Reimagining Demand-Side Management with Mean Field Learning
This addresses the challenge of integrating renewable energy into the power grid by improving demand-side management for grid operators, though it appears incremental as it builds on existing mean-field control methods.
The paper tackles the problem of controlling a large population of electrical devices to follow a desired consumption signal in demand-side management, proposing a new algorithm (MD-MFC) that directly solves the target tracking problem without regularization, with experiments on a realistic dataset.
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.