Demand response for thermostatically controlled loads using belief propagation

This paper explores a belief propagation algorithm approach, for state estimation of thermal states of aggregated populations of thermostatically controlled loads to provide balancing services in power systems. Balancing services such as frequency control require high accuracy and thus estimation is crucial, especially when sensing and communication in real time is not available or partially. We use Markov chain models to describe the thermal states of aggregated thermostatically controlled loads populations and belief propagation for state estimation. Kalman filter, which has been used in similar studies, is known to be an instance of belief propagation framework. This fact motivates us to introduce belief propagation in this framework, and demonstrate that it provides the same results as Kalman filter. Moreover, belief propagation algorithm is fully decentralized, offering higher flexibility of system modeling using factor graphs. Besides demand response, belief propagation can be applied for different purposes in power systems, either in fully distributed or in mixed systems, and is easily integrated with Kalman filter or similar probabilistic frameworks.