IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1302-d746880.html
   My bibliography  Save this article

The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer

Author

Listed:
  • Ashraf Ramadan

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

  • Mohamed Ebeed

    (Faculty of Engineering, Sohag University, Sohag 82524, Egypt)

  • Salah Kamel

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

  • Ahmed M. Agwa

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 1321, Saudi Arabia
    Prince Faisal bin Khalid bin Sultan Research Chair in Renewable Energy Studies and Applications (PFCRE), Northern Border University, Arar 1321, Saudi Arabia)

  • Marcos Tostado-Véliz

    (Department of Electrical Engineering, University of Jaén, 23700 EPS Linares, Spain)

Abstract

Renewable distributed generators (RDGs) are widely embedded in electrical distribution networks due to their economic, technological, and environmental benefits. However, the main problem with RDGs, photovoltaic generators, and wind turbines, in particular, is that their output powers are constantly changing due to variations in sun irradiation and wind speed, leading to power system uncertainty. Such uncertainties should be taken into account when selecting the optimal allocation of RDGs. The main innovation of this paper is a proposed efficient metaheuristic optimization technique for the sizing and placement of RDGs in radial distribution systems considering the uncertainties of the loading and RDG output power. A Monte Carlo simulation method, along with the backward reduction algorithm, is utilized to create a set of scenarios to model these uncertainties. To find the positions and ratings of the RDGs, the artificial gorilla troops optimizer (GTO), a new efficient strategy that minimizes the total cost, is used to optimize a multiobjective function, total emissions, and total voltage deviations, as well as the total voltage stability boosting. The proposed technique is tested on an IEEE 69-bus network and a real Egyptian distribution grid (East Delta Network (EDN) 30-bus network). The results indicate that the proposed GTO can optimally assign the positions and ratings of RDGs. Moreover, the integration of RDGs into an IEEE 69-bus system can reduce the expected costs, emissions, and voltage deviations by 28.3%, 52.34%, and 66.95%, respectively, and improve voltage stability by 5.6%; in the EDN 30-bus system, these values are enhanced by 25.97%, 51.1%, 67.25%, and 7.7%, respectively.

Suggested Citation

  • Ashraf Ramadan & Mohamed Ebeed & Salah Kamel & Ahmed M. Agwa & Marcos Tostado-Véliz, 2022. "The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer," Energies, MDPI, vol. 15(4), pages 1-22, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1302-:d:746880
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1302/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/4/1302/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed Ginidi & Sherif M. Ghoneim & Abdallah Elsayed & Ragab El-Sehiemy & Abdullah Shaheen & Attia El-Fergany, 2021. "Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    2. Sam Weckx & Reinhilde D'hulst & Johan Driesen, 2015. "Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration," Energies, MDPI, vol. 8(5), pages 1-22, May.
    3. Chu Donatus Iweh & Samuel Gyamfi & Emmanuel Tanyi & Eric Effah-Donyina, 2021. "Distributed Generation and Renewable Energy Integration into the Grid: Prerequisites, Push Factors, Practical Options, Issues and Merits," Energies, MDPI, vol. 14(17), pages 1-34, August.
    4. Ali Selim & Salah Kamel & Amal A. Mohamed & Ehab E. Elattar, 2021. "Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique," Sustainability, MDPI, vol. 13(12), pages 1-31, June.
    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. Fathy, Ahmed, 2022. "A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems," Applied Energy, Elsevier, vol. 323(C).
    2. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    3. Abdelmonem Draz & Mahmoud M. Elkholy & Attia A. El-Fergany, 2023. "Automated Settings of Overcurrent Relays Considering Transformer Phase Shift and Distributed Generators Using Gorilla Troops Optimizer," Mathematics, MDPI, vol. 11(3), pages 1-25, February.

