A D ISTRIBUTED D EMAND R ESPONSE A LGORITHM AND I TS A PPLICATIONS TO PHEV C HARGING IN S MART G RID 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 , 2012.

C ONTENTS Demand Response Model Distributed PHEV Charging Leveraging Networking Concepts into Smart Grid Load Leveling 2

I - D EMAND R ESPONSE (DR) IN S MART G RID 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. 3

DR M ODEL – C ONGESTION P RICING 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]. 4 [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.

DR M ODEL AND U SER A DAPTION (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: 5

DR M ODEL AND U SER A DAPTION (2) User adaption: user i adapts its demand according to: The convergence of the adaption: The error of demand estimate: 6 : optimal demand : equilibrium price

DR M ODEL AND U SER A DAPTION (3) Implementation Consideration 7

DR M ODEL – N UMERICAL R ESULTS (1) 8 Basic simulation The effect of gamma

DR M ODEL – N UMERICAL R ESULTS (2) 9 Heterogeneous initial demands and adaption rates

DR M ODEL – N UMERICAL R ESULTS (3) 10

II - D ISTRIBUTED PHEV C HARGING Price function: User adaption: Charging dynamics: Difference: Finish service (Charging done, y=1) 11

D IFFERENTIAL Q O S? Total charging cost for PHEV i: If we assume the price remains constant (p) Equilibrium price: 12

D IFFERENTIAL Q O S? 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

S IMULATION R ESULTS Basic simulation Differential QoS and total cost of charging Impact of WTPs on system performance Maximum charging rate Different number of PHEVs 14

B ASIC SIMULATION 15 ParameterValue Number of PHEVs100 Unit of demand100 kW Unit of time slot0.01 h Initial SOC15% Charging efficiency85% WTP0.01+i*0.01

D IFFERENTIAL Q O S AND TOTAL COST OF CHARGING 16 ParameterValue WTP of PHEV502, if charging rate <0.2 Uniform [0,1], other WTP of other PHEVUniform [0,1] Total charging cost: PHEV1 only 7% less than PHEV50

I MPACT OF WTP S ON SYSTEM PERFORMANCE 17

M AXIMUM CHARGING RATE 18 ParameterValue Maximum charging rate10 kW WTPUniform [0,30]

M AXIMUM CHARGING RATE 19 ParameterValue Maximum charging rate10 kW WTPUniform [0,30]

D IFFERENT NUMBER OF PHEV S 20 ParameterValue Number of PHEVs20, 60, 100 WTPUniform [0,2]

S OME F UTURE W ORK 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]. 21 [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.

III - I NCORPORATING N ETWORKING I DEAS AND M ETHODS INTO THE R ESEARCH 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 22 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.

L OAD L EVELING AS A R ESOURCE U SAGE O PTIMIZATION P ROBLEM 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

L OAD A DMISSION C ONTROL 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 A LLOCATION 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

C OOPERATIVE 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

T HANKS ! 27