State Estimation for the Individual and the Population in Mean Field Control with Application to Demand Dispatch

TL;DR

This paper develops state estimation techniques in mean field control models, applying them to demand dispatch in power grids, demonstrating effective load regulation with minimal communication and operational cost.

Contribution

It introduces a Kalman filter-based state estimation method for mean field control models, enhancing demand dispatch efficiency with reduced information exchange.

Findings

Kalman filter reduces communication needs in demand dispatch

Local control algorithms enable accurate aggregate load regulation

Operational costs are minimized in the proposed framework

Abstract

This paper concerns state estimation problems in a mean field control setting. In a finite population model, the goal is to estimate the joint distribution of the population state and the state of a typical individual. The observation equations are a noisy measurement of the population. The general results are applied to demand dispatch for regulation of the power grid, based on randomized local control algorithms. In prior work by the authors it has been shown that local control can be carefully designed so that the aggregate of loads behaves as a controllable resource with accuracy matching or exceeding traditional sources of frequency regulation. The operational cost is nearly zero in many cases. The information exchange between grid and load is minimal, but it is assumed in the overall control architecture that the aggregate power consumption of loads is available to the grid operator. It is shown that the Kalman filter can be constructed to reduce these communication requirements,