TY - JOUR
T1 - Designing, optimizing and comparing distributed generation technologies as a substitute system for reducing life cycle costs, CO2 emissions, and power losses in residential buildings
AU - Sadeghi, Delnia
AU - Ahmadi, Seyed Ehsan
AU - Amiri, Nima
AU - Satinder,
AU - Marzband, Mousa
AU - Abusorrah, Abdullah
AU - Rawa, Muhyaddin
N1 - Funding information: The authors acknowledge the support provided by King Abdullah City for Atomic and Renewable Energy (K.A.CARE) under K.A.CARE-King Abdulaziz University Collaboration Program. The authors are also thankful to Deanship of Scientific Research, King Abdulaziz University for providing financial support vide grant number (RG-37-135-42).
PY - 2022/8/15
Y1 - 2022/8/15
N2 - 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.
AB - 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.
KW - demand response program
KW - electric vehicle
KW - Taguchi method
KW - Monte Carlo simulation
KW - Multi-objective particle swarm optimization
KW - non-dominated sorting genetic algorithm II
U2 - 10.1016/j.energy.2022.123947
DO - 10.1016/j.energy.2022.123947
M3 - Article
SN - 0360-5442
VL - 253
JO - Energy
JF - Energy
M1 - 123947
ER -
