IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v323y2022ics0306261922009126.html
   My bibliography  Save this article

A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems

Author

Listed:
  • Fathy, Ahmed

Abstract

Improving the performance of the electric distribution network is essential to meet the needs of the customer and guarantee the service continuity. Installing generators with small sizes known as distributed generators (DGs) can contribute to enhance the network operation by mitigating the network loss and improving the voltage profile. Integrating these generators in inappropriate places can cause serious consequences to the network operation. Therefore, this paper proposes a novel metaheuristic approach of artificial hummingbirdalgorithm (AHA) to identify the best locations and sizes of biomass-based DGs in radial distribution network. The proposed approach has enriched exploration and exploitation phases that enhancing its search capability and avoiding stuck in local optima. The network active power loss and the voltage deviation are selected as the targets to be minimized. Moreover, a new version of AHA is programmed to solve multi-objective problem with the purpose of mitigating both targets. The analysis is conducted on three radial distribution networks of IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus. Three scenarios are implemented in each network, the first one is minimizing the active power loss, the second one is mitigating the voltage deviation, and the last one is multi-objective problem. Also, biomass-based DGs with unity, fixed, and optimal power factors are analyzed. Excessive comparison to fractal search algorithm, particle swarm optimizer, genetic algorithm, the whale optimization algorithm, sperm swarm optimization, tunicate swarm algorithm, pathfinder algorithm, seagull optimization algorithm, and sine cosine algorithm, multi-objective water cycle algorithm, multi-objective grey wolf optimizer, and multi-objective sparrow search algorithm is conducted. Moreover, statistical tests of Wilcoxon, Friedman, ANOVA, and Kruskal Wallis are performed to assess the performance of the proposed approach. The gotten results confirmed the preference and competence of the proposed approach in integrating the biomass-based DGs in radial distribution networks.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922009126
    DOI: 10.1016/j.apenergy.2022.119605
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922009126
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119605?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ayman Awad & Hussein Abdel-Mawgoud & Salah Kamel & Abdalla A. Ibrahim & Francisco Jurado, 2021. "Developing a Hybrid Optimization Algorithm for Optimal Allocation of Renewable DGs in Distribution Network," Clean Technol., MDPI, vol. 3(2), pages 1-15, May.
    2. 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.
    3. Rezaee Jordehi, Ahmad, 2016. "Allocation of distributed generation units in electric power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 893-905.
    4. Prem Prakash & Duli Chand Meena & Hasmat Malik & Majed A. Alotaibi & Irfan Ahmad Khan, 2022. "A Novel Analytical Approach for Optimal Integration of Renewable Energy Sources in Distribution Systems," Energies, MDPI, vol. 15(4), pages 1-23, February.
    5. Akorede, Mudathir Funsho & Hizam, Hashim & Pouresmaeil, Edris, 2010. "Distributed energy resources and benefits to the environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(2), pages 724-734, February.
    6. Mulusew Ayalew & Baseem Khan & Issaias Giday & Om Prakash Mahela & Mahdi Khosravy & Neeraj Gupta & Tomonobu Senjyu, 2022. "Integration of Renewable Based Distributed Generation for Distribution Network Expansion Planning," Energies, MDPI, vol. 15(4), pages 1-17, February.
    7. Ehsan, Ali & Yang, Qiang, 2018. "Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques," Applied Energy, Elsevier, vol. 210(C), pages 44-59.
    8. Lipowski, Adam & Lipowska, Dorota, 2012. "Roulette-wheel selection via stochastic acceptance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(6), pages 2193-2196.
    9. Barik, Soumyabrata & Das, Debapriya, 2020. "A novel Q−PQV bus pair method of biomass DGs placement in distribution networks to maintain the voltage of remotely located buses," Energy, Elsevier, vol. 194(C).
    10. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1205-1230.
    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. Emad A. Mohamed & Mokhtar Aly & Masayuki Watanabe, 2022. "New Tilt Fractional-Order Integral Derivative with Fractional Filter (TFOIDFF) Controller with Artificial Hummingbird Optimizer for LFC in Renewable Energy Power Grids," Mathematics, MDPI, vol. 10(16), pages 1-33, August.
    2. Sami M. Alshareef & Ahmed Fathy, 2023. "Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks," Mathematics, MDPI, vol. 11(15), pages 1-30, July.
    3. Elham Mahdavi & Seifollah Asadpour & Leonardo H. Macedo & Rubén Romero, 2023. "Reconfiguration of Distribution Networks with Simultaneous Allocation of Distributed Generation Using the Whale Optimization Algorithm," Energies, MDPI, vol. 16(12), pages 1-19, June.
    4. Biao Li & Tao Wang & Chunxiao Li & Zhen Dong & Hua Yang & Yi Sun & Pengfei Wang, 2022. "A Strategy for Determining the Decommissioning Life of Energy Equipment Based on Economic Factors and Operational Stability," Sustainability, MDPI, vol. 14(24), pages 1-24, December.

