Modelling The Aggregate Demand Response Of A Population Of Air Conditioners To Changes In Ambient Temperature

A substantial amount of research in recent years has investigated the direct load control (DLC) of populations of air conditioners (ACs) to provide demand-side services in the electricity grid. In many existing approaches, the control of aggregate power demand of these populations requires the knowledge of distributed physical parameters, such as the rated thermal power of the ACs and the thermal capacitances of the air-conditioned spaces. These parameters can be identified from DLC trials on a real-world population. However, such trials typically need to engage participants and fit their ACs with DLC-enabling devices for monitoring and control, which can be costly and may raise privacy concerns. This paper develops an alternative approach that allows the non-intrusive identification of distributed parameters for DLC. The proposed approach is based on a new mathematical model that describes the dynamic aggregate demand response of a population of ACs to changes in ambient temperature, rather than to a control signal. The parameters of the proposed model can then be identified from ambient temperature and aggregate demand data from sufficiently warm days, which are inexpensive to collect and do not need the direct engagement of participants in the target population. A key benefit of the new model is that its identified parameters also fit a previously developed dynamic model for DLC of aggregate demand of ACs, which completes a practical solution to model-based feedback control design for DLC in such populations.