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Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm

Author

Listed:
  • Masoud Zahedi Vahid

    (Department of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan 9816745785, Iran)

  • Ziad M. Ali

    (Electrical Engineering Department, Aswan faculty of Engineering, Aswan University, Aswan 81542, Egypt
    Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia)

  • Ebrahim Seifi Najmi

    (Roshdieh Higher Institue of Education, Tabriz 5166616471, Iran)

  • Abdollah Ahmadi

    (School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia)

  • Foad H. Gandoman

    (Research Group MOBI–Mobility, Logistics, and Automotive Technology Research Centre, Vrije Universiteit Brussel, 1050 Brussels, Belgium)

  • Shady H. E. Abdel Aleem

    (Department of Electrical Engineering and Electronics, Valley Higher Institute of Engineering and Technology, Science Valley Academy, Al-Qalyubia 44971, Egypt)

Abstract

In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cleared that optimal allocation and planning of the DGSCs can be improved the distribution network operation with reducing the power losses and also enhancing the voltage profile. The obtained results confirmed superiority of the MRFO compared with well-known particle swarm optimization (PSO) in the DGSCs allocation. The results also showed that increasing the number of DGSCs reduces more losses and improves more the network voltage profile. The achieved results demonstrated that the energy loss in smart network is less than the network with conventional load. In other words, any error in forecasting load demand leads to non-optimal operating point and more energy losses.

Suggested Citation

  • Masoud Zahedi Vahid & Ziad M. Ali & Ebrahim Seifi Najmi & Abdollah Ahmadi & Foad H. Gandoman & Shady H. E. Abdel Aleem, 2021. "Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm," Energies, MDPI, vol. 14(16), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4856-:d:611071
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    References listed on IDEAS

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    1. Rahiminejad, A. & Vahidi, B. & Hejazi, M.A. & Shahrooyan, S., 2016. "Optimal scheduling of dispatchable distributed generation in smart environment with the aim of energy loss minimization," Energy, Elsevier, vol. 116(P1), pages 190-201.
    2. Naderipour, Amirreza & Abdul-Malek, Zulkurnain & Nowdeh, Saber Arabi & Ramachandaramurthy, Vigna K. & Kalam, Akhtar & Guerrero, Josep M., 2020. "Optimal allocation for combined heat and power system with respect to maximum allowable capacity for reduced losses and improved voltage profile and reliability of microgrids considering loading condi," Energy, Elsevier, vol. 196(C).
    3. Stavros P. Adam & Stamatios-Aggelos N. Alexandropoulos & Panos M. Pardalos & Michael N. Vrahatis, 2019. "No Free Lunch Theorem: A Review," Springer Optimization and Its Applications, in: Ioannis C. Demetriou & Panos M. Pardalos (ed.), Approximation and Optimization, pages 57-82, Springer.
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    2. Yin, Mingjia & Li, Kang & Yu, James, 2022. "A data-driven approach for microgrid distributed generation planning under uncertainties," Applied Energy, Elsevier, vol. 309(C).

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