Master–slave game-based optimal scheduling of community-integrated energy system by considering incentives for peak-shaving and ladder-type carbon trading - Dai, Fengzhe; Jiang, Fei; Chen, Lei; Wu, Yongfei; Xiao, Changlin (Frontiers in Energy Research, 2023)
To alleviate the challenges posed by high energy consumption, significant carbon emissions, and conflicting interests among multiple parties in a community-level microgrid, the authors of this study propose a master-slave game-based optimal scheduling strategy for a community-integrated energy system (CIES). First, we analyze the decision variables and revenue-related objectives of each stakeholder in the CIES, and use the results to construct a framework of implementation. Second, we develop a model to incentivize peak regulation and a ladder-type carbon trading model that consider the correlation between the load owing to residential consumers, the load on the regional grid, and the sources of carbon emissions. Third, we propose a master-slave game-based mechanism of interaction and a decision-making model for each party to the game, and show that it has a Stackelberg equilibrium solution by combining genetic algorithms and quadratic programming. The results of evaluations showed that compared with an optimization strategy that considers only the master-slave game, the proposed strategy increased the consumption surplus of the user aggregator by 13.65%, the revenue of the community energy operator by 7.95%, increased the revenue of the energy storage operator, reduced CO2 emissions by 6.10%, and adequately responded to peak-cutting and valley-filling by the power grid company.