IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v64y2014icp71-81.html
   My bibliography  Save this article

A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation

Author

Listed:
  • Sansavini, G.
  • Piccinelli, R.
  • Golea, L.R.
  • Zio, E.

Abstract

The purpose of this work is the analysis of the uncertainties affecting an electric transmission network with wind power generation and their impact on its reliability. A stochastic model was developed to simulate the operations and the line disconnection and reconnection events of the electric network due to overloads beyond the rated capacity. We represent and propagate the uncertainties related to consumption variability, ambient temperature variability, wind speed variability and wind power generation variability. The model is applied to a case study of literature. Conclusions are drawn on the impact that different sources of variability have on the reliability of the network and on the seamless electric power supply. Finally, the analysis enables identifying possible system states, in terms of power request and supply, that are critical for network vulnerability and may induce a cascade of line disconnections leading to massive network blackout.

Suggested Citation

  • Sansavini, G. & Piccinelli, R. & Golea, L.R. & Zio, E., 2014. "A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation," Renewable Energy, Elsevier, vol. 64(C), pages 71-81.
    • Handle: RePEc:eee:renene:v:64:y:2014:i:c:p:71-81
    DOI: 10.1016/j.renene.2013.11.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148113005806
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2013.11.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Yanfu & Zio, Enrico, 2012. "Uncertainty analysis of the adequacy assessment model of a distributed generation system," Renewable Energy, Elsevier, vol. 41(C), pages 235-244.
    2. Safari, Bonfils & Gasore, Jimmy, 2010. "A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda," Renewable Energy, Elsevier, vol. 35(12), pages 2874-2880.
    3. Roy Billinton & Yi Gao, 2008. "Adequacy assessment of composite power generation and transmission systems with wind energy," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 2(1/2), pages 79-98.
    4. Gouveia, Eduardo M. & Matos, Manuel A., 2009. "Symmetric AC fuzzy power flow model," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1012-1018, September.
    5. 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.
    6. Hart, Elaine K. & Jacobson, Mark Z., 2011. "A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables," Renewable Energy, Elsevier, vol. 36(8), pages 2278-2286.
    7. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    8. Xu, M. & Zhuan, X., 2013. "Optimal planning for wind power capacity in an electric power system," Renewable Energy, Elsevier, vol. 53(C), pages 280-286.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. M. Jenabi & S. M. T. Fatemi Ghomi & S. A. Torabi & Moeen Sammak Jalali, 2022. "An accelerated Benders decomposition algorithm for stochastic power system expansion planning using sample average approximation," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1304-1336, December.
    2. Zio, Enrico, 2016. "Challenges in the vulnerability and risk analysis of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 137-150.
    3. Yan, Xingyu & Abbes, Dhaker & Francois, Bruno, 2017. "Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators," Renewable Energy, Elsevier, vol. 106(C), pages 288-297.
    4. Perera, A.T.D. & Wang, Z. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2021. "Towards realization of an Energy Internet: Designing distributed energy systems using game-theoretic approach," Applied Energy, Elsevier, vol. 283(C).
    5. Bracale, Antonio & Carpinelli, Guido & De Falco, Pasquale, 2017. "A new finite mixture distribution and its expectation-maximization procedure for extreme wind speed characterization," Renewable Energy, Elsevier, vol. 113(C), pages 1366-1377.
    6. Mo, Huadong & Sansavini, Giovanni, 2019. "Impact of aging and performance degradation on the operational costs of distributed generation systems," Renewable Energy, Elsevier, vol. 143(C), pages 426-439.
    7. Wang, Wei & Cova, Gregorio & Zio, Enrico, 2022. "A clustering-based framework for searching vulnerabilities in the operation dynamics of Cyber-Physical Energy Systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    8. Perera, A.T.D. & Nik, Vahid M. & Wickramasinghe, P.U. & Scartezzini, Jean-Louis, 2019. "Redefining energy system flexibility for distributed energy system design," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    9. Nik, Vahid M. & Moazami, Amin, 2021. "Using collective intelligence to enhance demand flexibility and climate resilience in urban areas," Applied Energy, Elsevier, vol. 281(C).
    10. Brouwer, Sander R. & Al-Jibouri, Saad H.S. & Cárdenas, Ibsen Chivatá & Halman, Johannes I.M., 2018. "Towards analysing risks to public safety from wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 77-87.
    11. Eryilmaz, Serkan & Devrim, Yilser, 2019. "Theoretical derivation of wind plant power distribution with the consideration of wind turbine reliability," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 192-197.
