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Modelling of solar power production at 65°35'N

Per Westerlund(1), Jesper Rydén(2) and Nils Persson(3)

1. Electric Power Engineering Luleå University of Technology, Sweden

2. Department of Energy and Technology Swedish University of Agricultural Sciences, Sweden

3. Department of Engineering Sciences and Mathematics Luleå University of Technology, Sweden

2026-01-20

Distributed generation feeds into the distributionpart of the electric grid. As it is constructed with power flows going from higher to lower voltages, there might be problems with overloading and high currents, caused by more production than consumption in the area fed by a substation.
To estimate the impact of generation connected to the distribution system, there is need for a statistical description of the production. Five years of data from a solar power plant in Luleå have been analysed statistically. Also, a physical model has been applied.
The results show that the physical model manages to predict reasonable the data. Of the five years, one has apparently a lower production, but it is due to the lack of data. There is a marked seasonality, where the power drops considerable in September and vanishes during winter. The variation at a certain day and a certain hour is large, as the interquartile distance is about one half of the maximum power in summer.

Key words: Solar power generation, power generation planning, time series analysis, extremes.

[1] M. Bollen, T. Tavares de Oliveira, N. Etherden, S. Bhattacharyya, and S. Bahramirad, 20 years of hosting capacity studies, 2004 - 2024, CIRED Workshop, Vienna, 2024.

[2] Swedish Energy Market Inspectorate, Ellagens bestämmelser om ersättning vid inmatning av el: Metod vid prövning av tvister om nätnyttoersättning, Ei PM2020:04, http://www.ei.se, (2020).

[3] G.M. Masters, Renewable and efficient electric power systems, John Wiley & Sons, Hoboken, NJ (2004), ch. 7.

[5] S.N. Wood, Generalized additive models. An introduction with R, Chapman & Hall/CRC Press, Boca Raton (2006).

[6] D.K. Dey and J. Yan, Extreme value modelling and risk analysis. Methods and applications, Chapman & Hall/CRC Press, Boca Raton (2016).

[7] I. Rychlik and J. Rydén, Probability and risk analysis: an introduction for engineers, Springer-Verlag, Berlin (2006).

[8] P. Westerlund and W. Naim, “Extreme value analysis of power system data”, International Conference on Time Series and Forecasting, Granada (2019), pp. 322-327.

[9] J, Rydén, Estimation of return levels with long return periods for extreme sea levels by the Average Conditional Exceedance Rate method, GeoHazards 2024 5(1), 165-175 (2024)

[10] J.R.M. Hosking and J.R. Wallis, Regional frequency analysis. An approach based on L-moments, Cambridge University Press, (1997).

[11] W.H. Asquith, lmomco -- L-moments, censored L-moments, trimmed L-moments, L-comoments, and many distributions. R package version 2.4.7, Texas Tech University, Lubbock, Texas, (2024).