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Article type: Research Article
Authors: Nayeripour, Majid | Mahboubi-Moghaddam, Esmaeil* | Khooban, Mohammad Hassan
Affiliations: Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
Correspondence: [*] Corresponding author. E. Mahboubi-Moghaddam, Depart-ment of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran. Tel.: +98 711 7264121; Fax: +98 711 7353502; E-mails: [email protected]; [email protected].
Abstract: Over the past few years, distribution system operators try their best in order to obtain the well-balanced distribution systems to reduce the power loss, decrease the operation cost and improve the reliability indices. This paper presents an efficient method to solve the multi-periods distribution feeder reconfiguration (DFR) with respect to the presence of Distributed Generators (DGs). Most studies so far have investigated reconfiguration problem as a single period problem considering a fixed level of load. However, in this study, time-varying characteristics of load profiles and line failure rates are considered. The proposed framework formulates and studies the direct and implied costs of power supply, reliability, energy loss, and switching operations, simultaneously. By considering these conditions to the DFR problem, the number of decision variables is significantly increased and the problem becomes more complicated than before. To this end a new modified particle swarm optimization (PSO) algorithm, compatible with the multi-periods problems, is presented. In the proposed algorithm, the costs of individual periods and the total cost are considered simultaneously in order to update the particles. To evaluate the performance of the proposed method, the results are compared with the original one. A typical distribution test system is used to demonstrate the performance of the proposed approach.
Keywords: Distribution feeder reconfiguration, distributed generation, reliability, particle swarm optimization algorithm
DOI: 10.3233/IFS-162144
Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 1, pp. 321-331, 2016
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