OPTIMIZED DESIGN AND CONTROL OF A MULTI-TEMPERATURE, MULTI-MODULE THERMAL ENERGY STORAGE ENSEMBLE

Latent thermal energy storage (LTES) systems which store energy in a phase change material (PCM) are effective in bridging the gap between renewable energy supply and demand for applications such as concentrated solar plants and building heating and cooling systems. Cascaded LTES systems which use multiple PCMs with different melt temperatures can be used to improve performance by increasing energy and exergy efficiencies and increasing rates for charging and discharging. A multi-temperature, multi-module (MTMM) LTES ensemble controlled by an Artificial Neural Network (ANN) can further improve performance by allowing for various working fluid flow paths through the system which allow for the ensemble to adapt to dynamically changing and unforeseen operating conditions. The MTMM ensemble is optimized to control to dual objectives of meeting a target working fluid outlet temperature and minimizing the instantaneous rate of exergy destruction allowing it to both achieve operational reliability and maximize second law efficiencies. In this investigation, design optimization of the MTMM ensemble is explored by comparing various parallel and series configurations of modules and exploring the effects of TES module layout, PCM melt temperatures, and the overall heat transfer coefficient of individual modules on the performance of the ensemble. The results demonstrate the impact of optimization of design parameters on the performance of MTMM ensembles and the potential for the use of data driven optimizers such as genetic algorithms for design of MTMM ensembles. The performance of the MTMM ensemble with optimized design and adaptive ANN controller is demonstrated to achieve the exergy efficiency of a cascaded PCM system while also maintaining the flexibility needed to respond to dynamically changing operating conditions. This novel approach widens the potential applications of cascaded PCM by providing a means to standardize manufacturing to drive down system cost and allowing the system to adapt to dynamic operating conditions and unforeseen design parameters.