Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability. This paper proposes a multi-agent reinforcement learning framework for managing energy transactions in microgrids. The framework addresses the challenges above: it seeks to optimize the usage of available resources by minimizing the carbon footprint while benefiting all stakeholders. The proposed architecture consists of three layers of agents, each pursuing different objectives. The first layer, comprised of prosumers and consumers, minimizes the total energy cost. The other two layers control the energy price to decrease the carbon impact while balancing the consumption and production of both renewable and conventional energy. This framework also takes into account fluctuations in energy demand and supply.

翻译：将波动性可再生能源整合到电网中，给系统运营商在实现能源可用性、成本可承受性与污染可控性之间的最优权衡提出了挑战。本文提出了一种用于管理微电网中能源交易的多智能体强化学习框架。该框架旨在应对上述挑战：在最小化碳足迹的同时惠及所有利益相关方，从而优化可用资源的利用。所提出的架构由三层智能体构成，每层追求不同的目标。第一层由产消者和消费者组成，旨在最小化总能源成本；其余两层则通过调控能源价格来降低碳排放影响，同时平衡可再生能源与常规能源的消费与生产。该框架还考虑了能源需求与供应的波动。