计及需求响应的分布鲁棒博弈区域综合能源系统运行优化策略

随着能源市场的发展,大量新兴市场主体参与竞争,区域综合能源系统(regional integrated energy system,RIES)源荷间日渐复杂的利益耦合关系给制定有效的协同优化策略带来困难。因此,文章提出基于分布鲁棒博弈的RIES源荷协同优化策略,将RIES运营商作为领导者,用户作为跟随者,建立考虑供能侧风电不确定性的主从博弈模型。该模型中,领导者考虑收益最优,建立计及风电不确定性的热电联供型RIES数据驱动分布鲁棒决策模型,跟随者依据分时能源价格建立考虑用能替代行为的综合效益优化模型。为了简化求解所提分布鲁棒博弈模型,采用卡鲁什·库恩·塔克条件(Karush Kuhn Tucker,KKT)把跟随者模型等效为均衡约束加入领导者决策模型中,将双层博弈模型转化为单层分布鲁棒优化模型;然后,运用McCormick方法线性化领导者目标函数中的双线性项;最后,采用列和约束生成(column and constraint generation,C&CG)算法求解。实验结果证明,该策略能在满足用户利益诉求的同时通过能源价差引导用户合理用能,有效应对风电出力不确定风险。

Optimal RIES Operation Strategy Based on Distributionally Robust Game Considering Demand Response

With the development of the energy market, many emerging market agents participate in competition. The increasingly complex coupling of interests between the operators and users of the regional integrated energy system (RIES) make it difficult to formulate effective collaborative optimization strategies. Therefore, this paper proposes an operator-user collaborative optimization strategy based on the distributionally robust game model for RIES. Regarding RIES operators as leaders and users as followers, a Stackelberg game model accounting the uncertainty of wind power on the energy supply side is established. In this model, the leader aims to maximize revenue and establishes a data-driven distributionally robust decision model of the combined heat and power RIES, which takes into account the uncertainty of wind power. Followers establish a comprehensive benefit optimization that considering energy substitution behavior according to time-of-use energy prices. In order to simplify the solution of the proposed distributionally robust game model, the follower model is equivalent to an equilibrium constraint and added to the leader’s decision-making model applying KKT condition, and the two-level game model is transformed into a single-level sub-Brue-bar optimization model. Then, the McCormick method is used to linearize the bilinear term in the leader’s objective function. Finally, the problem is solved by the C&CG algorithm. The experimental results show that, the strategy can satisfy the interests of users and guide them to use energy reasonably through energy price difference, and effectively deal with the uncertain risk of wind power output.