IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i9p704-d77176.html
   My bibliography  Save this article

Optimal Available Transfer Capability Assessment Strategy for Wind Integrated Transmission Systems Considering Uncertainty of Wind Power Probability Distribution

Author

Listed:
  • Jun Xie

    (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Lu Wang

    (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Qiaoyan Bian

    (State Grid Zhejiang Electric Power Company, Hangzhou 310007, China)

  • Xiaohua Zhang

    (School of Urban Rail Transportation, Changzhou University, Changzhou 213164, China)

  • Dan Zeng

    (China Electric Power Research Institute (Nanjing), Nanjing 210003, China)

  • Ke Wang

    (China Electric Power Research Institute (Nanjing), Nanjing 210003, China)

Abstract

Wind power prediction research shows that it is difficult to accurately and effectively estimate the probability distribution (PD) of wind power. When only partial information of the wind power probability distribution function is available, an optimal available transfer capability ( ATC ) assessment strategy considering the uncertainty on the wind power probability distribution is proposed in this paper. As wind power probability distribution is not accurately given, the proposed strategy can efficiently maximize ATC with the security operation constraints satisfied under any wind power PD function case in the uncertainty set. A distributional robust chance constrained (DRCC) model is developed to describe an optimal ATC assessment problem. To achieve tractability of the DRCC model, the dual optimization, S-lemma and Schur complement are adopted to eliminate the uncertain wind power vector in the DRCC model. According to the characteristics of the problem, the linear matrix inequality (LMI)-based particle swarm optimization (PSO) algorithm is used to solve the DRCC model which contains first and second-order moment information of the wind power. The modified IEEE 30-bus system simulation results show the feasibility and effectiveness of the proposed ATC assessment strategy.

Suggested Citation

  • Jun Xie & Lu Wang & Qiaoyan Bian & Xiaohua Zhang & Dan Zeng & Ke Wang, 2016. "Optimal Available Transfer Capability Assessment Strategy for Wind Integrated Transmission Systems Considering Uncertainty of Wind Power Probability Distribution," Energies, MDPI, vol. 9(9), pages 1-18, September.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:704-:d:77176
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/9/704/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/9/9/704/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Pan & Wang, Jiangfeng & Dai, Yiping, 2015. "Capacity allocation of a hybrid energy storage system for power system peak shaving at high wind power penetration level," Renewable Energy, Elsevier, vol. 75(C), pages 541-549.
    Full references (including those not matched with items on IDEAS)

    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. Fichter, Tobias & Soria, Rafael & Szklo, Alexandre & Schaeffer, Roberto & Lucena, Andre F.P., 2017. "Assessing the potential role of concentrated solar power (CSP) for the northeast power system of Brazil using a detailed power system model," Energy, Elsevier, vol. 121(C), pages 695-715.
    2. Guo, Cong & Xu, Yujie & Zhang, Xinjing & Guo, Huan & Zhou, Xuezhi & Liu, Chang & Qin, Wei & Li, Wen & Dou, Binlin & Chen, Haisheng, 2017. "Performance analysis of compressed air energy storage systems considering dynamic characteristics of compressed air storage," Energy, Elsevier, vol. 135(C), pages 876-888.
    3. Lan, Hai & Wen, Shuli & Hong, Ying-Yi & Yu, David C. & Zhang, Lijun, 2015. "Optimal sizing of hybrid PV/diesel/battery in ship power system," Applied Energy, Elsevier, vol. 158(C), pages 26-34.
    4. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    5. Hai Lan & Jinfeng Dai & Shuli Wen & Ying-Yi Hong & David C. Yu & Yifei Bai, 2015. "Optimal Tilt Angle of Photovoltaic Arrays and Economic Allocation of Energy Storage System on Large Oil Tanker Ship," Energies, MDPI, vol. 8(10), pages 1-16, October.
    6. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Forecasting error processing techniques and frequency domain decomposition for forecasting error compensation and renewable energy firming in hybrid systems," Applied Energy, Elsevier, vol. 313(C).
    7. Hasan, Nor Shahida & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Omar, Wan Zaidi Wan & Rosmin, Norzanah, 2016. "Improving power grid performance using parallel connected Compressed Air Energy Storage and wind turbine system," Renewable Energy, Elsevier, vol. 96(PA), pages 498-508.
    8. Jiang, Yinghua & Kang, Lixia & Liu, Yongzhong, 2019. "A unified model to optimize configuration of battery energy storage systems with multiple types of batteries," Energy, Elsevier, vol. 176(C), pages 552-560.
    9. Abdul Ghani Olabi & Tabbi Wilberforce & Mohammad Ali Abdelkareem & Mohamad Ramadan, 2021. "Critical Review of Flywheel Energy Storage System," Energies, MDPI, vol. 14(8), pages 1-33, April.
    10. Abbassi, Abdelkader & Dami, Mohamed Ali & Jemli, Mohamed, 2017. "A statistical approach for hybrid energy storage system sizing based on capacity distributions in an autonomous PV/Wind power generation system," Renewable Energy, Elsevier, vol. 103(C), pages 81-93.
    11. Fu-Cheng Wang & Kuang-Ming Lin, 2018. "Impacts of Load Profiles on the Optimization of Power Management of a Green Building Employing Fuel Cells," Energies, MDPI, vol. 12(1), pages 1-16, December.
    12. Wang, Xiaokui & Bamisile, Olusola & Chen, Shuheng & Xu, Xiao & Luo, Shihua & Huang, Qi & Hu, Weihao, 2022. "Decarbonization of China's electricity systems with hydropower penetration and pumped-hydro storage: Comparing the policies with a techno-economic analysis," Renewable Energy, Elsevier, vol. 196(C), pages 65-83.
    13. Hongxia Liu & Huiling Wang & Zongtang Xie, 2019. "Wind utilization and carbon emissions equilibrium: Scheduling strategy for wind-thermal generation system," Energy & Environment, , vol. 30(6), pages 1111-1131, September.
    14. Han, Jie & Ouyang, Leixin & Xu, Yuzhen & Zeng, Rong & Kang, Shushuo & Zhang, Guoqiang, 2016. "Current status of distributed energy system in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 288-297.
    15. Saboori, Hedayat & Hemmati, Reza, 2017. "Maximizing DISCO profit in active distribution networks by optimal planning of energy storage systems and distributed generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 365-372.
    16. Tong, Zheming & Cheng, Zhewu & Tong, Shuiguang, 2021. "A review on the development of compressed air energy storage in China: Technical and economic challenges to commercialization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    17. Heng Yang & Ziliang Jin & Jianhua Wang & Yong Zhao & Hejia Wang & Weihua Xiao, 2019. "Data-Driven Stochastic Scheduling for Energy Integrated Systems," Energies, MDPI, vol. 12(12), pages 1-21, June.
    18. Qing Kong & Michael Fowler & Evgueniy Entchev & Hajo Ribberink & Robert McCallum, 2018. "The Role of Charging Infrastructure in Electric Vehicle Implementation within Smart Grids," Energies, MDPI, vol. 11(12), pages 1-20, December.
    19. Yang, Yiqing & Chen, Peihao & Liu, Qiang, 2021. "A wave energy harvester based on coaxial mechanical motion rectifier and variable inertia flywheel," Applied Energy, Elsevier, vol. 302(C).
    20. Alessandro Serpi & Mario Porru & Alfonso Damiano, 2017. "An Optimal Power and Energy Management by Hybrid Energy Storage Systems in Microgrids," Energies, MDPI, vol. 10(11), pages 1-21, November.

    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:9:y:2016:i:9:p:704-:d:77176. 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.