区域综合能源系统多主体非完全信息下的双层博弈策略

针对区域综合能源系统多主体高耦合的特点,提出一种由供能商、配电网和用户组成的多主体双层博弈互动策略。博弈互动策略包括调度和竞价两个方面。调度部门协调各方可调资源,根据供能商的报价和配电网的分时电价预测多能负荷,以系统用能费用最小为目标协调优化,真正实现合作博弈下的多能互补。文中在非完全信息和有限理性的假设下设计供能商报价和系统结算策略。在此基础上,根据历史调度结果和自身机组特性,以追求自身最大利益为目标,模拟供能商代理的冷热电日前市场非合作竞价策略后上报调度部门,并采用Q-Learning算法优化多代理竞价策略。应用实际算例研究区域综合能源系统调度—竞价双层博弈过程的演化规律,并分析了所提策略的局部Nash均衡性。

Bi-level Game Strategy for Multi-agent with Incomplete Information in Regional Integrated Energy System

In view of the characteristics of multi-agent and strong coupling of regional integrated energy system(RIES), a multi-agent bi-level game interaction strategy consisting of energy suppliers, distribution networks, and users is proposed. The game strategy includes two aspects: dispatch and bidding. According to energy supplier''s bidding electricity price, multi-energy loads are forecasted. With the aim of minimizing system costs, the dispatch department coordinates all adjustable resources which belong to different participants. The cooperative game realizes the purpose of multi-energy complementary. Under the assumption of incomplete information and bounded rationality, this paper designs the energy supplier bidding strategy and corresponding clearing mechanism. On this basis, according to the history of scheduling results and unit characteristics, the non-cooperative bidding strategy of day-ahead energy markets with cold, thermal, and electric energies is simulated to pursue their own profits. And the Q-Learning algorithm is used to optimize the multi-agent bidding strategy which is then reported to the dispatch department. The evolutionary law of dispatch-bidding bi-level game of RIES is studied using a case study, and the local Nash equilibrium of the strategy is analyzed.