PhD thesis defense Energy management under …l2ep.univ-lille1.fr/pagesperso/francois/These_Xingyu_Yan...PhD thesis defense Thursday, May 18, 2017 Energy management under uncertainty:

PhD thesis defense Thursday, May 18, 2017

Energy management under uncertainty: Application to the day-ahead planning and power reserve allocation of an

urban microgrid with active photovoltaic (PV) generators and storages

Xingyu YAN

L2EP, Centrale Lille, FranceSupervisors: Bruno FRANCOIS & Dhaker ABBES

2

Wind and photovoltaic (PV) power production are highlyintermittent due to the influence of meteorological conditionson the primary energy resource.

I. Background: Problem, Goals and Methods

I.1 Problem: Variability of RES

Accurate forecasts of renewable energy generation are useful for Producers: Making bids in electricity markets, Planning maintenance of wind farms, etc.; Grid operators: Economic dispatch, planning power reserve, planning power exchanges

with interconnections and other stakeholders, etc.

Operate the power system in a secure and economic way.

0 100 200 300 400 500 600 700 800 9000

10

20

PV P

ower

(kW

)

T ime (minutes)

22/06/2010-24/06/2010

RES: renewable energy sources

3

Intermittent RES energy production is predictable thanks to weather and statistical tools.

I.1 Problem: Prediction Errors

However, forecasting errors can not be eliminated even with the best forecasting tools.


Uncertainty caused by forecasting errors

4I.1 Problem: Power Reserve to Cover the Uncertainty

PrefPref

Pref is given by the system operators (power dispatch)

Today the consumption/production balancing and OR provision are performed by conventional generators.

UncertainVariable Reserve

Constant Reserve

To cover the risk of an unexpected RES generation losses or load increasing, operatingreserve (OR) is scheduled one day ahead.

Massive RES generators increase the system uncertainty of power production and so thedifficulties to maintain the system security level.

To cover the risk: Additional OR is needed !

How much ? How to provide it ?


5I.2 Applied method

Active PV Generators (PV AGs): with an additional storage system (batteries and super-capacitors); MGTs: Micro gas-turbines.


Microgrid: From a centralized network to a decentralized network.

Distributed generators (DG) MGTs: constant power sources, with

controlled power output; PV AGs: can provide ancillary services

thanks to storage devices.

Uncertainty challenges of RES integration into the Microgrid ?

6I.3 General Organization of Energy Management System (EMS)

Microgrid supervision can be analyzed in different timing scales and functions.


Focuses on day-ahead optimization of the network operation.

7I.4 Goals and Methods for the Study Case

Predictive Analysis for Uncertainty: PV power and load forecasting

Operating Reserve Quantification:Loss of load probability (LOLP)

Day-ahead Optimization Planning:Unit commitment problem with

dynamic programming

OR Dispatching Strategies on Generators

A User-friendly EMS and Operational Supervisor


Active PV Generators (PV AGs): with an additional storage system (batteries and super-capacitors); MGTs: Micro gas-turbines.

8Roadmap: Part II




dynamic programming



9II.1 Data Management and Predictive Analysis (1)II. Uncertainty Analysis and Forecasting of PV Power and Load

Data collecting for database building

PV data collecting: (Sunways) three PV inverters (3 kW each), Centrale de Lille

Load data collecting (www.rte‐france.com)

Meteorological data collecting (www.wunderground.com)

010203040506070

05

10152025

Prec

ipita

tion

(mm

)

Tem

pera

ture

(°C

)

Month

Data from 2000 to 2012Average precipitation Average high in °C Average low in °C

10II. Operating Reserve (OR) Quantification to Cover Uncertainty

Data mining and predictive analysis (PV power)

II.1 Data Management and Predictive Analysis (2)

Mathematical Modeling of PV Generator

) )25)(( 0.0035 - 1 ( )( )( =)( tTtIAttPV rpvpvP

0 100 200 300 400 500 600 700 800 9000

10

20

PV P

ower

(kW

)

22/06/2010-24/06/2010

0 100 200 300 400 500 600 700 800 9000

0.4

0.8

1.2

Irra

diat

ion

(kW

/m2 )

0 100 200 300 400 500 600 700 800 9000

20

40

Time (every 5 minutes)

Tem

pera

ture

(°C

)

R2 is used to determine how closely the irradiance and the temperature fit PV power.

11II. Uncertainty Analysis and Forecasting of PV Power and Load

PV power and irradiance in 2010

0100200300400500600700800900

1000

Sola

r Rad

iatio

n (W

/m2 )

Time (hour)

Highly Variable-01/05

Overcasted-12/05

Low Variable-15/05

Clear Sky-23/05

II.2 PV Power variability quantification

020406080100120140160

0

500

1000

1500

2000

2500

Irrid

ianc

e (k

Wh/

m²)

PV P

ower

(kW

h)

Month

Irridiation (kWh/m²) PV Power (kWh)

Variable Index (VI) and Radiation Index (RI)

SC

N

k kkk

I

SRSRSRNVI 2

22122

1

SC

N

k k

ISR

RI 2

121

VI: calculates PV power variabilityRI: calculates daily solar radiation

: solar radiation during 5 minutes scale

12

Forecasting with Artificial Neural Networks (ANN) PV forecast database

PV power data

Meteorological dataSolar irradiation Humidity Air pressure Wind speed Temperature

PV power output AC voltage DC voltage

Dispatch data into 3 sets

Validation set

Test set

Training set

DATA BASE

Load forecast databaseLoad demand data

Meteorological dataSolar irradiation Humidity Air pressure Wind speed Temperature

Dispatch data into 3 sets

Validation set

Test set

Training set Historical load data

DATA BASE

Input layer Hidden layer Output layer

X ... ...

)1(bias

)1( )2(

. . . y

)2(bias

II.3 PV Power and Load Forecasting with ANN (1)

A three-layer ANN

Data description: PV power and Load forecasting databases

Error Computing Method

Data normalization

minmax

min:xx

xxxxf i

n

i kk yyn

nRMSE1

2)~(1 =

n

i kk yyn

nMAE1

~1 =

nRMSE: normalized Root Mean Square Error nMAE: the normalized Mean Absolute Error

II. Uncertainty Analysis and Forecasting of PV Power and Load

13II.3 PV Power and Load Forecasting with ANN (2)

PV Forecast

24~..., ,~

hh vPvP

ANN

124 ..., , hh PvPv

Last 24h sensed PV Power

Last 24h sensed irradiance

124~..., ,~

hh II 24~..., ,~hh TT

Day ahead Predicted

Temperature

D+1 D

124~..., ,~

hh vPvP

PV Power Forecasting with ANN


X . . . . . .

)1(bias

)1( )2(

... y

)2(bias

A three-layer ANN

0 5 10 15 20 250

0.5

1

Time (hours)

Inpu

t Dat

a (p

.u.)

24h-before PV Power24h-before Irradiance24h-ahead Temperature

0 5 10 15 20 250

0.5

1

Time (hours)

Out

put D

ata (

p.u.

)

Real Measured PV Power24h-ahead Predicted PV Power

nRMSE [%] nMAE [%]Training Set 6.09 3.69

Validation Set 5.58 3.13Test Set 5.95 3.12

Errors of the PV power forecasting with ANN.

0 4 8 12 16 20 24-0.4

-0.2

0

0.2

0.4

Time (hours)

Erro

r (p.

u.)


14

Load Forecast

24~..., ,~

hh LL

ANN

148 ..., , hh LL

Last 48h Sensed Load

D+1 D

24~..., ,~hh TT

24h ahead Predicted Temperature

124~..., ,~

hh LL

Load Forecasting with ANN

II.3 PV Power and Load Forecasting with ANN (3)

0 10 20 30 40 50

0.4

0.6

0.8

Inpu

t Dat

a (p

.u.)

48h-before Load

0 5 10 15 20 250

0.5

1

24h-ahead Temperature

0 5 10 15 20 250.2

0.4

0.6

Time (hours)

Out

put D

ata (

p.u.

)

Real LoadPredicted Load


X . . . . . .

)1(bias

)1( )2(

... y

)2(bias

A three-layer ANN

nRMSE [%] nMAE [%]Training Set 3.18 2. 45

Validation Set 3.57 2.76Test Set 3.67 2.84

Errors of the Load forecasting with ANN.

0 4 8 12 16 20 24-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Time (hours)

Erro

r (p.

u.)


