Abstract
Power system analyses increasingly use annual time series for temporal and spatial assessment of operational and also planning aspects. These analyses are often limited due to the computational time of the large amount of load flow calculation. By introducing algorithms which are capable of generating shorter and representative time series of measured load or power generation time series, the calculation time for load flow calculations can be reduced. We present a method which is capable of extracting features from the time series and use those features to create a representative time series. Furthermore, we show that our method is capable of maintaining the most important statistical features of the original time series by applying a Fisher-Pitman Permutation test.
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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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Henze, J., Kneiske, T., Braun, M., Sick, B. (2017). Identifying Representative Load Time Series for Load Flow Calculations. In: Woon, W., Aung, Z., Kramer, O., Madnick, S. (eds) Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. DARE 2017. Lecture Notes in Computer Science(), vol 10691. Springer, Cham. https://doi.org/10.1007/978-3-319-71643-5_8
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DOI: https://doi.org/10.1007/978-3-319-71643-5_8
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