1. Demand Side Management in Smart Grid Using Heuristic Optimization (IEEE TRANSACTIONS ON SMART GRID, VOL. 3, NO. 3, SEPTEMBER 2012) Author ： Thillainathan Logenthiran, Dipti Srinivasan and Tan Zong Shun 2013.01.30 Chen-Chou Tsai

2. Outline  Introduction  Related Work  Demand Side Management Techniques  Proposed DSM Strategy  Simulation Results  Conclusion 2

3. Introduction Demand side management(DSM) in smart grid that ▫allows customers to make informed decisions regarding their energy consumption ▫helps the energy providers reduce the peak load demand Increasing sustainability of the smart grid. Reducing overall operational cost and carbon emission. 3

4. Outline  Introduction  Related Work  Demand Side Management Techniques  Proposed DSM Strategy  Simulation Results  Conclusion 4

5. Related work There are several DSM techniques and algorithms ▫System specific ([4]-[6],[10],[13])  Which are not applicable to practical systems that have a wide variety of independent devices. ▫Dynamic programming([13]) ▫Linear programming([5],[10])  Cannot handle a large number of controllable devices from several types of devices which have several computation patterns and heuristics. 5

6. Related work(cont.) In addition, the transformation of today’s grid towards smart grid opens new perspectives on DSM 1.Renewable energy sources. 2.The communication infrastructure be-tween the central controller and controllable loads. 6

7. Related work(cont.) 3.Criteria for deciding the optimal load consumption  Maximizing the use of renewable energy resources.  Maximizing the economic benefit by offering bids to reduce demand during peak periods.  Minimizing the amount of power imported from the main grid.  Reducing peak load demand. 7

8. Outline  Introduction  Related Work  Demand Side Management Techniques  Proposed DSM Strategy  Simulation Results  Conclusion 8

9. DSM Techniques 9

10. DSM Techniques(cont.) 10

11. Outline  Introduction  Related Work  Demand Side Management Techniques  Proposed DSM Strategy  Simulation Results  Conclusion 11

12. Architecture for DSM 12

13. Problem Formulation 13 Forecast(t) is the forecasted consumption at time t PLoad(t) is the actual consumption at time t Objective(t) is the actual consumption at time t

14. Problem Formulation(cont.) 14 X kit is the number of devices of type k that are shifted from step i to t. P 1k is the power consumptions at time step 1 for device k. j is the total duration of consumption for device of type k.

15. Problem Formulation 15

16. Problem Formulation(cont.) 16 X ktq is the number of devices of type k that are delayed from step t to q. P 1k is the power consumptions at time step 1 for device k. j is the total duration of consumption for device of type k. m is the maximum allowable delay.

17. Problem Formulation(cont.) 17

18. Problem Formulation(cont.) 18 Constraint :

19. Proposed Algorithm 19 Characteristics : m is the maximum allowable delay.

20. Proposed Algorithm(cont.) 20 N: The maximum number of possible time steps. B: The number of bits required to represent the number of devices that are shifted in each time step. m is the maximum allowable delay.

21. Proposed Algorithm(cont.) 21

22. Proposed Algorithm(cont.) 22 Best crossover rate: 0.9 Best mutation rate: 0.1

23. Outline  Introduction  Related Work  Demand Side Management Techniques  Proposed DSM Strategy  Simulation Results  Conclusion 23

24. Setup 24  Test on three different areas of a smart grid.  Residential Area  Commercial Area  Industrial Area  Objective  To reduce the utility bills of consumers in these areas.

25. Schedule 25  Valley of the load consumption curve will be before the peak hours. X 0h(current day)~24h(current day) 8h(current day)~8h(following day)

26. Schedule(cont.) 26

27. Controllable Devices(residential area) 27

28. Controllable Devices(commercial area) 28

29. Controllable Devices(industrial area) 29

30. Simulation Results 30

31. Simulation Results 31

32. Simulation Results 32

33. Simulation Results 33

34. Simulation Results 34

35. Simulation Results 35

36. Simulation Results 36

37. Simulation Results 37 Without DSMWith DSM Residential$225.48$345.82 Commercial$-551.33$-157.52 Industrial$-695.51$-252.15 Whole Smart Grid$-53.70$103.70

38. Outline  Introduction  Related Work  Demand Side Management Techniques  Proposed DSM Strategy  Simulation Results  Conclusion 38

39. Conclusion  DSM has potential to provide many benefits to the entire smart grid.  The simulation outcomes  Be able to handle a large number of controllable devices of several types.  Achieve substantial savings while reducing the peak load demand of the smart grid. 39

40. Thanks for your listening. Q&A 40