Distributed Randomized Control for Demand Dispatch

TL;DR

This paper introduces two novel randomized control design techniques for demand dispatch in power grids, focusing on local load intelligence and system passivity, with theoretical and numerical validation.

Contribution

It presents the Individual Perspective Design and System Perspective Design methods, both based on solving a single ODE, advancing control strategies for demand dispatch.

Findings

Both methods are computationally efficient.

Numerical results validate theoretical models.

Enhanced control of demand response in power systems.

Abstract

The paper concerns design of control systems for Demand Dispatch to obtain ancillary services to the power grid by harnessing inherent flexibility in many loads. The role of "local intelligence" at the load has been advocated in prior work, randomized local controllers that manifest this intelligence are convenient for loads with a finite number of states. The present work introduces two new design techniques for these randomized controllers: (i) The Individual Perspective Design (IPD) is based on the solution to a one-dimensional family of Markov Decision Processes, whose objective function is formulated from the point of view of a single load. The family of dynamic programming equation appears complex, but it is shown that it is obtained through the solution of a single ordinary differential equation. (ii) The System Perspective Design (SPD) is motivated by a single objective of the grid operator: Passivity of any linearization of the aggregate input-output model. A solution is obtained that can again be computed through the solution of a single ordinary differential equation. Numerical results complement these theoretical results.