    12. Andrea Antenucci & Giovanni Sansavini, 2018. "Adequacy and security analysis of interdependent electric and gas networks," Journal of Risk and Reliability, , vol. 232(2), pages 121-139, April.
    13. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Zhou, Jing & Zhang, Li & Fan, Lin & Yang, Zhaoming & Xie, Fei & Zuo, Lili & Zhang, Jinjun, 2023. "A systematic framework for the assessment of the reliability of energy supply in Integrated Energy Systems based on a quasi-steady-state model," Energy, Elsevier, vol. 263(PB).
    14. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    15. Cadini, Francesco & Agliardi, Gian Luca & Zio, Enrico, 2017. "Estimation of rare event probabilities in power transmission networks subject to cascading failures," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 9-20.
    16. LM López-Manrique & EV Macias-Melo & KM Aguilar-Castro & I Hernández-Pérez & HP Díaz-Hernández, 2021. "Review on methodological and normative advances in assessment and estimation of wind energy," Energy & Environment, , vol. 32(1), pages 25-61, February.
    17. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    18. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Yang, Zhaoming & Zhang, Jinjun, 2021. "Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model," Renewable Energy, Elsevier, vol. 174(C), pages 952-970.
    19. Zhu, Yueying & Wang, Qiuping Alexandre & Li, Wei & Cai, Xu, 2017. "An analytic method for sensitivity analysis of complex systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 52-59.
    20. Perera, A.T.D. & Nik, Vahid M. & Mauree, Dasaraden & Scartezzini, Jean-Louis, 2017. "Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid," Applied Energy, Elsevier, vol. 190(C), pages 232-248.
    21. Jose R. Vargas-Jaramillo & Jhon A. Montanez-Barrera & Michael R. von Spakovsky & Lamine Mili & Sergio Cano-Andrade, 2019. "Effects of Producer and Transmission Reliability on the Sustainability Assessment of Power System Networks," Energies, MDPI, vol. 12(3), pages 1-21, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Can & Bie, Zhaohong & Xie, Min & Jiang, Jiangfeng, 2016. "Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation," Renewable Energy, Elsevier, vol. 93(C), pages 68-76.
    2. Kubik, M.L. & Coker, P.J. & Hunt, C., 2012. "The role of conventional generation in managing variability," Energy Policy, Elsevier, vol. 50(C), pages 253-261.
    3. Pablo González-Inostroza & Claudia Rahmann & Ricardo Álvarez & Jannik Haas & Wolfgang Nowak & Christian Rehtanz, 2021. "The Role of Fast Frequency Response of Energy Storage Systems and Renewables for Ensuring Frequency Stability in Future Low-Inertia Power Systems," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    4. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    5. Liu, Wen & Hu, Weihao & Lund, Henrik & Chen, Zhe, 2013. "Electric vehicles and large-scale integration of wind power – The case of Inner Mongolia in China," Applied Energy, Elsevier, vol. 104(C), pages 445-456.
    6. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    7. Popović, Željko N. & KovaÄ ki, Neven V. & Popović, Dragan S., 2020. "Resilient distribution network planning under the severe windstorms using a risk-based approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    8. 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.
    9. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    10. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    11. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    12. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    13. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2018. "A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods," Energies, MDPI, vol. 11(11), pages 1-19, October.
    14. Chandel, S.S. & Ramasamy, P. & Murthy, K.S.R, 2014. "Wind power potential assessment of 12 locations in western Himalayan region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 530-545.
    15. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
    16. Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    17. Hui Li & Gengyin Li & Yaowu Wu & Zhidong Wang & Jiaming Wang, 2016. "Operation Modeling of Power Systems Integrated with Large-Scale New Energy Power Sources," Energies, MDPI, vol. 9(10), pages 1-17, October.
    18. Bracco, Stefano & Delfino, Federico & Pampararo, Fabio & Robba, Michela & Rossi, Mansueto, 2013. "The University of Genoa smart polygeneration microgrid test-bed facility: The overall system, the technologies and the research challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 442-459.
    19. Soukissian, Takvor H. & Papadopoulos, Anastasios, 2015. "Effects of different wind data sources in offshore wind power assessment," Renewable Energy, Elsevier, vol. 77(C), pages 101-114.
    20. Foley, A.M. & Ó Gallachóir, B.P. & McKeogh, E.J. & Milborrow, D. & Leahy, P.G., 2013. "Addressing the technical and market challenges to high wind power integration in Ireland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 692-703.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:64:y:2014:i:c:p:71-81. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.