An integrated multi-objective optimization model for determining the optimal solution in the solar thermal energy system

The STE (solar thermal energy) system is considered an important new renewable energy resource. While various simulations are used as decision-making tools in implementing the STE system, it has a limitation in considering both diverse impact factors and target variables. Therefore, this study aimed to develop an integrated multi-objective optimization model for determining the optimal solution in the STE system. As the optimization algorithm, this study utilizes GA (genetic algorithm) to select optimal STE system solution. Using crossover and mutation, GA investigates optimal STE system solution. The proposed model used GA based on the software program Evolver 5.5. The proposed model presents high available and efficient results as decision-making tools. First, to determine the optimal solution, a total of 30,407,832 possible scenarios were generated by considering various factors in terms of their high availability. Second, in terms of efficiency, an average of 131 s were used to determine the optimal solution out of the previously proposed various scenarios. The proposed model can become a tool for consumers to decide on the optimal solution for the design of the STE system.