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Spatial and temporal analysis of electric wind generation intermittency and dynamics

Author

Listed:
  • Tarroja, Brian
  • Mueller, Fabian
  • Eichman, Joshua D.
  • Brouwer, Jack
  • Samuelsen, Scott
Abstract
A spatial and temporal analysis of wind power generation characteristics was conducted in order to determine the implications of intermittent wind generation dynamics on the profile of the electric loads that must be balanced by dispatchable electrical generators on the electric grid. A parametric analysis was conducted to evaluate the sensitivity of the typical magnitudes of wind power fluctuations on different timescales, power variation range, typical daily and seasonal wind profiles to wind farm size and regional distribution. A methodology to evaluate wind dynamics based on power spectral density analyses have been developed. Results indicate that increasing the size of a local wind farm significantly reduced the magnitude of wind power fluctuations on timescales faster than 12 h, with the largest reductions occurring at the fastest timescales. Additional reductions in power fluctuations can be achieved with the implementation of local and regional distribution of wind turbines in disperse high wind areas. In these cases, it was discovered that the timescale band within which the largest reductions in power fluctuations occurred was dependent on regional geographic features, and did not necessarily correspond to the fastest timescales. In addition, it was also discovered that the aggregation of wind power from different regions could produce a more uniform frequency distribution of power fluctuation reductions.

Suggested Citation

  • Tarroja, Brian & Mueller, Fabian & Eichman, Joshua D. & Brouwer, Jack & Samuelsen, Scott, 2011. "Spatial and temporal analysis of electric wind generation intermittency and dynamics," Renewable Energy, Elsevier, vol. 36(12), pages 3424-3432.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:12:p:3424-3432
    DOI: 10.1016/j.renene.2011.05.022
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    References listed on IDEAS

