Control-Guided Reinforcement Learning for Cooperative Energy Management
協調的エネルギー管理のための制御誘導型強化学習 (AI 翻訳)
Isabela Fons Moreno-Palancas, Raquel Salcedo‐Díaz, Rubén Ruiz‐Femenia, José A. Caballero, Ehecatl Antonio del Rio‐Chanona
🤖 gxceed AI 要約
日本語
本論文は、再生可能エネルギーとV2G機能を持つプロシューマーの協調制御に、クロスエントロピー法に基づく分散型マルチエージェント強化学習（MARL）を提案。PID制御からの行動模倣学習で方策を温起動し、サンプル効率とロバスト性を向上。現実的なマイクログリッドシミュレーションで、従来手法より低コストを達成した。
English
This paper proposes a decentralized multi-agent reinforcement learning approach based on the cross-entropy method (CEM) for coordinating prosumers with renewables and vehicle-to-grid. Behavior cloning from a PID controller warm-starts the policy, improving sample efficiency. Simulations show lower energy costs than PID or random initialization without sacrificing comfort.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、再生可能エネルギーの普及に伴い、地域マイクログリッドの重要性が増している。本手法は、プライバシー保護とスケーラビリティを備えた分散制御を実現し、日本の分散型エネルギーリソース（DER）運用に応用可能である。
In the global GX context
Globally, this work addresses the challenge of integrating distributed energy resources through scalable and privacy-preserving control. The hybrid BC-CEM framework offers a practical approach for energy flexibility coordination, relevant to microgrids and virtual power plants worldwide.
👥 読者別の含意
🔬研究者:The hybrid BC-CEM framework demonstrates a novel combination of imitation learning and derivative-free optimization for MARL in energy systems.
🏢実務担当者:The approach can be deployed in microgrid controllers to reduce operational costs and improve renewable integration.
🏛政策担当者:Highlights the potential of AI-based coordination for achieving grid stability and decarbonization goals through distributed energy resources.
📄 Abstract（原文）
Addressing the urgent transition to low-carbon energy systems requires microgrids capable of locally coordinating electricity generation, storage, and flexible consumption. Their efficient integration calls for control strategies that are scalable, privacy-preserving, and robust to uncertainty. To address such a challenging control problem, this work proposes a decentralised Multi-Agent Reinforcement Learning (MARL) approach based on the Cross-Entropy Method (CEM) for the coordination of prosumers, equipped with renewable generation and vehicle-to-grid capabilities. To improve sample efficiency and robustness, the policy is warm-started using Behaviour Cloning (BC) from a classical Proportional-Integral-Derivative (PID) controller, resulting in a hybrid BC–CEM framework. The proposed method is evaluated in a realistic microgrid simulation with stochastic demand and real weather and generation profiles. Results show that BC–CEM accelerates convergence and achieves lower energy costs compared to both PID control and randomly initialized CEM, without sacrificing comfort or mobility requirements. The findings highlight the effectiveness of combining derivative-free optimization with imitation learning in complex MARL tasks, such as energy flexibility coordination.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.69997/sct.165580first seen 2026-07-13 04:55:07
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