15




dynamic programming



Roadmap: Part III

16III. Operating Reserve (OR) Quantification to Cover Uncertainty

Frequency control and energy balancing

t

Reserve

Automatic Automatic/Manually

Manually

fnom

Primary Secondary

Tertiaire

30 s 15 min 30 min

Band of the normal frequency variation

Detection of the problem

Deployment of the reserve

Stabilization Recovery Maintain

f

Frequency

In this thesis, the OR is defined as the real power that can be called on instantaneously for the imbalance between power generation and load demand (Primary and secondary reserve).

III.1 Calculation of OR by considering RES uncertainties (1)

Deploying of the primary, secondary and tertiary regulation of frequency.

17III.1 Calculation of OR by considering RES uncertainties (2)III. Operating Reserve (OR) Quantification to Cover Uncertainty

The OR must be precisely quantified in order to maintain a specified level of system security with a minimum cost.

Most conventional utilities have adopted deterministic criteria by considering only two sources of uncertainty:

the possibility of multiple large generators failing: low probability but high impact; and load forecast errors: often but usually relatively small.

Deterministic methods do not match the stochastic nature of the OR quantification problem.

Probabilistic methods are adapted to the stochastic characteristics of RES based generators and loads. They can set a certain security level.

18III. Operating Reserve (OR) Quantification to Cover Uncertainty

Recent examples of probabilistic methods:

Probabilistic Model

Fixed Risk Indices

Uncertainty Analysis

Load Forecasting Uncertainty

PV Power Forecasting Uncertainty

Net Demand (ND) Uncertainty t

FtF

tF PVLND

Risk/Reserve Curve

Operating Reserve Quantification

Eastern Wind Integration and Transmission Study (EWITS): focused on the operational impacts of various wind penetrations. Load variability and wind power variability are considered independent [1].

Western Wind and Solar Integration Study (WWSIS): discussed the OR requirement that dynamically relied on both the load and wind penetration levels [1].

Other probabilistic methods

General Scheme for OR quantification

Decisions made under uncertainty must be informed by probabilistic information in order to correctly quantify the risk.

III.1 Calculation of OR by considering RES uncertainties (3)

[1]: L. E. Jones, Renewable energy integration: practical management of variability, uncertainty, and flexibility in power grids: Academic Press, 2014.

19III.2 Net Demand (ND) Uncertainty Analysis (1)

124~..., ,~

hh LL 124 ..., , hh PvPv

D+1 D

D+1D

+ _

+

Last 24h ND Last 24h ND forecast

124~..., ,~

hh DNDN

+ _

124 ..., , hh LL

ND Error Forecast ANN

NDh

NDh 124 ..., ,

Last 24h prediction errors

124~..., ,~

hh vPvP

Sensed Load Database

D+1 D

Sensed PV Database

D+1 D

Forecasted PV Database

Forecasted Load Database

_

NDh

NDh 241

~..., ,~

NDh

NDh

Mean average and Standard deviation

First Method: Day-ahead Net Demand Errors Forecast The real ND is composed of the forecasted ND and an error: ND

hhh DNND ~ =

NDhNDh

: Mean value: Standard deviation

III. Operating Reserve (OR) Quantification to Cover Uncertainty

NDh : ND forecasting errors

-10 0 100

10

20

30

40

Errors (% p.u.)C

ount

Frequency histFitted Gaussian

(a)Net Forecast Errors

20III.2 Net Demand (ND) Uncertainty Analysis (2)

Second Method: Calculation from the PV Power and the Load Forecast Errors Estimation.

Prediction Error Forecast

ANN

Lh

Lh 124 ..., ,

Lh

Lh 24

~..., ,~

Prediction Error Forecast

ANN

PVh

PVh 124 ..., ,

PVh

PVh 24

~..., ,~

Last 24h prediction errors

2)(, NDh

NDh

2)(, PVh

PVh 2)(, L

hLh


Equations


124~..., ,~

hh LL

124 ..., , hh PvPv

D+1 D

D+1D

+ + _

124 ..., , hh LL 124

~..., ,~ hh vPvP

Sensed Load Database

D+1 D

Sensed PV Database

D+1 D

Forecasted PV Database

Forecasted Load Database

_

PVh

Lh

NDh

222 )()()( PVh

Lh

NDh

The real ND is composed of the forecasted ND and an error: NDhhh DNND ~ =


NDhNDh

: Mean value: Standard deviation

NDh : ND forecasting errors

-40 0 400

15

30

45

Errors (% p.u.)

