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：将可变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、成本可负担性和污染可控性之间的最优平衡带来了挑战。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：通过最小化碳足迹来优化可用资源的使用，同时惠及所有利益相关者。所提出的架构由三层智能体组成，每层追求不同的目标。第一层由产消者和消费者构成，其目标是最小化总能源成本。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传统能源的消耗与生产。该框架还考虑了能源需求与供应的波动。