Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

Zhang, Ying; Yue, Meng; Wang, Jianhui

doi:10.1109/pesgm46819.2021.9638058

Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

Journal Article · Mon Jul 26 00:00:00 EDT 2021 · IEEE Power & Energy Society General Meeting

DOI:https://doi.org/10.1109/pesgm46819.2021.9638058· OSTI ID:1890215

Zhang, Ying ^[1]; Yue, Meng ^[1]; Wang, Jianhui ^[2]

Brookhaven National Lab. (BNL), Upton, NY (United States). Interdisciplinary Science Department
Southern Methodist Univ., Dallas, TX (United States)

Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in real-world power systems under contingencies. However, existing emergency control schemes are rule-based and cannot be adaptively applied to uncertain and floating operating conditions. Here, we propose an adaptive UVLS algorithm for emergency control via deep reinforcement learning (DRL) and expert systems. We first construct dynamic components for picturing the power system operation as the environment. The transient voltage recovery criteria, which poses time-varying requirements to UVLS, is integrated into the states and reward function to advise the learning of deep neural networks. The proposed method has no tuning issue of coefficients in reward functions, and this issue was regarded as a deficiency in the existing DRL-based algorithms. Case studies illustrate that the proposed method outperforms the traditional UVLS relay in both the timeliness and efficacy for emergency control.

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Electricity (OE), Advanced Grid Research & Development

Grant/Contract Number:: SC0012704

OSTI ID:: 1890215

Report Number(s):: BNL--223485-2022-JAAM

Journal Information:: IEEE Power & Energy Society General Meeting, Journal Name: IEEE Power & Energy Society General Meeting Vol. 2021; ISSN 1944-9925

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
artificial intelligence
expert systems
grid resiliency
load shedding
power system stability
process control
real-time systems
reinforcement learning
relays

Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

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References (11)

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