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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1384995
This article is part of the Research Topic Urban Energy System Planning, Operation, and Control with High Efficiency and Low Carbon Goals View all 29 articles

Optimizing Load Frequency Control in Isolated Island City Microgrids: A Deep Graph Reinforcement Learning Approach with Data Enhancement Across Extensive Scenarios

Provisionally accepted
Min Wu Min Wu 1Dakui Ma Dakui Ma 2*Kaiqing Xiong Kaiqing Xiong 1Linkun Yuan Linkun Yuan 1
  • 1 China Southern Power Grid (China), Guangzhou, Guangdong Province, China
  • 2 Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD., Guangdong, China

The final, formatted version of the article will be published soon.

    This study presents a Data-Enhanced Optimum Load Frequency Control (DEO-LFC) strategy for microgrids, targeting an optimal balance between generation costs and frequency stability amidst high renewable energy integration. By replacing traditional controls with agent-based systems and reinforcement learning, the DEO-LFC employs an optimal balance between generation costs and frequency stability amidst high renewable energy integration. By replacing traditional controls with agent-based systems and reinforcement learning, the DEO-LFC employs a Soft Graph Actor Critic (SGAC) algorithm, integrating deep reinforcement learning with graph sequence neural networks for effective frequency management. Proven effective in the China Southern Grid's island microgrid model, DEO-LFC offers a sophisticated solution to the challenges posed by the island microgrid model. Proven effective in the China Southern Grid's island microgrid model, DEO-LFC offers a sophisticated solution to the challenges posed by the variability of modern power grids.

    Keywords: Load frequency control, Deep graph reinforcement learning, Isolated island city microgrid, Soft graph actor critic, Data-enhanced

    Received: 11 Feb 2024; Accepted: 15 Apr 2024.

    Copyright: © 2024 Wu, Ma, Xiong and Yuan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Dakui Ma, Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD., Guangdong, China

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