    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. Ashraf K. Abdelaal & Elshahat F. Mohamed & Attia A. El-Fergany, 2022. "Optimal Scheduling of Hybrid Sustainable Energy Microgrid: A Case Study for a Resort in Sokhna, Egypt," Sustainability, MDPI, vol. 14(19), pages 1-13, October.
    2. Jayashree Piri & Puspanjali Mohapatra & Biswaranjan Acharya & Farhad Soleimanian Gharehchopogh & Vassilis C. Gerogiannis & Andreas Kanavos & Stella Manika, 2022. "Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data," Mathematics, MDPI, vol. 10(15), pages 1-31, August.
    3. Xinghua Liu & Siwei Qiao & Zhiwei Liu, 2023. "A Survey on Load Frequency Control of Multi-Area Power Systems: Recent Challenges and Strategies," Energies, MDPI, vol. 16(5), pages 1-22, February.
    4. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Martin Calasan & Mihailo Micev & Ziad M. Ali & Saad Mekhilef & Hussain Bassi & Hatem Sindi & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer," Mathematics, MDPI, vol. 10(7), pages 1-31, March.
    5. Yousef Alharbi & Ahmed Darwish & Xiandong Ma, 2023. "A Comprehensive Review of Distributed MPPT for Grid-Tied PV Systems at the Sub-Module Level," Energies, MDPI, vol. 16(14), pages 1-23, July.
    6. 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).
    7. Gustavo Adolfo Gómez-Ramírez & Carlos Meza & Gonzalo Mora-Jiménez & José Rodrigo Rojas Morales & Luis García-Santander, 2023. "The Central American Power System: Achievements, Challenges, and Opportunities for a Green Transition," Energies, MDPI, vol. 16(11), pages 1-20, May.
    8. Hegazy Rezk & A. G. Olabi & Tabbi Wilberforce & Enas Taha Sayed, 2023. "A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    9. Ragab El-Sehiemy & Abdallah Elsayed & Abdullah Shaheen & Ehab Elattar & Ahmed Ginidi, 2021. "Scheduling of Generation Stations, OLTC Substation Transformers and VAR Sources for Sustainable Power System Operation Using SNS Optimizer," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    10. Ashraf Ramadan & Mohamed Ebeed & Salah Kamel & Almoataz Y. Abdelaziz & Hassan Haes Alhelou, 2021. "Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    11. Rob Shipman & Rebecca Roberts & Julie Waldron & Chris Rimmer & Lucelia Rodrigues & Mark Gillott, 2021. "Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic," Energies, MDPI, vol. 14(21), pages 1-16, November.
    12. Ahmed M. Mahmoud & Shady H. E. Abdel Aleem & Almoataz Y. Abdelaziz & Mohamed Ezzat, 2022. "Towards Maximizing Hosting Capacity by Optimal Planning of Active and Reactive Power Compensators and Voltage Regulators: Case Study," Sustainability, MDPI, vol. 14(20), pages 1-34, October.
    13. Xiangang Peng & Lixiang Lin & Weiqin Zheng & Yi Liu, 2015. "Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem," Energies, MDPI, vol. 8(12), pages 1-19, December.
    14. Piotr Powroźnik & Paweł Szcześniak & Krzysztof Piotrowski, 2021. "Elastic Energy Management Algorithm Using IoT Technology for Devices with Smart Appliance Functionality for Applications in Smart-Grid," Energies, MDPI, vol. 15(1), pages 1-23, December.
    15. Wassim Daher & Jihad Elnaboulsi & Mahelet G. Fikru & Luis Gautier, 2024. "An Analysis of Mergers in the Presence of Uncertainty in Renewable Energy Integration Costs," Working Papers 2024-14, CRESE.
    16. Kongrit Mansiri & Sukruedee Sukchai & Chatchai Sirisamphanwong, 2018. "Fuzzy Control for Smart PV-Battery System Management to Stabilize Grid Voltage of 22 kV Distribution System in Thailand," Energies, MDPI, vol. 11(7), pages 1-19, July.
    17. Su Su & Yong Hu & Tiantian Yang & Shidan Wang & Ziqi Liu & Xiangxiang Wei & Mingchao Xia & Yutaka Ota & Koji Yamashita, 2018. "Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities," Energies, MDPI, vol. 11(2), pages 1-19, February.
    18. Adel A. Abou El-Ela & Ragab A. El-Sehiemy & Abdullah M. Shaheen & Aya R. Ellien, 2022. "Review on Active Distribution Networks with Fault Current Limiters and Renewable Energy Resources," Energies, MDPI, vol. 15(20), pages 1-30, October.
    19. Ayuketah, Yvan & Gyamfi, Samuel & Diawuo, Felix Amankwah & Dagoumas, Athanasios S., 2023. "A techno-economic and environmental assessment of a low-carbon power generation system in Cameroon," Energy Policy, Elsevier, vol. 179(C).
    20. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.

    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:15:y:2022:i:4:p:1302-:d:746880. 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.