A DISTRIBUTED DEMAND RESPONSE ALGORITHM AND ITS APPLICATIONS

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.

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

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. 3

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]. 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 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: 5

DR MODEL AND USER ADAPTION (2) User adaption: user i adapts its demand according to: The convergence of the adaption: : optimal demand : equilibrium price � The error of demand estimate: 6

DR MODEL – NUMERICAL RESULTS (1) 8 Basic simulation The effect of gamma

DR MODEL – NUMERICAL RESULTS (2) 9 Heterogeneous initial demands and adaption rates

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 If charging cost for PHEV i: 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 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 Qo. S and total cost of charging Impact of WTPs on system performance Maximum charging rate Different number of PHEVs 14

BASIC SIMULATION Parameter Value Number of PHEVs 100 Unit of demand 100 k. W Unit of time slot 0. 01 h Initial SOC 15% Charging efficiency 85% WTP 0. 01+i*0. 01 15

DIFFERENTIAL QOS AND TOTAL COST OF CHARGING Parameter Value WTP of PHEV 50 2, if charging rate <0. 2 Uniform [0, 1], other WTP of other PHEV Uniform [0, 1] Total charging cost: PHEV 1 only 7% less than PHEV 50 16

IMPACT OF WTPS ON SYSTEM PERFORMANCE 17

MAXIMUM CHARGING RATE Parameter Value Maximum charging rate 10 k. W WTP Uniform [0, 30] 18

MAXIMUM CHARGING RATE Parameter Value Maximum charging rate 10 k. W WTP Uniform [0, 30] 19

DIFFERENT NUMBER OF PHEVS Parameter Value Number of PHEVs 20, 60, 100 WTP Uniform [0, 2] 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]. 21 [2] C. Wang and M. de Groot, “Managing end-user preferences in the smart grid, ” in Proc. 1 st Int. Conf. Energy. Efficient Comput. Network. (ACM e-Energy), 2010.

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 22 S. Gormus, P. Kulkarni, and Z. Fan, “The power of networking: How networking can help power management, ” in Proc. 1 st IEEE Int. Conf. Smart Grid Commun. , 2010.

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 offpeak 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 throughput and network coverage by sharing available bandwidth and power resources cooperatively. 26

THANKS! 27