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Incorporating load variation and variable wind generation in service restoration plans for distribution systems

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  • Zidan, Aboelsood
  • El-Saadany, Ehab F.

Abstract

A service restoration process is achieved through the switching actions of the sectionalizing and tie switches in distribution feeders. After the faults have been located and isolated, restoration plans are applied in order to minimize the de-energized consumer load and the number of switching operations. All of these objectives are converted to monetary costs, which are then added together into a global objective. The solution to the problem, which is based on a genetic algorithm, is then aimed at achieving the minimum cost. In this work, numerous practical aspects related to service restoration have been considered, such as variations in the load and the priorities of the customers, price discounts for in-service customers based on their participation in a load-curtailment scheme that permits other customers to be supplied, the presence of manual and automated switches, and the incorporation of distributed generation (dispatchable and wind-based DG units) in the restoration process. The constraints involved include voltage limits, line current limits, and radial topology.

Suggested Citation

  • Zidan, Aboelsood & El-Saadany, Ehab F., 2013. "Incorporating load variation and variable wind generation in service restoration plans for distribution systems," Energy, Elsevier, vol. 57(C), pages 682-691.
    • Handle: RePEc:eee:energy:v:57:y:2013:i:c:p:682-691
    DOI: 10.1016/j.energy.2013.03.099
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    References listed on IDEAS

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    Cited by:

    1. Ehsan Gord & Rahman Dashti & Mojtaba Najafi & Hamid Reza Shaker, 2019. "Real Fault Section Estimation in Electrical Distribution Networks Based on the Fault Frequency Component Analysis," Energies, MDPI, vol. 12(6), pages 1-29, March.
    2. Aboelsood Zidan & Hossam A. Gabbar, 2016. "DG Mix and Energy Storage Units for Optimal Planning of Self-Sufficient Micro Energy Grids," Energies, MDPI, vol. 9(8), pages 1-18, August.
    3. El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    4. Duy Phuc Le & Duong Minh Bui & Cao Cuong Ngo & Anh My Thi Le, 2018. "FLISR Approach for Smart Distribution Networks Using E-Terra Software—A Case Study," Energies, MDPI, vol. 11(12), pages 1-33, November.
    5. Sadeghi, M. & Kalantar, M., 2023. "Fully decentralized multi-agent coordination scheme in smart distribution restoration: Multilevel consensus," Applied Energy, Elsevier, vol. 350(C).
    6. Zidan, Aboelsood & El-Saadany, E.F., 2015. "Incorporating customers' reliability requirements and interruption characteristics in service restoration plans for distribution systems," Energy, Elsevier, vol. 87(C), pages 192-200.
    7. Zidan, Aboelsood & Gabbar, Hossam A. & Eldessouky, Ahmed, 2015. "Optimal planning of combined heat and power systems within microgrids," Energy, Elsevier, vol. 93(P1), pages 235-244.
    8. Jung-Hun Lee & Seung-Gyu Jeon & Dong-Kyu Kim & Joon-Seok Oh & Jae-Eon Kim, 2020. "Temporary Fault Ride-Through Method in Power Distribution Systems with Distributed Generations Based on PCS," Energies, MDPI, vol. 13(5), pages 1-15, March.
    9. Li, Peng & Ji, Jie & Ji, Haoran & Song, Guanyu & Wang, Chengshan & Wu, Jianzhong, 2020. "Self-healing oriented supply restoration method based on the coordination of multiple SOPs in active distribution networks," Energy, Elsevier, vol. 195(C).

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