Energy Systems

, Volume 5, Issue 1, pp 85–121 | Cite as

Optimization of power plant investments under uncertain renewable energy deployment paths: a multistage stochastic programming approach

  • Michaela Fürsch
  • Stephan Nagl
  • Dietmar Lindenberger
Original Paper


Electricity generation from renewable energy sources (RES-E) is planned to increase significantly within the coming decades. However, due to uncertainty about the progress of necessary infrastructure investments, public acceptance and cost developments of renewable energies, the achievement of political plans is uncertain. Implementation risks of renewable energy targets are challenging for investment planning, because different shares of RES-E fundamentally change the optimal mix of dispatchable power plants. Specifically, uncertain future RES-E deployment paths induce uncertainty about the level and the steepness of the residual load duration curve and the hourly residual load structure. In this paper, we show how uncertain future RES-E penetration levels impact the electricity system and try to quantify effects for the Central European power market. We use a multi-stage stochastic investment and dispatch model to analyze effects on investment choices, electricity generation and system costs. Our main findings include that uncertainty about the achievement of RES-E targets significantly affects optimal investment and dispatch decisions. In particular, plants with a medium capital/operating cost ratio have a higher value under uncertainty. We find that this technology choice is mainly driven by the uncertainty about the level rather than about the structure of the residual load. Furthermore, given larger investments in plants with medium capital/operating cost ratio under uncertainty, optimal investments in storage units are lower than under perfect foresight. In the case of the Central European power market, costs induced by the implementation risk of renewable energies are rather small compared to total system costs.


