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Modelling Dynamic Constraints in Electricity Markets and the Costs of Uncertain Wind Output

Author

Listed:
  • Felix Müsgens

    (School of Risk Management and Institute of Energy Economics University of Köln)

  • Karsten Neuhoff

    (Faculty of Economics University of Cambridge)

Abstract

Building on models that represent inter-temporal constraints in the optimal production decisions for electricity generation, the paper analysis the resulting costs and their impact on prices during the day. We linearise the unit commitment problem to facilitate the interpretation of shadow prices. Analytic research gives insights for a system with one technology and numeric implementation provides results for the German power system. The model is expanded to a stochastic optimisation with recourse. The model is used to calculate the cost of wind uncertainty and the value of updating wind forecasts.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Felix Müsgens & Karsten Neuhoff, 2006. "Modelling Dynamic Constraints in Electricity Markets and the Costs of Uncertain Wind Output," Working Papers EPRG 0514, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg0514
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    File URL: https://www.jbs.cam.ac.uk/wp-content/uploads/2023/12/eprg-wp0514.pdf
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    Cited by:

    1. repec:hal:spmain:info:hdl:2441/53r60a8s3kup1vc9l564igg8g is not listed on IDEAS
    2. Iegor Riepin & Thomas Mobius & Felix Musgens, 2020. "Modelling uncertainty in coupled electricity and gas systems -- is it worth the effort?," Papers 2008.07221, arXiv.org, revised Sep 2020.
    3. Green, Richard & Vasilakos, Nicholas, 2010. "Market behaviour with large amounts of intermittent generation," Energy Policy, Elsevier, vol. 38(7), pages 3211-3220, July.
    4. repec:hal:wpspec:info:hdl:2441/53r60a8s3kup1vc9l564igg8g is not listed on IDEAS
    5. Hiroux, C. & Saguan, M., 2010. "Large-scale wind power in European electricity markets: Time for revisiting support schemes and market designs?," Energy Policy, Elsevier, vol. 38(7), pages 3135-3145, July.
    6. Ambec, Stefan & Crampes, Claude, 2012. "Electricity provision with intermittent sources of energy," Resource and Energy Economics, Elsevier, vol. 34(3), pages 319-336.
    7. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
    8. Corsatea, Teodora Diana & Giaccaria, Sergio & Covrig, Catalin-Felix & Zaccarelli, Nicola & Ardelean, Mircea, 2016. "RES diffusion and R&D investments in the flexibilisation of the European electricity networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1069-1082.
    9. Stefan Ambec & Claude Crampes, 2010. "Electricity Production with Intermittent Sources of Energy," LERNA Working Papers 10.07.313, LERNA, University of Toulouse.
    10. repec:spo:wpmain:info:hdl:2441/53r60a8s3kup1vc9l564igg8g is not listed on IDEAS
    11. Riepin, Iegor & Möbius, Thomas & Müsgens, Felix, 2021. "Modelling uncertainty in coupled electricity and gas systems—Is it worth the effort?," Applied Energy, Elsevier, vol. 285(C).
    12. repec:spo:wpecon:info:hdl:2441/53r60a8s3kup1vc9l564igg8g is not listed on IDEAS
    13. Fadoua Chiba & Sébastien Rouillon, 2020. "Intermittent Electric Generation Technologies and Smart Meters: Substitutes or Complements," Revue d'économie politique, Dalloz, vol. 130(4), pages 573-613.

    More about this item

    Keywords

    Electricity Markets; Energy Modelling; Optimisation Models;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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