Geodesy

Control (management)

Engineering

Statistics

Control engineering

Smart grid

Energy (signal processing)

Computer science

Distributed generation

Grid

Distributed computing

Energy management

Thermodynamics

Physics

Reinforcement learning

Renewable energy

Mathematics

Geography

Artificial intelligence

Geometry

Building automation

Structural engineering

Smart meter

Reinforcement

Building management system

Electrical engineering

Electric Power Systems Research

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%.

GridLearn: Multiagent reinforcement learning for grid-aware building energy management