Coun

t

(a) PV Forecast Errors

0 -20 0 200

15

30

45

Errors (% p.u.)

Coun

t

(b) Load Forecast Errors

Frequency histFitted Gaussian

21III.3 Uncertainty Assessment

Lower bound (Blower)

Normal Inverse Cumulative Distribution

Function (F-1)

Normal Probability

Density Function (F(B|µND,σND))

Upper bound (Bup)

+ +

_ +

NDh

NDh

Net Demand Forecast

241~..., ,~

hh DNDN max_

~hDN

min_~

hDN

x: Desired probability

NDhpdf

deBxB

NDh

NDh

NDh

NDh

NDh

2

2

)(2)(

21),(F

xBFBxB NDh

NDh

NDh

NDh ),(:),(F 1-

Hourly ND uncertainty assessment

0 4 8 12 16 20 240

5

10

15

20

Time [hours]

PV u

ncer

tain

ty [k

W]

90% probability

80% probability

70% probability

60% probability

Forecasted PV

0 4 8 12 16 20 240

20

40

60

80

100

120

Time [hours]

Loa

d D

eman

d U

ncer

tain

ty [k

W]

90% probability80% probability70% probability60% probabilityForecasted Load

0 4 8 12 16 20 240

20

40

60

80

100

Time [hours]

Net

Dem

and

Unc

erta

inty

[kW

]

90% probability80% probability70% probability60% probabilityNet Forecasted


Bound margin:

Probability:

22III.3 Uncertainty Assessment

Lower bound (Blower)

Normal Inverse Cumulative Distribution

Function (F-1)

Normal Probability

Density Function (F(B|µND,σND))

Upper bound (Bup)

+ +

_ +

NDh

NDh

Net Demand Forecast

241~..., ,~

hh DNDN max_

~hDN

min_~

hDN

x: Desired probability

NDhpdf

deBxB

NDh

NDh

NDh

NDh

NDh

2

2

)(2)(

21),(F

xBFBxB NDh

NDh

NDh

NDh ),(:),(F 1-

Hourly ND uncertainty assessment

0 4 8 12 16 20 240

5

10

15

20

Time [hours]

PV u

ncer

tain

ty [k

W]

90% probability

80% probability

70% probability

60% probability

Forecasted PV

0 4 8 12 16 20 240

20

40

60

80

100

120

Time [hours]

Loa

d D

eman

d U

ncer

tain

ty [k

W]

90% probability80% probability70% probability60% probabilityForecasted Load

0 4 8 12 16 20 240

20

40

60

80

100

Time [hours]

Net

Dem

and

Unc

erta

inty

[kW

]

90% probability80% probability70% probability60% probabilityNet Forecasted


Bound margin:

Probability:

High uncertainty

23III.4 OR Quantification Power reserve quantification

PRhhh pdfPLprobLOLP )d()0( =LOLP represents the probability that load exceeds PV power.


1

PV power forecast errors database

tPVcdf CDF CDF CDF

CDF CDF CDF

CDF CDF CDF

tLcdf

tNDcdf

2 T …

Load forecast errors database

Net load forecast errors database

…

…

…

0 0

0.02

0.04

0.06

0.08

0.1

Prob

abili

ty

Accepted risk

LOLP= x %

x %

RPower DeficitPower Surplus

0 1 2 3 4 5 6 7 8 9 10

0510

1520

250

5

10

15

x % of LOLPTime [Hour]

Pow

er R

eser

ve [k

W]

0 4 8 12 16 20 24 0

6

12

18Hourly power reserve with 1 % LOLP

Time (Hour)

Hou

rly P

ower

Res

erve

(kW

h)

First Method Second Method

CDF: cumulative distribution function

24




dynamic programming



Roadmap : Part IV

25IV.1 Optimal OR Dispatching Strategies

8h 10h 13h 16h 19h 22h 1h 4h 7h0

30

60

Fore

cast

PV (k

W)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

60

120

Fore

cast

Loa

d (k

W)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

10

20

Time (half hour)

PR (k

W)

With 1 % of LOLP

Forecasted PV, load and OR quantification

IV. Optimal Operating Reserve (OR) Dispatching Strategies

Day-ahead PV power forecasting Day-ahead load forecasting Statistic OR quantification

