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Active distribution networks planning with high penetration of wind power

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  • Mokryani, Geev
  • Hu, Yim Fun
  • Pillai, Prashant
  • Rajamani, Haile-Selassie

Abstract

In this paper, a stochastic method for active distribution networks planning within a distribution market environment considering multi-configuration of wind turbines is proposed. Multi-configuration multi-scenario market-based optimal power flow is used to maximize the social welfare considering uncertainties related to wind speed and load demand and different operational status of wind turbines (multiple-wind turbine configurations). Scenario-based approach is used to model the abovementioned uncertainties. The method evaluates the impact of multiple-wind turbine configurations and active network management schemes on the amount of wind power that can be injected into the grid, the distribution locational marginal prices throughout the network and on the social welfare. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system. It was shown that multi-wind turbine configurations under active network management schemes, including coordinated voltage control and adaptive power factor control, can increase the amount of wind power that can be injected into the grid; therefore, the distribution locational marginal prices reduce throughout the network significantly.

Suggested Citation

  • Mokryani, Geev & Hu, Yim Fun & Pillai, Prashant & Rajamani, Haile-Selassie, 2017. "Active distribution networks planning with high penetration of wind power," Renewable Energy, Elsevier, vol. 104(C), pages 40-49.
    • Handle: RePEc:eee:renene:v:104:y:2017:i:c:p:40-49
    DOI: 10.1016/j.renene.2016.12.007
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    Citations

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    Cited by:

    1. Chen Sun & Dong Liu & Yun Wang & Yi You, 2017. "Assessment of Credible Capacity for Intermittent Distributed Energy Resources in Active Distribution Network," Energies, MDPI, vol. 10(8), pages 1-24, July.
    2. Zubo, Rana H.A. & Mokryani, Geev & Abd-Alhameed, Raed, 2018. "Optimal operation of distribution networks with high penetration of wind and solar power within a joint active and reactive distribution market environment," Applied Energy, Elsevier, vol. 220(C), pages 713-722.
    3. Ilia Shushpanov & Konstantin Suslov & Pavel Ilyushin & Denis N. Sidorov, 2021. "Towards the Flexible Distribution Networks Design Using the Reliability Performance Metric," Energies, MDPI, vol. 14(19), pages 1-24, September.
    4. Samal, Rajat Kanti & Tripathy, M., 2019. "A novel distance metric for evaluating impact of wind integration on power systems," Renewable Energy, Elsevier, vol. 140(C), pages 722-736.
    5. Antonio Rubens Baran Junior & Thelma S. Piazza Fernandes & Ricardo Augusto Borba, 2019. "Voltage Regulation Planning for Distribution Networks Using Multi-Scenario Three-Phase Optimal Power Flow," Energies, MDPI, vol. 13(1), pages 1-21, December.
    6. Zhou, Siyu & Han, Yang & Yang, Ping & Mahmoud, Karar & Lehtonen, Matti & Darwish, Mohamed M.F. & Zalhaf, Amr S., 2022. "An optimal network constraint-based joint expansion planning model for modern distribution networks with multi-types intermittent RERs," Renewable Energy, Elsevier, vol. 194(C), pages 137-151.
    7. Gyanendra Singh Sisodia & Einas Awad & Heba Alkhoja & Bruno S. Sergi, 2020. "Strategic business risk evaluation for sustainable energy investment and stakeholder engagement: A proposal for energy policy development in the Middle East through Khalifa funding and land subsidies," Business Strategy and the Environment, Wiley Blackwell, vol. 29(6), pages 2789-2802, September.
    8. Pouya Pourghasem Gavgani & Salar Baghbannovin & Seyed Masoud Mohseni-Bonab & Innocent Kamwa, 2024. "Distributed Energy Resources Management System (DERMS) and Its Coordination with Transmission System: A Review and Co-Simulation," Energies, MDPI, vol. 17(6), pages 1-20, March.
    9. Mokryani, Geev & Hu, Yim Fun & Papadopoulos, Panagiotis & Niknam, Taher & Aghaei, Jamshid, 2017. "Deterministic approach for active distribution networks planning with high penetration of wind and solar power," Renewable Energy, Elsevier, vol. 113(C), pages 942-951.
    10. Bilal Amjad & Mohammad Ahmad A. Al-Ja’afreh & Geev Mokryani, 2021. "Active Distribution Networks Planning Considering Multi-DG Configurations and Contingency Analysis," Energies, MDPI, vol. 14(14), pages 1-16, July.
    11. Prajapati, Vijaykumar K. & Mahajan, Vasundhara, 2021. "Reliability assessment and congestion management of power system with energy storage system and uncertain renewable resources," Energy, Elsevier, vol. 215(PB).
    12. Yajing Gao & Wenhai Yang & Jing Zhu & Jiafeng Ren & Peng Li, 2017. "Evaluating the Effect of Distributed Generation on Power Supply Capacity in Active Distribution System Based on Sensitivity Analysis," Energies, MDPI, vol. 10(10), pages 1-14, September.
    13. Li, Zening & Su, Su & Jin, Xiaolong & Chen, Houhe, 2021. "Distributed energy management for active distribution network considering aggregated office buildings," Renewable Energy, Elsevier, vol. 180(C), pages 1073-1087.
    14. Xie, Shiwei & Hu, Zhijian & Zhou, Daming & Li, Yan & Kong, Shunfei & Lin, Weiwei & Zheng, Yunfei, 2018. "Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network," Applied Energy, Elsevier, vol. 219(C), pages 207-225.
    15. Ye, Chengjin & Ding, Yi & Song, Yonghua & Lin, Zhenzhi & Wang, Lei, 2018. "A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing," Applied Energy, Elsevier, vol. 232(C), pages 9-25.
    16. Lazo, Joaquín & Watts, David, 2024. "Stochastic model for active distribution networks planning: An analysis of the combination of active network management schemes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).

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