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Increasing amounts of distributed generation in distribution networks canprovide both challenges and opportunities for voltage regulation across thenetwork. Intelligent control of smart inverters and other smart building energymanagement systems can be leveraged to alleviate these issues. GridLearn is amultiagent reinforcement learning platform that incorporates both buildingenergy models and power flow models to achieve grid level goals, by controllingbehind-the-meter resources. This study demonstrates how multi-agentreinforcement learning can preserve building owner privacy and comfort whilepursuing grid-level objectives. Building upon the CityLearn framework whichconsiders RL for building-level goals, this work expands the framework to anetwork setting where grid-level goals are additionally considered. As a casestudy, we consider voltage regulation on the IEEE-33 bus network usingcontrollable building loads, energy storage, and smart inverters. The resultsshow that the RL agents nominally reduce instances of undervoltages and reduceinstances of overvoltages by 34%.

GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management