Game theory-based multi-agent capacity optimization for integrated energy systems with compressed air energy storage

Abstract

The capacity optimization of integrated energy systems (IESs) is directly related to economy and stability, while centralized optimization methods are difficult to solve for scenarios in which energy units belong to different operators. This study proposes a game theory-based multi-agent capacity optimization method for an IES to analyze the benefit interactions among independent operators in decision-making processes. The IES is composed of four plants: solar photovoltaic, wind turbine, combined heating and power system, and compressed air energy storage (CAES), wherein plant operators act as players, and the net present value (NPV) is selected as the utility function. The Nash equilibrium is proven to exist and is solved by the best response algorithm for analyzing self-interested optimization. The Shapley value method is adopted to deal with benefit allocation in cooperative coalition, considering both stability and fairness. Several case studies are conducted to analyze all fifteen possible game models among four players. The results show that the access of CAES could improve the environmental and economic performance of the IES. The completely cooperative game model yields better economic performance for the whole and for individuals; its total NPV is 20.15% higher than when individuals act alone.