Model Predictive Control for Demand Flexibility: Real-world Operation of a Commercial Building with Photovoltaic and Battery Systems
Published:
Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.
Recommended citation: Kun Zhang, Anand Prakash, Lazlo Paul, David Blum, Peter Alstone, James Zoellick, Richard Brown, Marco Pritoni. Model Predictive Control for Demand Flexibility: Real-world Operation of a Commercial Building with Photovoltaic and Battery Systems. Advances in Applied Energy. (2022). 100099,ISSN 2666-7924. https://doi.org/10.1016/j.adapen.2022.100099