A Date-Driven Voltage Control Strategy for Distribution Network Using Deep Reinforcement Learning

A data-driven voltage control strategy is proposed for real-time optimized distributed voltage control of distribution networks connected to the energy storage system (ESS) and distributed photovoltaics (PVs) with high penetration. Deep reinforcement learning (DRL) methods are used in the control of...

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Podrobná bibliografie
Vydáno v:	2023 IEEE 6th International Electrical and Energy Conference (CIEEC) s. 77 - 82
Hlavní autoři:	Shen, Jiawei, Han, Jing, Wang, Yihan, Dong, Yanhao, Li, Hao
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 12.05.2023
Témata:	data-driven method Deep learning distributed voltage control distribution network Distribution networks MATD3 Mathematical models Photovoltaic systems Real-time systems Reinforcement learning Training
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Popis
Shrnutí:	A data-driven voltage control strategy is proposed for real-time optimized distributed voltage control of distribution networks connected to the energy storage system (ESS) and distributed photovoltaics (PVs) with high penetration. Deep reinforcement learning (DRL) methods are used in the control of voltage in the distribution grid. Firstly, based on the division of distributed networks, the decision-making process of combined distributed voltage control is given, which is mainly based on the reactive power compensation of photovoltaic inverters and supplemented by ESS active power regulation. A multi-agent deep reinforcement learning (MADRL) model is then applied to the distributed voltage control model. The Multi-Agent Twin Delay Deep Deterministic Policy Gradient (MATD3) algorithm is then applied. Finally, offline training and online testing are performed on a 33-bus test network with a branch structure to demonstrate the effectiveness of the proposed method.
DOI:	10.1109/CIEEC58067.2023.10167323