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Modeling and Optimal Control of Micro-CSP and a Building HVAC System to Minimize Electricity Cost

[+] Author Affiliations
Chethan R. Reddy, Meysam Razmara, Mahdi Shahbakhti, Rush D. Robinett, III

Michigan Technological University, Houghton, MI

Mohamed Toub, Ghassane Aniba

Mohammed V University of Rabat, Rabat, Morocco

Paper No. DSCC2018-9131, pp. V002T28A004; 10 pages
  • ASME 2018 Dynamic Systems and Control Conference
  • Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems
  • Atlanta, Georgia, USA, September 30–October 3, 2018
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5190-6
  • Copyright © 2018 by ASME


This paper presents a model predictive control (MPC) framework to minimize the energy cost associated with the building heating, ventilation, and air-conditioning (HVAC) system integrated with a micro-scale concentrated solar power (MicroCSP) system. To this end, a MicroCSP model is developed and then integrated to the building model of an office building in Michigan Technological University. Then, an MPC framework is designed to optimize MicroCSP electrical and thermal energy flows for HVAC use in the building. The optimal control results show that the designed MPC framework reduces the HVAC energy cost by 37–42% for a sample sunny day by optimally utilizing the solar energy, compared to the HVAC system without MicroCSP with an MPC controller. The cost saving varies from 12% to 47% depending on seasonal weather variations.

Copyright © 2018 by ASME



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