Optimal reconfiguration and capacitor placement for power loss reduction of distribution system using improved binary particle swarm optimization

Sedighizadeh et al. International Journal of Energy and Environmental Engineering 2014, 5:3 http://www.journal-ijeee.com/content/5/1/3 ORIGINAL RESEARCH Open Access Optimal reconfiguration and capacitor placement for power loss reduction of distribution system using improved binary particle swarm optimization Mostafa Sedighizadeh1*, Marzieh Dakhem2, Mohammad Sarvi2 and Hadi Hosseini Kordkheili3,4 Abstract Optimal reconfiguration and capacitor placement are used to reduce power losses and keep the voltage within its allowable interval in power distribution systems considering voltage, current, and radial condition constraints. It is needed to solve two nonlinear discrete optimization problems simultaneously, so an intelligent algorithm is used to reach an optimum solution for network power losses. An effective method and a new optimization algorithm using ‘improved binary PSO’ is presented and discussed to minimize power losses in distribution network by simultaneous network reconfiguration and capacitor placement. The proposed model uses binary strings which represent the state of the network switches and capacitors. The algorithm is applied and tested on 16- and 33-bus IEEE test systems to find the optimum configuration of the network with regard to power losses. Five different cases are considered, and the effectiveness of the proposed technique is also demonstrated with improvements in power loss reduction compared to other previously researched methods, through MATLAB under steady-state conditions. Keywords: Optimal capacitor placement; Optimal reconfiguration; Power loss reduction; Power distribution network; Improved binary particle swarm optimization Background A power distribution network consists of a group of radial feeders which can be connected together by several tie switches and tie lines. Power loss reduction in the network is a major concern of electric distribution utilities. Among conventional methods, optimal reconfiguration and capacitor placement are two effective methods which can be applied on the network. Optimization methods have been used to find the optimal location and size of different devices such as capacitors, FACTS, and distributed generations, in power distribution systems [1-3]. In this paper, optimization methods are used for optimal reconfiguration and capacitor allocation in power systems. Feeder reconfiguration is the process of changing the distribution network topology by changing the status of * Correspondence: 1 Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G. C., Tehran, 1983963113, Iran Full list of author information is available at the end of the article its switches. There are two kinds of switches based on their open or close conditions: normally open (N.O.) and normally close (N.C.). N.O. switches are considered as tie switches in the network. During a reconfiguration process, the status of these switches will be changed optimally based on the proposed objective function. Opening and closing of these switches can change the amount of power losses. On the other hand, capacitors are mostly used for reactive power compensation in distribution networks. They are also used for power loss reduction and improvement of voltage profiles. The advantages of this kind of compensation depend on how and where to place the capacitors in the network. In recent years, according to proper results of optimal capacitor placement and optimal distribution network reconfiguration for power loss reduction, the idea of using these two methods simultaneously has been considered to maximize the amount of power loss reduction. The combination of these approaches makes the optimization process more complex. © 2014 Sedighizadeh et al.; licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Sedighizadeh et al. International Journal of Energy and Environmental Engineering 2014, 5:3 http://www.journal-ijeee.com/content/5/1/3 Optimal capacitor placement and feeder reconfiguration have been investigated in many papers separately, and various approaches have been used with different objective functions to model the network and optimize the problem solution. In [4], the branch and bound heuristic method has been used to solve the reconfiguration problem with minimum power losses. In this method, all network switches become closed and will be opened one by one to obtain a new radial configuration. In [5], the problem of power loss reduction and load balancing reconfiguration is formulated as an integer programming method. The branch exchange heuristic method is proposed in [6]. In this method, power loss reduction is obtained by closing a switch and opening another one. In [7], an algorithm has been proposed to obtain switch patterns as a function of time. A load flow method based on a heuristic algorithm has been introduced in [8] to determine a configuration with minimal power losses. An implementation using a genetic algorithm (GA) has been proposed in [9]. An improved genetic algorithm based on fuzzy multi-objective approach has been suggested in [10] to solve the problem. In [11], a method based on binary particle swarm optimization (BPSO) algorithm is presented to balance network loads. In [12], the authors have used ant colony search algorithm (ACSA) to solve the reconfiguration problem. Moreover, they have performed a comparison which shows better results for ACSA compared to GA and simulated annealing (SA). A solution procedure employing simulated annealing is proposed in [13] and [14] to search for an acceptable noninferior solution. In [15], the network reconfiguration problem has been solved by a harmony search algorithm, and an optimal switching combination in the network is obtained which results in minimum power loss. Many investigations have also been performed to solve the optimal capacitor placement problem. Grainger and Lee [16-18] formulated the problem using a nonlinear programming model and solved it by simple iterative procedures based on gradient search. In [19] and [20], the capacitor placement problem is modeled as a mixed integer nonlinear programming problem and is solved using a decomposition method and a power flow algorithm. In [21], tabu search (TS) algorithm is used and a sensitivity analysis is performed to reduce the search space. It is Figure 1 A string of random typical particle. Page 2 of 11 shown that TS has a better performance compared to SA. In [22], the placement problem is solved together with finding the amount of proper capacitors. An approach using genetic algorithm and a new type of sensitivity analysis method is used. In [23], the implementation of integrated evolutionary algorithms is investigated for solving the capacitor placemen (...truncated)