Volt-VAR Optimization in Distribution Networks Using Twin Delayed Deep Reinforcement Learning

Conference · 24 April 2022 · 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)

DOI:https://doi.org/10.1109/isgt50606.2022.9817477· OSTI ID:1894929

Hossain, Rakib ^[1]; Gautam, Mukesh ^[2]; Lakouraj, Mohammad Mansour ^[2]; Livani, Hanif ^[2]; Benidris, Mohammed ^[2]

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1. University of Nevada,Department of Electrical & Biomedical Engineering,Reno; NV Energy
2. University of Nevada,Department of Electrical & Biomedical Engineering,Reno

Modern distribution grids are undergoing new challenges due to the stochastic nature of distributed energy resources (DERs). High penetration of DERs has a significant impact on Volt-VAR profile and system power losses. This work proposes a deep reinforcement learning (DRL)-based Volt-VAR optimization approach for improving voltage profile and reducing system power loss under high penetration of distributed energy resources, such as battery energy storage and solar photovoltaic units in distribution grids. The twin delayed deep deterministic policy gradient (TD3) method-based DRL agent is proposed to configure optimal set-points of reactive power outputs of fast responding smart inverters. The agent schedules the reactive power of inverters according to their physical capabilities, such as minimum allowed power factor, e.g., 0.9 leading/lagging. The reward function of the proposed DRL scheme is designed carefully to ensure a proper voltage profile of the grids with effective scheduling of reactive power outputs from inverters. The performance of the proposed model is verified on modified IEEE 34- and 123-bus systems and compared with base case with no reactive supply by inverters, and local droop Volt-VAR control approach. The results show that the proposed method performs better than the local droop control and deep deterministic policy gradient (DDPG)-based DRL method for reducing voltage fluctuation and minimizing power loss.

• Research Organization: NV Energy
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: EE0009022
• OSTI ID: 1894929
• Journal Information: 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Journal Name: 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
• Country of Publication: United States
• Language: English