Introduction of adaptive clonal differential evolution in allocation and sizing of renewable-energy distributed generation units in distribution networks

Madihah M.D. Rasid, Junichi Murata, Ryohei Funaki

研究成果: ジャーナルへの寄稿記事

抄録

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.

元の言語英語
ページ(範囲)29-38
ページ数10
ジャーナルResearch Reports on Information Science and Electrical Engineering of Kyushu University
21
発行部数2
出版物ステータス出版済み - 7 1 2016
外部発表Yes

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Distributed power generation
Electric power distribution
Fuel consumption
Economics

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

これを引用

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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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