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A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach

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  • Prusty, B Rajanarayan
  • Jena, Debashisha

Abstract

A power system with large integration of renewable energy based generations is inherently associated with different types of uncertainties. In such cases, probabilistic load flow is a vital tool for delivering comprehensive information for power system planning and operation. Efforts have been made in this paper to perform a critical review on different probabilistic load flow models, uncertainty characterization and uncertainty handling methods, since from its inspection in 1974. An efficient analytical method named multivariate-Gaussian mixture approximation is proposed for precise estimation of probabilistic load flow results. The proposed method considers the uncertainties pertaining to photovoltaic generations and load demands. At the same time, it effectively incorporates multiple input correlations. In order to examine the performance of the proposed method, modified IEEE 118-bus test system is taken into consideration and results are compared with univariate-Gaussian mixture approximation, series expansion based cumulant methods and Monte Carlo simulation. Effect of various correlation cases on distribution of result variables is also studied. The effectiveness of the proposed method is justified in terms of accuracy and execution time.

Suggested Citation

  • Prusty, B Rajanarayan & Jena, Debashisha, 2017. "A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1286-1302.
    • Handle: RePEc:eee:rensus:v:69:y:2017:i:c:p:1286-1302
    DOI: 10.1016/j.rser.2016.12.044
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    1. Abdullah, M.A. & Agalgaonkar, A.P. & Muttaqi, K.M., 2013. "Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation," Renewable Energy, Elsevier, vol. 55(C), pages 532-543.
    2. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
    3. Carpinelli, Guido & Caramia, Pierluigi & Varilone, Pietro, 2015. "Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems," Renewable Energy, Elsevier, vol. 76(C), pages 283-295.
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    Cited by:

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    4. HyungSeon Oh, 2019. "A Unified and Efficient Approach to Power Flow Analysis," Energies, MDPI, vol. 12(12), pages 1-20, June.
    5. Filip Mišurović & Saša Mujović, 2022. "Numerical Probabilistic Load Flow Analysis in Modern Power Systems with Intermittent Energy Sources," Energies, MDPI, vol. 15(6), pages 1-20, March.
    6. Harshavardhan Palahalli & Paolo Maffezzoni & Giambattista Gruosso, 2021. "Gaussian Copula Methodology to Model Photovoltaic Generation Uncertainty Correlation in Power Distribution Networks," Energies, MDPI, vol. 14(9), pages 1-16, April.
    7. Prusty, B. Rajanarayan & Jena, Debashisha, 2018. "An over-limit risk assessment of PV integrated power system using probabilistic load flow based on multi-time instant uncertainty modeling," Renewable Energy, Elsevier, vol. 116(PA), pages 367-383.
    8. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    9. Hasan, Kazi Nazmul & Preece, Robin & Milanović, Jovica V., 2019. "Existing approaches and trends in uncertainty modelling and probabilistic stability analysis of power systems with renewable generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 168-180.
    10. Samet, Haidar & Khorshidsavar, Morteza, 2018. "Analytic time series load flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3886-3899.
    11. Mohamed A. M. Shaheen & Hany M. Hasanien & Said F. Mekhamer & Mohammed H. Qais & Saad Alghuwainem & Zia Ullah & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-23, August.
    12. Mohamed S. Hashish & Hany M. Hasanien & Haoran Ji & Abdulaziz Alkuhayli & Mohammed Alharbi & Tlenshiyeva Akmaral & Rania A. Turky & Francisco Jurado & Ahmed O. Badr, 2023. "Monte Carlo Simulation and a Clustering Technique for Solving the Probabilistic Optimal Power Flow Problem for Hybrid Renewable Energy Systems," Sustainability, MDPI, vol. 15(1), pages 1-25, January.
    13. Mohammad Rayati & Pasquale De Falco & Daniela Proto & Mokhtar Bozorg & Mauro Carpita, 2021. "Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids," Energies, MDPI, vol. 14(16), pages 1-21, August.
    14. Xiaoyang Deng & Jinghan He & Pei Zhang, 2017. "A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables," Energies, MDPI, vol. 10(10), pages 1-21, October.
    15. Esau Zulu & Ryoichi Hara & Hiroyuki Kita, 2023. "An Efficient Hybrid Particle Swarm and Gradient Descent Method for the Estimation of the Hosting Capacity of Photovoltaics by Distribution Networks," Energies, MDPI, vol. 16(13), pages 1-17, July.
    16. Ziwei Zhu & Shifan Lu & Sui Peng, 2018. "An Improved Stochastic Response Surface Method Based Probabilistic Load Flow for Studies on Correlated Wind Speeds in the AC/DC Grid," Energies, MDPI, vol. 11(12), pages 1-14, December.
    17. Ziqiang Zhou & Fei Tang & Dichen Liu & Chenxu Wang & Xin Gao, 2020. "Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators," Sustainability, MDPI, vol. 12(5), pages 1-20, February.
    18. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    19. González-Ordiano, Jorge Ángel & Mühlpfordt, Tillmann & Braun, Eric & Liu, Jianlei & Çakmak, Hüseyin & Kühnapfel, Uwe & Düpmeier, Clemens & Waczowicz, Simon & Faulwasser, Timm & Mikut, Ralf & Hagenmeye, 2021. "Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow," Applied Energy, Elsevier, vol. 302(C).
    20. Stavros Lazarou & Vasiliki Vita & Lambros Ekonomou, 2018. "Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-17, November.

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