Multi-Agent Deep Reinforcement Learning for Voltage Control With Coordinated Active and Reactive Power Optimization

The increasing penetration of distributed renewable energy resources causes voltage fluctuations in distribution networks. The controllable active and reactive power resources such as energy storage (ES) systems and electric vehicles (EVs) in active distribution networks play an important role in mi...

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Vydané v:	IEEE transactions on smart grid Ročník 13; číslo 6; s. 4873 - 4886
Hlavní autori:	Hu, Daner, Ye, Zhenhui, Gao, Yuanqi, Ye, Zuzhao, Peng, Yonggang, Yu, Nanpeng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Active control Algorithms Capacitor banks Controllability Costs Deep learning Deep reinforcement learning Electric potential Electric vehicles Energy policy Energy sources Energy storage experience augmentation Inverters Machine learning Mixed integer multi-agent Multiagent systems Optimization Quality control Reactive power Reinforcement learning Robustness (mathematics) soft actor-critic Tap changers Time Voltage Voltage control Voltage fluctuations
ISSN:	1949-3053, 1949-3061
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Abstract	The increasing penetration of distributed renewable energy resources causes voltage fluctuations in distribution networks. The controllable active and reactive power resources such as energy storage (ES) systems and electric vehicles (EVs) in active distribution networks play an important role in mitigating the voltage excursions. This paper proposes a two-timescale hybrid voltage control strategy based on a mixed-integer optimization method and multi-agent reinforcement learning (MARL) to reduce power loss and mitigate voltage violations. In the slow-timescale, the active and reactive power optimization problem involving capacitor banks (CBs), on-load tap changers (OLTC), and ES systems is formulated as a mixed-integer second-order cone programming problem. In the fast-timescale, the reactive power of smart inverters connected to solar photovoltaic systems and active power of EVs are adjusted to mitigate short-term voltage fluctuations with a MARL algorithm. Specifically, we propose an experience augmented multi-agent actor-critic (EA-MAAC) algorithm with an attention mechanism to learn high-quality control policies. The control policies are executed online in a decentralized manner. The proposed hybrid voltage control strategy is validated on an IEEE testing distribution feeder. The numerical results show that our proposed control strategy is not only sample-efficient and robust but also effective in mitigating voltage fluctuations.
AbstractList	The increasing penetration of distributed renewable energy resources causes voltage fluctuations in distribution networks. The controllable active and reactive power resources such as energy storage (ES) systems and electric vehicles (EVs) in active distribution networks play an important role in mitigating the voltage excursions. This paper proposes a two-timescale hybrid voltage control strategy based on a mixed-integer optimization method and multi-agent reinforcement learning (MARL) to reduce power loss and mitigate voltage violations. In the slow-timescale, the active and reactive power optimization problem involving capacitor banks (CBs), on-load tap changers (OLTC), and ES systems is formulated as a mixed-integer second-order cone programming problem. In the fast-timescale, the reactive power of smart inverters connected to solar photovoltaic systems and active power of EVs are adjusted to mitigate short-term voltage fluctuations with a MARL algorithm. Specifically, we propose an experience augmented multi-agent actor-critic (EA-MAAC) algorithm with an attention mechanism to learn high-quality control policies. The control policies are executed online in a decentralized manner. The proposed hybrid voltage control strategy is validated on an IEEE testing distribution feeder. The numerical results show that our proposed control strategy is not only sample-efficient and robust but also effective in mitigating voltage fluctuations.
Author	Yu, Nanpeng Hu, Daner Gao, Yuanqi Ye, Zhenhui Ye, Zuzhao Peng, Yonggang
Author_xml	– sequence: 1 givenname: Daner orcidid: 0000-0002-9400-2490 surname: Hu fullname: Hu, Daner organization: Department of Electrical Engineering, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Zhenhui orcidid: 0000-0002-7105-014X surname: Ye fullname: Ye, Zhenhui organization: Department of Electrical Engineering, Zhejiang University, Hangzhou, China – sequence: 3 givenname: Yuanqi orcidid: 0000-0003-4078-6143 surname: Gao fullname: Gao, Yuanqi organization: Department of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USA – sequence: 4 givenname: Zuzhao orcidid: 0000-0002-0428-662X surname: Ye fullname: Ye, Zuzhao organization: Department of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USA – sequence: 5 givenname: Yonggang orcidid: 0000-0002-0960-3807 surname: Peng fullname: Peng, Yonggang email: pengyg@zju.edu.cn organization: Department of Electrical Engineering, Zhejiang University, Hangzhou, China – sequence: 6 givenname: Nanpeng orcidid: 0000-0001-5086-5465 surname: Yu fullname: Yu, Nanpeng email: nyu@ece.ucr.edu organization: Department of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USA
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SubjectTerms	Active control Algorithms Capacitor banks Controllability Costs Deep learning Deep reinforcement learning Electric potential Electric vehicles Energy policy Energy sources Energy storage experience augmentation Inverters Machine learning Mixed integer multi-agent Multiagent systems Optimization Quality control Reactive power Reinforcement learning Robustness (mathematics) soft actor-critic Tap changers Time Voltage Voltage control Voltage fluctuations
Title	Multi-Agent Deep Reinforcement Learning for Voltage Control With Coordinated Active and Reactive Power Optimization
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