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Probabilistic optimal power flow in correlated hybrid wind-PV power systems: A review and a new approach

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  • Aien, Morteza
  • Rashidinejad, Masoud
  • Firuz-Abad, Mahmud Fotuhi

Abstract

Hastening the power industry reregulation juxtaposed with the unprecedented utilization of uncertain renewable energies (REs), faces power system operation with sever uncertainties. Consequently, uncertainty assessment of system performance is an obligation. This paper reviews the probabilistic techniques used for probabilistic optimal power flow (P-OPF) studies and proposes a novel and powerful approach using the unscented transformation (UT) method. The heart of the proposed method lies in how to produce the sampling points. Appropriate sampling points are chosen to perform the P-OPF with a high degree of accuracy and less computational burden compared with features of other existing methods. The proposed method can take into account the correlation between uncertain input variables. In order to examine performance of the suggested method, two case studies are conducted and obtained results are compared with those of Monte Carlo simulation (MCS) and two point estimation method (2PEM). Comparison of the results justifies the effectiveness of the proposed method with regards to both accuracy and execution time criteria.

Suggested Citation

  • Aien, Morteza & Rashidinejad, Masoud & Firuz-Abad, Mahmud Fotuhi, 2015. "Probabilistic optimal power flow in correlated hybrid wind-PV power systems: A review and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1437-1446.
    • Handle: RePEc:eee:rensus:v:41:y:2015:i:c:p:1437-1446
    DOI: 10.1016/j.rser.2014.09.012
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    1. Soroudi, Alireza & Ehsan, Mehdi, 2011. "A possibilistic-probabilistic tool for evaluating the impact of stochastic renewable and controllable power generation on energy losses in distribution networks--A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 794-800, January.
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    2. Shargh, S. & Khorshid ghazani, B. & Mohammadi-ivatloo, B. & Seyedi, H. & Abapour, M., 2016. "Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties," Renewable Energy, Elsevier, vol. 94(C), pages 10-21.
    3. Khaled Nusair & Feras Alasali, 2020. "Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method," Energies, MDPI, vol. 13(14), pages 1-46, July.
    4. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    5. Zhang, Mengling & Jiao, Zihao & Ran, Lun & Zhang, Yuli, 2023. "Optimal energy and reserve scheduling in a renewable-dominant power system," Omega, Elsevier, vol. 118(C).
    6. Habib, Arslan & Sou, Chan & Hafeez, Hafiz Muhammad & Arshad, Adeel, 2018. "Evaluation of the effect of high penetration of renewable energy sources (RES) on system frequency regulation using stochastic risk assessment technique (an approach based on improved cumulant)," Renewable Energy, Elsevier, vol. 127(C), pages 204-212.
    7. 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.
    8. Khaled Nusair & Lina Alhmoud, 2020. "Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy," Energies, MDPI, vol. 13(22), pages 1-35, November.
    9. Syranidis, Konstantinos & Robinius, Martin & Stolten, Detlef, 2018. "Control techniques and the modeling of electrical power flow across transmission networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3452-3467.
    10. Nadjemi, O. & Nacer, T. & Hamidat, A. & Salhi, H., 2017. "Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1352-1365.
    11. Hadis Moradi & Mahdi Esfahanian & Amir Abtahi & Ali Zilouchian, 2017. "Modeling a Hybrid Microgrid Using Probabilistic Reconfiguration under System Uncertainties," Energies, MDPI, vol. 10(9), pages 1-17, September.
    12. Mei, Fei & Zhang, Jiatang & Lu, Jixiang & Lu, Jinjun & Jiang, Yuhan & Gu, Jiaqi & Yu, Kun & Gan, Lei, 2021. "Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations," Energy, Elsevier, vol. 219(C).
    13. Samet, Haidar & Khorshidsavar, Morteza, 2018. "Analytic time series load flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3886-3899.
    14. Jithendranath, J. & Das, Debapriya & Guerrero, Josep M., 2021. "Probabilistic optimal power flow in islanded microgrids with load, wind and solar uncertainties including intermittent generation spatial correlation," Energy, Elsevier, vol. 222(C).

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