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

被引：0

作者：

Feng C. ^{[1
]}

Zhang Y. ^{[1
]}

Xie L. ^{[1
]}

Wen F. ^{[2
]}

Zhang K. ^{[1
]}

Zhang Y. ^{[1
]}

机构：

[1] College of Information Engineering, Zhejiang University of Technology, Hangzhou

[2] College of Electrical Engineering, Zhejiang University, Hangzhou

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Deep reinforcement learning; Distribution system; Dual-timescale; Markov decision process; Renewable energy generation; Voltage management;

D O I：

10.7500/AEPS20211220001

中图分类号：

学科分类号：

摘要：

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. © 2022 Automation of Electric Power Systems Press.

引用

页码：202 / 209

页数：7

共 25 条

[1] ZHUO Zhenyu, ZHANG Ning, XIE Xiaorong, Et al., Key technologies and developing challenges of power system with high proportion of renewable energy, Automation of Electric Power Systems, 45, 9, pp. 171-191, (2021)
[2] YANG Meihui, ZHOU Niancheng, WANG Qianggang, Et al., Unbalanced voltage control strategy of bipolar DC microgrid based on distributed cooperation, Transactions of China Electrotechnical Society, 36, 3, pp. 634-645, (2021)
[3] Interconnection and interoperability of distributed energy resources with associated electric power systems interfaces: IEEE Standard 1547-2018, (2018)
[4] FENG C S, LI Z Y, MOHAMMAD S, Et al., Decentralized short-term voltage control in active power distribution systems, IEEE Transactions on Smart Grid, 9, 5, pp. 4566-4576, (2018)
[5] ZHANG Hong, DONG Haiying, CHEN Zhao, Et al., Multi-time scale reactive power optimal control strategy of a CSP-PV system based on model predictive control, Power System Protection and Control, 48, 9, pp. 135-142, (2020)
[6] ZHU H, LIU H J., Fast local voltage control under limited reactive power: optimality and stability analysis, IEEE Transactions on Power Systems, 31, 5, pp. 3794-3803, (2016)
[7] LI J Y, XU Z, ZHAO J, Et al., Distributed online voltage control in active distribution networks considering PV curtailment, IEEE Transactions on Industrial Informatics, 15, 10, pp. 5519-5530, (2019)
[8] GUO Y F, GAO H L, WANG Z Y., Distributed online voltage control for wind farms using generalized fast dual ascent, IEEE Transactions on Power Systems, 35, 6, pp. 4505-4517, (2020)
[9] SINDRI M, QU G N, LI N., Distributed optimal voltage control with asynchronous and delayed communication, IEEE Transactions on Smart Grid, 11, 4, pp. 3469-3482, (2020)
[10] DING T, YANG Q R, YANG Y H, Et al., A data-driven stochastic reactive power optimization considering uncertainties in active distribution networks and decomposition method, IEEE Transactions on Smart Grid, 9, 5, pp. 4994-5004, (2018)

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