Development and evaluation of a generalized rule-based control strategy for residential ice storage systems

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In recent years, variable electricity pricing has become available to residential consumers to incentivize demand reductions during midday peak hours. Thermal energy storage (TES) systems enable consumers to store cooling energy when demand is low and assist A/C operation during peak demand periods. However, the cost savings achievable using TES are highly dependent on how the system is operated for a given utility rate structure. This study investigates control strategies for a packaged chiller unit integrated with ice storage that leverage available residential utility rate structures in the U.S. to reduce consumer electricity cost. The present work describes the development and evaluation of a generalized rule-based control strategy inspired by the performance of an optimal controller that minimizes monthly electricity cost considering both time-of-use energy and demand charges. The generalized rule-based controller is compared against the optimal controller as well as to heuristic control strategies for TES that were originally developed for commercial buildings for a range of equipment cooling capacities, TES sizes, geographic locations, and residential utility rates. The total electricity cost is determined using a simulation model that includes models for the chiller unit, ice storage tank, and secondary loop components, along with a building load model. Results show that the generalized rule-based controller can approximate the performance of the optimal controller within 20% for all cases tested, and within 10% of the optimal cost in 53% of the cases tested. The controller also performs significantly better than the heuristic strategies for commercial buildings that were evaluated.