IDEAS home Printed from https://ideas.repec.org/p/enp/wpaper/eprg1102.html

Planning electricity transmission to accommodate renewables: Using two-stage programming to evaluate flexibility and the cost of disregarding uncertainty

Author

Listed:
  • Adriaan Hendrik van der Weijde

    (Electricity Policy Research Group, University of Cambridge Department of Spatial Economics, VU Amsterdam)

  • Benjamin F. Hobbs

    (Electricity Policy Research Group, University of Cambridge Whiting School of Engineering, The Johns Hopkins University)

Abstract

We develop a stochastic two-stage optimisation model that captures the multistage nature of electricity transmission planning under uncertainty and apply it to a stylised representation of the Great Britain (GB) network. In our model, a proactive transmission planner makes investment decisions in two time periods, each time followed by a market response. This model allows us to identify robust first-stage investments and estimate the value of information in transmission planning, the costs of ignoring uncertainty, and the value of flexibility. Our results show that ignoring risk has quantifiable economic consequences, and that considering uncertainty explicitly can yield decisions that have lower expected costs than traditional deterministic planning methods. Furthermore, the best plan under a risk-neutral criterion can differ from the best under risk-aversion.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Adriaan Hendrik van der Weijde & Benjamin F. Hobbs, 2011. "Planning electricity transmission to accommodate renewables: Using two-stage programming to evaluate flexibility and the cost of disregarding uncertainty," Working Papers EPRG 1102, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg1102
    as

    Download full text from publisher

    File URL: https://www.jbs.cam.ac.uk/wp-content/uploads/2023/12/eprg-wp1102.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. De Jonghe, C. & Hobbs, B. F. & Belmans, R., 2011. "Integrating short-term demand response into long-term investment planning," Cambridge Working Papers in Economics 1132, Faculty of Economics, University of Cambridge.
    2. Marcelino Madrigal & Steven Stoft, 2012. "Transmission Expansion for Renewable Energy Scale-Up : Emerging Lessons and Recommendations," World Bank Publications - Books, The World Bank Group, number 9375, April.
    3. van der Weijde, Adriaan Hendrik & Hobbs, Benjamin F., 2012. "The economics of planning electricity transmission to accommodate renewables: Using two-stage optimisation to evaluate flexibility and the cost of disregarding uncertainty," Energy Economics, Elsevier, vol. 34(6), pages 2089-2101.
    4. Farrell, Niall & Devine, Mel T. & Soroudi, Alireza, 2018. "An auction framework to integrate dynamic transmission expansion planning and pay-as-bid wind connection auctions," Applied Energy, Elsevier, vol. 228(C), pages 2462-2477.
    5. Andreas Schröder & Maximilian Bracke, 2012. "Integrated Electricity Generation Expansion and Transmission Capacity Planning: An Application to the Central European Region," Discussion Papers of DIW Berlin 1250, DIW Berlin, German Institute for Economic Research.
    6. Vithayasrichareon, Peerapat & MacGill, Iain F., 2012. "A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 41(C), pages 374-392.
    7. Frew, Bethany A. & Becker, Sarah & Dvorak, Michael J. & Andresen, Gorm B. & Jacobson, Mark Z., 2016. "Flexibility mechanisms and pathways to a highly renewable US electricity future," Energy, Elsevier, vol. 101(C), pages 65-78.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

    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:enp:wpaper:eprg1102. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Ruth Newman (email available below). General contact details of provider: https://edirc.repec.org/data/jicamuk.html .

    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.