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Preprints, Working Papers, ... Year : 2023

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.
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Dates and versions

hal-03972660 , version 1 (15-02-2023)
hal-03972660 , version 2 (24-05-2023)
hal-03972660 , version 3 (02-04-2024)

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Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard, Nadia Oudjane. Reimagining Demand-Side Management with Mean Field Learning. 2023. ⟨hal-03972660v2⟩
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