Demand response (DR) can provide a costeffectiveness approach for reducing peak load while renewable energy sources (RES) can result in an environmental-friendly solution for solving the problem of power shortage. The increasing integration of DR and renewable energy bring challenging issues for energy policy makers, and electricity market regulators in the power grid. In this paper, a new two-stage stochastic game model is introduced to operate the electricity market, where Stochastic Stackelberg-Cournot-Nash (SSCN) equilibrium is applied to characterize the optimal energy bidding strategy of the forward market and the optimal energy trading strategy of the spot market. To obtain an SSCN equilibrium, sampling average approximation (SAA) technique is harnessed to address the stochastic game model in a distributed way. By this game model, the participation ratio of demand response can be significantly increased while the unreliability of power system caused by renewable energy resources can be considerably reduced. The effectiveness of the proposed model is illustrated by extensive simulations.
Funding
Effective and Efficient Query Processing over Dynamic Social Networks
Proceedings of the 27th International Joint Conference on Artificial Intelligence held jointly with 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018)
Name of conference
IJCAI-ECAI 2018: International Joint Conferences on Artificial Intelligence
Publisher
International Joint Conference on Artificial Intelligence, European Association for Artificial Intelligence and the Swedish Artificial Intelligence Society