Distribution Network Reconfiguration Using Selective Firefly Algorithm and a Load Flow Analysis Criterion for Reducing the Search Space

Abstract

This paper proposes an alternative to solve the distribution network reconfiguration (DNR) problem, aiming real power losses' minimization. For being a problem that has complexity for its solution, approximate techniques are adequate for solving it. Here, the proposition is a technique based on the firefly metaheuristic, named selective firefly algorithm, where the positioning of these insects is compressed in a selective range of values. The algorithm is applied to the DNR, and all its implementation and adequacy to the problem studied are presented. To define the search space, the methodology presented initially considers a set of candidate switches for opening based on the studied systems' mesh analysis. To reduce these possibilities, a refinement through a load flow analysis criterion (LFAC) is proposed. This LFAC considers the real power losses on each branch for a configuration with all switches closed, then, selecting possible switches to elimination from the set previously established. To demonstrate the behavior and the viability of the LFAC, it was initially applied on a 5 buses' and 7 branches' system. Also, to avoid getting stuck on results that may be considered not good, a disturbance resetting the population is set to occur every time a counter reaches a pre-defined number of times that the best solution does not change. Results found for simulations with 33, 70, and 84 buses are presented and comparisons with selective particle swarm optimization (SPSO) and selective bat algorithm (SBAT) are made.

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

“…This algorithm is inspired from the natural courtship signal transfer exhibited by the fireflies wherein a firefly with maximum brightness attracts other fireflies the most regardless of their sex . FAs are proposed to solve optimal DGs allocation problem by minimising active and reactive power losses and improving line loading . Nadhir et al differently used FA for finding optimal sizes and locations of multiple DGs on a balanced radial network aimed at minimising power loss.…”

“…217 FAs are proposed to solve optimal DGs allocation problem by minimising active and reactive power losses and improving line loading. 218,219 Nadhir et al 220,221 differently used FA for finding optimal sizes and locations of multiple DGs on a balanced radial network aimed at minimising power loss. Othman et al 222 modified the traditional FA to efficiently solve constrained optimisation problems.…”

Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

“…These may result in significant power losses in distribution systems, which may cost electric utilities millions of dollars per day. Not only that, but also poor service quality may cause severe damage to many modern electrical devices due to its sensitivity to voltage variations [4]. In such stressed circumstances, the distribution systems planners and operators must face such challenges and provide quantitative as well as qualitative power service to satisfy consumers' satisfaction and reduce dissipated energy.…”

“…A number of meta-heuristic techniques has been introduced in literatures such as; one rank cuckoo search algorithm [27], which is formulated with multi-objectives of power loss minimization, voltage deviation minimization and voltage stability improvement; grey wolf optimizer [28]; stochastic fractal search algorithm [29]; particle swarm optimization [7]; multi-objective chaotic differential evolution [30]. As well, numerous meta-heuristic techniques have been introduced for DNR to enhance the distribution system performance such as; fireworks algorithm [31]; cuckoo search algorithm [32]; genetic algorithm with varying population [33], [34]; harmony search algorithm [6]; selective firefly algorithm [4]. Combining CBs, DGs and DNR with each other greatly improves the performance of distribution systems, but in return the number of control variables is increased, which increases the complexity of this problem.…”

Optimal Allocation of Renewable Energy Hybrid Distributed Generations for Small-Signal Stability Enhancement

“…Intelligent search (IS) based methods are differently employed to solve the optimal sizing and placement of DGs problems. IS methods utilize artificial intelligence (AI) algorithms like the genetic algorithm (GA) [21,22], particle swarm optimization (PSO) [23,24], simulated annealing (SA) [25][26][27], harmony search (HS) [28,29], big bang crunch (BBC) [30,31], the fireworks algorithm (FA) [32,33], and the water drop algorithm (WDA) [34,35].…”