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    1. Dalili, N. & Edrisy, A. & Carriveau, R., 2009. "A review of surface engineering issues critical to wind turbine performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 428-438, February.
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    1. Coker, Phil & Barlow, Janet & Cockerill, Tim & Shipworth, David, 2013. "Measuring significant variability characteristics: An assessment of three UK renewables," Renewable Energy, Elsevier, vol. 53(C), pages 111-120.
    2. Tadeusz Mączka & Halina Pawlak-Kruczek & Lukasz Niedzwiecki & Edward Ziaja & Artur Chorążyczewski, 2020. "Plasma Assisted Combustion as a Cost-Effective Way for Balancing of Intermittent Sources: Techno-Economic Assessment for 200 MW el Power Unit," Energies, MDPI, vol. 13(19), pages 1-16, September.
    3. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    4. Álvarez-García, Francisco J. & Fresno-Schmolk, Gonzalo & OrtizBevia, María J. & Cabos, William & RuizdeElvira, Antonio, 2020. "Reduction of aggregate wind power variability using Empirical Orthogonal Teleconnections: An application in the Iberian Peninsula," Renewable Energy, Elsevier, vol. 159(C), pages 151-161.
    5. Alexis Tantet & Marc Stéfanon & Philippe Drobinski & Jordi Badosa & Silvia Concettini & Anna Cretì & Claudia D’Ambrosio & Dimitri Thomopulos & Peter Tankov, 2019. "e 4 clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy," Energies, MDPI, vol. 12(22), pages 1-37, November.
    6. Ye, Lin & Zhao, Yongning & Zeng, Cheng & Zhang, Cihang, 2017. "Short-term wind power prediction based on spatial model," Renewable Energy, Elsevier, vol. 101(C), pages 1067-1074.
    7. Schmidt, Johannes & Cancella, Rafael & Junior, Amaro Olímpio Pereira, 2016. "The effect of windpower on long-term variability of combined hydro-wind resources: The case of Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 131-141.
    8. Harrison-Atlas, Dylan & Murphy, Caitlin & Schleifer, Anna & Grue, Nicholas, 2022. "Temporal complementarity and value of wind-PV hybrid systems across the United States," Renewable Energy, Elsevier, vol. 201(P1), pages 111-123.
    9. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren & Söder, Lennart, 2018. "Analysis of wind power intermittency based on historical wind power data," Energy, Elsevier, vol. 150(C), pages 482-492.
    10. Han, Shuang & Zhang, Lu-na & Liu, Yong-qian & Zhang, Hao & Yan, Jie & Li, Li & Lei, Xiao-hui & Wang, Xu, 2019. "Quantitative evaluation method for the complementarity of wind–solar–hydro power and optimization of wind–solar ratio," Applied Energy, Elsevier, vol. 236(C), pages 973-984.
    11. Eichman, Joshua D. & Mueller, Fabian & Tarroja, Brian & Schell, Lori Smith & Samuelsen, Scott, 2013. "Exploration of the integration of renewable resources into California's electric system using the Holistic Grid Resource Integration and Deployment (HiGRID) tool," Energy, Elsevier, vol. 50(C), pages 353-363.
    12. He, Y.X. & Xia, T. & Liu, Z.Y. & Zhang, T. & Dong, Z., 2013. "Evaluation of the capability of accepting large-scale wind power in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 509-516.
    13. Ikegami, Takashi & Urabe, Chiyori T. & Saitou, Tetsuo & Ogimoto, Kazuhiko, 2018. "Numerical definitions of wind power output fluctuations for power system operations," Renewable Energy, Elsevier, vol. 115(C), pages 6-15.
    14. Johannes Schmidt & Rafael Cancella & Amaro Olímpio Pereira Junior, 2014. "Combing windpower and hydropower to decrease seasonal and inter-annual availability of renewable energy sources in Brazil," Working Papers 562014, University of Natural Resources and Life Sciences, Vienna, Department of Economics and Social Sciences, Institute for Sustainable Economic Development.
    15. Yan, Jie & Liu, Yongqian & Han, Shuang & Qiu, Meng, 2013. "Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 613-621.
    16. repec:zbw:inwedp:562014 is not listed on IDEAS
    17. Yuan, Qiheng & Zhou, Keliang & Yao, Jing, 2020. "A new measure of wind power variability with implications for the optimal sizing of standalone wind power systems," Renewable Energy, Elsevier, vol. 150(C), pages 538-549.
    18. François, B. & Borga, M. & Creutin, J.D. & Hingray, B. & Raynaud, D. & Sauterleute, J.F., 2016. "Complementarity between solar and hydro power: Sensitivity study to climate characteristics in Northern-Italy," Renewable Energy, Elsevier, vol. 86(C), pages 543-553.
    19. Doepfert, Markus & Castro, Rui, 2021. "Techno-economic optimization of a 100% renewable energy system in 2050 for countries with high shares of hydropower: The case of Portugal," Renewable Energy, Elsevier, vol. 165(P1), pages 491-503.
    20. Yongqian Liu & Yanhui Qiao & Shuang Han & Yanping Xu & Tianxiang Geng & Tiandong Ma, 2021. "Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid," Energies, MDPI, vol. 14(16), pages 1-14, August.
    21. Daniel Suchet & Adrien Jeantet & Thomas Elghozi & Zacharie Jehl, 2020. "Defining and Quantifying Intermittency in the Power Sector," Energies, MDPI, vol. 13(13), pages 1-12, July.
    22. Tarroja, Brian & Mueller, Fabian & Eichman, Joshua D. & Samuelsen, Scott, 2012. "Metrics for evaluating the impacts of intermittent renewable generation on utility load-balancing," Energy, Elsevier, vol. 42(1), pages 546-562.
    23. Baltas, A.E. & Dervos, A.N., 2012. "Special framework for the spatial planning & the sustainable development of renewable energy sources," Renewable Energy, Elsevier, vol. 48(C), pages 358-363.
    24. Schmidt, Johannes & Cancella, Rafael & Junior, Amaro Olímpio Pereira, 2014. "Combing windpower and hydropower to decrease seasonal and inter-annual availability of renewable energy sources in Brazil," Discussion Papers DP-56-2014, University of Natural Resources and Life Sciences, Vienna, Department of Economics and Social Sciences, Institute for Sustainable Economic Development.
    25. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.

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