TY - JOUR
T1 - Optimal Placement of Non-Site Specific DG for Voltage Profile Improvement and Energy Savings in Radial Distribution Networks
AU - Funsho Akorede, Mudathir
AU - Pouresmaeil, Edris
AU - Hizam, Hashim
AU - Aris, Ishak
AU - Zainal Ab-Kadir, Mohd
PY - 2014
Y1 - 2014
N2 - This paper proposes a model based on Fuzzy Genetic Algorithm (FGA) to determine the optimal capacity and location of a DG unit in a radial distribution network. In the FGA, a fuzzy controller is integrated into GA to adjust the crossover and mutation rates dynamically to maintain the proper population diversity during GA’s operation. This effectively overcomes the premature convergence problem of the simple genetic algorithm (SGA). The main objective functions considered in this study are maximisation of cost savings arising from energy loss, minimisation of voltage drops across all lines, and maximisation of the transfer capability of the system. The model takes into account the peculiarities of radial distribution networks, such as high R/X ratio, voltage dependency and composite nature of loads. The proposed model is evaluated on three radial test distribution systems, and the results obtained are very impressive, with high computational efficiency, when compared with those of the existing approaches cited in the literature.
AB - This paper proposes a model based on Fuzzy Genetic Algorithm (FGA) to determine the optimal capacity and location of a DG unit in a radial distribution network. In the FGA, a fuzzy controller is integrated into GA to adjust the crossover and mutation rates dynamically to maintain the proper population diversity during GA’s operation. This effectively overcomes the premature convergence problem of the simple genetic algorithm (SGA). The main objective functions considered in this study are maximisation of cost savings arising from energy loss, minimisation of voltage drops across all lines, and maximisation of the transfer capability of the system. The model takes into account the peculiarities of radial distribution networks, such as high R/X ratio, voltage dependency and composite nature of loads. The proposed model is evaluated on three radial test distribution systems, and the results obtained are very impressive, with high computational efficiency, when compared with those of the existing approaches cited in the literature.
KW - Distributed generation
KW - Fuzzy genetic algorithm
KW - Radial distribution systems
KW - Voltage profile
U2 - 10.15598/aeee.v12i5.1080
DO - 10.15598/aeee.v12i5.1080
M3 - Journal article
VL - 12
SP - 392
EP - 406
JO - Advances in Electrical and Electronic Engineering (Online)
JF - Advances in Electrical and Electronic Engineering (Online)
SN - 1804-3119
IS - 5
ER -