Multi-agent reinforcement learning for renewable integration in the electric power grid

Abstract

As part of the fight against climate change, the electric power system is transitioning from fuel-burning generators to renewable sources of power like wind and solar. To allow for the grid to rely heavily on renewables, important operational changes must be done. For example, novel approaches for frequency regulation, i.e., for balancing in real-time demand and generation, are required to ensure the stability of a renewable electric system. Demand response programs in which loads adjust in part their power consumption for the grid’s benefit, can be used to provide frequency regulation. In this proposal, we present and motivate a collaborative multi-agent reinforcement learning approach to meet the algorithmic requirements for providing real-time power balancing with demand response.

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.