Optimizing load frequency control in isolated island city microgrids: a deep graph reinforcement learning approach with data enhancement across extensive scenarios - Wu, Min; Ma, Dakui; Xiong, Kaiqing; Yuan, Linkun (Frontiers in Energy Research, 2025)
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.