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Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales

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  • Zhang, Jie
  • Cui, Mingjian
  • Hodge, Bri-Mathias
  • Florita, Anthony
  • Freedman, Jeffrey

Abstract

The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most of the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.

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  • Zhang, Jie & Cui, Mingjian & Hodge, Bri-Mathias & Florita, Anthony & Freedman, Jeffrey, 2017. "Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales," Energy, Elsevier, vol. 122(C), pages 528-541.
    • Handle: RePEc:eee:energy:v:122:y:2017:i:c:p:528-541
    DOI: 10.1016/j.energy.2017.01.104
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    Cited by:

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    15. Hu, Jianming & Zhang, Liping & Tang, Jingwei & Liu, Zhi, 2023. "A novel transformer ordinal regression network with label diversity for wind power ramp events forecasting," Energy, Elsevier, vol. 280(C).
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