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<p>The electrical distribution network is a critical and complex system in terms of safety and reliability, because it is composed of different components (switches, reclosers, etc.). The improvement of its reliability is therefore one of the most important tasks through the good management of remote-controlled switches and reclosers in this network. This paper presents an analytical model based on graph theory to evaluate SAIDI and SAIFI indices based on the network architecture and the location of remote-controlled reclosers and switches. These indicators have been used to formalize a multi-objective mathematical model that respects the real operation constraints of equipments in smart grid. The applied model, in this article, was evaluated on an IEEE 13 bus network using the TOPSIS method to determine the optimal location of the switches and reclosers and to improve the overall reliability of the distribution network.</p>

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<p>Since they are fast, remote-controlled, automated and intelligent, reclosers<br />and switches are an inevitable solution for improving the reliability of<br />intelligent electrical distribution networks at optimal cost. However, their<br />location and coordination have great effects on the protection and automation<br />strategies of complex electrical distribution networks against multiple<br />unpredictable faults. Which requires a flexible and multi-criteria optimization<br />method. In this article, we propose a multi-objective method based on an<br />analytical model by considering the fault rate, restoration times, outage cost<br />and coordination between devices. The non-dominated genetic sorting<br />algorithm II was proposed to obtain the optimal Pareto solutions, and a<br />technique of performance control by similarity with the ideal solution was<br />used to classify them. The objective criteria weights are based on the entropy<br />method which allows solutions to be obtained and better classified with the<br />minimum of subjectivity. The IEEE33 and IEEE13 bus test networks were<br />used to verify the method. The results obtained are compared to a binary<br />multi-objective particle swarm optimization method and the results show that<br />the proposed method reduces the overall costs, reduces the undelivered<br />energy of the system and improves the reliability of the service.</p>