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

Min Wu¹ Dakui Ma^2* Kaiqing Xiong¹ Linkun Yuan¹

• ¹China Southern Power Grid (China), China
• ²Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD., China

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.