Fully autonomous load frequency control for integrated energy system with massive energy prosumers using multi-agent deep meta reinforcement learning

Jiawen Li and Tao Zhou

Renewable and Sustainable Energy Reviews, 2025, vol. 213, issue C

Abstract: In Interconnected Integrated Energy Systems (IIES), grid operators face the challenge of dealing with intermittent and stochastic disturbances caused by energy prosumers, while considering the multi-energy constraints and exploiting the fast response capability of prosumers to optimize the benefits of both regulation service providers and the grid. To address these challenges, this paper proposes a Fully Autonomous Load Frequency Control (FA-LFC) approach for IIES in performance-based frequency regulation markets. This approach considers each regulation service provider as an independent decision-making agent that can autonomously adjust its policy based on the state of its area and the global objective. The agents use a novel reinforcement learning algorithm called Maximum Entropy Multi-Agent Deep Meta Actor Critic (MEMA-DMAC), which combines meta-learning and multi-agent learning with a maximum entropy exploration policy. The MEMA-DMAC algorithm enables the agents to learn from high-value demonstrations of different regulation tasks, as well as to account for the multi-energy constraints through centralized learning. The proposed method is validated on a four-area LFC model of China Southern Grid (CSG).

Keywords: Load frequency control; Energy prosumers; Performance-based frequency regulation market; Integrated energy system; Large-scale multi-agent deep reinforcement learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2025.115489

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