IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v35y2010i5p2033-2042.html
   My bibliography  Save this article

Short term decisions for long term problems – The effect of foresight on model based energy systems analysis

Author

Listed:
  • Keppo, Ilkka
  • Strubegger, Manfred

Abstract

This paper presents the development and demonstration of a limited foresight energy system model. The presented model is implemented as an extension to a large, linear optimization model, MESSAGE. The motivation behind changing the model is to provide an alternative decision framework, where information for the full time frame is not available immediately and sequential decision making under incomplete information is implied. While the traditional optimization framework provides the globally optimal decisions for the modeled problem, the framework presented here may offer a better description of the decision environment, under which decision makers must operate. We further modify the model to accommodate flexible dynamic constraints, which give an option to implement investments faster, albeit with a higher cost. Finally, the operation of the model is demonstrated using a moving window of foresight, with which decisions are taken for the next 30 years, but can be reconsidered later, when more information becomes available. We find that the results demonstrate some of the pitfalls of short term planning, e.g. lagging investments during earlier periods lead to higher requirements later during the century. Furthermore, the energy system remains more reliant on fossil based energy carriers, leading to higher greenhouse gas emissions.

Suggested Citation

  • Keppo, Ilkka & Strubegger, Manfred, 2010. "Short term decisions for long term problems – The effect of foresight on model based energy systems analysis," Energy, Elsevier, vol. 35(5), pages 2033-2042.
  • Handle: RePEc:eee:energy:v:35:y:2010:i:5:p:2033-2042
    DOI: 10.1016/j.energy.2010.01.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544210000216
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Messner, S. & Golodnikov, A. & Gritsevskii, A., 1996. "A stochastic version of the dynamic linear programming model MESSAGE III," Energy, Elsevier, vol. 21(9), pages 775-784.
    2. Fredrik Hedenus, Christian Azar and Kristian Lindgren, 2006. "Induced Technological Change in a Limited Foresight Optimization Model," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 109-122.
    3. Kanudia, Amit & Loulou, Richard, 1998. "Robust responses to climate change via stochastic MARKAL: The case of Quebec," European Journal of Operational Research, Elsevier, vol. 106(1), pages 15-30, April.
    4. Niclas Mattsson, 2002. "Introducing uncertain learning in an energy system model: a pilot study using GENIE," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 18(2/3/4), pages 253-265.
    5. Azar, Christian & Lindgren, Kristian & Andersson, Bjorn A., 2003. "Global energy scenarios meeting stringent CO2 constraints--cost-effective fuel choices in the transportation sector," Energy Policy, Elsevier, vol. 31(10), pages 961-976, August.
    6. Shilpa Rao, Ilkka Keppo and Keywan Riahi, 2006. "Importance of Technological Change and Spillovers in Long-Term Climate Policy," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 123-140.
    7. Martinsen, Dag & Krey, Volker & Markewitz, Peter, 2007. "Implications of high energy prices for energy system and emissions--The response from an energy model for Germany," Energy Policy, Elsevier, vol. 35(9), pages 4504-4515, September.
    8. Leonardo Barreto, Socrates Kypreos, 2002. "Multi-regional technological learning in the energysystems MARKAL model," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 17(3), pages 189-213.
    9. Alan Manne & Richard Richels, 1992. "Buying Greenhouse Insurance: The Economic Costs of CO2 Emission Limits," MIT Press Books, The MIT Press, edition 1, volume 1, number 026213280x.
    10. Shilpa Rao and Keywan Riahi, 2006. "The Role of Non-CO2 Greenhouse Gases in Climate Change Mitigation: Long-term Scenarios for the 21st Century," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 177-200.
    Full references (including those not matched with items on IDEAS)

    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:energy:v:35:y:2010:i:5:p:2033-2042. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.journals.elsevier.com/energy .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.