Energy optimization strategy with Model Predictive Control and demand response

The overall price of energy is gradually increasing as a result of a constant escalating demand and limited supply. Consequently, the idea of demand response is being entertained by researchers and policy makers as a viable solution to the challenges ahead. Thus, new methods that aim to reduce the energy consumption in the residential sector are required to face such challenges. However, in order to optimize the consumption of energy while guaranteeing a certain level of comfort in the interior of the building could generate several control challenges. The goal of this paper is to compare the performance of control methods such as the Model Predictive Control (MPC), ON/OFF, and proportional-integral-derivative (PID) of a domestic heating, ventilation and air conditioning (HVAC) system controlling the temperature of a room. The house with local solar microgeneration is modelled approximating a location in a Portuguese city — Évora — pilot in a demand response project. The residence of the case study is subject to the local solar irradiance, temperature and six Time-of-Use (ToU) electricity rates applied on an entire week of July 2016. The aim of this paper is to accomplish the best compromise between temperature comfort levels and energy costs given by the performance of the fittest control method under different ToU rate options.