Exergy-wise predictive control framework for optimal performance of MicroCSP systems for HVAC applications in buildings

Abstract

The paper presents a new control method to optimize energy flows of a micro-scale concentrated solar power (MicroCSP) system in order to minimize the electrical energy consumption of a building heating, ventilation, and air conditioning (HVAC) system integrated with a MicroCSP system. A new real-time optimal control method is proposed using exergy-based model predictive control (XMPC) techniques. To achieve this, the first law of thermodynamics (FLT) and the second law of thermodynamics (SLT) based mathematical models of MicroCSP are developed and integrated into FLT and SLT based models of an office building located at Michigan Technological University. Then, an XMPC framework is designed to optimize MicroCSP operation in accordance with the building HVAC energy demand. The new controller shows 45% grid electrical energy saving, compared to a common rule-based controller. Furthermore, a probability analysis using Monte-Carlo simulations shows energy saving ranges from 44% to 46.5% in the presence of prediction uncertainties and 35% to 57.5% energy savings considering seasonal variations of the weather.