Optimizing the regulation of aggregated thermostatically controlled loads by jointly considering consumer comfort and tracking error

Abstract

Thermostatically controlled loads (TCLs) are promising to offer demand-side regulation with proper control. In this paper, the aggregate power of TCLs is used to track the automatic generation control (AGC) signal by changing the temperature setpoint. The dynamics of the indoor temperature are described by a Monte Carlo model, and population dissatisfaction is described by the predicted percentage of dissatisfied (PPD). The objective is optimization from two aspects, minimizing both population dissatisfaction and tracking error. We propose an improved active target particle swarm optimization (APSO) algorithm to optimize the model, making it possible to ensure that the user's dissatisfaction is as small as possible while the aggregate power tracks the AGC signal. The novelty of this paper is to introduce PPD into the model and at the same time establish three models using PPD as the objective function and constraints. The simulation results are shown to verify the efficiency of the designed model.