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

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  • 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
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    References listed on IDEAS

    as
    1. Enzo Sauma & Shmuel Oren, 2006. "Proactive planning and valuation of transmission investments in restructured electricity markets," Journal of Regulatory Economics, Springer, vol. 30(3), pages 358-387, November.
    2. Sinden, Graham, 2007. "Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand," Energy Policy, Elsevier, vol. 35(1), pages 112-127, January.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.

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    More about this item

    Keywords

    Decision making; electricity; transmission; planning; uncertainty;
    All these 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

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