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.

翻译：将可再生能源整合到电网中并平衡供需，因其间歇性特征而成为复杂问题。需求侧管理（DSM）为这一挑战提供了解决方案。我们提出一种DSM新方法，具体针对控制大量用电设备以跟随期望消耗信号的问题。将其建模为有限时域马尔可夫均值场控制问题，我们开发了新算法MD-MFC，该算法为凸函数和利普希茨目标函数提供了理论保证。MD-MFC与现有负荷控制文献的区别在于，它能有效直接求解目标跟踪问题，而无需对主问题采用正则化技术。基于镜像下降方案的非标准布雷格曼散度，使得动态规划可被用于获得简单闭式解。此外，我们证明通用均值场博弈算法可应用于此问题，这拓展了解决负荷控制问题的可能性。我们通过真实数据集上的实验验证了上述论断。