mlsanityData to 14 Oct 2026

Cooperative Energy Scheduling of Multi-Microgrids Based on Risk-Sensitive Reinforcement Learning

arXiv 2512.17246 ↗First posted 19 Dec 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

With the rapid development of distributed renewable energy, multi-microgrids play an increasingly important role in improving the flexibility and reliability of energy supply. Reinforcement learning has shown great potential in coordination strategies due to its model-free nature. Current methods lack explicit quantification of the relationship between individual and joint risk values, resulting in obscured credit assignment. Moreover, they often depend on explicit communication, which becomes inefficient as system complexity grows. To address these challenges, this paper proposes a risk-sensitive reinforcement learning framework with shared memory (RRL-SM) for multi-microgrid scheduling. Specifically, a risk-sensitive value factorization scheme is proposed to quantify the relationship between individual and joint risk values by leveraging distributional modeling and attention-based representations, thereby aligning local decisions with global risk objectives. An implicit shared-memory coordination mechanism is implemented through a global memory space to enhance the overall efficiency of decentralized decision-making. Collectively, the integrated approach delivers more reliable cooperative scheduling under renewable energy uncertainty. Simulation results show that RRL-SM reduces load-shedding risk by 84.5%, demonstrating a favorable balance between reliability and economic performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 27 references that are in the map. First posted on arXiv 19 Dec 2025.

Builds on

7 of 27 references.

References · 7

  1. 2020Value-Decomposition Multi-Agent Actor-Criticscited by 48
  2. 2023SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfindingcited by 37
  3. 2023Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learningcited by 29
  4. 2023RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorizationcited by 20
  5. 2021CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clusteringcited by 12
  6. 2023Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithmcited by 11
  7. 2023SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimizationcited by 4

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  1. 2025Independent policy gradient-based reinforcement learning for economic and reliable energy management of multi-microgrid systems
  2. 2025Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
  3. 2026Multiagent Reinforcement Learning in Enhancing Resilience of Microgrids under Extreme Weather Events ↗
  4. 2024A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications
  5. 2024Safe Reinforcement Learning for Power System Control: A Review
  6. 2025A Reinforcement Learning Approach for Optimal Control in Microgrids
  7. 2026Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources
  8. 2024Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes