MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids
This addresses grid management problems for system operators and stakeholders in renewable energy integration, but appears incremental as it builds on existing multi-agent and reinforcement learning approaches.
The paper tackles the challenge of integrating variable renewable energy into microgrids by proposing a multi-agent reinforcement learning framework to optimize energy transactions, aiming to minimize carbon footprint and benefit stakeholders while balancing cost, availability, and pollution.
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.