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配电系统双时间尺度电压管理的深度强化学习方法
作者:
作者单位:

1.浙江工业大学信息工程学院,浙江省杭州市 310023;2.浙江大学电气工程学院,浙江省杭州市 310027

摘要:

随着可再生能源发电渗透率的不断增大,配电系统的电压越限问题愈发频繁,亟需高效的电压管理策略以保证配电系统的安全经济运行。首先,文中建立了双时间尺度的配电系统电压管理模型,实现不同时间响应特性的调压设备协调控制。然后,将2个时间尺度的电压管理模型建模为马尔可夫决策过程,在有效考虑两者的时间耦合关系和可控设备物理特性的基础上,分别利用多智能体深度确定性策略梯度算法和双深度Q网络算法求解模型,实现了双时间尺度的实时电压管理。最后,基于IEEE 33节点配电系统进行算例分析,验证了所提模型和方法的有效性。

关键词:

基金项目:

国家自然科学基金资助项目(52107129)。

通信作者:

作者简介:

冯昌森(1990—),男,博士,讲师,硕士生导师,主要研究方向:电力系统优化控制、电力市场、机器学习。E-mail:fcs@zjut.edu.cn
张瑜(1996—),女,硕士研究生,主要研究方向:电力系统优化控制、机器学习。E-mail:1304150284@qq.com
谢路耀(1984—),男,博士,讲师,主要研究方向:新能源发电技术、储能技术、交直流微电网技术、电能质量治理技术、大功率中压多电平变流技术。E-mail:xieluyao@zjut.edu.cn
张有兵(1971—),男,通信作者,教授,博士生导师,主要研究方向:智能电网、分布式发电及新能源优化控制、电动汽车入网、电力系统通信、电能质量监控。E-mail:youbingzhang@zjut.edu.cn


Deep Reinforcement Learning Approach for Dual-timescale Voltage Management in Distribution System
Author:
Affiliation:

1.College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China;2.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Abstract:

With the increasing penetration rate of renewable energy generation, the problem of voltage violation in the distribution system becomes more frequent, and efficient voltage management strategies are urgently needed to ensure the secure and economic operation of the distribution system. First, this paper establishes a dual-timescale voltage management model for the distribution system to realize the coordinated control of voltage regulators with different time response characteristics. Then, the voltage management models of the two time scales are modeled as Markov decision process (MDP). Effectively considering the temporal coupling relationship between the two time scales and the physical characteristics of controllable devices, the dual-timescale real-time voltage management is realized by using the multi-agent deep deterministic policy gradient algorithm and the double deep Q network algorithm to solve the model, respectively. Finally, the effectiveness of the proposed model and method is demonstrated by case studies on the IEEE 33-bus standard distribution system.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 52107129).
引用本文
[1]冯昌森,张瑜,谢路耀,等.配电系统双时间尺度电压管理的深度强化学习方法[J].电力系统自动化,2022,46(12):202-209. DOI:10.7500/AEPS20211220001.
FENG Changsen, ZHANG Yu, XIE Luyao, et al. Deep Reinforcement Learning Approach for Dual-timescale Voltage Management in Distribution System[J]. Automation of Electric Power Systems, 2022, 46(12):202-209. DOI:10.7500/AEPS20211220001.
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  • 收稿日期:2021-12-20
  • 最后修改日期:2022-01-28
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  • 在线发布日期: 2022-06-27
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