Model predictive control for optimum integration of active and passive energy sources

Abstract

The primary objective of this research is to develop an energy management system for the Co-creation center (CCC) that maximizes the use of passive energy sources while maintaining indoor thermal comfort. Passive energy sources have the potential to significantly reduce the energy consumption of the building. However, to achieve optimal energy savings, it is necessary to integrate multiple passive energy sources and develop a control strategy that can manage them effectively.

Model Predictive Control (MPC) strategies have been extensively researched in the literature as a means of optimizing energy consumption in buildings. However, most studies only consider a single passive energy source or energy distribution in multiple zones. There is limited research on the optimal management of multiple passive energy sources. To address this gap, this thesis investigates the use of an MPC strategy to optimize the operation of multiple passive energy sources in a building. Specifically, the research focuses on four solar blinds, a Phase Change Material (PCM) battery, sky windows, heat recuperation, natural ventilation, and an active energy source.

Grey-box modeling is used to model the building, and the model is calibrated using experimental data. The MPC problem is then set up to minimize energy supplied by Heat pump (HP) while ensuring indoor thermal comfort during occupied periods. An adaptive comfort model is used as a criterion to satisfy during occupied periods. The proposed MPC control is then implemented in the building.

The results show that the proposed MPC outperforms the rule-based controller in terms of energy consumption and maintaining thermal comfort. The research further provides insights into the potential of MPC strategy to increase the energy flexibility of buildings. The final parts of this research focused on varying the PCM temperatures and using a more flexible thermal comfort model and studying its effects on the energy demand of the building. The findings could be used to inform the design of energy-efficient buildings and the development of smart energy management systems