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Assessment of large scale wind power generation with new generation locations without measurement data

Author

Listed:
  • Ekström, Jussi
  • Koivisto, Matti
  • Mellin, Ilkka
  • Millar, John
  • Saarijärvi, Eero
  • Haarla, Liisa

Abstract

Large amounts of new wind power are currently under construction or planning in many countries. The constantly increasing percentage of wind power in the electricity generation mix has to be taken into consideration when planning power systems. This paper introduces a Monte Carlo simulation based methodology that can be used to assess the effects (e.g. need for new transmission lines, reserves, wind curtailment or demand side management) of large amounts of existing and planned wind power generation on the power system. The presented methodology is able to assess new wind power scenarios spread over a wide geographical area, comprising numerous existing and planned wind generation locations. The Monte Carlo simulation results are verified against measured aggregated wind power generation in Finland from 2008 to 2014. In addition, case studies of future scenarios with 232 individual wind generation locations are presented to show the applicability of the methodology as a tool in power system planning.

Suggested Citation

  • Ekström, Jussi & Koivisto, Matti & Mellin, Ilkka & Millar, John & Saarijärvi, Eero & Haarla, Liisa, 2015. "Assessment of large scale wind power generation with new generation locations without measurement data," Renewable Energy, Elsevier, vol. 83(C), pages 362-374.
    • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:362-374
    DOI: 10.1016/j.renene.2015.04.050
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    References listed on IDEAS

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    1. Goić, R. & Krstulović, J. & Jakus, D., 2010. "Simulation of aggregate wind farm short-term production variations," Renewable Energy, Elsevier, vol. 35(11), pages 2602-2609.
    2. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    3. González-Longatt, F. & Wall, P. & Terzija, V., 2012. "Wake effect in wind farm performance: Steady-state and dynamic behavior," Renewable Energy, Elsevier, vol. 39(1), pages 329-338.
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    Cited by:

    1. Murthy, K.S.R. & Rahi, O.P., 2016. "Preliminary assessment of wind power potential over the coastal region of Bheemunipatnam in northern Andhra Pradesh, India," Renewable Energy, Elsevier, vol. 99(C), pages 1137-1145.
    2. 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.
    3. Jussi Ekström & Matti Koivisto & Ilkka Mellin & Robert John Millar & Matti Lehtonen, 2018. "A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations," Energies, MDPI, vol. 11(9), pages 1-18, September.
    4. Arslan Ahmad Bashir & Andreas Lund & Mahdi Pourakbari-Kasmaei & Matti Lehtonen, 2021. "Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities," Energies, MDPI, vol. 14(7), pages 1-20, March.
    5. Mubbashir Ali & Jussi Ekström & Matti Lehtonen, 2018. "Sizing Hydrogen Energy Storage in Consideration of Demand Response in Highly Renewable Generation Power Systems," Energies, MDPI, vol. 11(5), pages 1-11, May.
    6. Arslan Ahmad Bashir & Matti Lehtonen, 2019. "Optimal Coordination of Aggregated Hydro-Storage with Residential Demand Response in Highly Renewable Generation Power System: The Case Study of Finland," Energies, MDPI, vol. 12(6), pages 1-16, March.
    7. Mubbashir Ali & Jussi Ekström & Matti Lehtonen, 2017. "Assessing the Potential Benefits and Limits of Electric Storage Heaters for Wind Curtailment Mitigation: A Finnish Case Study," Sustainability, MDPI, vol. 9(5), pages 1-15, May.

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