Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings

Active storage is capable of shifting peak demand for peak load management (PLM) as well as providing longer duration and larger capacity for demand response (DR). In this research, a model-based optimal design method using genetic algorithm is developed to optimize the capacity of active CTES for maximizing the life-cycle cost saving including capital cost associated with storage capacity as well as incentives from both fast DR and PLM. In the method, the active CTES operates under a fast DR control strategy during DR events and under the storage-priority operation mode to shift peak demand during normal days. The optimal storage capacities, maximum annual net cost saving and corresponding power reduction set-points during DR events are obtained by using the proposed optimal design method. This research provides guidance in comprehensive evaluation of the cost-saving potential of active CTES integrated with HVAC system for building demand management including both fast DR and PLM.