An Evolutionary Algorithm-Based Model Predictive Control for Combined Electrical and Thermal Energy Systems

Abstract: Energy storage is crucial to increase renewable energy adoption in construction. By optimizing their control strategies, operational costs decrease and return on investment improves. Model Predictive Controls (MPC) have been used to optimize the use of energy storage but are costly to implement. This paper presents an MPC with a generalized mathematical model for electrical and thermal storage. A methodology is introduced to account for physical restrictions. Three evolutionary algorithms were compared for the optimization and a Genetic Algorithm was found to best reduce the energy bill with average daily savings of 38.7%.