A robust game-theoretic optimization model for battery energy storage in multi-microgrids by considering of renewable based DGs uncertainty

Abstract

With the development of electricity market, in the future, various stakeholders such as battery energy storage system (BESS), multi-microgrid (MMG), photovoltaic (PV), and wind turbine (WT) clusters, can trade with the distribution network or each other to meet their power balance needs and to maximizing their profits. In order to predict the charging and discharging power of the battery, a Markov Decision Process (MDP) model of random variables is established. Then, a robust game-theoretic optimization (GO) model is established for MMG's economical operation by considering the renewable based DGs uncertainty. And suggestions for battery operation modes under different compensation coefficients are given. In addition, BESS capacity and demand response (DR) for each microgrid are considered in this proposed model, and an uncertainty adjustment parameter is introduced to adjust the level of conservatism of the robust solution against the modeled uncertainty. In order to search the robust Nash equilibrium solution, this paper proposes a two-level game model based on game theory to study the operation strategy of BESS in the distribution network by considering of renewable based DGs uncertainty. This model is decomposed into a master problem (1st level) and a sub-problem (2nd level), which are solved based on the column and constraint generation algorithm , particle swarm optimization (PSO) and MDP model for random variables of BESS. Simulation result shows that the proposed approach has a well performance in tackling the uncertainty of renewable based distributed energy generation.