Grid emulator

Element Parameter Device

Storage (serial 8

battery units)

96V/130Ah Sonnenschein Solar

S12/130 A

PV array (16 PV panel

in series)

IMPP=7.14A, STC

VMPP=280V, STC

PV panel: Solar-Fabrik SF-

130/2-125

Grid emulator 3kVA Bidirectional linear

amplifier

Programmable DC

electronic load

2.6kW Chroma 63202

Controller board dSPACE 1103

Power electronic

converter

600V-100A SEMIKRON

SKM100GB063D

2. URBAN MICROGRIDS


Platform STELLA: Smart Transport and Energy Living Lab

9 parking spot at Innovation Center of UTC

84 PV Sunpower: 28,9kW STC

Storage Li-ion, supercapacitors

Public grid connection

Building grid supply connection

Charging terminals: AC and DC


18

2. URBAN MICROGRIDS


Platform STELLA: Smart Transport and Energy Living

19


URBAN DC MICROGRIDS


Real-Time Control

Outline


2. Urban microgrids









Results

6. Conclusion


20

3. MICROGRIDS MODELING

Energetic Macroscopic Representation (EMR)

Systematic approach to design all the interactions between the different subsystems of a complex system

Synthetic graphic tool using causal or functional representation

Four basic elements interconnected following the action and reaction principle using exchange variables and respecting the integral causality

integral causality defines accumulation element by a time-dependent relationship between its variables (output is an integral function of its inputs)

other elements are described using relationships without time dependence

Instantaneous power exchanged between two elements is the result of the product of action and reaction variables represented by arrows (inputs and outputs)

21


Source of energyElectrical converter

(without energy

accumulation)

Element with energy

accumulation

Electrical coupling

(without energy

accumulation)


Building-integrated microgrid

22


IPV

PV Installation Public Grid

DC Load

Storage

System

PVi

PVv

LPVi PVL

IPV

C

'LPVi

'PVv

Cv

SL LL

RL

LSi LL

i

LRi

'Sv 'LvSv Lv

Rv

'Rv

Li

'i 'LLi

'LSi

'LRi

PVC LC

1B 2B 3B

4B 5Bréseau extérieur

système de stockage charge'IPV et adaptateur d impédanceDC

Load

Public Grid

Storage PV & impedance adaptor


EMR of PV installation and the impedance adaptor

EMR of the DC common bus

23


IPVIPV

PVi

PVv

'LPV

iPVi PVv LPV

i

'PVv

PVm CvPVv LPVi


(without energy

accumulation)

Element with energy

accumulation

Electrical coupling

(without energy

accumulation)

PVi

PVv

LPVi PVL

IPV

C

'LPVi

'PVv

Cv

SL LL

RL

LSi LL

i

LRi

'Sv 'LvSv Lv

Rv

'Rv

Li

'i 'LLi

'LSi

'LRi

PVC LC

1B 2B 3B


système de stockage charge'IPV et adaptateur d impédancePV & impedance adaptor

C

'LPVi

Cv

'i 'LLi

'LSi

'LRi

C

'LPVi

Cv

'i 'LLi

'LSi

'LRi

'LPV

i

'LS

i

'iCv

CvCv

Cv

Cv

'LL

i

'LR

i

'LPV

i

'LS

i

'iCv

CvCv

Cv

Cv

'LL

i

'LR

i

a b

0;1PVm


EMR of the DC load

EMR of the storage

EMR of the public grid

24



(without energy

accumulation)

Element with energy

accumulation

Electrical coupling

(without energy

accumulation)

'LL

i

LLi

LL

Lm LLi

Lv Li

Cv 'Lv Lv

'LS

i

SS

Sm LSi

Sv

Cv LSi'

Sv

RR

RmLR

i

'Rv

Rv

Cv

'LR

i

LRi

PVi

PVv

LPVi PVL

IPV

C

'LPVi

'PVv

Cv

SL LL

RL

LSi LL

i

LRi

'Sv 'LvSv Lv

Rv

'Rv

Li

'i 'LLi

'LSi

'LRi

PVC LC

1B 2B 3B


système de stockage charge'IPV et adaptateur d impédanceDC

Load

Public Grid

Storage

0;1Lm

0;1Sm

1;1Rm


EMR of the microgrid

25



(without energy

accumulation)

Element with energy

accumulation

Electrical coupling

(without energy

accumulation)

