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Quantifying the predictability of renewable energy data for improving power systems decision-making.

Sahand Karimi-Arpanahi1,2, S Ali Pourmousavi1, Nariman Mahdavi2

  • 1School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia.

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Summary

Accurately measuring renewable energy data predictability is crucial for power system decision-making. This study identifies the best predictability measure, offering guidance to save millions in the electricity sector.

Keywords:
PV generation time serieselectricity market analysisgeneration predictabilitypower systems data analysispower systems decision makingrenewable generation forecastingtime series predictabilityweighted permutation entropy

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Area of Science:

  • Power Systems Engineering
  • Time Series Analysis
  • Renewable Energy Forecasting

Background:

  • Accurate renewable generation forecasting is vital for power system stability and economic efficiency.
  • Existing forecasting methods' accuracy is constrained by the inherent predictability of the time series data.
  • A reliable measure for assessing data predictability is currently lacking in the power systems domain.

Purpose of the Study:

  • To systematically evaluate various predictability measures for renewable generation time series.
  • To identify the most suitable predictability measure for this specific data type.
  • To provide practical guidance on tuning the selected predictability measure.

Main Methods:

  • Literature review of existing predictability measures.
  • Systematic assessment of selected measures using renewable generation datasets.
  • Statistical analysis to determine measure suitability and performance.
  • Development of tuning guidelines for the optimal measure.

Main Results:

  • Identified and ranked the performance of multiple predictability measures for renewable energy data.
  • Determined the most effective predictability measure, outperforming others in accuracy and relevance.
  • Provided a validated method for tuning the chosen predictability measure for practical application.

Conclusions:

  • The proposed predictability measure offers a significant advancement over existing methods for renewable energy forecasting.
  • Implementing this measure can lead to more reliable energy predictions, enhancing grid stability.
  • Quantifiable economic benefits, including millions in savings for end-users and investors, are achievable through improved predictability assessment.