Optimal network reconfiguration in distribution system for loss reduction and voltage-profile improvement using hybrid algorithm of PSO and ACO

Abstract

Considering time varying nature of load in conventional distribution system, network reconfiguration is combinatorial complex optimization problem. In this paper, a hybrid configuration of particle swarm optimization (PSO) method with ant colony optimization (ACO) algorithm called hybrid PSO-ACO algorithm is presented for optimal network reconfiguration in distribution network in presence of distributed generation (DG) resources. The objectives consist of minimizing power losses and improving the voltage profile. MATLAB based simulation conducted on 33-bus IEEE test feeder to verify the effectiveness of proposed method. Results demonstrate accuracy and efficiency of PSO-ACO algorithm.

Heuristic optimization techniques for voltage stability enhancement of radial distribution network with simultaneous consideration of network reconfiguration and DG sizing and allocations

“…base case (without reconfiguration and DG allocation and sizing), reconfiguration only, DG allocation only, DG allocation 37 33,34,35,36,37 33,34,35,36,37 33,34,35,36,37 33,34,35,36,37 33,34,35,36,37 DG (MW) (Bus No.) 0.8044 (14) 0.9142 (12) 0.8049 (14) 0.7628 (14) 0.7798 (14) 0.5897 (14) 0.1070 181.1063 241.2001 (30) 1.1059 241.1072 241.1251 (24) 0.1895 (18) 0.5724 171.3917(30) 291.287 291.2024 (29) 0.6158 (29) 0.5586 (31) 0.4173 (32) 0.7602 (18) 0.8463 (18) 0.765 (18) 0.7127 (18) 0.5315 (18) 0.5840 (33) Power 28 6,14,10,32,37 7,13,11,32,27 33,13,9,28,30 33,34,11,31 after reconfiguration, reconfiguration after DG allocation, simultaneous reconfiguration and DG sizing, and simultaneous reconfiguration and DG allocation and sizing were considered. The optimization problem was formulated with multiple objectives of minimization of power loss along with maximization of system VSI and was evaluated for IEEE 33-and 69-bus radial distribution networks.…”

“…In [10][11][12], ant colony optimization (ACO) is proposed to solve feeder reconfiguration problem with the objective to minimize power loss in the network. In [13,14], hybrid algorithm based on PSO and ACO was used to solve feeder reconfiguration. Recently, other population-based metaheuristic optimizations, such as simulated annealing (SA) [15], hybrid algorithm based on SA and Tabu search [16], enhanced gravitational search algorithm (EGSA) [17], runner root algorithm [18], genetic algorithm (GA) [19][20][21], discrete firefly algorithm [22], modified plant growth simulation algorithm [23], and fuzzy firefly algorithm [24], have been applied to the network reconfiguration problem.…”

Dynamic Robust Reconfiguration of Distribution Network with Low-wind-speed Wind Turbine Integrated

“…The main difference is that the dynamic distribution network reconfiguration (DDNR) takes into consideration the time-varying nature of loads [1]. Some researches have been done on static distribution network reconfiguration (SDNR) from the perspective of strategy improvement and solving algorithm [2][3][4][5][6][7][8][9]. However, with the loads of distribution network varying, SDNR cannot guarantee the optimal operation economy of the network since only a single time section is considered.…”

Reconfiguração de Redes de Distribuição de energia elétrica utilizando unineuron e nullneuron

“…Entre os métodos Heurístico e Metaheurísticos mais aplicados a problemas de otimização para reconfiguração de redes de distribuição, destacam-se, Branch and Bound (Merlin and Back, 1975;Romano et al, 2013) e Branch Exchange (Civanlar et al, 1988; Baran and Wu, 1989;Peponis and Papadopoulos, 1997;Bernardon et al, 2009;Junior, 2014) como métodos Heurísticos e PSO (Particle Swarm Optmization) (Tofis et al, 2017;Macedo, 2012;Atteya et al, 2017), Inteligência Artificial (Pandiarajan and Babulal, 2011;Bernardon et al, 2009;Berredo et al, 2011;Neto and Vellasco, 2016) e Colônia de formigas (Amin Heidari, 2017;Silva, 2010;Ghorbani et al, 2008) como métodos Metaheuríscos.…”