TY - JOUR
T1 - A PSO-BPSO technique for hybrid power generation system sizing
AU - Llerena-Pizarro, Omar
AU - Proenza-Perez, Nestor
AU - Tuna, Celso Eduardo
AU - Silveira, Jose Luz
N1 - Publisher Copyright:
© 2003-2012 IEEE.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - The Particle Swarm Optimization (PSO) algorithm has been widely used in the field of optimization mainly due to its easy implementation, robustness, fast convergence, and low computational cost. However, due to its continuous nature, the PSO cannot be applied directly to real-life problems such as hybrid energy generating systems (HEGS) sizing, which contain continuous and discrete decision variables. In this context, the present work proposes the combination of the original version of the PSO with the binary version of the same algorithm (BPSO) for the sizing of HEGS. The transfer function is the main difference between these two algorithms. In this paper, an S-type transfer function is used to map the continuous space into a discrete space. All components of the HEGS are modeled and simulated during the optimization process. The net present value is defined as the unique objective function. The state of charge (SOC) of the batteries is the main constraint. The proposed PSO-BPSO is used for sizing hybrid power generating systems in the Galapagos Islands in Ecuador. Results show that the best configuration for the studied case is a hybrid system with solar panels, batteries, and diesel generators. Configurations that contain only photovoltaic panels and batteries imply a higher cost due to the oversizing of the battery bank. The proposed PSO-BPSO algorithm revealed to be a simple and powerful tool for efficient energy systems sizing.
AB - The Particle Swarm Optimization (PSO) algorithm has been widely used in the field of optimization mainly due to its easy implementation, robustness, fast convergence, and low computational cost. However, due to its continuous nature, the PSO cannot be applied directly to real-life problems such as hybrid energy generating systems (HEGS) sizing, which contain continuous and discrete decision variables. In this context, the present work proposes the combination of the original version of the PSO with the binary version of the same algorithm (BPSO) for the sizing of HEGS. The transfer function is the main difference between these two algorithms. In this paper, an S-type transfer function is used to map the continuous space into a discrete space. All components of the HEGS are modeled and simulated during the optimization process. The net present value is defined as the unique objective function. The state of charge (SOC) of the batteries is the main constraint. The proposed PSO-BPSO is used for sizing hybrid power generating systems in the Galapagos Islands in Ecuador. Results show that the best configuration for the studied case is a hybrid system with solar panels, batteries, and diesel generators. Configurations that contain only photovoltaic panels and batteries imply a higher cost due to the oversizing of the battery bank. The proposed PSO-BPSO algorithm revealed to be a simple and powerful tool for efficient energy systems sizing.
KW - Hybrid generation energy systems
KW - Mathematical modeling
KW - Optimal sizing
KW - PSO-BPSO
UR - http://www.scopus.com/inward/record.url?scp=85086462287&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/dc87da97-5c5e-3d2e-a216-8ef1f6483e0a/
U2 - 10.1109/TLA.2020.9111671
DO - 10.1109/TLA.2020.9111671
M3 - Article
AN - SCOPUS:85086462287
SN - 1548-0992
VL - 18
SP - 1362
EP - 1370
JO - Ieee Latin America Transactions
JF - Ieee Latin America Transactions
IS - 8
M1 - 9111671
ER -