Integration of a Grey-Box Refrigerated Case Model in EnergyPlus via Python Plugin

Journal Article · Mon Oct 13 00:00:00 EDT 2025 · Building Simulation

DOI:https://doi.org/10.1007/s12273-025-1352-5· OSTI ID:3013275

Chinde, Venkatesh ^[1]; Woldekidan, Korbaga ^[1]; Bonnema, Eric ^[1]; Xiong, Jie ^[1]; Saha, Avijit ^[1]; Saunders, Mike ^[2]; Wheeler, Grant ^[1]

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1. National Renewable Energy Lab., Golden, CO (United States)
2. Copeland

Commercial buildings, in particular grocery stores (due mainly to their large refrigeration load), provide opportunities for energy cost reductions. Grocery stores could offer substantial load flexibility to the power grid through participation in demand response programs because of their usage patterns and relatively high energy intensity. This load flexibility could come from modifying the control of heating, ventilation, and air conditioning (HVAC) systems, refrigeration systems, or both. Although estimation of the HVAC system's load flexibility potential is relatively targeted in the literature, estimating load flexibility of refrigeration systems is nascent and has been a challenge, in part because of the lack of proper simulation tools that capture the dynamics in the refrigeration cases. The existing refrigerated case model within EnergyPlus, a whole building energy simulation program, assumes a constant case temperature throughout the simulation period and does not explicitly model the cycling of the compressor serving the refrigerated case. In addition, it does not encompass modeling of temperatures of the product inside the refrigerated case. This difference between modeled and actual operation can be a barrier to the development of demand control algorithm and accurate analysis of load flexibility potential. In this paper, we present a grey-box model for modeling refrigerated cases in grocery stores, which include medium temperature and low temperature. Four cases are modeled; two are low-temperature closed cases and two are medium-temperature cases with one closed and one open. Data from an experimental facility are used to train and test the models. Results demonstrate the efficacy of the grey-box models in predicting the temperatures. This model is integrated into EnergyPlus to capture the dynamic effects of case temperature on the environment and enhance the calculation of sensible and latent heat exchange with the environment (case credits). These enhancements can be leveraged more broadly to model advanced refrigeration controls such as defrost, develop and test unique algorithms that could affect refrigeration interactions with HVAC, and refine store design for any commercial building with refrigeration. enhance the calculation of sensible and latent heat exchange with the environment (case credits)..

• 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
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 3013275
• Report Number(s): NREL/JA-5500-88789
• Journal Information: Building Simulation, Journal Name: Building Simulation
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
97 MATHEMATICS AND COMPUTING
EnergyPlus
PythonAPI
case credits
grey-box model
refrigeration