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A statistical algorithm for predicting the energy storage capacity for baseload wind power generation in the future electric grids

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  • Shokrzadeh, Shahab
  • Jafari Jozani, Mohammad
  • Bibeau, Eric
  • Molinski, Tom

Abstract

We propose a statistical algorithm for sizing the energy storage system required for delivering baseload electricity to a selected confidence level for a wind farm. The proposed algorithm can be utilized by utilities to assess wind integration and to investigate better capacity credits for wind farms connected to the grid, by wind farm operators to potentially increase their return on investment by designing a baseload wind farm to a selected confidence level, and by financial institutions to calculate the confidence level for baseload wind farm projects. Methods introduced are based on parametric and nonparametric statistical models using wind resource assessment data and available wind turbine information that reflect different stages of a wind farm project—from site selection to operational status. To study the performance of each method, we apply these to a North America operational wind farm data set. We use averaged 10-min and hourly data to calculate and compare the firm capacity of the wind turbine for each proposed method. The results show that for different stages of the wind farm development, and depending on the available information, the proposed algorithm can properly estimate the energy storage capacity required to deliver constant power to a user selected confidence level.

Suggested Citation

  • Shokrzadeh, Shahab & Jafari Jozani, Mohammad & Bibeau, Eric & Molinski, Tom, 2015. "A statistical algorithm for predicting the energy storage capacity for baseload wind power generation in the future electric grids," Energy, Elsevier, vol. 89(C), pages 793-802.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:793-802
    DOI: 10.1016/j.energy.2015.05.140
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2018. "Battery energy storage system size determination in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 109-125.
    3. Basetti, Vedik & Chandel, Ashwani K. & Chandel, Rajeevan, 2016. "Power system dynamic state estimation using prediction based evolutionary technique," Energy, Elsevier, vol. 107(C), pages 29-47.
    4. Paweł Ziemba, 2021. "Multi-Criteria Fuzzy Evaluation of the Planned Offshore Wind Farm Investments in Poland," Energies, MDPI, vol. 14(4), pages 1-19, February.
    5. Barelli, L. & Bidini, G. & Bonucci, F., 2016. "A micro-grid operation analysis for cost-effective battery energy storage and RES plants integration," Energy, Elsevier, vol. 113(C), pages 831-844.
    6. Ahmadian, Ali & Sedghi, Mahdi & Aliakbar-Golkar, Masoud & Elkamel, Ali & Fowler, Michael, 2016. "Optimal probabilistic based storage planning in tap-changer equipped distribution network including PEVs, capacitor banks and WDGs: A case study for Iran," Energy, Elsevier, vol. 112(C), pages 984-997.
    7. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2021. "Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes," Energy, Elsevier, vol. 218(C).
    8. Babaeiyazdi, Iman & Rezaei-Zare, Afshin & Shokrzadeh, Shahab, 2021. "State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach," Energy, Elsevier, vol. 223(C).
    9. Jiang, Hou & Zhang, Xiaotong & Yao, Ling & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2023. "High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles," Applied Energy, Elsevier, vol. 348(C).
    10. Shokrzadeh, Shahab & Bibeau, Eric, 2016. "Sustainable integration of intermittent renewable energy and electrified light-duty transportation through repurposing batteries of plug-in electric vehicles," Energy, Elsevier, vol. 106(C), pages 701-711.

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