Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Zhang, Kun; Prakash, Anand; Paul, Lazlo; Blum, David; Alstone, Peter; Zoellick, James; Brown, Richard; Pritoni, Marco

doi:10.1016/j.adapen.2022.100099

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Journal Article · Mon Jul 04 00:00:00 EDT 2022 · Advances in Applied Energy

DOI:https://doi.org/10.1016/j.adapen.2022.100099· OSTI ID:1894319

^[1]; Prakash, Anand ^[2]; Paul, Lazlo ^[2]; Blum, David ^[2]; Alstone, Peter ^[3]; Zoellick, James ^[3]; Brown, Richard ^[2]; Pritoni, Marco ^[2]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Natural Resources Canada, Edmonton, AB (Canada)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
California Polytechnic State Univ. (CalPoly), Humbolt, CA (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: California Energy Commission; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1894319

Journal Information:: Advances in Applied Energy, Journal Name: Advances in Applied Energy Vol. 7; ISSN 2666-7924

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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