Radial distribution network reconfiguration for power losses reduction using a modified particle swarm optimisation

Abstract

Recently, losses reduction gained a great deal of attention in distribution system due to low-voltage level and the high-current passing through the lines, pushing the distribution utilities to improve their profit margins on one hand by reducing the unnecessary operational cost, and improving their delivered power quality on the other hand by maintaining the system reliability, and the continuity of supply for varying load demand. Load balancing, voltage regulation, network reconfiguration and others are different techniques used to reduce the losses. This study addresses the distribution network reconfiguration to minimise the network losses. A new modified form of particle swarm optimisation (PSO) is used to identify the optimal configuration of distribution network effectively. The difference between the modified PSO (MPSO) algorithms and the typical one is the filtered random selective search space for initial position, which is proposed to accelerate the algorithm for reaching the optimum solution. The suggested MPSO is tested via 33 and 69 IEEE networks. A benchmark comparison has been conducted to prove the effectiveness of MPSO compared with previous optimisation techniques. 3 Networks description 3.1 33-Bus test distribution system The 33 IEEE network, 12.6 kV, as shown in Fig. 1, consists of 37 branches, 32 normally closed switches (sectionalising switches) and 5 normally open switches (tie line switches). Interactive power 24th International Conference & Exhibition on Electricity Distribution (CIRED)

Augmentation of situational awareness by fault passage indicators in distribution network incorporating network reconfiguration

“…To limit these losses, the status of normally closed sectionalizing switches and normally opened tie switches is modified by keeping up the radial topology of the system. This is called the reconfiguration of the system [22]. The merits of system reconfiguration are load balancing, minimizing power losses, improvement of voltage profile, enhancing power system security & reliability and upgrade of power quality [23].…”

Optimal Network Reconfiguration and DG Integration in Power Distribution Systems Using Enhanced Water Cycle Algorithm

“…These researchers have believed that other methods such as capacitor placement and cable size upgrading might add more additional cost burdens to the distribution system utilities. In [4][5][6][7], the system losses and voltage profile are improved by formulating the network reconfiguration problem as a mixed-integer nonlinear optimization problem using various metaheuristic algorithms with considering operational constraints. Due to the rapid growth of the integration of the distributed generations (DGs) into the distribution system, the use of the network reconfiguration technique without considering the presence of the DGs is no longer applicable.…”

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.…”

“…O método foi aplicado por (Tofis et al, 2017;Macedo, 2012;Ghorbani et al, 2008) para reconfiguração de uma rede de distribuição de energia elétrica e apresentou a desvantagem de considerar apenas uma função objetivo. Atteya et al (2017) propôs também uma modificação do PSO (MPSO), onde a diferença entre ele e o o típico PSO está no espaço de busca seletiva aleatório filtrado para posição inicial, queé proposto para acelerar o algoritmo para alcançar a soluçãoótima. Silva (2010), utiliza um novo algoritmo baseado na estrutura do método de otimização por colônia de formigas (ACO) para reconfiguração do sistema de distribuição.…”