1. Laboratoire des systèmes électriques et industriels
Institute of Energy System Technology
Genetic Algorithms-based Battery Predictive Management in INES Smart Grid System
Merci monsieur le président
بسم الله الرحمان الرحيم
Monsieur le président , madame, messieurs les membres de jury, honorable assistance, j’ai le plaisir de vous présenter mon travail dans le cadre d’une soutenance du mémoire de magistère intitulé par: Stockage et récupération d’énergie dans un système multi-sources, application au véhicule électrique
Supervisors
Dr. A A LADJICI
Pr. E BOLLIN
PhD student
Mustapha HABIB

2. PhD main axis
Forecasting tools for PV power and building power demand: ANN, ANFIS
Short-term power management: frequency/voltage control
Long-term power management: Daily power dispatching of grid connected hybrid system
Experimental validation of battery predictive management: Developing a control algorithm for XTM Xtenders for batteries daily power management

3. Predictive controller
Forecasting tools: ANN and fitting tools
Control variables: grid energy and battery SOC
System model: power flow equation
Actuator variables: battery current and grid relay
Cost function and solver: genetic algorithms

4. Forecasting tools

5. PV power forecasting with ANN Time Series tool
Forecasting tools
PV power forecasting with ANN Time Series tool
Prediction horizon
Prediction precision
6 past steps
1 step ahead

6. Forecasting tool
PV power forecasting with ANN Time Series tool

7. ANN forecasting system
Forecasting tools
ANN-based building forecasting tool
ANN forecasting system
Hour of day
Day of week
Building power demand
Temperature
Holyday indicator

8. Forecasting tools
ANN-based building forecasting tool

9. Forecasting tools
Building power demand forecasting with fitting tool

10. Building power demand forecasting with fitting tool
Forecasting tools
Building power demand forecasting with fitting tool
Polynomial
Sum of sin
Coefficient
Value a1
1340 b1
0.1405 c1 a2
624.8 b2
0.2942 c2
2.055 a3
239 b3
0.5406 c3
2.274 a4
87.47 b4
0.9153 c4
-2.906 a5
55.9 b5
1.773 c5
-3.962 a6
48.87 b6
1.346 c6
0.1323
Coefficient
Value a9
1.472e-06 a8 a7 a6 a5 a4
6.069 a3
-46.94 a2
163 a1
-290.3 a0
511.4
RMSE=74.96
R=0.9714
RMSE=51.91
R=0.9863

11. Control variables

12. 3. Battery charging mode: the Xtender works as charger
Control variables
Grid energy
1. Power network energy
1. Grid feeding mode: the Xtender works as inverter SCI (current source inverter)
2. Island mode: the Xtender works as inverter VSI (voltage source inverter)
3. Battery charging mode: the Xtender works as charger

13. Control variables
SOC (%)
Battery SOC 90
2. Battery SOC 50
1. Maximum limit (90%): keep enough capacity to absorb any excess PV power in island mode
2. Minimum limit (50%): to save batteries life time by minimizing DOD level

14. System model

15. Genetic Algorithms-based Predictive Management
System model
Power flow equation in grid-connected mode
Power flow equation in island mode
Battery current calculation
Battery SOC estimation

16. Actuator variables

17. Genetic Algorithms-based Predictive Management
Power topology and the role of GA-PM

18. Cost function and solver

19. Genetic Algorithms-based Predictive Management
Cost function
Actuator variables : grid relay and battery current
Battery and power converter efficiency
Weighting coefficients
Battery voltage
Reduce the used grid energy/increase the energy injected into the grid
Prediction of the evolution of battery current (SOC) in island mode
Giving priority to the island mode compared to
grid-connected mode

20. Genetic Algorithms-based Predictive Management
System constraints

21. Global GA-PM
Algorithm

22. Genetic Algorithms-based Predictive Management
Global control algorithm
MATLAB program

23. Experimental results

24. INES smart grid parameters
Experimental results
INES smart grid parameters
Component
Subcomponent
Parameter
Value
PV system
PV module
Power at MPP
240 Wp
Voltage at MPP
30.0 V
Current at MPP
8.1 A
Open circuit voltage
37.4 V
Short circuit current
8.6 A
Temperature coefficient
-0.46 %/K
Module model
Bosch solar module c-Si M 60
PV power plant
Number of modules 27
Inclination
9 x 35°
18 x 30°
Alignment
180° south
Power
6.3 KWp
Batteries system
Battery cell
Voltage
4 V
Nominal capacity
546 Ah
Battery model
Rolls Battery 4CS17P
Batteries bank
Number of cell in series 12
Number of cells in parallel 1
4.5 KW
Programmable load -
Nominal power
3.6 KW
Load mode
Constant power
Control mode
Remote
Model
Chroma 63803

25. Experimental results
Reference method: rules-based power management

26. Experimental results
Rules-based power management
June 8th (sunny day)

27. June 14th (sunny day) Experimental results
GA Predictive power management
June 14th (sunny day)

28. 21.36 % !! ΔE=580 Wh Economy of 6.23 % for just 7 hours
Simulation results with MATLAB/Simulink
Comparison
Power management technique
Produced PV energy (EPV)
Consumed Energy (ELoad)
Exchanged energy with grid (Eg)
EPV/ELoad
Estimated exchanged energy with grid when EPV/Eload = 1
Rules management
34.11 KWh
27.88 KWh
KWh
1.22
-8.72
Predictive management
31.9 KWh
27.88
-10.61
1.14
-9.30
ΔE=580 Wh
Economy of 6.23 % for just 7 hours
Estimated economy in
the case of 24 hours
21.36 % !!

29. June 9th (cloudy day) Experimental results
GA Predictive power management
June 9th (cloudy day)

30. System model is not required
Advantages and disadvantages
Rules-based power management
Simple algorithm
System model is not required
The control behavior is not on real time

31. On-line control behavior
Advantages and disadvantages
Predictive-based power management
On-line control behavior
Energy economy and battery SOC control performance
The effectiveness depends highly on system model exactitude and on weather forecasting accuracy

32. Fuzzy-based power management

33. PLoad-PPV Grid relay SOC Battery current Electricity price
Fuzzy-based power management
Controller structure
PLoad-PPV
Grid relay
SOC
Battery current
Electricity price
MATLAB program

34. 45 Rules ! Fuzzy-based power management Fuzzy rules PLoad-PPV
Controller response
dP++
dP+
dP0
dP-
dP--
EP-L
SOC-H P+ R1 R0
SOC-M P-
SOC-L
P--
EP-M
P++ P0
EL-H
Energy price
SOC
Rules
Battery charging mode
18 Rules !
45 Rules !
Grid feeding mode
18 Rules !
09 Rules !
Island mode

35. Fuzzy-based power management
Fuzzy rules
1. Inject more power into the grid when the energy price is high
2. Using the grid power to charge batteries when the energy price is low
3. When a low difference between building power demand and supplied PV power is detected an island mode is to be applied

36. Fuzzy-based power management
Surfaces

37. Forecasted energy price in Germany for July 27th 2017
Fuzzy-based power management
Energy price profile (€/MWh)
Forecasted energy price in Germany for July 27th 2017 ©

38. Fuzzy-based power management
Experimental results

39. Perspective works

40. Perspective works
Repeat the same tests by introducing wind energy in the local network
Improve the PV and building power forecasting tools: using on-line weather forecast with ANN
Taking the batteries aging in consideration
Taking the frequency/voltage control in consideration
Using other solvers for the cost function: PSO
Using INES offices as a load when doing new tests

41. Thank You for your Attention
Any Question !