Abstract
Renewable energies are interesting as an alternative and sustainable resource for air conditioning applications. But initial investment cost of equipment, whose employed for converting the renewable energy into usable shape and also for air conditioning duty, are significant. Therefore, determining the optimum sizing has high priority. In current study, water cooled vapor compression refrigeration cycle powered by wind energy and storage tank is proposed, simulated and optimized. To contribute the total effective aspects in system optimum size, the thermo-economic-environmental criteria is defined. By the help of databank of parametric analysis, the optimum design variables are determined by employing the GA optimization algorithm. In the following, an intelligence neural network is developed to learn the reliable correlation between the inputs and outputs data. Finally, the optimum size of each subsystem is determined by using triple-objective MPSO. Based on detailed economic analysis, the system payback period is estimated about 450 days which is 41% less than the conventional system. The daily COP and exergy efficiency of the whole system has improved up to 98% and 40%, after substituting the optimum design variable parameters. Triple-objective MPSO results show that, the ice storage tank should be selected 22% smaller than the initial amount.