Optimal Distribution Feeder Reconfiguration with Distributed Generation Using Intelligent Techniques

Feeder reconfiguration is performed by changing the open/close status of switches. Primary distribution networks contain two types of switches, known as tie switches (normally open) and sectionalizing switches (normally closed). These switches are designed for both protection and configuration purposes. A whole feeder or part of a feeder may be served from another feeder by closing a tie switch linking the two while an appropriate sectionalizing switch must be opened to maintain the radial structure of the system. In this paper the problem is formulated as a multi-objective problem considering four objectives related to minimization of the system power loss, minimization of the deviations of the nodes voltage, minimization of branch current violation and minimization of feeder's currents imbalance. Since these objectives are different and difficult to be solved by the conventional approaches that may optimize only a single objective. A new approach, based on the Differential Evolution algorithm (DE), is presented, allowing the topological and electrical constraints to be satisfied. DE is a well-known and uncomplicated population based probabilistic approach for comprehensive optimization. This paper also aims to include the effects of Distributor Generators (DGs) outputs. The validity and effectiveness of the proposed algorithm is demonstrated using two different test systems, 16 and 33-bus distribution network having three substations and one substation respectively. The proposed algorithm is also compared to some alternative methods such as Genetic Algorithm.