Lawrence Berkeley National Laboratory

In California, commercial refrigeration accounts for 14% of electric energy usage, corresponding to 9,014 GWh per year and affecting more than 110,000 commercial establishments. A single walk-in freezer uses more energy than 5 single-family houses. Despite more stringent standards and utility programs promoting hardware improvements, controls systems used for commercial refrigerators are still primitive. In fact, in traditional refrigerators, the vapor-compression cycle is controlled with a simple hysteresis controller to keep the air cabinet temperature within a specific range. The controller does not account for system dynamics, energy consumption, utility prices or recurrent events such as food loading schedules and business opening hours. Also, demand response is precluded with these unsophisticated controls. With the help advanced control theory and efficient optimization algorithms, computerbased real-time optimization is now feasible and applicable in commercial refrigeration systems, but its practical use to date has been limited to industrial systems requiring expensive on-premise equipment and complex operations. This paper presents a novel hardware and software architecture that allows advanced control algorithms for commercial refrigerators to be developed, tested and deployed inexpensively. The aim of this new control framework is to optimize energy consumption as a software task, utilizing the benefits of lower cost computational resources inherent to cloud computing, minimizing on net overall energy usage of the refrigeration system. A prototype of the proposed system has been developed and tested under a California Energy Commission grant