A Distributed Demand Response Algorithm and Its Applications to PHEV Charging in Smart Grid

1. A Distributed Demand Response Algorithm and Its Applications to PHEV Charging in Smart Grid Zhong Fan IEEE Trans. on Smart Grid. Z. Fan. A Distributed Demand Response Algorithm and Its Applications to PHEV Charging in Smart Grid. IEEE Trans. on Smart Gird, vol. 3, num. 3, pp. 1280-1290, 2012.

2. Contents • Demand Response Model • Distributed PHEV Charging • Leveraging Networking Concepts into Smart Grid Load Leveling

3. I - Demand Response (DR) in Smart Grid • Demand Response (DR): a mechanism for achieving energy efficiency through managing customer consumption of electricity in response to supply conditions. • Ex. Reducing customer demand at critical times (or in response to market price) • Advanced communication will enhance the DR capability (E.g., real-time pricing). • PHEVs require enhanced demand response mechanism.

4. DR Model – Congestion Pricing • Fully distributed system (only price is known) • A principle of congestion control in IP networks – Proportionally Fair Pricing (PFP) • Each user declares a price he is willing to pay per unit time. • The network resource (bandwidth) is shared in proportion to the prices paid by the users. • If each user chooses the price that maximizes his utility, then the total utility of the network is maximized [1]. [1] F. Kelly, A. Maulloo, and D. Tan, “Rate control for communication networks: Shadow prices, proportional fairness and stability,” J. Oper. Res. Soc., vol. 49, no. 3, pp. 237–252, 1998.

5. DR Model and User Adaption (1) • A discrete time slot system • N users • demand of user i at slot n • user i’s willingness to pay (WTP) parameter • Price of energy in slot n: • Utility function of user i: • The users choose demand to maximize:

6. DR Model and User Adaption (2) • User adaption: user i adapts its demand according to: • The convergence of the adaption: • The error of demand estimate: : optimal demand : equilibrium price

7. DR Model – Numerical Results (1) Basic simulation The effect of gamma

8. DR Model – Numerical Results (2) Heterogeneous initial demands and adaption rates Heterogeneous initial demands

9. DR Model – Numerical Results (3)

10. II - Distributed PHEV Charging • Price function: • User adaption: • Charging dynamics: • Difference: Finish service (Charging done, y=1)

11. Differential QoS? • Total charging cost for PHEV i: • If we assume the price remains constant (p) • Equilibrium price:

12. Differential QoS? • Several observation • WTPs affect the price of energy. • WTPs decide the charging time of individual PHEVs • PHEVs with same total charging demand and different WTPs will have almost same total charging cost. • After some PHEVs finish charging, the price will go down, which results in slight differences of the charging cost between PHEVs with different WTPs.

13. Simulation Results • Basic simulation • Differential QoS and total cost of charging • Impact of WTPs on system performance • Maximum charging rate • Different number of PHEVs

14. Basic simulation

15. Differential QoS and total cost of charging Total charging cost: PHEV1 only 7% less than PHEV50

16. Impact of WTPs on system performance

17. Maximum charging rate

18. Maximum charging rate

19. Different number of PHEVs

20. Some Future Work • How should PHEVs adapt their WTPs according to the price policy and their own charging preference? • In-depth analysis of how maximum charging rate impacts the performance. • Game theoretical analysis of the proposed demand response model (the social optimum is a Nash bargaining solution[1]) • The impact of PHEVs as energy storage on the SG. • The introduction of energy service company (like charging station) will bring about new problems of optimization, security and social-economic interactions[2]. [2] C.Wang and M. de Groot, “Managing end-user preferences in the smart grid,” in Proc. 1st Int. Conf. Energy-Efficient Comput. Network. (ACM e-Energy), 2010.

21. III - Incorporating Networking Ideas and Methods into the Research of SG • Load leveling as a resource usage optimization problem • Resource allocation ideas from networking to the smart grid. • Load admission control • OFDMA allocation • Cooperative energy trading S. Gormus, P. Kulkarni, and Z. Fan, “The power of networking: How networking can help power management,” in Proc. 1st IEEE Int. Conf. Smart Grid Commun., 2010.

22. Load Leveling as a Resource Usage Optimization Problem • Resource allocation: • Optimization goals • Environmental impact – load will be shifted to when the renewable resources have higher general mix. • Cheapest resource available – load will be shifted to the off-peak time when the price is low. • When outage? • Hierarchical priority manner • Low priority appliances of low priority customer should be black out first.

23. Load Admission Control • Like “call admission control” • Customers send request before accessing SG to the Power Management System (PMS) • Granted • Rejected • If the request with high priority

24. OFDMA Allocation • OFDMA: deciding which frequencies to allocate at what times to users • Resource allocation in SG: what loads to allocate at what times to which users to optimize resource utilization and hence improve energy efficiency. • Learn from the OFDMA with the allocation methods

25. Cooperative energy trading • Future smart grid: micro grids with local generation plants (solar, wind, etc.) and users while connected to the macro grid. • The idea here is a better utilization of the available power resources by cooperatively using available generation resources. • Similar to the cooperative communication philosophy where the nodes in a wireless network try to increase the throughput and network coverage by sharing available bandwidth and power resources cooperatively.

26. Thanks!