Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Journal Article · Thu May 08 00:00:00 EDT 2025 · Energy and Buildings

DOI:https://doi.org/10.1016/j.enbuild.2025.115828· OSTI ID:2573863

Guittet, Darice ^[1]; Bonnema, Eric ^[1]; Mitchell, Matt ^[1]; Mahvi, Allison ^[2]; Woods, Jason ^[1]

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1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2. Univ. of Wisconsin, Madison, WI (United States)

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 2573863
• Alternate ID(s): OSTI ID: 2565992
• Report Number(s): NREL/JA--7A40-88779
• Journal Information: Energy and Buildings, Journal Name: Energy and Buildings Vol. 341; ISSN 0378-7788
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
25 ENERGY STORAGE
demand management
ice tank
nonlinear model predictive control
optimal control
thermal energy storage
utility cost minimization