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

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  • 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.

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  • 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
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    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.
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    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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    5. 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.
    6. Abid, Md. Shadman & Ahshan, Razzaqul & Al Abri, Rashid & Al-Badi, Abdullah & Albadi, Mohammed, 2024. "Techno-economic and environmental assessment of renewable energy sources, virtual synchronous generators, and electric vehicle charging stations in microgrids," Applied Energy, Elsevier, vol. 353(PA).
    7. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    8. 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.
    9. 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.

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