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CSEE Journal of Power and Energy Systems 2022, 8(3): 933-940 https://doi.org/10.17775/CSEEJPES.2020.05660
Open Access | Issue | Published: 30 December 2021

Robust Switch Selection in Radial Distribution Systems Using Combinatorial Optimization

Show Author's Information Hani Mavalizadeh,  Omid Homaee,  Reza Dashti,  Josep M. Guerrero,  Hassan Haes Alhelou( ),  Pierluigi Siano
Fanavaran Moj Khatam, Tehran 16844, Iran
School of Electrical Engineering, Iran University of Science and Technology, Tehran 16844, Iran
School of Advanced Technologies, Iran University of Science and Technology, Tehran 16844, Iran
Centre for Research on Microgrids (CROM). Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark
Department of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia 2230, Syria
Department of Management & Innovation Systems, University of Salerno, Fisciano 84084, Italy
Keywords:
reliability, multi-objective optimization, robustness, Combinatorial optimization, Monte Carlo Simulation, radial distribution system
Cite this article:
Mavalizadeh H, Homaee O, Dashti R, et al. Robust Switch Selection in Radial Distribution Systems Using Combinatorial Optimization. CSEE Journal of Power and Energy Systems, 2022, 8(3): 933-940. https://doi.org/10.17775/CSEEJPES.2020.05660
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Abstract Full text About this article

Selecting the best type of equipment among available switches with different prices and reliability levels is a significant challenge in distribution system planning. In this paper, the optimal type of switches in a radial distribution system is selected by considering the total cost and reliability criterion and using the weighted augmented epsilon constraint method and combinatorial optimization. A new index is calculated to assess the robustness of each Pareto solution. Moreover, for each failure, repair time is considered based on historical data. Monte Carlo simulations are used to consider the switch failure uncertainty and fault repair time uncertainty in the model. The proposed framework is applied to an RTBS Bus-2 test system. Furthermore, the model is also applied to an industrial system to verify the proposed method’s excellent performance in larger practical engineering problems.


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About this article

Robust Switch Selection in Radial Distribution Systems Using Combinatorial Optimization

Show Author's information Hani Mavalizadeh, Omid Homaee, Reza Dashti, Josep M. Guerrero, Hassan Haes Alhelou( ), Pierluigi Siano
Fanavaran Moj Khatam, Tehran 16844, Iran
School of Electrical Engineering, Iran University of Science and Technology, Tehran 16844, Iran
School of Advanced Technologies, Iran University of Science and Technology, Tehran 16844, Iran
Centre for Research on Microgrids (CROM). Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark
Department of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia 2230, Syria
Department of Management & Innovation Systems, University of Salerno, Fisciano 84084, Italy

Abstract

Selecting the best type of equipment among available switches with different prices and reliability levels is a significant challenge in distribution system planning. In this paper, the optimal type of switches in a radial distribution system is selected by considering the total cost and reliability criterion and using the weighted augmented epsilon constraint method and combinatorial optimization. A new index is calculated to assess the robustness of each Pareto solution. Moreover, for each failure, repair time is considered based on historical data. Monte Carlo simulations are used to consider the switch failure uncertainty and fault repair time uncertainty in the model. The proposed framework is applied to an RTBS Bus-2 test system. Furthermore, the model is also applied to an industrial system to verify the proposed method’s excellent performance in larger practical engineering problems.

Keywords: reliability, multi-objective optimization, robustness, Combinatorial optimization, Monte Carlo Simulation, radial distribution system

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Received: 26 October 2020
Revised: 26 February 2021
Accepted: 21 April 2021
Published: 30 December 2021
Issue date: May 2022

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