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The benefit of long-term high resolution wind data for electricity system analysis

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  • Henckes, Philipp
  • Knaut, Andreas
  • Obermüller, Frank
  • Frank, Christopher

Abstract

Future energy systems rely increasingly on the wind power supply. Understanding its characteristics is essential for the functioning of future electricity systems. Critical low wind situations may endanger the security of supply. So far, historical observations of wind power production are limited to few recent historical years and may not suffice to quantify the expected overall wind contribution, its variability, and its regional balancing effects for future electricity systems. With a novel long-term high-resolution wind power production dataset (hourly on a 6 × 6 km grid for 20 years) we derive new insights. First, we find advantages of our high-resolution dataset compared to previous studies. Second, we find a strong variation in annual wind production (variation of up to 14% for Germany). And third, we find a potential benefit from electricity exchange with neighboring countries in low wind conditions (for Germany in 81% of the low wind situations). The results are highly relevant for further investigation on the level of secured capacity or to identify optimal power transmission capacities within energy market modeling.

Suggested Citation

  • Henckes, Philipp & Knaut, Andreas & Obermüller, Frank & Frank, Christopher, 2018. "The benefit of long-term high resolution wind data for electricity system analysis," Energy, Elsevier, vol. 143(C), pages 934-942.
    • Handle: RePEc:eee:energy:v:143:y:2018:i:c:p:934-942
    DOI: 10.1016/j.energy.2017.10.049
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    References listed on IDEAS

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    1. Hagspiel, Simeon & Knaut, Andreas & Peter, Jakob, 2017. "Reliability in Multy-Regional Power Systems - Capacity Adequacy and the Role of Interconnectors," EWI Working Papers 2017-7, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), revised 29 Jun 2018.
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    Cited by:

    1. Jung, Christopher & Schindler, Dirk, 2018. "On the inter-annual variability of wind energy generation – A case study from Germany," Applied Energy, Elsevier, vol. 230(C), pages 845-854.
    2. Klie, Leo & Madlener, Reinhard, 2022. "Optimal configuration and diversification of wind turbines: A hybrid approach to improve the penetration of wind power," Energy Economics, Elsevier, vol. 105(C).
    3. 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).
    4. Klie, Leo & Madlener, Reinhard, 2020. "Concentration Versus Diversification: A Spatial Deployment Approach to Improve the Economics of Wind Power," FCN Working Papers 2/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised May 2021.
    5. Henckes, Philipp & Frank, Christopher & Küchler, Nils & Peter, Jakob & Wagner, Johannes, 2020. "Uncertainty estimation of investment planning models under high shares of renewables using reanalysis data," Energy, Elsevier, vol. 208(C).
    6. Frank, Christopher & Fiedler, Stephanie & Crewell, Susanne, 2021. "Balancing potential of natural variability and extremes in photovoltaic and wind energy production for European countries," Renewable Energy, Elsevier, vol. 163(C), pages 674-684.
    7. Peter, Jakob & Wagner, Johannes, 2018. "Optimal Allocation of Variable Renewable Energy Considering Contributions to Security of Supply," EWI Working Papers 2018-2, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    8. Drücke, Jaqueline & Borsche, Michael & James, Paul & Kaspar, Frank & Pfeifroth, Uwe & Ahrens, Bodo & Trentmann, Jörg, 2021. "Climatological analysis of solar and wind energy in Germany using the Grosswetterlagen classification," Renewable Energy, Elsevier, vol. 164(C), pages 1254-1266.
    9. Frank, Christopher W. & Pospichal, Bernhard & Wahl, Sabrina & Keller, Jan D. & Hense, Andreas & Crewell, Susanne, 2020. "The added value of high resolution regional reanalyses for wind power applications," Renewable Energy, Elsevier, vol. 148(C), pages 1094-1109.

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