One Day-ahead OR dispatching

Collected data for system

uncertainty analysis

PV power and load forecasting

OR quantification

Control System of the nth PVAG

(fig. 1-7)

E-Box t

FPV tFL

tORP

tnAGP _

Forecasting errors

Central EMS

OR Dispatching

And AG planning Optimal

MGT planning

tmMGTP _

tmMGT

M

mP _

1

Local

controller mth MGT

The flowchart of the energy management

26IV.2 Non-linear Constraints

Security: OR assessment with x % of LOLP;

Power balancing:

Maximization of RES usage: considering the battery capacity limitation (more PV power, larger battery storage !)

0))()(()()()( =)( _

N

1n 1_

tPttPtPtPt iMGT

M

iinAGresL

Non-linear characteristic of MGTs: )(%100)()(%50 max___max__ tPtPtP iMiMiM

Power output (p.u.)

CO

2eq

uiva

lent

(kg)

Power output (%)

Fuel

con

sum

ptio

n (k

Wh)

IV. Operating Power Reserve Dispatching

Load and OR

27IV.3 Management Strategy of OR Dispatching IV. Operating Power Reserve Dispatching

Two considered scenarios

OR dispatching strategies according to power sources: First strategy: OR on three MGTs only Second strategy: OR on three MGTs and PV AGs including batteries

According to PV energy during the day Uncertainty from

Scenario H ∗ _ Used PV power and load forecasting

Scenario L ∗ _ Stored in the batteries Only load forecasting

-20 -10 0 10 200

0.05

0.1

0.15

0.2

Generation Margin (kW)

Prob

abili

ty

2 p.m.

-30 -15 0 15 300

0.02

0.04

0.06

0.08

Generation Margin (kW)

Prob

abili

ty

2 p.m.(a) (b)

Scenario L Scenario H

Pre-processing of the uncertainty by energy management of batteries

OR quantification for the electrical system

28IV.4 OR Dispatching StrategiesIV. Operating Power Reserve Dispatching

More details can be found: X. Yan, D. Abbes, B. Francois, and Hassan Bevrani “Day-ahead Optimal Operational and Reserve Power Dispatching in a PV-based Urban Microgrid,” EPE 2016, ECCE Europe, Karlsruhe/ Germany.

Starting

PV energy is more than battery energy sizing

1<t<tnight

tF

N

n

tnAG LP

1_

tOR

M

m

tmMGT PP

1_

At time step t, if tF

tF LPV

Day Night

tF

N

n

tnAG PVP

1_

tOR

tF

tF

M

m

tmMGT PPVLP

1_

H L

1<t<tnightDay

tOR

tF

M

m

tmMGT PLP

1_

End

•

• •

•

•

0_ tnAGPt

ORP

0tMGTP

1st strategy

2nd strategy

Autonomous PV AG mode

1st strategy

2nd strategy

PV sure PV Not sure

tOR

tF

tF

M

m

tmMGT PPVLP

1_

Autonomous PV mode

tF

N

n

tnAG LP

1_

tOR

M

m

tmMGT PP

1_

0tMGTP

1st strategy

2nd strategy

PV AG sure

Full MGTs mode

PV AG sure

Forced PV AG to be sure But costly

•).( t

FtF

tBat PVLE t

FtBat LE

).( tOR

tF

tF

tBat PPVLE ).( t

ORtF

tBat PLE

1st strategy

2nd strategy

1st strategy

2nd strategy

Both strategies

Case 1.2 Case 1.1 Case 2.2 Case 2.1 Case 3.2 Case 3.1 Case 4

tOR

tF

N

n

tnAG PPVP

1_

tOR

tF

N

n

tnAG PLP

1_

29



Day-ahead Optimal Planning:Unit commitment problem with

dynamic programming



Roadmap : Part V

30V.1 Day-ahead Optimal OR Planning on MGTsV. Day-ahead Unit Commitment (UC) Problem with Dynamic Programming (DP)

Technological features

SoC(t)

Dynamic Programming

Optimization

Operational Planning of MGT

J

Day-ahead Optimal Operational Planning Constraints Power Balance MGT Efficiency Battery Power

& Energy Limitation

Day-ahead PV

Power, Load and

Operating R

eserve References

Criteria Objective Function

? (t)

u(t)

z(t)

tORP

Electrical SystemM odeling

Loads

PV AG

Economic Cost

CO2 EmissionsMGT

tAGP t

MGTP

...... _t

mMGTP

Focus on the design of the microgrid central EMS. Unit commitment (UC) problem with dynamic programming (DP) is developed in order to

reduce the economic cost and CO2 equivalent emissions.

