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On Start-up Costs of Thermal Power Plants in Markets with Increasing Shares of Fluctuating Renewables

Author

Listed:
  • Wolf-Peter Schill
  • Michael Pahle
  • Christian Gambardella

Abstract

The emerging literature on power markets with high shares of fluctuating renewables suggests that more frequent start-up procedures of thermal power plants may become an increasing concern, both for costs and possibly also for market design. Based on official scenario assumptions, we investigate how start-ups and related costs develop in Germany, where the share of fluctuating renewables quadruples between 2010 and 2030. We find that the overall number of start-ups decreases by a third, while related costs increase by half. The relative share of start-up costs in overall variable costs of thermal plants grows only slightly and remains below 1%. Several overlapping effects drive these results. The expansion of fluctuating renewables alone would strongly increase start-up costs. In contrast, increased flexibility of biomass power plants, additional power storage and larger block sizes have opposite effects. While the relevance of start-up costs grows only moderately under baseline assumptions, it may increase further under alternative developments of system flexibility. Future power market design reforms should thus aim to ensure proper remuneration of quasi-fixed start-up costs. Our findings are also relevant for many other countries with thermal power systems that plan to undergo comparable transitions toward fluctuating renewables.

Suggested Citation

  • Wolf-Peter Schill & Michael Pahle & Christian Gambardella, 2016. "On Start-up Costs of Thermal Power Plants in Markets with Increasing Shares of Fluctuating Renewables," Discussion Papers of DIW Berlin 1540, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1540
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    References listed on IDEAS

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    Cited by:

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    2. Poplavskaya, Ksenia & Lago, Jesus & Strömer, Stefan & de Vries, Laurens, 2021. "Making the most of short-term flexibility in the balancing market: Opportunities and challenges of voluntary bids in the new balancing market design," Energy Policy, Elsevier, vol. 158(C).
    3. Eren Çam, 2020. "Optimal Dispatch of a Coal-Fired Power Plant with Integrated Thermal Energy Storage," EWI Working Papers 2020-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    4. McPherson, Madeleine & Ismail, Malik & Hoornweg, Daniel & Metcalfe, Murray, 2018. "Planning for variable renewable energy and electric vehicle integration under varying degrees of decentralization: A case study in Lusaka, Zambia," Energy, Elsevier, vol. 151(C), pages 332-346.
    5. Gregor Brändle & Max Schönfisch & Simon Schulte, 2020. "Estimating Long-Term Global Supply Costs for Low-Carbon Hydrogen," EWI Working Papers 2020-4, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    6. Petitet, Marie & Perrot, Marie & Mathieu, Sébastien & Ernst, Damien & Phulpin, Yannick, 2019. "Impact of gate closure time on the efficiency of power systems balancing," Energy Policy, Elsevier, vol. 129(C), pages 562-573.
    7. McPherson, Madeleine & Harvey, L.D. Danny & Karney, Bryan, 2017. "System design and operation for integrating variable renewable energy resources through a comprehensive characterization framework," Renewable Energy, Elsevier, vol. 113(C), pages 1019-1032.
    8. McPherson, Madeleine & Tahseen, Samiha, 2018. "Deploying storage assets to facilitate variable renewable energy integration: The impacts of grid flexibility, renewable penetration, and market structure," Energy, Elsevier, vol. 145(C), pages 856-870.
    9. Andreas Knaut & Frank Obermüller & Florian Weiser, 2017. "Tender Frequency and Market Concentration in Balancing Power Markets," EWI Working Papers 2017-4, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    10. McPherson, Madeleine & Karney, Bryan, 2017. "A scenario based approach to designing electricity grids with high variable renewable energy penetrations in Ontario, Canada: Development and application of the SILVER model," Energy, Elsevier, vol. 138(C), pages 185-196.

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    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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