Multi-objective optimization of a proposed multi-generation cycle based on Pareto diagrams: Performance improvement, cost reduction, and CO2 emissions

Energy system optimization is a significant task aimed at optimizing system operations and reducing costs and emissions. In this paper, an energy system is presented for multi-energy generation of power, heating/cooling, and desalination. First, the cycle's exergy, exergoeconomics, and environmental impact are analyzed. Then, optimization is performed using MATLAB software by applying a genetic algorithm (GA) and adopting 10 design parameters with two objective functions. The goal of optimization is to find the design variable values based on a Pareto plot that will increase the exergy efficiency while reducing cost and CO2 emissions. Scatter plots of the decision variables for the population indicate that objective functions can be optimized. Optimal values for the objective functions can be found by selecting lower values for the heat exchanger and evaporator's pinch-point temperature differences and higher values for the compressor pressure ratio, inlet temperature of the gas turbine (GT), isentropic efficiencies of air compressor (AC) and GT, and temperature of Rankine cycle evaporator. After optimization, exergy efficiency increased around 8.4%, cost dropped 14.8%, CO2 emissions were reduced by 1.2%, and the production of desalinated water increased about 7.6% using the proposed cycle. At the end of work, the influences of design variables on CO2 emissions as well as the total cycle cost are investigated in a parametric study.

Automated summary

This study optimized an energy system for multi-energy generation using MATLAB software and a genetic algorithm, resulting in increased exergy efficiency, reduced costs, and decreased CO2 emissions. By optimizing design parameters, the system achieved significant improvements in various aspects.