Operational and Reserve Power Dispatching

31

)(),...,(),( =(t) _2_1_ tPtPtPx iMGTMGTMGT

)(),...,(),( =(t) 21 tttu i

UC Problem: is an operation scheduling function.

V.2 Unit Commitment (UC) Problem

Problem: optimal operation of a cluster of MGTs (three in our case)

MGT1(30kW)

MGT2(30kW)

MGT3(60kW)

Three MGTs with different characteristics

Number of states Generators states

1 δ1=1; δ2=1; δ3=1

2 δ1=1; δ2=1; δ3=0

3 δ1=1; δ2=0; δ3=1

4 δ1=1; δ2=0; δ3=0

5 δ1=0; δ2=1; δ3=1

6 δ1=0; δ2=1; δ3=0

7 δ1=0; δ2=0; δ3=1

8 δ1=0; δ2=0; δ3=0

V. Day-ahead Unit Commitment (UC) Problem with Dynamic Programming (DP)

32V.3 Optimization Goals

Economic criteria: minimize the total fuel cost;

Environmental criteria: minimize the equivalent CO2 emission;

Best compromise criteria: make a compromise of economic and environmental criteria.

),( = 1___

ti

ti

CostiP

tiM

ti

tiC CCJ

T M

i

tiCJJ

1t 1_Cost =

),(2 = 12___2

ti

ti

COiP

tiM

ti

tiCO CCOJ

T M

i

tiCOJJ

1t 1_2CO2

)),(2)(1()),( ( = 12__

1___

ti

ti

COiP

tiM

ti

ti

ti

CostiP

tiM

ti

tiBC CCOCCJ

T M

i

tiBCJJ

1t 1_BC =

M: the number of MGTs;T: the number of operational steps.

the proportion rate, from 0 to 1

Fuel cost Start/stop penalty


33

DP: Systematically evaluates a large number of possible decisions in a multi-step problem considering the "transition costs".

Start

t=1

t=T

Stop

t=t-1

Optimization: calculate the minimum J(t) with vectors x(t) and u(t), using recursive equation

F(t, x(t), u(t)).

Calculate F(T, x(T), u(T)) with x(T) and u(T) to minimize J(T).

F1(1) F1(2) … F1(T-1) F1(T) F2(1) F2(2)… F2(T-1) F2(T) F3(1) F3(2)… F3(T-1) F3(T)F4(1) F4(2)… F4(T-1) F4(T) … . … . … . … .

F1(t) F1(T) F2(t) F2(T) F3(t) F3(T) F4(t) F4(T) … . … .

F1(T) F2(T) F3(T)F4(T) … .

No

Yes

V.3 Dynamic Programming (DP)

Multistage decision process formulation with backward recursion:

State

back-track optimal path

An evaluation of all possible configurations in each time step (Stages and States); A “back-track” operation from the end back to the beginning (Recursive Optimization).

Stage


34V.4 Case Study and Simulation Results (1)

In this case: rated load (110 kW), rated PV power (55 kW) and the OR (with 1 % of LOLP) coming from the net demand uncertainty assessment.

8h 10h 13h 16h 19h 22h 1h 4h 7h0

30

60

Fore

cast

PV (k

W)

Scenario HScenario L

8h 10h 13h 16h 19h 22h 1h 4h 7h0

60

120

Fore

cast

Load

(kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

10

20

Time (half hour)

OR

(kW

) With 1 % of LOLP

Scenario HScenario L

Scenario H, sunny day, 269.5 kWh; Scenario L, cloudy day, 128.4 kWh; The total battery capacity (150 kWh).

The daily load is 1082 kWh.


OR calculation with two scenarios.

