Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Cao, Di; Zhao, Junbo; Hu, Weihao; Ding, Fei; Yu, Nanpeng; Huang, Qi; Chen, Zhe

doi:10.1016/j.apenergy.2021.117982

Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Journal Article · Sat Nov 06 04:00:00 EDT 2021 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2021.117982· OSTI ID:1834046

Cao, Di; Zhao, Junbo; Hu, Weihao; Ding, Fei; Yu, Nanpeng; Huang, Qi; Chen, Zhe

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Key Research and Development Program of China

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 1834046

Report Number(s):: NREL/JA-5D00-81589; MainId:82362; UUID:6a889dbc-f16a-4888-8ca7-1e4897d276f3; MainAdminID:63387

Journal Information:: Applied Energy, Journal Name: Applied Energy Journal Issue: Part A Vol. 306

Country of Publication:: United States

Language:: English

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