Abstract
Power system analysis algorithms increasingly use time series with a high temporal resolution to assess operational and planning aspects of the power grid. By using time series with high temporal resolution, information is getting more detailed, but at the same time, the computational costs of the algorithms increase. With the help of our algorithm, we create representative time series that have similar characteristics to the original time series. With the help of these representative time series, it is possible to reduce the computational cost of power system analysis algorithms having nearly the same results as with the original time series. In this work, we improve our previous algorithm with the help of specialized sampling strategies. Furthermore, we provide a new method to compare power analysis results achieved with the representative time series to the original time series.
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Acknowledgment
This work was created within the PrIME (03EK3536A) project and funded by BMBF: Deutsches Bundesministerium für Bildung und Forschung/German Federal Ministry of Education and Research.
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Appendices
Appendix A Detailed Results - Univariate
Appendix B Detailed Results - Segmentwise Correlation
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Henze, J., Kutzner, S., Sick, B. (2018). Sampling Strategies for Representative Time Series in Load Flow Calculations. In: Woon, W., Aung, Z., Catalina Feliú, A., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. Technologies, Systems and Society. DARE 2018. Lecture Notes in Computer Science(), vol 11325. Springer, Cham. https://doi.org/10.1007/978-3-030-04303-2_3
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DOI: https://doi.org/10.1007/978-3-030-04303-2_3
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