Model predictive control applied to a heating system with PV panels and thermal energy storage

Abstract

Two thirds of the total buildings final energy are used for heating purposes, specifically during the peak period. There is a mismatch between the power generation from renewable energy resources and demand. Thermal energy storage systems have been used not only to fill the gap between supply and demand, but also to take advantage of the time-of-use tariff structures. Nowadays, the application of smart controls to regulate heating systems is growing in popularity. Within this context, a model predictive control strategy to improve the operation of a space-heating system coupled with renewable resources is proposed. This model uses a dynamic approach based on forecasting all energy inputs into the system over a given period of time in advance, before taking any operational decisions. The model predictive control strategy was applied to minimize annual energy costs of the heating system of a detached house located in Puigverd de Lleida (Spain) and, based on a heat pump coupled to a thermal energy storage unit and photovoltaic panels. The results show the potential of the model predictive control with a 24-h horizon. In that case, energy cost savings of 58% can be achieved, compared to the same heating system without smart control.