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Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network

Author

Listed:
  • Mohammed Hamouda Ali

    (Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt)

  • Ahmed Tijani Salawudeen

    (Department of Electrical and Electronics Engineering, University of Jos, Jos 930222, Nigeria)

  • Salah Kamel

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Habeeb Bello Salau

    (Department of Computer Engineering, Ahmadu Bello University Zaria, Zaria 810107, Nigeria)

  • Monier Habil

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Mokhtar Shouran

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

Abstract

Nowadays, the electrical power system has become a more complex, interconnected network that is expanding every day. Hence, the power system faces many problems such as increasing power losses, voltage deviation, line overloads, etc. The optimization of real and reactive power due to the installation of energy resources at appropriate buses can minimize the losses and improve the voltage profile, especially for congested networks. As a result, the optimal distributed generation allocation (ODGA) problem is considered a more proper tool for the processes of planning and operation of power systems due to the power grid changes expeditiously based on the type and penetration level of renewable energy sources (RESs). This paper modifies the AO using a quasi-oppositional-based learning operator to address this problem and reduce the burden on the primary grid, making the grid more resilient. To demonstrate the effectiveness of the MAO, the authors first test the algorithm performance on twenty-three competitions on evolutionary computation benchmark functions, considering different dimensions. In addition, the modified Aquila optimizer (MAO) is applied to tackle the optimal distributed generation allocation (ODGA) problem. The proposed ODGA methodology presented in this paper has a multi-objective function that comprises decreasing power loss and total voltage deviation in a distribution system while keeping the system operating and security restrictions in mind. Many publications investigated the effect of expanding the number of DGs, whereas others found out the influence of DG types. Here, this paper examines the effects of different types and capacities of DG units at the same time. The proposed approach is tested on the IEEE 33-bus in different cases with several multiple DG types, including multi-objectives. The obtained simulation results are compared to the Aquila optimizer, particle swarm optimization algorithm, and trader-inspired algorithm. According to the comparison, the suggested approach provides a superior solution for the ODGA problem with faster convergence in the DNs.

Suggested Citation

  • Mohammed Hamouda Ali & Ahmed Tijani Salawudeen & Salah Kamel & Habeeb Bello Salau & Monier Habil & Mokhtar Shouran, 2022. "Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network," Mathematics, MDPI, vol. 10(12), pages 1-39, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2129-:d:842469
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    References listed on IDEAS

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    1. Paschalis A. Gkaidatzis & Aggelos S. Bouhouras & Kallisthenis I. Sgouras & Dimitrios I. Doukas & Georgios C. Christoforidis & Dimitris P. Labridis, 2019. "Efficient RES Penetration under Optimal Distributed Generation Placement Approach," Energies, MDPI, vol. 12(7), pages 1-32, April.
    2. Chandrasekaran Venkatesan & Raju Kannadasan & Mohammed H. Alsharif & Mun-Kyeom Kim & Jamel Nebhen, 2021. "A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems," Sustainability, MDPI, vol. 13(6), pages 1-34, March.
    3. Minh Quan Duong & Thai Dinh Pham & Thang Trung Nguyen & Anh Tuan Doan & Hai Van Tran, 2019. "Determination of Optimal Location and Sizing of Solar Photovoltaic Distribution Generation Units in Radial Distribution Systems," Energies, MDPI, vol. 12(1), pages 1-24, January.
    4. Vedik Basetti & Shriram S. Rangarajan & Chandan Kumar Shiva & Sumit Verma & Randolph E. Collins & Tomonobu Senjyu, 2021. "A Quasi-Oppositional Heap-Based Optimization Technique for Power Flow Analysis by Considering Large Scale Photovoltaic Generator," Energies, MDPI, vol. 14(17), pages 1-16, August.
    5. Jordehi, A. Rezaee, 2015. "Optimisation of electric distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1088-1100.
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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. Mohammed Hamouda Ali & Ali M. El-Rifaie & Ahmed A. F. Youssef & Vladimir N. Tulsky & Mohamed A. Tolba, 2023. "Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm," Energies, MDPI, vol. 16(2), pages 1-29, January.
    3. 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).

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