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Technical Constraints on and Efficient Strategies for the Integration of Wind Energy

Author

Listed:
  • Marian Klobasa

    (Fraunhofer Institute System and Innovation Research, Breslauer Strasse 48, 76139 Karlsruhe, Germany)

  • Carlo Obersteiner

    (Energy Economics Group, Vienna University of Technology, Gusshausstrasse 25-29/373-2, 1040 Vienna, Austria)

Abstract

Based on simulations of the German and Austrian electricity systems, the impacts of wind generation on power plant operation are analysed. Accurate forecasts play the major role when integrating wind power because they determine additional reserve capacity as well as any additional imbalance. Additional balancing power can be limited to less than 10 % of the installed wind capacity if improved forecast tools or shorter forecast horizons are used. Additional balancing costs are below 5 € per MWh wind energy depending on the forecast accuracy and the available balancing capacity. Balancing markets need developments which improve the accessibility for demand-side options as simulations have shown potential cost reductions of 25%.

Suggested Citation

  • Marian Klobasa & Carlo Obersteiner, 2006. "Technical Constraints on and Efficient Strategies for the Integration of Wind Energy," Energy & Environment, , vol. 17(6), pages 885-906, November.
    • Handle: RePEc:sae:engenv:v:17:y:2006:i:6:p:885-906
    DOI: 10.1260/095830506779398867
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    Cited by:

    1. N. Aravindhan & M. P. Natarajan & S. Ponnuvel & P.K. Devan, 2023. "Recent developments and issues of small-scale wind turbines in urban residential buildings- A review," Energy & Environment, , vol. 34(4), pages 1142-1169, June.
    2. Zehir, Mustafa Alparslan & Batman, Alp & Bagriyanik, Mustafa, 2016. "Review and comparison of demand response options for more effective use of renewable energy at consumer level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 631-642.
    3. Farzamkia, Saleh & Ranjbar, Hossein & Hatami, Alireza & Iman-Eini, Hossein, 2016. "A novel PSO (Particle Swarm Optimization)-based approach for optimal schedule of refrigerators using experimental models," Energy, Elsevier, vol. 107(C), pages 707-715.

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