Open Access
| Issue |
E3S Web Conf.
Volume 723, 2026
2026 International Conference on Artificial Intelligence in Energy and Infrastructure (AIEI 2026)
|
|
|---|---|---|
| Article Number | 03006 | |
| Number of page(s) | 9 | |
| Section | Smart Grids, Energy Management & Sustainable Energy Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202672303006 | |
| Published online | 08 July 2026 | |
- D. D. Le, V. D. Ngo, T. A. N. Nguyen, and V. K. Huynh, “Study on uncertainty of operating parameters to calculate and analyze working condition of power systems”, Journal of Science and Technology - The University of Danang, vol. 5, no. 90, pp. 34–38, May 2015. [Google Scholar]
- P. Chen, Z. Chen, and B. Bak-Jensen, “Probabilistic load flow: A review,” 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, Nanjing, China, 2008, pp. 1586–1591. [Google Scholar]
- D. D. Le, N. T. A. Nguyen, V. D. Ngo, and A. Berizzi, “Advanced probabilistic power flow methodology for power systems with renewable resources,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 25, no. 2, pp. 1154–1162, 2017. [Google Scholar]
- A. İskenderoğlu and Ö. Gül, “Probabilistic Power Flow Analysis of Power Systems with PV and Wind Using Monte Carlo Method”, in Proc. 10th Int. Conf. Electr. Electron. Eng. (ICEEE), 2023. [Google Scholar]
- Z. Ren, K. Wang, W. Li, L. Jin, and Y. Dai, “Probabilistic Power Flow Analysis of Power Systems Incorporating Tidal Current Generation”, IEEE Trans. Sustain. Energy, vol. 8, no. 3, pp. 1195–1203, Jul. 2017. [Google Scholar]
- D. S. Kumar, H. Quan, K. Y. Wen, and D. Srinivasan, “Probabilistic risk and severity analysis of power systems with high penetration of photovoltaics,” Solar Energy, vol. 230, pp. 1156–1164, Dec. 2021. [Google Scholar]
- R. Y. Rubinstein and D. P. Kroese, Simulation and the Monte Carlo Method, 2nd Ed. Wiley, Hoboken, NJ, USA, 2008. [Google Scholar]
- J. C. Helton and F. J. Davis, “Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems”, Reliability Engineering & System Safety, vol. 81, no. 1, pp. 23–69, Jul. 2003. [Google Scholar]
- V. W. Berger and Y. Zhou, “Kolmogorov–Smirnov Test: Overview”, in Encyclopedia of Statistics in Behavioral Science. Wiley, 2014. [Google Scholar]
- J. E. Cavanaugh, “The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements”, Wiley Interdiscip. Rev. Comput. Stat., vol. 11, no. 3, Art. no. e1460, May/Jun. 2019. [Google Scholar]
- J. Zang, B. M. Hodge, and A. Florita, “Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection”, in Proc. ASME 7th Int. Conf. Energy Sustain. and 11th Fuel Cell Sci., Eng., Technol. Conf., USA, Jul. 2013. [Google Scholar]
- L. Li, Y. Fan, X. Su, and G. Qiu, “Probabilistic Load Flow Calculation of Power System Integrated with Wind Farm Based on Kriging Model”, Energy Eng., vol. 118, no. 2, pp. 565–580, 2021. [Google Scholar]
- Siemens Power Technologies International, Program Operation Manual PSS®E 36.1.0. USA: Siemens PTI, Jul. 2024. [Google Scholar]
- Siemens Power Technologies International, Application Program Interface PSS®E 36.1.0. USA: Siemens PTI, Jul. 2024. [Google Scholar]
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