IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i20p5088-d1497843.html

Medium- and Long-Term Power System Planning Method Based on Source-Load Uncertainty Modeling

Author

Listed:
  • Wenfeng Yao

    (Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Ziyu Huo

    (Department of Electrical Engineering, Tsinghua University, Beijing 610213, China)

  • Jin Zou

    (Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Chen Wu

    (Yunnan Power Grid Co., Ltd., Kunming 100084, China)

  • Jiayang Wang

    (Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Xiang Wang

    (Department of Electrical Engineering, Tsinghua University, Beijing 610213, China)

  • Siyu Lu

    (Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Yigong Xie

    (Yunnan Power Grid Co., Ltd., Kunming 100084, China)

  • Yingjun Zhuo

    (Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Jinbing Liang

    (Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Run Huang

    (Yunnan Power Grid Co., Ltd., Kunming 100084, China)

  • Ming Cheng

    (Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Zongxiang Lu

    (Department of Electrical Engineering, Tsinghua University, Beijing 610213, China)

Abstract

In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region.

Suggested Citation

  • Wenfeng Yao & Ziyu Huo & Jin Zou & Chen Wu & Jiayang Wang & Xiang Wang & Siyu Lu & Yigong Xie & Yingjun Zhuo & Jinbing Liang & Run Huang & Ming Cheng & Zongxiang Lu, 2024. "Medium- and Long-Term Power System Planning Method Based on Source-Load Uncertainty Modeling," Energies, MDPI, vol. 17(20), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5088-:d:1497843
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/20/5088/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/20/5088/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Valizadeh Haghi, H. & Tavakoli Bina, M. & Golkar, M.A. & Moghaddas-Tafreshi, S.M., 2010. "Using Copulas for analysis of large datasets in renewable distributed generation: PV and wind power integration in Iran," Renewable Energy, Elsevier, vol. 35(9), pages 1991-2000.
    2. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    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. Wang, Ziren & Li, Wei & Zhang, Yuyuan, 2025. "Two-tier optimal scheduling of integrated energy systems in parks considering P2G-CCS-CHP coupling and electricity-gas-heat-cooling price-demand response," Energy, Elsevier, vol. 338(C).

    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. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
    2. Li, M.S. & Lin, Z.J. & Ji, T.Y. & Wu, Q.H., 2018. "Risk constrained stochastic economic dispatch considering dependence of multiple wind farms using pair-copula," Applied Energy, Elsevier, vol. 226(C), pages 967-978.
    3. Rand Kwong Yew Low, 2018. "Vine copulas: modelling systemic risk and enhancing higher‐moment portfolio optimisation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 423-463, November.
    4. F. Durante & A. Gatto & F. Ravazzolo, 2024. "Understanding relationships with the Aggregate Zonal Imbalance using copulas," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 513-554, April.
    5. Roger M. Cooke & Harry Joe & Bo Chang, 2020. "Vine copula regression for observational studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 141-167, June.
    6. David Zimmer, 2025. "A copula-based method for accommodating feedback in random effects panel models," Empirical Economics, Springer, vol. 69(4), pages 2093-2108, October.
    7. Sleire, Anders D. & Støve, Bård & Otneim, Håkon & Berentsen, Geir Drage & Tjøstheim, Dag & Haugen, Sverre Hauso, 2022. "Portfolio allocation under asymmetric dependence in asset returns using local Gaussian correlations," Finance Research Letters, Elsevier, vol. 46(PB).
    8. Hongjun Zeng & Abdullahi D. Ahmed, 2026. "Dependency structure and volatility connectedness among China-ASEAN stock market, cryptocurrencies, and crude oil," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-39, December.
    9. Yao, Yinhong & Chen, Xiuwen & Chen, Zhensong, 2025. "Portfolio tail risk forecasting for international financial assets: A GARCH-MIDAS-R-Vine copula model," The North American Journal of Economics and Finance, Elsevier, vol. 77(C).
    10. Liu, Zixuan & Mo, Li & Zhang, Mi & Kang, Jiangrui & Liu, Wan & Sun, Xutong & Xiao, Wenjing, 2025. "High-dimensional scenario generation method joint-driven by multiple correlations for hydro-wind-photovoltaic," Applied Energy, Elsevier, vol. 400(C).
    11. Li, Feng & Kang, Yanfei, 2018. "Improving forecasting performance using covariate-dependent copula models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 456-476.
    12. Zhang, Dalu, 2014. "Vine copulas and applications to the European Union sovereign debt analysis," International Review of Financial Analysis, Elsevier, vol. 36(C), pages 46-56.
    13. Portier, François & Segers, Johan, 2018. "On the weak convergence of the empirical conditional copula under a simplifying assumption," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 160-181.
    14. Elberg, Christina & Hagspiel, Simeon, 2015. "Spatial dependencies of wind power and interrelations with spot price dynamics," European Journal of Operational Research, Elsevier, vol. 241(1), pages 260-272.
    15. Hasankhani, Arezoo & Hakimi, Seyed Mehdi, 2021. "Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market," Energy, Elsevier, vol. 219(C).
    16. Reboredo, Juan C. & Ugolini, Andrea, 2015. "A vine-copula conditional value-at-risk approach to systemic sovereign debt risk for the financial sector," The North American Journal of Economics and Finance, Elsevier, vol. 32(C), pages 98-123.
    17. Sun, Fuqiang & Fu, Fangyou & Liao, Haitao & Xu, Dan, 2020. "Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    18. Zhichao Zhang & Fan Zhang & Zhuang Zhang, 2013. "Strategic Asset Allocation for China's Foreign Reserves: A Copula Approach," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 21(6), pages 1-21, November.
    19. Zhiwei Bai & Hongkui Wei & Yingying Xiao & Shufang Song & Sergei Kucherenko, 2021. "A Vine Copula-Based Global Sensitivity Analysis Method for Structures with Multidimensional Dependent Variables," Mathematics, MDPI, vol. 9(19), pages 1-20, October.
    20. Zhi, Bangdong & Wang, Xiaojun & Xu, Fangming, 2022. "Managing inventory financing in a volatile market: A novel data-driven copula model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jeners:v:17:y:2024:i:20:p:5088-:d:1497843. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.