Systematic review on model predictive control strategies applied to active thermal energy storage systems

Abstract This paper presents a review of the application of model predictive control strategies to active thermal energy storage systems. To date, model predictive control has been used to manage such energy systems as heating, ventilation and air conditioning equipment or power generation plants. In all cases, the aim of the strategy has been to anticipate both production and consumption decisions to optimize the system performance, reducing the final energy cost. This ability of the strategy to forecast weather conditions and predict demand requirements in advance exceeds the performance of conventional control methods and made the strategy a very effective option to be coupled with active thermal energy storage systems. In this regard, this review paper presents the progress and results of the combination of these two technologies. The key contributions consist of a summary of the technical parameters employed, such as the prediction horizon length, the computational architecture approaches, the thermal energy storage material used and the influence of renewables in this kind of system. Additionally, the review summarises the latest enhancements to overcome computational issues and an analysis of the objective functions employed in each study, which were mainly focused to minimize the energy cost, the peak power and CO2 emissions. A discussion about the strengths and weaknesses of this technology is provided, highlighting the difficulty of the strategy to operate with complicated physical models as the key limitation to overcome. Finally, some future guidelines to enhance the application of this strategy to control different sort of systems are detailed.