Designing, optimizing and comparing distributed generation technologies as a substitute system for reducing life cycle costs, CO2 emissions, and power losses in residential buildings

The optimization of distributed generation technologies and storage systems are essential for a reliable, cost-effective, and secure system due to the uncertainties of Renewable Energy Sources (RESs) and load demand. In this study, two algorithms, the Multi-Objective Particle Swarm Optimization (MOPSO) and the Non-Dominant Sorting Genetic Algorithm II (NSGA-II) were utilized to design five different case studies (CSs) (photovoltaic (PV)/ wind turbine (WT)/ battery/ diesel generator (DG), PV/ WT/ battery/ fuel cell (FC)/ electrolyzer (EL)/ hydrogen tank (HT), PV/ WT/ battery/ grid-connected, PV/ WT/ battery/ grid-connected with Demand Response Program (DRP), and PV/ WT/ battery/ electric vehicle (EV)) to minimize life cycle cost (LCC), loss of power supply probability (LPSP), and $CO_{\text{2}}$ emissions. In fact, different backups are provided for (PV/ WT/ battery), which is considered as the base system. Further, the uncertainties in RES and load were modeled by the Taguchi method, and Monte Carlo simulation (MCS) was used to model the uncertainties in EV to achieve accurate results. In addition, in CS4, a Demand Response Program (DRP) based on Time-of-Use (TOU) price is considered to study the effect on the number of specific components and other parameters. Finally, the simulation results verify that the NSGA-II calculation provides accurate and reliable outcomes compared to the MOPSO method, and the PV/WT/battery/ EV combination is the most suitable option among the five designed scenarios.