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

TL;DR

GridLearn employs multiagent reinforcement learning to optimize building energy management for voltage regulation in power grids, balancing grid stability with building owner privacy and comfort.

Contribution

This work extends the CityLearn framework to a network setting, integrating grid-level goals into multiagent reinforcement learning for energy management.

Findings

RL agents reduced undervoltages and overvoltages by 34%

Demonstrated privacy-preserving control of behind-the-meter resources

Achieved grid stability while maintaining building comfort

Abstract

Increasing amounts of distributed generation in distribution networks can provide both challenges and opportunities for voltage regulation across the network. Intelligent control of smart inverters and other smart building energy management systems can be leveraged to alleviate these issues. GridLearn is a multiagent reinforcement learning platform that incorporates both building energy models and power flow models to achieve grid level goals, by controlling behind-the-meter resources. This study demonstrates how multi-agent reinforcement learning can preserve building owner privacy and comfort while pursuing grid-level objectives. Building upon the CityLearn framework which considers RL for building-level goals, this work expands the framework to a network setting where grid-level goals are additionally considered. As a case study, we consider voltage regulation on the IEEE-33 bus network using controllable building loads, energy storage, and smart inverters. The results show that the RL agents nominally reduce instances of undervoltages and reduce instances of overvoltages by 34%.