Multi-objective optimum design of energy systems based on particle swarm optimization

Improving and enhancing methodologies for efficiently and effectively design of the energy systems is one of the most important challenges that energy engineers face. In this work, a multi-objective particle swarm optimization algorithm is applied for a highly constrained cogeneration problem named CGAM problem as a standard cycle to verify all optimization methods. The regarded objective functions are the exergetic efficiency that should be maximized and the total cost rate that should be minimized, simultaneously. In order to determine the polar effects of the pressure ratio and the turbine inlet temperature on the specified objective functions, a sensitivity analysis is performed. The related Pareto fronts with different values of equivalence ratios, unit costs of fuel and NOx emissions are represented and their effects on the system are studied. Furthermore, the comparison of the obtained results with those of other evolutionary algorithms demonstrates the superiority and efficiency of the considered multi-objective particle swarm optimization algorithm.