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The Korean Institute of Electrical Engineers (대한전기학회)
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Economic Comparison of Wind Power Curtailment and ESS Operation for Mitigating Wind Power Forecasting Error

풍력발전 출력 예측오차 완화를 위한 출력제한운전과 ESS운전의 경제성 비교

  • Wi, Young-Min (School of Electrical Engineering, Gwangju University) ;
  • Jo, Hyung-Chul (BK21 Plus Humanware Information Technology Division, Korea University) ;
  • Lee, Jaehee (Dept. of Information and Electronic Engineering, Mokpo National University)
  • Received : 2017.09.27
  • Accepted : 2018.01.11
  • Published : 2018.02.01
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Abstract

Wind power forecast is critical for efficient power system operation. However, wind power has high forecasting errors due to uncertainty caused by the climate change. These forecasting errors can have an adverse impact on the power system operation. In order to mitigate the issues caused by the wind power forecasting error, wind power curtailment and energy storage system (ESS) can be introduced in the power system. These methods can affect the economics of wind power resources. Therefore, it is necessary to evaluate the economics of the methods for mitigating the wind power forecasting error. This paper attempts to analyze the economics of wind power curtailment and ESS operation for mitigating wind power forecasting error. Numerical simulation results are presented to show the economic impact of wind power curtailment and ESS operation.

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References

  1. K. Dragoon, Valuing Wind Generation on Integrated Power Systems, Norwich: William Andrew, 2010, Chapter 5.
  2. M. Tsili and S. Papathanassiou, "A Review of Grid Code Technical Requirements for Wind Farms," IET Renew. Power Gen., vol. 3, no. 3, Mar. 2009.
  3. S. Fink, C. Mudd, K. Porter, and B. Morgenstern, "Wind Energy Curtailment Case Studies: May 2008 - May 2009," National Renewable Energy Laboratory, Oct. 2009.
  4. Y. Gu and L. Xie, "Fast Sensitivity Analysis Approach to Assessing Congestion Induced Wind Curtailment," IEEE Trans. Power Syst., vol. 29, no. 1, Jan. 2014.
  5. MISO, Dispatchable Intermittent Resources, available at: https://www.misoenergy.org.
  6. H. Bludszuweit, J. A. Dominguez-Navarro, and A. Llombart, "Statistical Analysis of Wind Power Forecast Error," IEEE Trans. on Power Syst., vol. 23, no. 3, Aug. 2008.
  7. H. Bludszuweit and J. A. Dominguez-Navarro, "A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty," IEEE Trans. on Power Syst., vol. 26, no. 3, Dec. 2010.
  8. F. Zhang, K. Meng, Z. Xu, Z. Dong, L. Zhang, C. Wan, and J. Liang, "Battery ESS Planning for Wind Smoothing via Variable-Interval Reference Modulation and Self-Adaptive SOC Control Strategy," IEEE Trans. on Sustainable Energy, vol. 8, no. 2, Oct, 2016.
  9. S. Schoenung, "Energy Storage Systems Cost Update - A Study for the DOE Energy Storage Systems Program," Snadia Report, Apr. 2011.
  10. C. A. Collier, Engineering Economic and Cost Analysis, Melon Park, CA: Addison Wesley Longman, 1998.
  11. Korea Power Exchange, Electric Power System Information System (EPSIS), available at: https://www.kpx.or.kr.