Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads


To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale variations in power generation. Frequency regulation through demand response has the potential to coordinate temporally flexible loads, such as air conditioners, to counteract these variations. Existing approaches for discrete control with dynamic constraints struggle to provide satisfactory performance for fast timescale action selection with hundreds of agents. We propose a decentralized agent trained with multi-agent proximal policy optimization with localized communication. We explore two communication frameworks – hand-engineered, or learned through targeted multi-agent communication. The resulting policies perform well and robustly for frequency regulation, and scale seamlessly to arbitrary numbers of houses for constant processing times.

In AAMAS 2023 Extened Abstract
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Tianyu Zhang
Tianyu Zhang
Ph.D. Student in Machine Learning

My research interests include Algorithmic Game Theory, Agent-based Model Simulator, AI for Climate Change, Multi-agent Reinforcement Learning, Self-supervised Learning, Domain Adaptation. I am still exploring and learning slowly.