'LPV

i

'LS

i

RR

IPVIPV LL

PVi PVv LPVi

'PVv

PVm'i

Lm

Rm

LLi

LRi

'Rv

Rv

Lv Li

SS

Sm LSi

Sv

CvPVv LPVi

CvCv

Cv

Cv

'LL

i

'LR

i

LRi

'Lv LL

iLv

LSi'

Sv

'IPV et adaptateur d impédance

système de stockage

charge

réseau extérieur

DC Load

Public Grid

Storage

PV & impedance adaptor

State variables: vPV ; iLPV ; vC ; vL ; iLL

; iLS ; iLR

Control variables: mPV ; mL ; mS ; mR


Maximum Control Structure (MCS)

MCS deduced through specific inversion rules

direct inversion (without controller) applied for items that are not time

function (conversion elements)

EMR formalism does not allow derivative causality (a direct inversion of time

function item is not possible)

indirect inversion (with controller) applied for items that are time function

(accumulation elements are inverted using a close-loop control)

Three basic elements

26


Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy


MCS of PV installation and the impedance adaptor

27


Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

'LPV

i

IPVIPV

PVi PVv LPVi

'PVv

PVm Cv

*PVv *

LPVi ' *

PVv

S1

PVv LPVi

1PV

PV LPVPV

dvi i

dt C * *

1L PV PV PVPVi C v v i

PVi

PVv

LPVi PVL

IPV

C

'LPVi

'PVv

Cv

SL LL

RL

LSi LL

i

LRi

'Sv 'LvSv Lv

Rv

'Rv

Li

'i 'LLi

'LSi

'LRi

PVC LC

1B 2B 3B


système de stockage charge'IPV et adaptateur d impédancePV & impedance adaptor

' * *2PV L L PVPV PV

v C i i v

' ** PV

PVC

vm

v

'1LPV

PV PVPV

div v

dt L

'

'L LPV PV

PVCPV

i im

vv


MCS of PV installation and the impedance adaptor

MCS of the DC load

28


Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

'LPV

i

IPVIPV

PVi PVv LPVi

'PVv

PVm Cv

*PVv *

LPVi ' *

PVv

S1

PVv LPVi

'LPV

i

'LS

i

LL

'iLm LL

iLv LiCv

CvCv

Cv

Cv

'LL

i

'LR

i

'Lv LL

iLv

*Lv*

LLi' *

Lv

* *3L L L LL

i C v v i

' * *4L L L LL L

v C i i v

' ** L

LC

vm

v

* *1L PV PV PVPV

i C v v i

' * *2PV L L PVPV PV

v C i i v ' *

* PVPV

C

vm

v


MCS of the storage system

29


Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

'LPV

i

'LS

i

'i

SS

Sm LSi

Sv

Cv

CvCv

Cv

Cv

'LL

i

'LR

i

LSi'

Sv

' *Sv *

LSi

'LPV

i

'LS

i

RR

'i

RmLR

i

'Rv

Rv

Cv

CvCv

Cv

Cv

'LL

i

'LR

i

LRi

*LR

i' *Rv

MCS of the public grid

' * *5S L L SS S

v C i i v

' ** S

SC

vm

v

' * *6R L L RR R

v C i i v

' ** R

RC

vm

v

3. MICROGRIDS MODELING 30


Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

SMINv

'LPV

i

'LS

i

RR

IPVIPV LL

PVi PVv LPVi

'PVv

PVm'i

Lm

Rm

LLi

LRi

'Rv

Rv

Lv Li

SS

Sm LSi

Sv

Cv

*PVv *

LPVi ' *

PVv

S1

PVv LPVi

CvCv

Cv

Cv

'LL

i

'LR

i

LRi

*LR

i' *Rv

'Lv LL

iLv

*Lv*

LLi' *

Lv

LSi'

