AUTHOR=Wu Min , Ma Dakui , Xiong Kaiqing , Yuan Linkun TITLE=Optimizing load frequency control in isolated island city microgrids: a deep graph reinforcement learning approach with data enhancement across extensive scenarios JOURNAL=Frontiers in Energy Research VOLUME=12 YEAR=2025 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1384995 DOI=10.3389/fenrg.2024.1384995 ISSN=2296-598X ABSTRACT=

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.