    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. Eshan Karunarathne & Jagadeesh Pasupuleti & Janaka Ekanayake & Dilini Almeida, 2020. "Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm," Energies, MDPI, vol. 13(23), pages 1-25, November.
    2. Rahmat Khezri & Amin Mahmoudi & Hirohisa Aki & S. M. Muyeen, 2021. "Optimal Planning of Remote Area Electricity Supply Systems: Comprehensive Review, Recent Developments and Future Scopes," Energies, MDPI, vol. 14(18), pages 1-29, September.
    3. Pereira, Luan D.L. & Yahyaoui, Imene & Fiorotti, Rodrigo & de Menezes, Luíza S. & Fardin, Jussara F. & Rocha, Helder R.O. & Tadeo, Fernando, 2022. "Optimal allocation of distributed generation and capacitor banks using probabilistic generation models with correlations," Applied Energy, Elsevier, vol. 307(C).
    4. Zubo, Rana.H.A. & Mokryani, Geev & Rajamani, Haile-Selassie & Aghaei, Jamshid & Niknam, Taher & Pillai, Prashant, 2017. "Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1177-1198.
    5. Ehsan, Ali & Yang, Qiang, 2019. "State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review," Applied Energy, Elsevier, vol. 239(C), pages 1509-1523.
    6. 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).
    7. Yi, Ji Hyun & Ko, Woong & Park, Jong-Keun & Park, Hyeongon, 2018. "Impact of carbon emission constraint on design of small scale multi-energy system," Energy, Elsevier, vol. 161(C), pages 792-808.
    8. Domenech, B. & Ferrer-Martí, L. & Pastor, R., 2015. "Including management and security of supply constraints for designing stand-alone electrification systems in developing countries," Renewable Energy, Elsevier, vol. 80(C), pages 359-369.
    9. Virgilio Alfonso Murillo Rodríguez & Noé Villa Villaseñor & José Manuel Robles Solís & Omar Alejandro Guirette Barbosa, 2023. "Impact of Automation on Enhancing Energy Quality in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 16(17), pages 1-25, August.
    10. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Xiong, Wen & Wang, Li, 2017. "Typical scenario set generation algorithm for an integrated energy system based on the Wasserstein distance metric," Energy, Elsevier, vol. 135(C), pages 153-170.
    11. Ismail, M.S. & Moghavvemi, M. & Mahlia, T.M.I., 2013. "Energy trends in Palestinian territories of West Bank and Gaza Strip: Possibilities for reducing the reliance on external energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 117-129.
    12. Chandel, S.S. & Shrivastva, Rajnish & Sharma, Vikrant & Ramasamy, P., 2016. "Overview of the initiatives in renewable energy sector under the national action plan on climate change in India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 866-873.
    13. Weifeng Xu & Bing Yu & Qing Song & Liguo Weng & Man Luo & Fan Zhang, 2022. "Economic and Low-Carbon-Oriented Distribution Network Planning Considering the Uncertainties of Photovoltaic Generation and Load Demand to Achieve Their Reliability," Energies, MDPI, vol. 15(24), pages 1-15, December.
    14. Urooj Javed & Saif Ullah & Muhammad Imran & Asif Iqbal Malik & Nokhaiz Tariq Khan, 2021. "Power Distribution Network Expansion and Location Optimization of Additional Facilities: A Case Study," Sustainability, MDPI, vol. 13(14), pages 1-26, July.
    15. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    16. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    17. Li, Haoran & Zhang, Chenghui & Sun, Bo, 2021. "Optimal design for component capacity of integrated energy system based on the active dispatch mode of multiple energy storages," Energy, Elsevier, vol. 227(C).
    18. Moroni, Stefano & Antoniucci, Valentina & Bisello, Adriano, 2016. "Energy sprawl, land taking and distributed generation: towards a multi-layered density," Energy Policy, Elsevier, vol. 98(C), pages 266-273.
    19. Wilkinson, Sam & Maticka, Martin J. & Liu, Yue & John, Michele, 2021. "The duck curve in a drying pond: The impact of rooftop PV on the Western Australian electricity market transition," Utilities Policy, Elsevier, vol. 71(C).
    20. Nasreddine Belbachir & Mohamed Zellagui & Samir Settoul & Claude Ziad El-Bayeh & Ragab A. El-Sehiemy, 2023. "Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Al," Energies, MDPI, vol. 16(4), pages 1-24, February.

    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:eee:appene:v:323:y:2022:i:c:s0306261922009126. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.