IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v7y2015i4p4582-4599d48350.html
   My bibliography  Save this article

Research on the Multi-Period Small-Signal Stability Probability of a Power System with Wind Farms Based on the Markov Chain

Author

Listed:
  • Rundong Ge

    (Electrical and Electronic Engineering Institute, North China Electric Power University, Mailbox 435, No. 2 Beinong Road, Changping District, Beijing 102206, China
    These authors contributed equally to this work.)

  • Wenying Liu

    (Electrical and Electronic Engineering Institute, North China Electric Power University, Mailbox 435, No. 2 Beinong Road, Changping District, Beijing 102206, China
    These authors contributed equally to this work.)

  • Huiyong Li

    (Electrical and Electronic Engineering Institute, North China Electric Power University, Mailbox 435, No. 2 Beinong Road, Changping District, Beijing 102206, China)

  • Jianzong Zhuo

    (Electrical and Electronic Engineering Institute, North China Electric Power University, Mailbox 435, No. 2 Beinong Road, Changping District, Beijing 102206, China
    These authors contributed equally to this work.)

  • Weizhou Wang

    (Gansu Electrical Power Research Institute, No.8 Binhe East Road, Chengguan North District, Lanzhou 730030, China
    These authors contributed equally to this work.)

Abstract

In the traditional studies on small-signal stability probability of a power system with wind farms, the frequency of wind speed was often assumed to obey to some extent a particular probability distribution. The stability probability that is thus obtained, however, actually only reflects the power system stability characteristics on long time scales. In fact, there is a direct correlation between the change of wind speed and the current state of wind speed, resulting in the system stability characteristics in different time periods having a great difference compared with that of long time scales. However, the dispatchers are more concerned about the probability that the power system remains stable in the next period or after several periods, namely the stability characteristics of the power system in a short period or multi-period. Therefore, research on multi-period small-signal stability probability of a power system with wind farms has important theoretical value and practical significance. Based on the Markov chain, this paper conducted in-depth research on this subject. Firstly, the basic principle of the Markov chain was introduced, based on which we studied the uncertainty of wind power by adopting the transition matrix and the wind speed−power output transformation model and established the probability distribution model of multi-period wind power. Then the boundary-based small-signal stability probability evaluation method was used to establish an evaluation model of multi-period small-signal stability probability of power system with wind farms. Finally, taking the power system with two wind farms as an example, we analyzed its small-signal stability probability and studied the influence of the initial states of wind speed and different periods on the probability of stability. This study provides a new method and support for analyzing the small-signal stability probability of a power system with wind farms.

Suggested Citation

  • Rundong Ge & Wenying Liu & Huiyong Li & Jianzong Zhuo & Weizhou Wang, 2015. "Research on the Multi-Period Small-Signal Stability Probability of a Power System with Wind Farms Based on the Markov Chain," Sustainability, MDPI, vol. 7(4), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:4:p:4582-4599:d:48350
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/7/4/4582/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/7/4/4582/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wenying Liu & Rundong Ge & Huiyong Li & Jiangbei Ge, 2014. "Impact of Large-Scale Wind Power Integration on Small Signal Stability Based on Stability Region Boundary," Sustainability, MDPI, vol. 6(11), pages 1-24, November.
    2. Wenying Liu & Rundong Ge & Quancheng Lv & Huiyong Li & Jiangbei Ge, 2015. "Research on a Small Signal Stability Region Boundary Model of the Interconnected Power System with Large-Scale Wind Power," Energies, MDPI, vol. 8(4), pages 1-25, March.
    3. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    4. Hernández-Escobedo, Q. & Saldaña-Flores, R. & Rodríguez-García, E.R. & Manzano-Agugliaro, F., 2014. "Wind energy resource in Northern Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 890-914.
    5. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    6. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    7. Fernández, Luis M. & Jurado, Francisco & Saenz, José Ramón, 2008. "Aggregated dynamic model for wind farms with doubly fed induction generator wind turbines," Renewable Energy, Elsevier, vol. 33(1), pages 129-140.
    Full references (including those not matched with items on IDEAS)

    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. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    2. Wenying Liu & Rundong Ge & Huiyong Li & Jiangbei Ge, 2014. "Impact of Large-Scale Wind Power Integration on Small Signal Stability Based on Stability Region Boundary," Sustainability, MDPI, vol. 6(11), pages 1-24, November.
    3. Tang, Jie & Brouste, Alexandre & Tsui, Kwok Leung, 2015. "Some improvements of wind speed Markov chain modeling," Renewable Energy, Elsevier, vol. 81(C), pages 52-56.
    4. Hernández-Escobedo, Q. & Rodríguez-García, E. & Saldaña-Flores, R. & Fernández-García, A. & Manzano-Agugliaro, F., 2015. "Solar energy resource assessment in Mexican states along the Gulf of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 216-238.
    5. Montoya, Francisco G. & García-Cruz, Amós & Montoya, Maria G. & Manzano-Agugliaro, Francisco, 2016. "Power quality techniques research worldwide: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 846-856.
    6. Amirinia, Gholamreza & Kamranzad, Bahareh & Mafi, Somayeh, 2017. "Wind and wave energy potential in southern Caspian Sea using uncertainty analysis," Energy, Elsevier, vol. 120(C), pages 332-345.
    7. Li, Gong & Shi, Jing & Zhou, Junyi, 2011. "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, Elsevier, vol. 36(1), pages 352-359.
    8. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
    9. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    10. Juaidi, Adel & Montoya, Francisco G. & Gázquez, Jose A. & Manzano-Agugliaro, Francisco, 2016. "An overview of energy balance compared to sustainable energy in United Arab Emirates," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1195-1209.
    11. Jónsdóttir, Guðrún Margrét & Milano, Federico, 2019. "Data-based continuous wind speed models with arbitrary probability distribution and autocorrelation," Renewable Energy, Elsevier, vol. 143(C), pages 368-376.
    12. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    13. Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.
    14. Kim, Sunwoo & Choi, Yechan & Park, Joungho & Adams, Derrick & Heo, Seongmin & Lee, Jay H., 2024. "Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    15. Arenas-López, J. Pablo & Badaoui, Mohamed, 2020. "The Ornstein-Uhlenbeck process for estimating wind power under a memoryless transformation," Energy, Elsevier, vol. 213(C).
    16. Domenech, B. & Ferrer-Martí, L. & Pastor, R., 2015. "Including management and security of supply constraints for designing stand-alone electrification systems in developing countries," Renewable Energy, Elsevier, vol. 80(C), pages 359-369.
    17. Shahmohammadi, Ali & Sioshansi, Ramteen & Conejo, Antonio J. & Afsharnia, Saeed, 2018. "Market equilibria and interactions between strategic generation, wind, and storage," Applied Energy, Elsevier, vol. 220(C), pages 876-892.
    18. Ismail, M.S. & Moghavvemi, M. & Mahlia, T.M.I., 2013. "Energy trends in Palestinian territories of West Bank and Gaza Strip: Possibilities for reducing the reliance on external energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 117-129.
    19. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    20. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.

    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:gam:jsusta:v:7:y:2015:i:4:p:4582-4599:d:48350. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.