基于多主体博弈和强化学习的微电网群与配电网协调优化调度方法
摘要：随着“煤改电”工程的推进，农村薄弱配电网因供暖季负荷突增可能出现短时线路容量不足的问题，传统的线路扩容改造方案面临着电网投资回收期长、非供暖季资产利用率低等问题，依托农村新能源资源禀赋，构建具备友好互动和需求响应能力的微电网是解决此问题的有效手段。为此，结合河北省某农村微电网群示范工程的应用背景，提出一种基于多主体博弈和强化学习的微电网群与配电网协调优化调度方法。各微电网以日内滚动优化调度计划为基准，考虑辅助服务策略对未来有限时域内运行经济性的影响，上报最优竞价策略；配电网结合辅助服务功率需求和各微电网的报价，以运行成本最小为目标确定辅助服务市场出清方案。最终，采用狼爬山(win or learn fast-policy hill-climbing, WoLF-PHC) 算法实现对多主体博弈问题的快速求解，并基于微电网群示范工程实际案例验证了所提方法的有效性。
Abstract:With the promotion of the "coal to electricity" project, the rural weak distribution networks may face the problems such as line congestion due to the increase in heating loads, the long payback period of power grid investment as well as low utilization rate of power grid assets during the non-heating season in the traditional line expansion scheme. It is an effective means to solve these problems that establish a microgrid that has the ability of friendly interaction and demand response based on the local renewable energy resource. For that reason, a coordinated optimization method for microgrids and distribution networks based on a multi-agent game and reinforcement learning was proposed for a practical microgrid project in Hebei Province. Each microgrid reports the optimal bidding strategy based on the intra-day rolling dispatching plan, considering the impact of the auxiliary service strategy on the future operation economy in the finite time domain; Taking minimizing the operation cost as an object, the distribution network determines the clearing scheme of the auxiliary service market based on the power demand of the auxiliary service and the quotation of each microgrid. Finally, The win or learn fast-policy hill-clipping (WoLF-PHC) algorithm was used to realize the fast solution of multi-agent game problems, and the effectiveness of the proposed method was verified based on the actual case of the micro-electric network group demonstration project.
标题：基于多主体博弈和强化学习的微电网群与配电网协调优化调度方法
英文标题：Coordinated Optimization Dispatching Method for Microgrids and Distribution Networks Based on Multi-agent Game and Reinforcement Learning
作者：韩璟琳, 胡平, 赵辉, 陈志永, 侯若松,
关键词：微电网群, 薄弱配电网, 多主体博弈, 强化学习, 协调优化,
发表日期：2024-10-10
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