SMART RECONFIGURATION OF DISTRIBUTION NETWORKS HANDLING DG PENETRATION FOR POWER LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT

Abstract

Power loss minimization and voltage stability improvement are important areas of power systems due to existing transmission line contingency, financial loss of utility and power system blackouts. Distribution network reconfiguration (DNR) can significantly reduce power losses, improve the voltage profile, and increase the power quality. DNR studies require implementation of the power flow analysis and complex optimization procedures capable of handling large combinatorial problems. In addition, optimal allocation (i.e. siting, sizing, and operating power factor) of Distributed Generation (DG) is one of the best ways to strengthen the efficiency of power system along with network reconfiguration. Power system operators and researchers put forward their efforts to solve the distribution system problem related to power loss, energy loss, voltage profile, and voltage stability based on optimal DG allocation. Furthermore, optimal DG allocation secures distribution system from unwanted events and allows the operator to run the system in islanding mode. The size of the distribution network influences the type of the optimization method to be applied. In particular, straight forward approaches can be computationally expensive or even prohibitive whereas heuristic or metaheuristic approaches can yield acceptable results with less computation cost. In the problems like DNR, there is extensive search procedure involved in finding the optimum solution. In addition, the solution improves in various stages of search procedure and in each iteration. In the optimization problems like the one in this thesis work, large number of variables have to be optimized. Distribution network reconfiguration and placement of DGs involves fourteen variables when five disconnecting switches and three DGs are considered – five for the disconnecting switches, three variables for the placement of DGs, three variables for the sizing of DGs, and three variables for the optimum power factor of DGs. Only the most efficient algorithms are able to find the optimum solution in minimum iteration and minimum time. Artificial Bee Colony (ABC) algorithm has been used in this thesis work as it is very easy to implement and efficient in finding optimum solution when compared to other popular metaheuristic algorithms like Genetic Algorithm, and Particle Swarm Optimization Algorithm.