TY - JOUR
T1 - Leveraging PSO algorithms to achieve optimal stand-alone microgrid performance with a focus on battery lifetime
AU - Kusuma, Vicky Andria
AU - Firdaus, Aji Akbar
AU - Suprapto, Sena Sukmananda
AU - Putra, Dimas Fajar Uman
AU - Prasetyo, Yuli
AU - Filliana, Firillia
N1 - Publisher Copyright:
© 2023, Intelektual Pustaka Media Utama. All rights reserved.
PY - 2023/9
Y1 - 2023/9
N2 - This research endeavors to increase the lifespan of a battery utilized in a standalone microgrid system, a self-sufficient electrical system that consists of multiple generators that are not connected to the main power grid. This type of system is ideal for use in remote locations or areas where the grid connection is not possible. The sources of energy for this system include photovoltaic panels, wind turbines, diesel generators, and batteries. The state of charge (SOC) of the battery is used to determine the amount of energy stored in it. The particle swarm optimization (PSO) method is applied to minimize energy generation costs and maximize battery life. The results show that battery optimization can decrease energy generation costs from Rp 5,271,523.03 ($338.64 in USD) to Rp 13,064,979.20 ($839.30 in USD) while increasing the battery's lifespan by 0.42%, with losses of 7.22 kW and 433.29 kVAR, and also a life loss cost of Rp 5,499/$0.35.
AB - This research endeavors to increase the lifespan of a battery utilized in a standalone microgrid system, a self-sufficient electrical system that consists of multiple generators that are not connected to the main power grid. This type of system is ideal for use in remote locations or areas where the grid connection is not possible. The sources of energy for this system include photovoltaic panels, wind turbines, diesel generators, and batteries. The state of charge (SOC) of the battery is used to determine the amount of energy stored in it. The particle swarm optimization (PSO) method is applied to minimize energy generation costs and maximize battery life. The results show that battery optimization can decrease energy generation costs from Rp 5,271,523.03 ($338.64 in USD) to Rp 13,064,979.20 ($839.30 in USD) while increasing the battery's lifespan by 0.42%, with losses of 7.22 kW and 433.29 kVAR, and also a life loss cost of Rp 5,499/$0.35.
KW - Battery management
KW - Cost-effectiveness
KW - Lifetime
KW - Optimization
KW - PSO algorithm
UR - http://www.scopus.com/inward/record.url?scp=85167787198&partnerID=8YFLogxK
U2 - 10.11591/ijape.v12.i3.pp293-299
DO - 10.11591/ijape.v12.i3.pp293-299
M3 - Article
AN - SCOPUS:85167787198
SN - 2252-8792
VL - 12
SP - 293
EP - 299
JO - International Journal of Applied Power Engineering
JF - International Journal of Applied Power Engineering
IS - 3
ER -