Sv

' *Sv

*p

*Cv

*Sp*

'ip'*i

PVp

S2

SMAXv

Lp

Sv

rk

*Rp

*LS

i

MCS of DC common bus and the system

control of 7 state variables

with 4 control variables

2 strategies

* *

* *1

S r

R r

p k p

p k p

0 1rk

S1: MPPT

S2: power balancing

**

**

SLS

S

RLR

R

pi

vp

iv

'* * '7 C C LPV

i C v v i

* '*' Ci

p v i

* *' Li

p p p


EV charging station based on microgrid

31


'i

PVAv

Af

C

Bf Cf

PVA

L

Bi L

Ai L

'ACu

'BCu

PVAi

LLoadiLoadi

LoadC

Loadv 'Loadv

Loadf

'LLoadi

LoadL

Load

ACu

BCu

' Av ' Bv 'Cv

Ci

LPEVsiPEVsi

PEVsC

PEVsv 'PEVsv

PEVsf

'LPEVsi

PEVsL

PEVs

PublicGrid

i

PEVs

PVA

DC loadGrid

connection

Direct DC power use

DC bus voltage 1000V

Public grid 230/400V, 50Hz


EMR of the system

32



(without energy

accumulation)

Element with energy

accumulation

Electrical coupling

(without energy

accumulation)

State variables:

vPEVs ; iLPEVs ; vLoad ; iLLoad

; vPVA ; iA ; iB

Control variables: mPEVs ; mLoad ; mA ; mB

0;1Loadm

1;1A

B

m

m

0;1PEVsm

A

B

mm

PG

PEVsi

PEVsv

PEVsv

LPEVsi

LPEVsi

'PEVsv

'LPEVsi

PVAv

PVAv

PVAv

PVAi

i PVAv

'i

''AC

BC

uu

A

B

ii

A

B

ii

AC

BC

uu

PEVs

PEVsm

PVA

Loadi

Loadv

Loadv

LLoadi

LLoadi

'Loadv

'LLoadi

PVAv

Load

Loadm

3. MICROGRIDS MODELING 33


Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

Control block

without controller

Control block

with controller

Block strategy

MCS of the system

A

B

mm

PG

PEVsi

PEVsv

PEVsv

LPEVsi

LPEVsi

'PEVsv

'LPEVsi

PVAv

PVAv

PVAv

PVAi

i PVAv

'i

''AC

BC

uu

A

B

ii

A

B

ii

AC

BC

uu

PEVs

PEVsm

PVA

Loadi

Loadv

Loadv

LLoadi

LLoadi

'Loadv

'LLoadi

PVAv

Load

*PEVsv*LPEVs

i ' *PEVsv

S

*PVAv

Loadm

*Loadv*LLoad

i ' *Loadv

'*i *p

* 0q

**

ii

vv

ii

• mPEVs , mLoad impose constant DC voltage

(vPEVs ; vLoad) vPEVs* ; vLoad *

• mA , mB impose variable DC voltage

(vPVA)vPVA* imposed by P&O MPPT

• power balance: * * * '*PVAp v i v i v i


EMR modeling for DC microgrid operation analysis

unified and comprehensible graphical representation

physical modeling

inversion rules applied to EMR system's control structure is easily

deduced using the MCS representation

Local DC microgrid based on PV sources

Building integrated microgrid

Charging station integrated microgrid

DC microgrid EMR model based on the interaction principle

graphical description

DC microgrid MCS inversion-based control structure

graphical description

34


URBAN DC MICROGRIDS


Real-Time Control

Outline


2. Urban microgrids









Results

6. Conclusion


35

4. MICROGRIDS OPTIMIZATION

Generic system overview

Local DC Microgrid, DC bus distribution, AC bus distribution, appliances


36


Building integrated DC microgrid

37


DC micro-grid:

- efficiently integration of other

renewable sources and

storage

- absence of phase

synchronization

- only the voltage must be

stabilized

- a single inverter is required to

connect an AC load

DC bus and DC load:

- improving overall performance

by removing multiple energy

conversions

- use of existing infrastructure

cables with the same power

transfer as in AC distribution

network

- positive-energy building

- electric vehicle connection

PVA

v

StoragePublicGrid

DC MICROGRID SYSTEM

DC DC PVA

Control AC DC

DC DC

*PVv *

Gi*Si

LK

Power subsystem states

MULTI-SOURCE POWER SUBSYSTEM

SMART GRID MESSAGES

USER DEMAND

METADATA

PVA: PV array

SUPERVISORY SUBSYSTEM


From hybrid dynamic system to supervisory and control principle

38


0 0 0 0

( ) ( ( ), ( ), ( )) ( ) ( ) ( )

( ) ( )

