Optimal sizing of combined cooling, heating, and power system based on cluster analysis

Accurate prediction of the performance of the key devices in combined cooling, heating, and power systems, especially prime mover under off-design operations, plays a key role in determining the flexibilities and the overall techno-economic performance of the system. Although a variety of optimization methods can be applied to size a prime mover based on a representative performance curve, the optimized capacity may not be available in the marketplace and the real performance curve for the optimized size may deviate the representative curve. The present study aims to provide a comparative analysis to elucidate the effects of the clustering of the potential prime movers on the sizing of the key devices in the combined cooling, heating, and power system. Case study results show that compared to the method with 31 individual performance curves of the real-world potential prime movers, the clustering method effectively produces accurate representative performance curves and thus sizing results. Furthermore, the optimization process is significantly accelerated by using the clustering method.