Reconfiguration of distribution power system using Evolutionary Algorithm and Branch exchange method for Power Loss Reduction Messaoud Belazzoug, Karim Sebaa Abstract: In this paper, we use an Evolutionary Algorithm (EA) and the branch exchange method to solve the optimal reconfiguration in radial distribution systems for power loss reduction that determine the optimal switches. The EA is a relatively powerful intelligence evolution method for solving optimization problems. It is a population based approach that is inspired from natural behaviour of species. In this paper EA is applied to a realistic distribution system (106 buses) located in the Medea city (Algeria). For the comparison purposes, our method is validated with the classical Branch and Bound (BB) method, widely used by the Distribution Companies. The results confirm the superiority of the EA. Keywords: Distribution power system, Branch and Bound, Evolutionary Algorithm. I. INTRODUCTION In the Distribution Power System (DPS); to reduce the losses and improving the voltage profile the reconfiguration of DPS is an alternative way which doesn’t require any investment. This operation must be considered when planning the operation due to the high variable costs associated with these systems [1]. Messaoud Belazzoug is with the LABSET, Department of electronic, University of Blida, Algeria Karim Sebaa is with the Department of Electrical Engineering, University of Medea, Algeria The problem of reconfiguration involves the definition of the states (open or closed) of the maneuverable switches attached to certain sections of the distribution network [2]. In the MV PDS these devices include (i) sectionalizing or normally closed (NC) switches and (ii) tie or normally open (NO) switches. This option is used to determine a radial network topology that minimizes losses and voltage deviations [3]. As this problem includes combinatorial variable (0 and 1) with nonlinear objective function and constrained, models of integer nonlinear programming (INLP) are used. These models must consider the integer nature of the problem because the number of possible solutions grows exponentially with the number of discrete variables [4]. Also, the radial and connected topographies of the DPS present additional complexity for the solution techniques. The heuristic-based methods have been proposed [5–11] in order to reduce the search space associated with reconfiguration problem. The use of meta-heuristics for INLP problems gives a well exploration of the search space. These algorithms permit the transition between local optima of the feasible region, as well as a more focused search in each subspace. Algorithms based on meta-heuristics, such as Genetic Algorithms [12–16], Simulated Annealing [17,18], Artificial Ant Colony [19] and Tabu Search [20,21], have been used to solve the problem of EDS reconfiguration. With the same purpose in mind, Ref. [22] presents an algorithm based on Artificial Immune Systems to reduce active power losses. In [23], a method based on the bacterial foraging optimization algorithm is INTERNATIONAL JOURNAL OF SYSTEMS APPLICATIONS, ENGINEERING & DEVELOPMENT Volume 12, 2018 ISSN: 2074-1308 102