A Potential Game Based Distributed Optimization Strategy for the Electricity Retailer Considering Residential Demand Response

With the opening of electricity market and the popularization of residential intelligent home, the design and implementation of demand response (DR) projects for large-scale residents to participate in grid interaction will be one of the key points for the operation optimization of electricity retailers. In view of decision-making of the electricity retailer considering residential DR, the characteristics of residential load were analyzed in detail, and then a decision model of the electricity retailer in day-ahead market was established based on potential game, aiming at maximizing its profits with the premise of no increasing of the residential electricity cost after DR. Thereafter, in order to solve the strategies iterative problems with the large-scale residential players, a distributed parallel iterative algorithm was put forward, which could coordinate strategies updating of residential players via their community agents. The simulation results show that the proposed model can protect the residential users' interests as well as effectively reduce the cost of purchasing electricity and the peak load of the residents. The proposed algorithm can converge to Nash equilibrium in a short time, and still keep a good convergence for the large-scale residential DR. © 2020 Chin. Soc. for Elec. Eng.