35

Scenarios Optimization Criteria Cost (€) Pollution (kg) OR on AG (%) Ebat_Max (kWh)

H 1st strategy: Only MGTs

None 173 1224 0 78.6Environmental 169 1141 0 78.6

Economic 167 1167 0 78.6Best compromise 171 1156 0 78.6

H 2nd strategy: MGTs and AGs

None 173 1027 35.4 52.7Environmental 171 937 35.4 52.7

Economic 168 976 35.4 52.7Best compromise 170 952 35.4 52.7

None 185 1290 0 132.4L 1st Environmental 182 1181 0 132.4

strategy Economic 180 1245 0 132.4Best compromise 183 1195 0 132.4

None 188 1119 11.8 132.4L 2nd Environmental 184 993 11.8 132.4

strategy Economic 181 1063 11.8 132.4Best compromise 185 1006 11.8 132.4

V.4 Case Study and Simulation Results (2) Day-ahead Operational Planning Results.

71%

10%

19%

H 1st Strategy

MGT1MGT2MGT3AG=0

8h 10h 13h 16h 19h 22h 1h 4h 7h0

10

20

OR

(kW

)

PR in MGT1PR in MGT2PR in MGT3

20

(a) H1


36








None 185 1290 0 132.4L 1st Environmental 182 1181 0 132.4

strategy Economic 180 1245 0 132.4Best compromise 183 1195 0 132.4

None 188 1119 11.8 132.4L 2nd Environmental 184 993 11.8 132.4

strategy Economic 181 1063 11.8 132.4Best compromise 185 1006 11.8 132.4

V.4 Case Study and Simulation Results (2)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

5

10

15

20

Time (half hour)

OR

(kW

)

PR in MGT1PR in MGT2PR in MGT3PR in AG

(b) H2

30%

12%13.4%

35.4%

H 2nd strategy

MGT1

MGT2

MGT3

AG

35.4% of OR ison PV AGs

Day-ahead Operational Planning Results.

Each PV AG equally contributed to the OR


37V.4 Case Study and Simulation Results (2)

Day-ahead Operational Planning Results.








None 185 1290 0 132.4L Environmental 182 1181 0 132.4

1st strategy Economic 180 1245 0 132.4Best compromise 183 1195 0 132.4

None 188 1119 11.8 132.4L Environmental 184 993 11.8 132.4

2nd strategy Economic 181 1063 11.8 132.4Best compromise 185 1006 11.8 132.4


Cost and pollution are increased

Battery size is larger

38V.4 Results (3): MGTs Load Ratio H 1st Strategy: OR on MGTs

00,10,20,30,40,50,60,70,80,9

1

MGT1 MGT2 MGT3

MG

T P

ower

Rat

io

MGT Power Ratio with Strategy 2

None Environmental Economic

00,10,20,30,40,50,60,70,80,9

1

MGT1 MGT2 MGT3

MG

T P

ower

Rat

io

MGT Power Ratio with Strategy 1

None Environmental Economic

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MGT

1 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MGT

2 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P MGT

3 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P AG (k

W)

T ime (half hour)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MG

T1 (k

W)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MG

T2 (k

W)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P MGT

3 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P AG (k

W)

T ime (half hour)

H 2nd Strategy: OR on MGTs and PV AGs

0.5

0.8

Better use of MGTs in their higher efficiency operation domain.


Turnoff all the MGTs

39V.4 Results (3): Obtained system security H 1st Strategy: OR on MGTs

7h 10h 13h 16h 19h 22h 1h 4h 7h0

0.5

1

LO

LP

(%) Strategy 1

7h 10h 13h 16h 19h 22h 1h 4h 7h0

1

2Strategy 2

LO

LP

(%)

Time (half hour)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MGT

1 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MGT

2 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P MGT

3 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P AG (k

W)

T ime (half hour)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MG

T1 (k

W)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MG

T2 (k

W)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P MGT

3 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P AG (k

W)

T ime (half hour)

H 2nd Strategy: OR on MGTs and PV AGs

This is the risk we need to face with.


40V.4 Results (4): Battery State of Charge

8h 10h 13h 16h 19h 22h 1h 4h 7h

8h 10h 13h 16h 19h 22h 1h 4h 7h-40

0

40

P Batt

ery

(kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

20

40

60E Ba

tter

y (k

Wh)

Time (half hour)

9 7

8h 10h 13h 16h 19h 22h 1h 4h 7h-20

0

20

P Batt

ery

(kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

100

E Batt

ery

(kW

h)

T ime (half hour)

H 1st Strategy: OR on MGTs

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MGT

1 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MGT

2 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P MGT

3 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P AG (k

W)

T ime (half hour)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MG

T1 (k

W)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

102030

P MG

T2 (k

W)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P MGT

3 (kW

)

8h 10h 13h 16h 19h 22h 1h 4h 7h0

50

P AG (k

W)

T ime (half hour)

H 2nd Strategy: OR on MGTs and PV AGs78.6 kWh

52.7 kWh


41



Day-ahead Optimal Planning:Unit commitment problem with

dynamic programming



Roadmap : Part VI

42VI.1 Application VI. A User-friendly EMS and Operational Supervisor

….

