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Optimal Long-Term Distributed Generation Planning and Reconfiguration of Distribution Systems: An Accelerating Benders’ Decomposition Approach

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Abstract

In this paper, we study the multi-period distributed generation planning problem in a multistage hierarchical distribution network. We first formulate the problem as a non-convex mixed-integer nonlinear programming problem. Since the proposed model is non-convex and generally hard to solve, we convexify the model based on semi-definite programming. Then, we use a customized Benders’ decomposition method with valid cuts to solve the convex relaxation model. Computational results show that the proposed algorithm provides an efficient way to solve the problem for relatively large-scale networks.

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Acknowledgements

The authors are grateful to the editors for their constructive comments, which helped us improve the presentation of this paper substantially.

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Authors and Affiliations

  1. Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), Gavazang Road, Zanjan, Iran

    Salman Khodayifar

  2. Faculty of Mathematical Sciences and Computer, Kharazmi University, Tehran, Iran

    Mohammad A. Raayatpanah

  3. Department of Electrical Engineering, University of Zanjan, Zanjan, Iran

    Abbas Rabiee

  4. Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, 43210, USA

    Hamed Rahimian

  5. Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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  1. Salman Khodayifar
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  2. Mohammad A. Raayatpanah
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  3. Abbas Rabiee
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  4. Hamed Rahimian
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  5. Panos M. Pardalos
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Correspondence to Salman Khodayifar.

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Khodayifar, S., Raayatpanah, M.A., Rabiee, A. et al. Optimal Long-Term Distributed Generation Planning and Reconfiguration of Distribution Systems: An Accelerating Benders’ Decomposition Approach. J Optim Theory Appl 179, 283–310 (2018). https://doi.org/10.1007/s10957-018-1367-5

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  • DOI: https://doi.org/10.1007/s10957-018-1367-5

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