A new approach to solve the distribution system planning problem considering automatic reclosers

Abstract

This article describes two new methodologies for optimal distribution system planning considering automatic reclosers to improve reliability indices. The first methodology proposed in this paper employs a two-stage cascade strategy to solve the problem. In the first stage a mono-objective Tabu Search (TS) algorithm is used to find the optimal distribution system topology. The installation cost of new elements, increasing capacity on existing elements and operational costs associated with technical losses are considered. In the second stage a Non-Dominated Sorting Genetic Algorithm known as NSGA II is employed, considering the optimal distribution system topology found by the first stage, and thus, solving the problem of optimal location of automatic reclosers in distribution systems. The non-supplied energy level of the network and installation cost of reclosers are considered as objective functions; and the min-max criterion to select a point on the optimal Pareto front is used. In the second methodology, a master-slave based optimization algorithm is used to find the best network topology including the associated to automatic reclosers. Master algorithm is a TS algorithm which is responsible to direct the optimization process through solution space, whereby, for each neighbor generated, the slave algorithm NSGA II is applied and min-max criterion is used to determine a configuration of reclosers for actual network topology. The proposed methodologies are applied on three-phase version of IEEE 34 nodes test feeder, with high quality results.Keywords: Distribution systems, min-max criterion, multi-objective optimization, NSGA-II Algorithm, reclosers, reliability. RESUMEN

On the Efficiency in Electrical Networks with AC and DC Operation Technologies: A Comparative Study at the Distribution Stage

“…Presently electrical distribution networks are essential systems in economic development around the word [1,2]; these grids are also responsible for distributing energy from large-scale power systems to all end users at medium and low voltage levels [3], which implies that in terms of size, the distribution networks are the lengthiest infrastructure inside of the power system [4,5]. This is important since higher losses can be presented at distribution networks in comparison to power systems (transmission and sub-transmission networks), e.g., in the Colombian context, energy losses at distribution networks can be between 6% and 18% while losses at transmission networks can be between 1% and 2% [6].…”

“…Regarding AC distribution networks multiple works related with power system planning and operation have been proposed in scientific literature. Some of these works are: optimal reconfiguration of distribution networks [17][18][19], optimal location of shunt capacitor banks and distributed generators [20][21][22], optimal selection of wire gauges in radial distribution networks [23][24][25], optimal location and coordination of protective devices [26][27][28], optimal location and operation of battery energy storage systems [7,14,29], and optimal planning of AC distribution networks including new substations [5,30].…”

Improved Genetic Algorithm for Phase-Balancing in Three-Phase Distribution Networks: A Master-Slave Optimization Approach

“…As indicated by existing literature, the problem of phase balancing has been solved using multiple optimization approaches, including the classical Chu and Beasley genetic algorithms [12][13][14][15]; tabu search algorithm [16,17]; ant colony optimization [18,19]; immune optimization algorithm [20]; branch and bound and convex methods [4,21]; bat optimization algorithm [22]; vortex search algorithm [2]; particle swarm optimization methods [8,23,24]; differential evolution algorithm [25]; and simulated annealing optimization method [26]. The main characteristics of these approaches are the hybridization of metaheuristic discrete optimization methods (with binary and integer codifications) with three-phase power flow methods, which are typically based on sweep iterative backward/forward power flow methods, and the minimization of the amount of power loss for a particular load condition, which typically corresponds to the peak load condition.…”

Heuristic Methodology for Planning AC Rural Medium-Voltage Distribution Grids

“…Numerical results were tested in a distribution network composed of 54 buses; however, the authors considered the single-phase equivalent of the network in the formulation, and they did not compare with exact or metaheuristic approaches to validate their proposal. In [9], the authors presented an optimization methodology for planning distribution networks using a three-phase representation of the network by combining a genetic algorithm with the tabu search algorithm (TSA). The reported numerical results demonstrated the efficiency of the TSA approach in determining the set of calibers that must be assigned to the network once the grid topology is defined through a heuristic graph generator.…”

“…In order to contribute beyond the previous studies available in the literature, we propose an easily implementable optimization algorithm to plan rural distribution grids that is composed of three stages and that can be applied to single-phase or three-phase distribution grids [9]. The first stage solves the problem of route selection by formulating the problem of minimum spanning tree (linear integer programming model) for minimizing the total length of the conductors [11,12].…”