Introduction of Adaptive Clonal Differential Evolution in Allocation and Sizing of Renewable-Energy Distributed Generation Units in Distribution Networks

DOI HANDLE Open Access
  • Rasid Madihah MD
    Department of Electrical and Electronic Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University : Graduate Student
  • Murata Junichi
    Department of Electrical Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University : Professor
  • Funaki Ryohei
    Department of Electrical Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University : Assistant Professor

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Abstract

Renewable-Energy Distributed Generation units (REDGs) that are installed in distribution network offer a lot of advantages in terms of technical, economic and environmental benefits. However, REDGs have both positive and negative effects depending on their sizes and locations. Thus, the purpose of this study is to determine the optimal locations and sizes of REDGs that attains fuel consumption reduction and system reliability improvement while satisfying various constraints and considering relevant uncertainties. To optimize the locations and sizes of REDGs, Adaptive Clonal Differential Evolution (ACDE) is proposed to improve the performance of Clonal Differential Evolution (CDE) by updating the control parameters in an adaptive manner. Previously, CDE with randomized scaling factor was introduced. CDE algorithm is capable of enhancing the exploration and searching ability, hence accelerates the convergence of the algorithm. However, the randomized scaling factor does not guarantee the robustness of the algorithm. Therefore, control parameter adaptation that utilizes collected data is introduced to favour providing information on good parameter values. The proposed algorithm is verified on a 33-bus test system. The comparative studies are carried out and the simulation results show that the proposed algorithm is more stable and robust than CDE.

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Details 詳細情報について

  • CRID
    1390290699820538240
  • NII Article ID
    120005837907
  • NII Book ID
    AN10569524
  • DOI
    10.15017/1669720
  • ISSN
    21880891
    13423819
  • HANDLE
    2324/1669720
  • Text Lang
    en
  • Data Source
    • JaLC
    • IRDB
    • CiNii Articles
  • Abstract License Flag
    Allowed

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