Optimal integration of renewable energy sources for autonomous tri-generation combined cooling, heating and power system based on evolutionary particle swarm optimization algorithm

Abstract Renewable energy (RE) sources can be integrated to serve autonomous tri-generation combined cooling, heating and power (CCHP) systems, so that the advantages of zero environmental emissions as well as higher energy efficiencies in generation and consumption are realized simultaneously. However, to override the inherent intermittent availability of RE sources and to enhance the performance of RE-CCHP systems, it is necessary to include thermal and electrical storage mechanisms. The objective of this study is to develop a simulation model for optimization of different configuration alternatives of autonomous RE-CCHP system for meeting cooling, heating and electrical loads, based on photovoltaic-thermal (PVT) panel, wind turbine (WT), thermal energy storage (TES), electrical energy storage (EES), absorption chiller (CHABS), electric chiller (CHELEC) and electric heater (EH). For operation of autonomous RE-CCHP system, two operational strategies, namely, following electric load (FEL) and following thermal load (FTL), are used. For optimization, a newly developed evolutionary particle swarm optimization (E-PSO) algorithm is examined and validated. It is demonstrated that the most cost effective configuration alternative of the autonomous RE-CCHP system is PVT+WT+EES+TES+CHABS+EH operating based on FTL operational strategy, where utilization of CHELEC is not needed.