Collaborative Optimization of Multi-energy Complementary System via Game Theory
Tongqing Song, Haotian Tang, Guo Tao, Ling Qin, Shenghong Ju · 2023
With the reformation of the energy market, the integrated multi-energy complementary system has achieved rapid development during the past decade. By coupling and interconnecting different energy sources, the integrated energy system has shown great potential in enhancing energy efficiency and diminishing carbon emissions. In this work, we constructed a multi-energy microgrid model that includes electricity, heat, cooling, gas, and a hydro-solar-wind multi-energy complementary system. Subsequently, a collaborative optimization model for multi-microgrid energy sharing is established, which is decomposed into sub-problems of microgrid alliance cost minimization and power trading. To ensure a fair and mutually beneficial interaction between the comprehensive energy system and users, we adopted game theory principles. By incorporating the complementary properties of hydro, solar, and wind energy, our approach enhances the efficiency and environmental sustainability of the integrated energy system through the day-ahead scheduling. To achieve this, the mixed integer linear programming is employed to optimize microgrid systems by considering multiple economic and environmental indicators. Additionally, the concept of Nash equilibrium game theory from economics is employed to guide peer-to-peer energy trading among multiple microgrid systems. Ultimately, through collaborative optimization among multiple microgrids, the renewable energy consumption rate of the system is increased to 100%. Simultaneously, the operating costs of the three microgrids are reduced by 40.98%, 65.74%, and 34.69% respectively, effectively enhancing the environmental sustainability of the system and lowering energy costs for users.