( ), ( ) ( ( ), ( ), ( )) if ( ) occurs

( ) , ( ) q

c

x t F x t q t u t A q x t B u t

y t C x t

x t q t G x t q t v t v t

x t x q t


Multilayer microgrid supervisory and control principle

39


4. MICROGRIDS OPTIMIZATION 40


KL_lim


Human-machine interface

To define operating criteria: total load shedding amount, period

41


Load power parameters

Appliances shedding parameters


Prediction layer

Load prediction pL_PRED by statistic data, BMS information

PV prediction pPV_PRED by weather forecast data, sun position, PV model

42


KL_lim

4. MICROGRIDS OPTIMIZATIONROGRID

Prediction layer

Load prediction pL_PRED by

Statistic data,

Building Manag. System

Other source information

43


* pL_PRED pL

KL_lim


Prediction layer

PV prediction pPV_PRED by weather forecast data, sun position, PV model

44


KL_lim


Energy management layer

Objective: minimized energy cost

Grid connected mode: reduce grid power peak demand

Off-grid mode: minimize diesel generator fuel consumption

Both modes: avoid load shedding and PV power limiting

Optimization result : KD

45




Problem formulation for grid-connected operating mode


46

_ _G G I G Sp p p

( ) ( ) ( ) ( ) G S L PVp t p t p t p t

* * * S Gp p p* *S Dp K p

[0,1]DK

_C _S S S Dp p p

_

_

L MAX

LL LIM

Pp

p

_

_

PV MPPTPV

PV LIM

pp

p



Problem formulation for grid-connected operating mode


47

_ _G G I G Sp p p

( ) ( ) ( ) ( ) G S L PVp t p t p t p t

* * * S Gp p p* *S Dp K p

[0,1]DK

_C _S S S Dp p p

_

_

L MAX

LL LIM

Pp

p

_

_

PV MPPTPV

PV LIM

pp

p

_ _

0 0 0

_ _ _ _

_ _

_ _

Minimize

for { , , 2 ,..., } and with respect to:

( ) ( ) ( ) ( ) ( ) ( )

( ) ( ) ( )

( ) ( ) ( )

( )

total G S PV S L S

i F

L i G I i S C i G S i S D i PV i

S i S C i S D i

PV i PV MPPT i PV S i

L i LD

C C C C C

t t t t t t t

p t p t p t p t p t p t

p t p t p t

p t p t p t

p t p _

_ _

_

_

_

_ _

min max

0 _

( ) ( )

if ( ) ( ) then ( ) 0

( ) 0if ( ) ( ) then

( ) 0

if ( ) ( ) then ( ) 0

( )

1( ) ( (

3600

i L S i

PV MPPT i LD i L S i

L S i

PV MPPT i LD i

PV S i

PV MPPT i LD i PV S i

i

i S C

S REF

t p t

p t p t p t

p tp t p t

p t

p t p t p t

SOC soc t SOC

soc t SOC p tv C

0

_

_

_

_ max _ max

_ _ _lim

_ _ _lim

1

) ( ))

( ) 0

( ) 0

( ) 0

( ) 0

( )

0 ( )

0 ( )

Limit ( ) ( )

F

i

t

i S D i

t t

PV i

L i

PV S i

L S i

S S S

G I i G I

G S i G S

G i G i

p t t

p t

p t

p t

p t

P p t P

p t P

p t P

p t p t

_ max

Limit

( ) 0, ( ) 0 if ( ) ( ) 0

( ) 0, ( ) 0 if ( ) ( ) 0

( ) 0 if ( )

G i S i PV i LD i

G i S i PV i LD i

PV S

p t p t p t p t

p t p t p t p t

p t soc t SOC



Energy cost optimization


48

Human-machine interface User demand

Metadata

SUPERVISION SYSTEM

Operation layer


Prediction layer

Power system

states

MULTI-SOURCE POWER SYSTEM

Optimization solved by

Mixed Integer Linear Programming

IBM ILOG CPLEX

Problem modeling

according to CPLEX

INPUT FILES

DATA :

CONSTRAINTS : _ _ lim _ _ lim _

_

, ,

, ,

energy tariff , ...

