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

This paper presents a novel modeling and control approach for the aggregation of large numbers of heterogeneous thermostatically controlled loads, such as refrigerators, electric water heaters, and air conditioners, and their usage for Demand Response. Unlike traditional Demand Response methods that act on time scales of hours, this approach 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 temperature 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 applicability of the approach to realistic systems, and includes a comparison of control performance depending on available state information and controller parameterization.