Reimagining Demand-Side Management with Mean Field Learning

• URL: http://arxiv.org/abs/2302.08190v2
• Date: Thu, 25 May 2023 06:37:32 GMT
• Title: Reimagining Demand-Side Management with Mean Field Learning
• Authors: Bianca Marin Moreno (EDF R&D, Thoth), Margaux Br\'eg\`ere (SU, LPSM (UMR\_8001), EDF R&D), Pierre Gaillard (Thoth), Nadia Oudjane (EDF R&D)
• Abstract summary: 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 develop a new algorithm, MD-MFC, which provides theoretical guarantees for convex and Lipschitz objective functions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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