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NARI Technology Co., Ltd., Nanjing 211106, China
State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210009, China
Jiangsu Fangtian Electric Power Technology Co., Ltd., Nanjing 211102, China
Received：10 April 2026，
Revised：2026-05-08，
Published：25 June 2026
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XU Huiguang, ZHANG Liudong, ZHANG Siyuan, et al. Dual-time-scale Intelligent Voltage Control Strategy for Distribution Networks with High-penetration Distributed Photovoltaics[J]. Power Capacitor & Reactive Power Compensation, 2026, 47(3): 85-95.
XU Huiguang, ZHANG Liudong, ZHANG Siyuan, et al. Dual-time-scale Intelligent Voltage Control Strategy for Distribution Networks with High-penetration Distributed Photovoltaics[J]. Power Capacitor & Reactive Power Compensation, 2026, 47(3): 85-95. DOI： 10.14044/j.1674-1757.pcrpc.2026.03.009.
针对高比例分布式光伏接入条件下配电网中易出现的电压越限、网络损耗增加及机械调压设备频繁动作等问题，提出一种基于强化学习的双时间尺度电压调控策略。首先，构建配电网双时间尺度电压调控优化模型，以节点电压偏差和系统网络损耗为优化目标，并计及机械调压设备运行约束。其次，在慢时间尺度上，以有载调压变压器（on-load tap changer，OLTC）和并联电容器为控制对象，采用深度Q网络（deep Q-network，DQN）实现机械调压设备离散决策。然后，在快时间尺度上，以逆变器群体为控制对象，采用ASRN-MAD3PG算法实现无功快速调节。最后，通过算例分析验证了所提模型及求解方法的有效性。
Aiming at the problems such as voltage limit violation，increased network loss and frequent operation of mechanical voltage regulation equipment that are prone to occur in distribution networks with a high proportion of distributed photovoltaic access，a dual-time-scale voltage regulation strategy based on reinforcement learning is proposed. Firstly，a dual-time-scale voltage regulation optimization model for distribution networks is constructed，with node voltage deviation and system network loss as the optimization objectives，and the operation constraints of mechanical voltage regulation equipment are considered. Secondly，on the slow time scale，the on-load tap changer（OLTC）and parallel capacitors are taken as the control objects，and the deep Q-network（DQN）is used to achieve discrete decision-making of mechanical voltage regulation equipment. Then，on the fast time scale，the inverter group is taken as the control object， and the ASRN-MAD3PG algorithm is used to achieve rapid reactive power regulation. Finally，the effectiveness of the proposed model and solution method is verified through case studies.
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