Modeling and Control of Aggregated Heterogeneous Thermostatically Controlled Loads for Ancillary Services

TLDR

The authors develop a novel modeling and control framework for aggregating heterogeneous thermostatically controlled loads—such as refrigerators, water heaters, and air conditioners—for demand response. They model the aggregate temperature dynamics with a Markov chain, identify the transition matrix from load population data, and employ a predictive controller to track a reference signal, enabling second‑to‑second ancillary services like balancing and frequency control. Simulations on realistic systems confirm the approach’s feasibility and show that control performance varies with the amount of available state information and controller tuning.

Abstract

This paper presents a novel modeling and con- trol approach for the aggregation of large numbers of hetero- geneous thermostatically controlled loads, such as refrigera- tors, electric water heaters, and air conditioners, and their usage for Demand Response. Unlike traditional Demand Re- sponse methods that act on time scales of hours, this ap- proach is able to provide short-term (e.g., second-to-second) ancillary services, such as balancing and frequency control. A statistical modeling approach based on Markov Chains is used to describe the evolution of probability mass in a tem- perature state space. The Markov state transition matrix is identified using state information from the population of thermostatically controlled loads. A predictive controller is used to control the aggregate population of loads such that it tracks a signal. A simulation example shows the applicabil- ity of the approach to realistic systems, and includes a com- parison of control performance depending on available state information and controller parameterization.