Model-based predictive control for optimal MicroCSP operation integrated with building HVAC systems

•Integrates a MicroCSP system into the building HVAC system.•Designs an MPC to minimize the energy consumption and cost of the combined system.•Compares the MicroCSP integration into the building HVAC with the PV integration.•Discusses the importance of proper energy storage sizing.•Conducts Monte Carlo analysis to assess the effect of inputs variations. This paper presents a model predictive control (MPC) framework to minimize the energy consumption and the energy cost of the building heating, ventilation, and air-conditioning (HVAC) system integrated with a micro-scale concentrated solar power (MicroCSP) system that cogenerates electricity and heat. The mathematical model of a MicroCSP system is derived and integrated into the building thermal model of an office building at Michigan Technological University. Then, the MPC framework is used to optimize thermal energy storage (TES) system usage, the energy conversion in the Organic Rankine Cycle (ORC), and the thermal energy flows to the HVAC system. The MPC results for energy and cost savings show the significance of understanding system dynamics and designing a real-time predictive controller to maximize the benefits of MicroCSP thermal and electrical energies production. Indeed, the designed MPC framework provided 37% energy saving and 70% cost saving compared to the conventional rule-based controller (RBC). Furthermore, the MicroCSP integration into the building HVAC is compared to the alternative of integrating photovoltaic (PV) panels and battery energy storage (BES) system to address the building HVAC needs. The results show the MicroCSP system outperforms PV solar panels for energy saving, while the PV panels outperform the MicroCSP system for cost saving when dynamic pricing is applied.