基于双层博弈的配电网与多综合能源微网协调优化

为充分挖掘综合能源微网(integrated energy microgrid, IEM)的潜在价值，促进可再生能源消纳，针对同一配电网下的多个IEM协同管理问题进行研究，提出了一种基于双层博弈的配电网-多IEM协同优化模型。对于IEM模型的构建，考虑在热电联产机组中加入碳捕集系统以及电转气装置，用来获取低碳效益。同时，针对IEM中可再生能源与负荷不确定性问题，采用鲁棒区间规划进行处理。首先，构建配电网运营商(distribution system operator,DSO)与IEM联盟系统模型框架，分析其不同主体间的博弈关系。其次，对于双层博弈，分为主从博弈与合作博弈。DSO作为博弈领导者，以自身效益最大为目标制定电价引导IEM联盟响应。IEM联盟作为博弈跟随者，以自身运行成本最小为目标，通过成员间互相合作能源共享响应DSO的决策。同时采用纳什谈判理论解决IEM联盟的合作运行问题，使用二分法与交替方向乘子法结合求解模型。最后，在算例中验证所提模型与方法的可行性和有效性。

Coordinated optimization of a distribution network and multi-integrated energy microgrid based on a double-layer game

To fully explore the potential value of the integrated energy microgrid (IEM) and promote the consumption of renewable energy, a two-layer game-based distribution network-multiple IEM cooperative optimization model is proposed to address the problem of cooperative management of multiple IEMs in the same distribution network. For the construction of the IEM model, a carbon capture system and an electricity-to-gas conversion device are added to the cogeneration units to obtain low-carbon benefits. At the same time, robust interval planning is used to deal with the problems of renewable energy and load uncertainty in the IEM. This paper initially establishes the framework of the alliance system between the DSO and IEMs, analyzing the game dynamics among various players. Subsequently, the two-layer game is further categorized into a master-slave game and a cooperative game. The DSO, as the game leader, sets the tariff to guide the IEM coalition to optimize with the goal of maximizing its own benefit. And the IEM coalition, as the game follower, responds to the DSO''s decision by cooperating with members to share energy with the goal of minimizing its own operating cost. At the same time, Nash negotiation theory is used to solve the cooperative operation problem of the IEM coalition, and the dichotomous method and alternating direction multiplier method (ADMM) are combined to analyze the model. Finally, an illustrative example is presented to validate the feasibility and effectiveness of the proposed model and methodology.