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Impact Evaluation of Wind Power Geographic Dispersion on Future Operating Reserve Needs

Author

Listed:
  • Fernando Manuel Carvalho da Silva Santos

    (College of Electrical Engineering, Federal University of Pará, Pará 66075-110, Brazil)

  • Leonardo Elizeire Bremermann

    (Department of Electrical and Electronics Engineering, Federal University of Santa Catarina, Santa Catarina 88040-900, Brazil
    Department of Energy and Production, Institute of Science and Technology, INESC P&D Brasil, Barrio Gonzaga Santos 11055-300, Brazil)

  • Tadeu Da Mata Medeiros Branco

    (College of Electrical Engineering, Federal University of Pará, Pará 66075-110, Brazil)

  • Diego Issicaba

    (Department of Electrical and Electronics Engineering, Federal University of Santa Catarina, Santa Catarina 88040-900, Brazil
    Department of Energy and Production, Institute of Science and Technology, INESC P&D Brasil, Barrio Gonzaga Santos 11055-300, Brazil)

  • Mauro Augusto da Rosa

    (Department of Electrical and Electronics Engineering, Federal University of Santa Catarina, Santa Catarina 88040-900, Brazil
    Department of Energy and Production, Institute of Science and Technology, INESC P&D Brasil, Barrio Gonzaga Santos 11055-300, Brazil)

Abstract

This paper evaluates the potential of diverse wind power patterns to balance the global power output of wind farms using the concept of operating reserve assessment. To achieve this, operating reserve assessment models are utilized to evaluate bulk generation systems under several conditions of wind power geographic distribution. Different wind behavior patterns and wind power penetration levels are tested using a modified configuration of the Institute of Electrical and Electronics Engineers Reliability Test System 96 (IEEE RTS-96). The results highlight that on a large country scale system with different wind characteristics, the diversification of wind behavior might be conducive to a compensation of wind power fluctuations, which may significantly decrease the need for system operating reserves. This effect is verified using probability distribution functions of reserve needs estimated by sequential Monte Carlo simulations (SMCS), such that useful information regarding generation capacity flexibility is drawn from the evaluations.

Suggested Citation

  • Fernando Manuel Carvalho da Silva Santos & Leonardo Elizeire Bremermann & Tadeu Da Mata Medeiros Branco & Diego Issicaba & Mauro Augusto da Rosa, 2018. "Impact Evaluation of Wind Power Geographic Dispersion on Future Operating Reserve Needs," Energies, MDPI, vol. 11(11), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2863-:d:177552
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    References listed on IDEAS

    as
    1. Chia-An Chang & Yuan-Kang Wu & Bin-Kwie Chen, 2016. "Determination of Maximum Wind Power Penetration in an Isolated Island System by Considering Spinning Reserve," Energies, MDPI, vol. 9(9), pages 1-16, August.
    2. Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
    3. Yan Li & Ming Zhou & Dawei Wang & Yuehui Huang & Zifen Han, 2017. "Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation," Energies, MDPI, vol. 10(11), pages 1-15, November.
    4. Yingying Zhao & Dongsheng Li & Ao Dong & Dahai Kang & Qin Lv & Li Shang, 2017. "Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data," Energies, MDPI, vol. 10(8), pages 1-17, August.
    5. Jiashen Teh, 2018. "Adequacy Assessment of Wind Integrated Generating Systems Incorporating Demand Response and Battery Energy Storage System," Energies, MDPI, vol. 11(10), pages 1-12, October.
    6. Hejun Yang & Lei Wang & Yeyu Zhang & Xianjun Qi & Lei Wang & Hongbin Wu, 2018. "Reliability Assessment of Wind Farm Electrical System Based on a Probability Transfer Technique," Energies, MDPI, vol. 11(4), pages 1-16, March.
    7. Drake, Ben & Hubacek, Klaus, 2007. "What to expect from a greater geographic dispersion of wind farms?--A risk portfolio approach," Energy Policy, Elsevier, vol. 35(8), pages 3999-4008, August.
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    Cited by:

    1. Pedro Vieira & Mauro Rosa & Leonardo Bremermann & Erika Pequeno & Sandy Miranda, 2020. "Long-term Static and Operational Reserves Assessment Considering Operating and Market Agreements Representation to Multi-Area Systems," Energies, MDPI, vol. 13(6), pages 1-17, March.

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