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Optimal Allocation of Distributed Generators in Active Distribution Networks Using a New Oppositional Hybrid Sine Cosine Muted Differential Evolution Algorithm

Author

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  • Subrat Kumar Dash

    (Department of Electrical Engineering, GCEK, Bhawanipatna 766002, India)

  • Sivkumar Mishra

    (Department of Electrical Engineering, CAPGS, BPUT, Rourkela 769015, India)

  • Almoataz Y. Abdelaziz

    (Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Mamdouh L. Alghaythi

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Ahmed Allehyani

    (Department of Electrical and Electronic Engineering, College of Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia)

Abstract

The research proposes a new oppositional sine cosine muted differential evolution algorithm (O-SCMDEA) for the optimal allocation of distributed generators (OADG) in active power distribution networks. The suggested approach employs a hybridization of the classic differential evolution algorithm and the sine cosine algorithm in order to incorporate the exploitation and exploration capabilities of the differential evolution algorithm and the sine cosine algorithm, respectively. Further, the convergence speed of the proposed algorithm is accelerated through the judicious application of opposition-based learning. The OADG is solved by considering three separate mono-objectives (real power loss minimization, voltage deviation improvement and maximization of the voltage stability index) and a multi-objective framework combining the above three. OADG is also addressed for DGs operating at the unity power factor and lagging power factor while meeting the pragmatic operational requirements of the system. The suggested algorithm for multiple DG allocation is evaluated using a small test distribution network (33 bus) and two bigger test distribution networks (118 bus and 136 bus). The results are also compared to recent state-of-the-art metaheuristic techniques, demonstrating the superiority of the proposed method for solving OADG, particularly for large-scale distribution networks. Statistical analysis is also performed to showcase the genuineness and robustness of the obtained results. A post hoc analysis using Friedman–ANOVA and Wilcoxon signed-rank tests reveals that the results are of statistical significance.

Suggested Citation

  • Subrat Kumar Dash & Sivkumar Mishra & Almoataz Y. Abdelaziz & Mamdouh L. Alghaythi & Ahmed Allehyani, 2022. "Optimal Allocation of Distributed Generators in Active Distribution Networks Using a New Oppositional Hybrid Sine Cosine Muted Differential Evolution Algorithm," Energies, MDPI, vol. 15(6), pages 1-35, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2267-:d:775433
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    References listed on IDEAS

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    1. Mohamed A. Tolba & Hegazy Rezk & Vladimir Tulsky & Ahmed A. Zaki Diab & Almoataz Y. Abdelaziz & Artem Vanin, 2018. "Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms," Energies, MDPI, vol. 11(1), pages 1-33, January.
    2. Pesaran H.A., Mahmoud & Nazari-Heris, Morteza & Mohammadi-Ivatloo, Behnam & Seyedi, Heresh, 2020. "A hybrid genetic particle swarm optimization for distributed generation allocation in power distribution networks," Energy, Elsevier, vol. 209(C).
    3. Eshan Karunarathne & Jagadeesh Pasupuleti & Janaka Ekanayake & Dilini Almeida, 2021. "The Optimal Placement and Sizing of Distributed Generation in an Active Distribution Network with Several Soft Open Points," Energies, MDPI, vol. 14(4), pages 1-20, February.
    4. Ibrahim Diaaeldin & Shady Abdel Aleem & Ahmed El-Rafei & Almoataz Abdelaziz & Ahmed F. Zobaa, 2019. "Optimal Network Reconfiguration in Active Distribution Networks with Soft Open Points and Distributed Generation," Energies, MDPI, vol. 12(21), pages 1-31, November.
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    Cited by:

    1. Sunday Adeleke Salimon & Gafari Abiola Adepoju & Isaiah Gbadegesin Adebayo & Harun Or Rashid Howlader & Samson Oladayo Ayanlade & Oludamilare Bode Adewuyi, 2023. "Impact of Distributed Generators Penetration Level on the Power Loss and Voltage Profile of Radial Distribution Networks," Energies, MDPI, vol. 16(4), pages 1-32, February.
    2. Asad Abbas & Saeed Mian Qaisar & Asad Waqar & Nasim Ullah & Ahmad Aziz Al Ahmadi, 2022. "Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System under a 24 h Load Horizon," Energies, MDPI, vol. 15(10), pages 1-32, May.
    3. Subrat Kumar Dash & Sivkumar Mishra & Almoataz Youssef Abdelaziz & Junhee Hong & Zong Woo Geem, 2022. "Optimal Planning of Multitype DGs and D-STATCOMs in Power Distribution Network Using an Efficient Parameter Free Metaheuristic Algorithm," Energies, MDPI, vol. 15(9), pages 1-35, May.
    4. Elseify, Mohamed A. & Hashim, Fatma A. & Hussien, Abdelazim G. & Kamel, Salah, 2024. "Single and multi-objectives based on an improved golden jackal optimization algorithm for simultaneous integration of multiple capacitors and multi-type DGs in distribution systems," Applied Energy, Elsevier, vol. 353(PA).
    5. Cinthia Peraza & Patricia Ochoa & Oscar Castillo & Zong Woo Geem, 2022. "Interval-Type 3 Fuzzy Differential Evolution for Designing an Interval-Type 3 Fuzzy Controller of a Unicycle Mobile Robot," Mathematics, MDPI, vol. 10(19), pages 1-17, September.

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