IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v228y2018icp546-555.html

A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration

Author

Listed:
  • Sun, Mingyang
  • Cremer, Jochen
  • Strbac, Goran

Abstract

Transmission expansion planning (TEP) is facing unprecedented challenges with the rise of integrated renewable energy resources (RES), flexible load elements, and the potential electrification of transport and heat sectors. Under this reality, the inadequate information of the stochastic parameters’ behavior may lead to inefficient expansion decisions, especially in the context of very high renewable penetration. This paper proposes a novel data-driven scenario generation framework for the TEP problem to generate unseen but important load and wind power scenarios while capturing inter-spatial dependencies between loads and wind generation units’ output in various locations, using a vine-copula based high-dimensional stochastic variable modeling approach. The superior performance of the proposed model is demonstrated through a case study on a modified IEEE 118-bus system. The expected result of using the expected value problem solution (EEV) and the net benefits of transmission expansion (NBTE) are used as the evaluation metrics to quantitatively illustrate the advantages of the proposed approach. In addition, the case of very high wind penetration is carried out to further highlight the importance of the multivariate stochastic dependence of load and wind power generation. The results demonstrate that the proposed scenario generation method can result in near-optimal investment decisions for the TEP problem that make more net benefits than using limited number of historical data.

Suggested Citation

  • Sun, Mingyang & Cremer, Jochen & Strbac, Goran, 2018. "A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration," Applied Energy, Elsevier, vol. 228(C), pages 546-555.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:546-555
    DOI: 10.1016/j.apenergy.2018.06.095
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918309656
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.06.095?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Daeyoung & Kim, Jong-Min & Liao, Shu-Min & Jung, Yoon-Sung, 2013. "Mixture of D-vine copulas for modeling dependence," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 1-19.
    2. Anthony Papavasiliou & Shmuel S. Oren, 2013. "Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network," Operations Research, INFORMS, vol. 61(3), pages 578-592, June.
    3. Zhang, Ning & Lu, Xi & McElroy, Michael B. & Nielsen, Chris P. & Chen, Xinyu & Deng, Yu & Kang, Chongqing, 2016. "Reducing curtailment of wind electricity in China by employing electric boilers for heat and pumped hydro for energy storage," Applied Energy, Elsevier, vol. 184(C), pages 987-994.
    4. Frahm, Gabriel & Junker, Markus & Schmidt, Rafael, 2005. "Estimating the tail-dependence coefficient: Properties and pitfalls," Insurance: Mathematics and Economics, Elsevier, vol. 37(1), pages 80-100, August.
    5. Díaz, Guzmán & Gómez-Aleixandre, Javier & Coto, José, 2016. "Wind power scenario generation through state-space specifications for uncertainty analysis of wind power plants," Applied Energy, Elsevier, vol. 162(C), pages 21-30.
    6. Teng, Fei & Aunedi, Marko & Strbac, Goran, 2016. "Benefits of flexibility from smart electrified transportation and heating in the future UK electricity system," Applied Energy, Elsevier, vol. 167(C), pages 420-431.
    7. 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.
    8. Baringo, L. & Conejo, A.J., 2013. "Correlated wind-power production and electric load scenarios for investment decisions," Applied Energy, Elsevier, vol. 101(C), pages 475-482.
    9. Sun, Yanlong & Kang, Chongqing & Xia, Qing & Chen, Qixin & Zhang, Ning & Cheng, Yaohua, 2017. "Analysis of transmission expansion planning considering consumption-based carbon emission accounting," Applied Energy, Elsevier, vol. 193(C), pages 232-242.
    10. Sun, M. & Teng, F. & Konstantelos, I. & Strbac, G., 2018. "An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources," Energy, Elsevier, vol. 145(C), pages 871-885.
    11. Wang, Zhiwen & Shen, Chen & Liu, Feng, 2018. "A conditional model of wind power forecast errors and its application in scenario generation," Applied Energy, Elsevier, vol. 212(C), pages 771-785.
    12. PAPAVASILIOU, Anthony & OREN, Schmuel S., 2013. "Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network," LIDAM Reprints CORE 2500, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    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. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
    2. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    3. Hu, Jinxing & Yan, Pengqian & Tan, Guoqiang, 2025. "Spatiotemporal dependence modeling of wind speeds via adaptive-selected mixture pair copulas for scenario-based applications," Renewable Energy, Elsevier, vol. 244(C).
    4. 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.
    5. Munoz, F.D. & Hobbs, B.F. & Watson, J.-P., 2016. "New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints," European Journal of Operational Research, Elsevier, vol. 248(3), pages 888-898.
    6. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Pan, Bo & Qi, Shiqiang, 2020. "Two-stage stochastic optimal operation of integrated electricity and heat system considering reserve of flexible devices and spatial-temporal correlation of wind power," Applied Energy, Elsevier, vol. 275(C).
    7. Mazo, Gildas & Averyanov, Yaroslav, 2019. "Constraining kernel estimators in semiparametric copula mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 170-189.
    8. Victor M. Zavala & Kibaek Kim & Mihai Anitescu & John Birge, 2017. "A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties," Operations Research, INFORMS, vol. 65(3), pages 557-576, June.
    9. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    10. Noori, Ehsan & Khazaei, Ehsan & Tavaro, Mehdi & Bardideh, Farhad, 2019. "Economically Operation of Power Utilities Base on MILP Approach," MPRA Paper 95910, University Library of Munich, Germany.
    11. Howard, B. & Waite, M. & Modi, V., 2017. "Current and near-term GHG emissions factors from electricity production for New York State and New York City," Applied Energy, Elsevier, vol. 187(C), pages 255-271.
    12. Abdul Rauf & Mahmoud Kassas & Muhammad Khalid, 2022. "Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    13. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    14. Munoz, Francisco D. & Pumarino, Bruno J. & Salas, Ignacio A., 2017. "Aiming low and achieving it: A long-term analysis of a renewable policy in Chile," Energy Economics, Elsevier, vol. 65(C), pages 304-314.
    15. Le Cadre, Hélène & Mezghani, Ilyès & Papavasiliou, Anthony, 2019. "A game-theoretic analysis of transmission-distribution system operator coordination," European Journal of Operational Research, Elsevier, vol. 274(1), pages 317-339.
    16. De Vos, K. & Stevens, N. & Devolder, O. & Papavasiliou, A. & Hebb, B. & Matthys-Donnadieu, J., 2019. "Dynamic dimensioning approach for operating reserves: Proof of concept in Belgium," Energy Policy, Elsevier, vol. 124(C), pages 272-285.
    17. Weiß, Gregor N.F. & Scheffer, Marcus, 2015. "Mixture pair-copula-constructions," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 175-191.
    18. Trine K. Boomsma, 2019. "Comments on: A comparative study of time aggregation techniques in relation to power capacity-expansion modeling," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 406-409, October.
    19. Jongheon Lee & Kyungsik Lee, 2025. "Column-and-constraint generation approach to partition-based risk-averse two-stage stochastic programs," Annals of Operations Research, Springer, vol. 349(3), pages 1717-1747, June.
    20. Pavel Krupskii & Harry Joe, 2015. "Tail-weighted measures of dependence," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 614-629, March.

    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:eee:appene:v:228:y:2018:i:c:p:546-555. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.