IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i16p4856-d611071.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/16/4856/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/16/4856/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gang Liang & Bing Sun & Yuan Zeng & Leijiao Ge & Yunfei Li & Yu Wang, 2022. "An Optimal Allocation Method of Distributed PV and Energy Storage Considering Moderate Curtailment Measure," Energies, MDPI, vol. 15(20), pages 1-19, October.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Jianguo & Xu, Zhongtian, 2023. "Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China," Renewable Energy, Elsevier, vol. 202(C), pages 1110-1137.
    2. Jilong Zhang & Yuan Diao, 2024. "Hierarchical Learning-Enhanced Chaotic Crayfish Optimization Algorithm: Improving Extreme Learning Machine Diagnostics in Breast Cancer," Mathematics, MDPI, vol. 12(17), pages 1-26, August.
    3. Azad-Farsani, Ehsan, 2017. "Loss minimization in distribution systems based on LMP calculation using honey bee mating optimization and point estimate method," Energy, Elsevier, vol. 140(P1), pages 1-9.
    4. Mohsen Khalili & Touhid Poursheykh Aliasghari & Ebrahim Seifi Najmi & Almoataz Y. Abdelaziz & A. Abu-Siada & Saber Arabi Nowdeh, 2022. "Optimal Allocation of Distributed Thyristor Controlled Series Compensators in Power System Considering Overload, Voltage, and Losses with Reliability Effect," Energies, MDPI, vol. 15(20), pages 1-25, October.
    5. Feng, Li & Liu, Jiajun & Lu, Haitao & Liu, Bingzhi & Chen, Yuning & Wu, Shenyu, 2022. "Robust operation of distribution network based on photovoltaic/wind energy resources in condition of COVID-19 pandemic considering deterministic and probabilistic approaches," Energy, Elsevier, vol. 261(PB).
    6. Tan, Zhongfu & Wang, Guan & Ju, Liwei & Tan, Qingkun & Yang, Wenhai, 2017. "Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand r," Energy, Elsevier, vol. 124(C), pages 198-213.
    7. Iraj Faraji Davoudkhani & Farhad Zishan & Saeedeh Mansouri & Farzad Abdollahpour & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya, 2023. "Allocation of Renewable Energy Resources in Distribution Systems While Considering the Uncertainty of Wind and Solar Resources via the Multi-Objective Salp Swarm Algorithm," Energies, MDPI, vol. 16(1), pages 1-17, January.
    8. Abdulaziz Alanazi & Mohana Alanazi, 2022. "Artificial Electric Field Algorithm-Pattern Search for Many-Criteria Networks Reconfiguration Considering Power Quality and Energy Not Supplied," Energies, MDPI, vol. 15(14), pages 1-27, July.
    9. Marco-Antonio Moreno-Ibarra & Yenny Villuendas-Rey & Miltiadis D. Lytras & Cornelio Yáñez-Márquez & Julio-César Salgado-Ramírez, 2021. "Classification of Diseases Using Machine Learning Algorithms: A Comparative Study," Mathematics, MDPI, vol. 9(15), pages 1-21, July.
    10. Saman Shahrokhi & Adel El-Shahat & Fatemeh Masoudinia & Foad H. Gandoman & Shady H. E. Abdel Aleem, 2021. "Sizing and Energy Management of Parking Lots of Electric Vehicles Based on Battery Storage with Wind Resources in Distribution Network," Energies, MDPI, vol. 14(20), pages 1-21, October.
    11. Masood, Nahid-Al- & Mahmud, Sajjad Uddin & Ansary, Md Nazmuddoha & Deeba, Shohana Rahman, 2022. "Improvement of system strength under high wind penetration: A techno-economic assessment using synchronous condenser and SVC," Energy, Elsevier, vol. 246(C).
    12. José-Luis Velázquez-Rodríguez & Yenny Villuendas-Rey & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez, 2020. "A Novel and Simple Mathematical Transform Improves the Perfomance of Lernmatrix in Pattern Classification," Mathematics, MDPI, vol. 8(5), pages 1-46, May.
    13. Anatolyy Dzyuba & Irina Solovyeva & Aleksandr Semikolenov, 2023. "Raising the Resilience of Industrial Manufacturers through Implementing Natural Gas-Fired Distributed Energy Resource Systems with Demand Response," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    14. Czekała, Wojciech & Bartnikowska, Sylwia & Dach, Jacek & Janczak, Damian & Smurzyńska, Anna & Kozłowski, Kamil & Bugała, Artur & Lewicki, Andrzej & Cieślik, Marta & Typańska, Dorota & Mazurkiewicz, Ja, 2018. "The energy value and economic efficiency of solid biofuels produced from digestate and sawdust," Energy, Elsevier, vol. 159(C), pages 1118-1122.
    15. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.
    16. Mokhtar Said & Ali M. El-Rifaie & Mohamed A. Tolba & Essam H. Houssein & Sanchari Deb, 2021. "An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem," Mathematics, MDPI, vol. 9(21), pages 1-14, November.
    17. Ali Sulaiman Alsagri & Abdulrahman A. Alrobaian, 2022. "Optimization of Combined Heat and Power Systems by Meta-Heuristic Algorithms: An Overview," Energies, MDPI, vol. 15(16), pages 1-34, August.
    18. Eliton Smith dos Santos & Marcus Vinícius Alves Nunes & Manoel Henrique Reis Nascimento & Jandecy Cabral Leite, 2022. "Rational Application of Electric Power Production Optimization through Metaheuristics Algorithm," Energies, MDPI, vol. 15(9), pages 1-31, April.
    19. Ahmed S. Menesy & Hamdy M. Sultan & Ibrahim O. Habiballah & Hasan Masrur & Kaisar R. Khan & Muhammad Khalid, 2023. "Optimal Configuration of a Hybrid Photovoltaic/Wind Turbine/Biomass/Hydro-Pumped Storage-Based Energy System Using a Heap-Based Optimization Algorithm," Energies, MDPI, vol. 16(9), pages 1-26, April.
    20. Peesapati, Rajagopal & Yadav, Vinod Kumar & Kumar, Niranjan, 2018. "Flower pollination algorithm based multi-objective congestion management considering optimal capacities of distributed generations," Energy, Elsevier, vol. 147(C), pages 980-994.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4856-:d:611071. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.