TY - GEN
T1 - Multi-objective optimization of system configuration and component capacity in AC mini-grid hybrid power system
AU - Mohammad-Alikhani, Arta
AU - Mahmoudi, Amin
AU - Kahourzade, Solmaz
PY - 2020/12/16
Y1 - 2020/12/16
N2 - This paper optimally determines the configuration of the system, as well as each component capacity, in order to design a cost-effective and environment-friendly AC mini-grid hybrid power system for a remote area in South Australia. To this purpose, a modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied. In each iteration of the modified proposed MOPSO, first, renewable power sources, including photovoltaic arrays and wind turbines along with energy storage components, are selected to form a hybrid power system along with the diesel generator. Then, the capacities of the components are optimized based on two objective functions, including Levelized Cost of Energy (LCOE) and CO2 emission, by analyzing the system using real data in hourly time-step for the load, solar insolation, ambient temperature, and wind speed over a 20-year timespan. Afterward, positions for system configuration and component capacities are updated according to the objective functions.
AB - This paper optimally determines the configuration of the system, as well as each component capacity, in order to design a cost-effective and environment-friendly AC mini-grid hybrid power system for a remote area in South Australia. To this purpose, a modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied. In each iteration of the modified proposed MOPSO, first, renewable power sources, including photovoltaic arrays and wind turbines along with energy storage components, are selected to form a hybrid power system along with the diesel generator. Then, the capacities of the components are optimized based on two objective functions, including Levelized Cost of Energy (LCOE) and CO2 emission, by analyzing the system using real data in hourly time-step for the load, solar insolation, ambient temperature, and wind speed over a 20-year timespan. Afterward, positions for system configuration and component capacities are updated according to the objective functions.
KW - Component capacity
KW - Hybrid power system
KW - Multi-objective optimization
KW - System Configuration
UR - http://www.scopus.com/inward/record.url?scp=85103918742&partnerID=8YFLogxK
U2 - 10.1109/PEDES49360.2020.9379526
DO - 10.1109/PEDES49360.2020.9379526
M3 - Conference contribution
AN - SCOPUS:85103918742
T3 - 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
BT - 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
PB - Institute of Electrical and Electronics Engineers
CY - Jaipur, India
T2 - 9th IEEE International Conference on Power Electronics, Drives and Energy Systems
Y2 - 16 December 2020 through 19 December 2020
ER -
