基于多智能体深度强化学习的配电网双时间尺度电压控制策略OA北大核心
Distribution Network Dual Time Scale Voltage Control Strategy Based on Multi-Agent Deep Reinforcement Learning
风电、光伏(photovoltaics,PV)在新型电力系统中的渗透率日益增加,使得配电网电压波动加剧,而储能(energy storage,ES)、电动汽车(electric vehicles,EV)对降低配电网电压波动有重要作用.与此同时,智能电表、智能传感器以及改进的通信网络广泛部署,可获取的数据量越来越大,数据驱动技术兴起.提出了一种基于多智能体深度强化学习(multi-agent deep reinforcement learning,…查看全部>>
The increasing penetration rate of wind power and photovoltaics(PV)in new power systems exacerbates voltage fluctua-tions in distribution networks,while energy storage(ES)and electric vehicles(EV)play important roles in reducing voltage fluctua-tions in distribution networks.At the same time,smart meters,smart sensors and improved communication networks are widely deployed,the amount of data available is increasing,and data-driven technology is emerging.This…查看全部>>
赵晶晶;张超立;王涵;盛杰
上海电力大学电气工程学院,上海 200090上海电力大学电气工程学院,上海 200090上海电力大学电气工程学院,上海 200090上海电力大学电气工程学院,上海 200090
动力与电气工程
数据驱动多智能体深度强化学习双时间尺度电压控制功率优化
data drivenmulti-agent deep reinforcement learningdual time scalesvoltage controlpower optimization
《南方电网技术》 2025 (2)
68-79,12
国家自然科学基金资助项目(52007112). Supported by the National Natural Science Foundation of China(52007112).
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