基于改进Nash-Q均衡迁移算法的源网荷储协同优化策略

为了充分发挥多类型储能资源的调度潜力，实现源网荷储协同优化调度，提出了计及电池储能、抽水蓄能、电动汽车的多类型储能调度策略。以低碳经济为目标，构建了考虑多主体博弈的源网荷储协同优化调度模型。为了在保证源网荷三侧整体利益的同时兼顾自身利益，基于Nash均衡理论，利用强化迁移学习技术，提出了一种基于改进Nash-Q的均衡迁移算法。利用K-means聚类使数据离散化，增设双结构经验池以提高样本利用率，从而有效提高了模型的泛化能力。基于实际区域电网的数据进行仿真验证，结果表明所提策略能有效降低系统的经济成本和碳处理费用，提高新能源消纳能力。

Collaborative optimization strategy of source-grid-load-energy storage based on improved Nash-Q equilibrium transfer algorithm

In order to give full play to the scheduling potential of multi-type energy storage resources and realize the collaborative optimization scheduling of source-grid-load-energy storage, a multi-type energy storage scheduling strategy including battery energy storage, pumped storage and electric vehicles is proposed. With the goal of low-carbon economy, the collaborative optimization scheduling model of source-grid-load-energy storage considering multi-agent game is established. In order to ensure the overall interests of source side, grid side and load side while taking into account their own interests, based on Nash equilibrium theory and using the reinforcement transfer learning technology, an equilibrium transfer algorithm based on improved Nash-Q is proposed. K-means clustering is used to discretize the data, and a dual-structure experience pool is added to improve the sample utilization rate, thus effectively improving the generalization ability of the model. Based on the data of an actual regional power grid, the simulative results show that the proposed strategy can effectively reduce the economic cost and carbon treatment cost of the system, and improve the new energy consumption capacity.