【摘要】 【目的】充分考虑目前分布式光伏用户的供用能的双重属性，将其灵活有功负荷响应与主动配电网的无功支撑能力结合，提出了一种基于深度强化学习的多智能体有功无功协同电压控制方法。【方法】首先，通过分析光伏接入对节点电压和线路潮流的影响，对分布式光伏用户有功倒送配额及过电压责任进行计算；其次，基于配额与责任构建激励政策来引导分布式光伏产消者的灵活有功负荷响应，并设计了主动配电网和多个分布式光伏产消者的多智能体电压控制模型。【结果】IEEE33节点系统算例分析表明，所提有功无功协同电压控制方法能够有效降低光伏发电量过高导致的电压越限和网络损耗，并改善光伏用户的收益分配。更多还原

【Abstract】 【Purposes】In this paper, considering the current distributed photovoltaic(PV) prosumers’ supply/use dual attributes, a multi-agent active and reactive coodinated voltage control method based on deep reinforcement learning is proposed, which combines the PV prosumers’ flexible active load response with the reactive power support capability of the active distribution network. 【Methods】First, by analyzing the influence of PV access on node voltage and line currents, the active power reverse quota and overvoltage responsibility of distributed PV prosumers were calculated. Second, an incentive policy was constructed based on the quota and responsibility to guide PV prosumers’ flexible active load response, and a multi-agent voltage control model was designed for the active distribution network and multiple PV prosumers. 【Results】The analyses of the IEEE 33-bus system demonstrate that the proposed active and reactive power coordinated voltage control method can effectively reduce voltage violations and power losses caused by excessive PV generation, and improve the revenue distribution of PV prosumers.更多还原