Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads

This work investigates an uncertainty quantification (UQ) framework that analyses the uncertainty involved in modelling control systems to improve control strategy performance. The framework involves solving four problems: identifying uncertain parameters, propagating uncertainty to the quantity of interest, data assimilation and making decisions under quantified uncertainties. A specific group of UQ approaches, known as the ensemble-based methods, are adopted to solve these problems. This UQ framework is applied to coordinating a group of thermostatically controlled loads, which relies on simulating a second-order equivalent thermal parameter model with some uncertain parameters. How this uncertainty affects the prediction and the control of total power is examined. The study shows that uncertainty can be effectively reduced using the measurement of air temperatures. Also, the control objective is achieved fairly accurately with a quantification of the uncertainty.