Distribution Network Reconfiguration with Different Load Models using Adaptive Quantum inspired Evolutionary Algorithm

Abstract

Distribution network has high power losses and poor voltage regulation as compared with transmission system due to high admittance ratio. Reconfiguration is one of the methods used in distribution system to reduce the power losses. Distribution networks are primarily designed in meshed structure, under normal operating conditions they are operated in radial configuration. Two types of switches viz., sectionalizing and tie line switches are normally used in distribution system. The operational performance of distribution system can be improved by changing the functional links between the switches or branches. Service restoration, improvement in voltage profile, load balancing and reduction in power loss are some of major advantages produced by reconfiguration. Since many switching combinations are possible, the optimal switching configuration of a power distribution network is a difficult non linear combinatorial optimization problem. In this paper, an Adaptive Quantum inspired Evolutionary Algorithm (AQiEA) approach is used to determine the optimal switching configuration of a power distribution network. Voltage dependent load models viz., constant current load, constant impedance and constant power load are used to reduce the power losses with reconfiguration. The main criterion addressed in this paper is minimizing the active power loss with network reconfiguration for different load models. A class of mix load model is also considered. Comparative study is performed on IEEE 33 bus system which demonstrates the performance, accuracy and effectiveness of the proposed algorithm.