Reimagining Demand-Side Management with Mean Field Learning
Abstract
Integrating renewable energy into the power grid while balancing supply and demand is a complex issue, given its intermittent nature. Demand side management (DSM) offers solutions to this challenge. We propose a new method for DSM, in particular the problem of controlling a large population of electrical devices to follow a desired consumption signal. We model it as a finite horizon Markovian mean field control problem. We develop a new algorithm, MD-MFC, which provides theoretical guarantees for convex and Lipschitz objective functions. What distinguishes MD-MFC from the existing load control literature is its effectiveness in directly solving the target tracking problem without resorting to regularization techniques on the main problem. A non-standard Bregman divergence on a mirror descent scheme allows dynamic programming to be used to obtain simple closed-form solutions. In addition, we show that general mean-field game algorithms can be applied to this problem, which expands the possibilities for addressing load control problems. We illustrate our claims with experiments on a realistic data set.
Cite
@article{arxiv.2302.08190,
title = {Reimagining Demand-Side Management with Mean Field Learning},
author = {Bianca Marin Moreno and Margaux Brégère and Pierre Gaillard and Nadia Oudjane},
journal= {arXiv preprint arXiv:2302.08190},
year = {2023}
}