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Optimal allocation strategy of photovoltaic- and wind turbine-based distributed generation units in radial distribution networks considering uncertainty

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Abstract

This paper proposes an improved version of the artificial ecosystem-based optimization (AEO) algorithm called artificial ecosystem-based optimization–opposition-based learning (AEO-OBL), with the aim of improving the performance of the original AEO. In addition, it is utilized for determining the optimal allocation of distributed generation (DG) units in radial distribution networks (RDNs) with the aim of minimizing power and energy losses. The stochastic nature of renewable DGs such as wind turbine and photovoltaic generation is taken in consideration using appropriate probability models. The Loss Sensitivity Index is used to assess the most suitable busses for the integration of DG units in the RDN. AEO is nature-inspired optimization algorithm which imitates the flow of energy in an ecosystem on earth. In the proposed AEO-OBL, the search ability and the balance between the exploration and exploitation phases in the original AEO are enhanced. In the AEO-OBL, five efficient strategies are used to avoid falling on a local optimal: (1) enhanced linear weight coefficient a, (2) production operator, (3) modified consumption operator, (4) modified decomposing operator and (5) opposition-based learning (OBL). The performance of the proposed technique is validated on IEEE 33-bus and 85-bus RDNs. To emphasize the superiority of the proposed technique, the results are compared with the original AEO, Henry gas solubility optimizer (HGSO) and Harris hawks optimization (HHO) algorithm results. Besides, the developed algorithm is compared with other optimization algorithms in literature that solved the same problem. The outcomes indicate a better performance of AEO-OBL relative to other algorithms. Accordingly, AEO-OBL can be a very suitable algorithm in solving the problem of optimal DG allocation in RDNs.

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Acknowledgements

The authors thank the support of the National Research and Development Agency of Chile (ANID), ANID/Fondap/15110019.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Tashkent State Technical University, Tashkent, Uzbekistan, 100095

    Mansur Khasanov

  2. School of Electrical Engineering, Chongqing University, Chongqing, 400044, China

    Mansur Khasanov

  3. Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt

    Salah Kamel

  4. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam Halim Houssein

  5. Department of Electrical Engineering, University of Chile, Santiago, Chile

    Claudia Rahmann

  6. Faculty of Engineering, Helwan University, Cairo, Egypt

    Fatma A. Hashim

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  1. Mansur Khasanov
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  5. Fatma A. Hashim
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Correspondence to Salah Kamel.

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Appendix A

Appendix A

The PV module characteristics used in this study are given in Table

Table 11 Manufacturer's specifications of the PV modules used in the study
Full size table

11 [16].

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Khasanov, M., Kamel, S., Halim Houssein, E. et al. Optimal allocation strategy of photovoltaic- and wind turbine-based distributed generation units in radial distribution networks considering uncertainty. Neural Comput & Applic 35, 2883–2908 (2023). https://doi.org/10.1007/s00521-022-07715-2

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