G I G S L MAX

MIN MAX S MAX

P P P

SOC SOC P

_ _, PV PRED L PREDp p

_ _ _ _, , , ,G S C S D PV S L Sp p p p p

OUTPUT OPTIMAL POWER EVOLUTION

_ _min ( )t G S PV S L SC C C C C

_ _: S G PV S L STariff T T T T



Energy cost optimization


49

Human-machine interface User demand

Metadata

SUPERVISION SYSTEM

Operation layer


Prediction layer

Power system

states

MULTI-SOURCE POWER SYSTEM

Optimization solved by

Mixed Integer Linear Programming

IBM ILOG CPLEX

Problem modeling

according to CPLEX

INPUT FILES

DATA :

CONSTRAINTS : _ _ lim _ _ lim _

_

, ,

, ,

energy tariff , ...

G I G S L MAX

MIN MAX S MAX

P P P

SOC SOC P

_ _, PV PRED L PREDp p

_ _ _ _, , , ,G S C S D PV S L Sp p p p p

OUTPUT OPTIMAL POWER EVOLUTION

DK

_ _

_ _( )

S C S D

D

G S C S D

p pK

p p p

_ _min ( )t G S PV S L SC C C C C

_ _: S G PV S L STariff T T T T


Operational layer

50


Interface:

optimization by KD

Robust:

power balancing with

any KD value

Self-correcting:

load shedding

PV power limiting

URBAN DC MICROGRIDS


Real-Time Control

Outline


2. Urban microgrids









Results

6. Conclusion


51

52


Communication

Subsystem

Forecast

Subsystem

Operational

Algorithm Load shedding/restoration

optimization algorithm

Load Control PV Control

Human-Machine Interface

PV power prediction calculation

Smart Grid

Estimation

soc

Local

Weather

Forecast

System parameters

Measurement

Local load power

prediction

Optimization algorithm

( )f G ( )f S( )f PV

Dk

_PV Sp _L Sp

_L DEMp

, AIRg

min max _ max _ crit, , ,S LSOC SOC P k

_PV prep

Sp

_ , ,L DEM PV Sp p p

_L prep

_L Sp

min max, ,origCoP T T

soc

PVp

_ _max _ _max,G I G SP P

Energy Tariff

_ _ _ _,G I pre G S preP PEconomic Dispatch Layer

Operational Subsystem

Demand Side

Management

Subsystem

( )f L

,PV Sp p

5. MICROGRIDS REAL TIME CONTROL

Demand side management (load shedding optimization)

53


1

0 if appliance is off

1 if appliance is on

max ( ) : 1

0

0

th

i th

n

i i

i

orig

i rated u c

ix

i

f x CoP x i n

CoP

W W k k

1

max

max

_ min

with respect to:

W

50 if _ T

if _ T

n

D i i AVL

i

orig count off

i

orig count on

count off

P x P

CoP TCoP

CoP T

T T

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00100

200

300

400

500

600

700

800

900

1000

Po

wer

(W)

PAVL

PD

PS

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00100

200

300

400

500

600

700

800

900

1000

Po

wer

(W)

PAVL

PD

PS


Demand side management (load shedding optimization)

54



• Results


55

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:000

500

1000

1500

2000

2500

pP

V (

W)

Raw data prediction Corrected prediction Measure

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:000

500

1000

1500

2000

2500

pP

V (

W)


8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:000

500

1000

1500

2000

2500

pP

V (

W)


Case operation Ctotal (€) Load

shedding (€)

PVA power

limiting (€)

Optimization based predictions -0.777 0 0

Experiment 0.225 0.244 0

A postiori optimization based

real conditions

-0.247 0 0


shedding (€)

PVA power

limiting (€)


Experiment 0.929 0.266 0.052


real conditions

0.357 0 0


shedding (€)

PVA power

limiting (€)


Experiment 3.219 1.300 0


real conditions

2.165 0.257 0

URBAN DC MICROGRIDS


Real-Time Control

Outline


2. Urban microgrids









Results

6. Conclusion


56

6. CONCLUSION

Energy cost optimization and predictive control

Flexible and reconfigurable algorithm

Power balancing following KD parameter as

predictive control parameter

Limits

Near-optimal cost due to the forecast

uncertainties

Real time optimization

Microgrid for urban areas offers interface with

the future smart grid

Multilayer supervisory hierarchical design allow

smart communication

Experimental validation technical feasibility

Work in progress

Dynamic converter efficiencies nonlinear

optimization

Electromobility: V2G, V2H, I2H


57


Research Unit AVENUES EA 7284

Thanks for listening

urban dc microgrids - saaei - manuela sechilariu.pdf · public grid s grid data super capacitors ....

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