Optimization of autonomous combined heat and power system including PVT, WT, storages, and electric heat utilizing novel evolutionary particle swarm optimization algorithm

Renewable energy (RE) sources can be incorporated in design of combined heat and power (CHP) systems, so that the advantages of zero environmental emissions as well as higher energy efficiencies are realized simultaneously. Further, due to inability to dispatch renewable energy sources, the integration of thermal and electricity storages is necessary to enhance the performance of RE-CHP systems in terms of overall cost and reliability to meet thermal and electrical loads. In addition, the utilization of excess electrical energy for conversion to heat could be critical to meeting thermal load and, hence, maintaining the autonomous operation of RE-CHP systems. The goal of this study is to develop a simulation model for optimization of an autonomous RE-CHP system, where thermal and electrical loads are met utilizing photovoltaic (PV)-thermal (PVT) panel, wind turbines (WTs), thermal energy storage, electrical energy storage, and electric heater (EH). For optimization, a newly developed evolutionary particle swarm optimization (E-PSO) algorithm is introduced and validated. It is shown that, as an autonomous RE-CHP system, the combination of PVT, WT, storages, and EH can effectively meet thermal and electrical loads with an acceptable reliability. Moreover, the results confirm the superiority of the proposed E-PSO algorithm among other methods.