A Comprehensive Review of Metaheuristic Methods for the Reconfiguration of Electric Power Distribution Systems and Comparison With a Novel Approach Based on Efficient Genetic Algorithm

Abstract

The distribution system reconfiguration (DSR) is a complex large-scale optimization problem, which is usually formulated with one or more objective functions and should satisfy multiple sets of linear and non-linear constraints. As the exploration of feasible solutions in large and nonconvex search space of DSR is typically hard, it is important to develop efficient algorithms and methods for finding optimal solutions for DSR problem in reasonably short computational times. In traditional DSR, the configuration of distribution network can be changed by opening and closing sectional and tie switches, where active power losses are minimized, while radial network configuration and supply to all connected loads are both preserved. Accordingly, this paper provides a comprehensive review of a number of existing metaheuristic reconfiguration methods and introduces a novel efficient genetic algorithm (efficient GA) for DSR with loss minimization. In order to demonstrate benefits and effectiveness of the proposed efficient GA for DSR, the paper also provides a detailed comparison of results with an improved genetic algorithm (improved GA) for several test systems and real distribution networks. The obtained simulation results clearly show higher accuracy and improved convergence performance of the proposed efficient GA method, compared to the improved GA and other considered reconfiguration methods.

Multi-Objective Optimization for Distribution Network Reconfiguration With Reactive Power Optimization of New Energy and EVs

“…Researchers in different regions have had different understandings of DNR over time, and performed numerous meaningful viewpoints in modeling and optimization, as discussed below. From the perspective of the solution procedure, literature [14] employed an efficient Genetic Algorithm (GA) to accelerate the calculation and show higher accuracy in DNR. In [15], researchers proposed an improved Particle Swarm Optimization (PSO) which involved a new acceleration coefficient.…”

A Novel Radiality Maintenance Algorithm for the Metaheuristic Based Co-Optimization of Network Reconfiguration With Battery Storage

“…In addition, the feasible search space of the ONR problem is nonconvex due to the AC load flow constraints and binary nature of the switches [5]. Thus, finding the optimal switching combination subjected to different DN constraints is a challenging and computationally intensive task that places ONR in the category of combinato-rial NP-hard mixed-integer nonlinear programming (MINLP) problems [6]. The non-deterministic polynomial-time (NP) problems are those whose solution can be verified in polynomial time but not necessarily be solved in polynomial time [7].…”

“…The normal practice in the available literature is to discard the non-radial generated topologies during the update process and temporarily stop the update process until the new radial topologies are generated [6]. For example, reference [20] restored the radiality of DN by opening the switches of the DN loops one by one until a new radial configuration is found.…”

“…For instance, references [22] and [23] maintain the radiality of the DN by discarding the newly formed non-radial topologies and continuing the update process with the previously ob-tained radial topologies, which may cause a significant delay in the solution convergence. In [6], an attempt was made to avoid the generation of non-radial configurations during the update process using a heuristic technique combined with the genetic algorithm. However, the proposed technique was not generic as it cannot be seamlessly integrated into other metaheuristic algorithms to solve the ONR problem.…”

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

“…DG sources are modeled as negative load, and its effect on power flow is considered as voltage constraints. So far, meta-heuristic algorithms with various efficiency and accuracy and with different objective functions have been presented for the reconfiguration and placement problem of DG such as GA (Saonerkar and Bagde, 2014;Ajmal et al, 2021;Mahdavi et al, 2021), PSO (Jena and Chauhan, 2016;Saleh et al, 2018;Rafi and Dhal, 2020b), SFLA (Arandian et al, 2014;Azizivahed et al, 2017;Onlam et al, 2019), fuzzy (Sedighizadeh and Bakhtiary, 2016;Mohammadi et al, 2017;Hosseinimoghadam et al, 2020), and ABC (Jamian et al, 2014;Quadri and Bhowmick, 2020;Dashtdar et al, 2022c).…”