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About this paper
Aisling Pigott, Constance Crozier, Kyri Baker, Zoltan Nagy
GridLearn introduces a framework that integrates multiagent reinforcement learning with building energy management and power flow models to achieve grid-level voltage regulation. This approach reduced overvoltages by 34% and improved overall voltage stability in a high-photovoltaic penetration scenario.
Key insights
- Multiagent reinforcement learning enables decentralized control of distributed energy resources, achieving grid-level objectives while preserving building owner privacy.
- RL agents can learn dynamic and adaptive control strategies for behind-the-meter devices that surpass static rule-based methods in complex grid scenarios.
- Integrating building energy models with power flow simulations provides a comprehensive environment for developing and evaluating coordinated building-to-grid control.
Summary by alphaXiv.
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