Adopted Methods for the Urban

Microgrid Simulator

Environmental, Economic, Best Compromise

LOLP

Scenarios H&L

OR Quantify

Mining Collecting

Test Validation Training

Big Data

Predictive Analysis

Forecast with ANN

LOLP

PV Uncertainty

Load Uncertaitny

ND Uncertainty Assessment

Uncertainties Assessment for OR

Quantification

Constrains

Unit Commitment

Dispatching Strategies

Dynamic Programming

MGT & PV AG

Optimization Ceriteria

Data Collection and System Uncertainty

Analysis

OR Dispatching for UC Problem

with DP

Objective: to provide a complete set of user-friendly GUI to properly model uncertainties and optimal manage the details of PV AGs, loads, and MGTs one day-ahead.

43VI.2 Microgrid EMS Supervisor Frame Design

Main Interfaces Individual Modules

Data Collect and System Uncertainty

Analysis

Historical Data Collect for ANN Training Day-ahead Data Download: Weather

Information, Load, and PV Power Data PV Power and Load Demand Forecast by

Using Well Trained ANN

System Uncertainties Assessment and OR

Power Quantification

PV Power Uncertainty Load Demand Uncertainty Net Demand Uncertainty OR Quantification

Operational and OR Dispatching

Dispatching Strategies PV AGs and MGTs Power References

VI. A User-friendly EMS and Operational Supervisor

44



Analysis









VI. A User-friendly EMS and Operational Supervisor VI.2 Development software

45



Analysis









VI. A User-friendly EMS and Operational Supervisor VI.2 Microgrid EMS Supervisor Frame Design

46VI.3 DemonstrationVI. A User-friendly EMS and Operational Supervisor

47VII. Conclusions and perspectives

PV power variability and load demand variability are analyzed; A probabilistic method for the OR calculation is proposed (with two different kinds of

ND uncertainty assessment methods); The dynamic joint operational and OR dispatching strategies are developed; Day-ahead operational and OR planning with DP is proposed by considering different

optimization strategies; A User-friendly EMS and Operational Supervisor is developed.

“Big data” for distributed RES uncertainty analysis, prediction for more then one day and with multiple time steps, and intraday adjustment;

Optimization method to improve the battery efficiency and its lifespan;

Considering load shedding;

How the uncertainties are propagated in the electrical system.

Prospectives

Contributions

Thank you for your attention !

1. X. Yan, B. Francois, and D. Abbes, “Uncertainty Analysis for Power Reserve Quantification in an Urban Microgrid Including PV Generators”, Elsevier, Renewable Energy, Vol (106), June 2017, pp. 288–297. (Accepted 9 January, 2017)

2. X. Yan, B. Francois, and D. Abbes, “Operating Reserve Quantification and Day-ahead Optimal Dispatching of a Microgrid with Active PV Generators,” Elsevier, Sustainable Energy, Grids and Networks, under review.

3. X. Yan, B. Francois, and D. Abbes, ”Solar radiation forecasting using artificial neural network for local power reserve,” in Electrical Sciences and Technologies in Maghreb (CISTEM), 2014 International Conference, pp. 1-6.

4. X. Yan, B. Francois, and D. Abbes, “Operating power reserve quantification through PV generation uncertainty analysis of a microgrid,” in PowerTech, 2015 IEEE Eindhoven, 2015, pp. 1-6.

5. Yan, X., Abbes, D., Francois, B. and Bevrani, H., 2016, October. Day-ahead optimal operational and reserve power dispatching in a PV-based urban microgrid. In Power Electronics and Applications (EPE'16 ECCE Europe), 2016 18th European Conference on (pp. 1-10). IEEE.

Related Publications (https://www.researchgate.net/profile/Xingyu_Yan)

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