Stochastic modelling of a low voltage distribution network operation
DOI:
https://doi.org/10.18690/jet.19.1.%25p.2026Keywords:
stochastic modelling, low voltage network, probability density, histogram, load flow, development planningAbstract
Modelling a low-voltage network operation is becoming increasingly challenging. In the past, deterministic methods adopted from sub-transmission and transmission network analysis were sufficient for operators to assess the operating conditions. However, changes in customer behaviour, household appliance structure, and the growing penetration of residential photovoltaics, EV charging, heat pumps, and air conditioning are altering operating conditions in low voltage networks significantly. Given the strongly stochastic nature of individual customer consumption and generation, deterministic methods are becoming less suitable for proper grid operation analysis and development planning.
This article presents a simple method for stochastic modelling of a network operation, where customer behaviour is described using a system of histograms. The presented approach enables in-depth investigation of operating conditions in low voltage networks, identifying potential operational extremes that compromise safety of operation, while also quantifying the frequency and probability of such events. Application of the method is demonstrated for a typical rural network.
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Damianakis, N., Mouli, G.R.C, Bauer, P. (2025) Grid impact of photovoltaics, electric vehicles and heat pumps on distribution grids — An overview, Applied Energy, vol. 380, doi:10.1016/j.apenergy.2024.125000
Jang, H., Kang, J. (2016) A stochastic model of integrating occupant behaviour into energy simulation with respect to actual energy consumption in high-rise apartment buildings, Energy and Buildings, vol. 121, doi: 10.1016/j.enbuild.2016.03.037
Yildirim, F., Arslan, H. (2024), Energy Consumption Forecasting Using Time Series Analysis Methods, International Conference on Science, Engineering Management and Information Technology SEMIT 2023, Ankara, Turkey, doi: 10.1007/978-3-031-72287-5_9
Martellotta, F., Stefanizzi, A. U., Sacchetti, A., Riganti, G. (2017) On the use of artificial neural networks to model household energy consumptions, Energy Procedia, vol. 126, pp. 250-257, doi: 10.1016/j.egypro.2017.08.149
Nelsen, R. B.(2006) An Introduction to Copulas, Springer Nature
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82. https://doi.org/10.1109/4235.585893
Kalos, M.H., Whitlock, P.A. (2008) Monte Carlo Methods, 2nd ed., Wiley-VCH
Beláň, A, et al. (2022). Stochastic Analysis of Battery Storage Systems Integration to the Real Distribution Network with Variable Penetration Levels of PV Systems. Proceedings of International Conference on Smart Systems and Technologies SST 2022, Osijek, Croatia, pp. 237-242, doi: 10.1109/SST55530.2022.9954873
Bahernik, M., Höger, M., Petrik, F. (2026) Stochastic Modelling of Low-Voltage Distribution Feeder Operation. Proceeding of the 26th International Conference on Electric Power Engineering EPE2026, Opole, Poland
Höger, M., Bracinik, P., Bahernik, M. (2026) Stochastic modelling of low voltage distribution networks. 7th International Scientific Conference Energy and Responsibility EnRe, Velenje, Slovenia
Devroye, L. (1986). Non-uniform random variate generation, Springer, New York, USA, doi:10.1007/978-1-4613-8643-8
Stagg, G.W., El-Abiad, A.H. (2019) Computer Methods in Power System Analysis, Medtech, ISBN:9388716159
Höger, M., Bahernik, M., Petrik, F. (2026) Modified Jacobi Method for Efficient Multi-Scenario Load Flow Analysis, Advanced Power Systems Conference 2026, Cluj-Napoca, Romania
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