Multi-stage stochastic programming Renewable energy  Power plant optimization 


  1. 1.
    Birge, J.R., Louveaux, F.: Glynn, P., Robinson, S. (eds.) Introduction to Stochastic Programming. Springer, Berlin (1997)Google Scholar
  2. 2.
    BMU: Erneuerbare Energien in Zahlen–Internet-Update ausgewählter Daten. Tech. rep., Bundesministerium fur Umwelt, Naturschutz und Reakorsicherheit (BMU) (2011)Google Scholar
  3. 3.
    Cramton, P., Ockenfels, A.: Economics and design of capacity markets for the power sector. Zeitschrift fur Energiewirtschaft 36, 113–134 (2012)CrossRefGoogle Scholar
  4. 4.
    Cramton, P., Stoft, S.: A capacity market that makes sense. Electr. J. 18, 43–54 (2005)CrossRefGoogle Scholar
  5. 5.
    Cramton, P., Stoft, S.: Forward reliability markets: less risk, less market power, more efficiency. Util. Policy 16, 194–201 (2008)CrossRefGoogle Scholar
  6. 6.
    Dantzig, G.B.: Linear programming under uncertainty. Manage. Sci. 1, 197–206 (1955)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    De Jonghe, C., Delarue, E., Belmans, R., Dhaeseleer, W.: Determining optimal electricity technology mix with high level of wind power penetration. Appl. Energy 88, 2231–2238 (2011)CrossRefGoogle Scholar
  8. 8.
    Dena: Planning of the grid integration of wind energy in Germany onshore and offshore up to the year 2020 (Dena grid study). Tech. rep., German Energy Agency (2008)Google Scholar
  9. 9.
    Energiekonzept: Energiekonzept für eine umweltschonende, zuverlässige und bezahlbare Energieversorgung. Tech. rep., BMWi/BMU (2010)Google Scholar
  10. 10.
    ENTSO-E: NTC Matrix summer 2010 and NTC Matrix winter 2009–2010 (2010).
  11. 11.
    Eurelectric: Statistics and Prospects for the European electricity sector. EURPROG 2008, 36th edn. Tech. rep., Eurelectric (2008)Google Scholar
  12. 12.
    Eurelectric: Statistics and Prospects for the European Electricity Sector. EURPROG 2009, 37th edn. Tech. rep., Eurelectric (2009)Google Scholar
  13. 13.
    EWI/energynautic: Roadmap 2050—a closer look. Cost-efficient RES-E penetration and the role of grid extensions. Tech. rep., Institute of Energy Economics at the University of Cologne and energynautics GmbH (2011)Google Scholar
  14. 14.
    EWI/Prognos/GWS: Energieszenarien für ein Energiekonzept der Bundesregierung. Tech. rep., Study on behalf of the German Federal Ministry of Economics and Technology (2010)Google Scholar
  15. 15.
    Fürsch, M., Hagspiel, S., Jägemann, C., Nagl, S., Lindenberger, D., Tröster, E.: The role of grid extensions in a cost-efficient transformation of the European electricity system until 2050. Appl. Energy 104, 642–652 (2013)Google Scholar
  16. 16.
    Gardner, D., Rogers, J.: Planning electric power systems under demand uncertainty with different technology lead times. Manage. Sci. 45, 1289–1306 (1999)CrossRefzbMATHGoogle Scholar
  17. 17.
    Gardner, D.T.: Flexibility in electric power planning: coping with demand uncertainty. Energy 21, 1207–1218 (1996)CrossRefGoogle Scholar
  18. 18.
    Hobbs, B.F., Maheshwari, P.: A decision analysis of the effect of uncertainty upon electric utility planning. Energy 15, 785–801 (1990)CrossRefGoogle Scholar
  19. 19.
    Leipzig, I.E.: Mittelfristprognose zur deutschlandweiten Stromerzeugung aus regenerativen Kraftwerken bis 2016. Tech. rep, Leipziger Institut für Energie GmbH (2011)Google Scholar
  20. 20.
    Joskow, P.: Capacity payments in imperfect electricity markets: need and design. Util. Policy 16, 159–170 (2008)CrossRefGoogle Scholar
  21. 21.
    Lamont, A.D.: Assessing the long-term system value of intermittent electric generation technologies. Energy Econ. 30, 1208–1231 (2008)CrossRefGoogle Scholar
  22. 22.
    Manne, A., Richels, R.: A decision analysis of the U.S. breeder reactor programm. Energy 3, 747–767 (1978)CrossRefGoogle Scholar
  23. 23.
    Manne, A.S.: Waiting for the breeder, pp. 47–65. In: Symposium on Review of Economic Studies (1974)Google Scholar
  24. 24.
    Miera, G., Gonzales, P., Vizcaino, I.: Analysing the impact of renewable electricity support schemes on power prices: the case of wind electricity in Spain. Energy Policy 36, 3345–3359 (2008)CrossRefGoogle Scholar
  25. 25.
    Mondiano, E.: Derived demand and capacity planning under uncertainty. Oper. Res. 35, 185–197 (1987)CrossRefGoogle Scholar
  26. 26.
    Murphy, F.H., Sen, S., Soyster, A.L.: Electric utility capacity expansion planning with uncertain load forecasts. IIE Trans. 14, 52–59 (1982)CrossRefGoogle Scholar
  27. 27.
    Nabe, C.: Effiziente Integration erneuerbarer Energien in den deutschen Elektrizitätsmarkt. Ph.D. thesis, TU Berlin (2006)Google Scholar
  28. 28.
    Nagl, S., Fürsch, M., Jägemann, C., Bettzüge, M.O.: The economic value of storage in renewable power systems—the case of thermal energy storage in concentrating solar plants. Workingpaper (Institute of Energy Economics at the University of Cologne) (2011)Google Scholar
  29. 29.
    Nagl, S., Fürsch, M., Lindenberger, D.: The costs of electricity systems with a high share of fluctuating renewables–a stochastic investment and dispatch optimization model for Europe. Workingpaper (Institute of Energy Economics at the University of Cologne). Energy J. (2012, accepted)Google Scholar
  30. 30.
    Patino-Echeverri, D., Fischebeck, P., Kriegler, E.: Economic and environmental costs of regulatory uncertainty for coal-fired power plants. Environ. Sci. Technol. 43, 578–584 (2009)CrossRefGoogle Scholar
  31. 31.
    Reinelt, P.S., Keith, D.W.: Carbon capture retrofits and the cost of regulatory uncertainty. Energy J. 28, 101–128 (2007)CrossRefGoogle Scholar
  32. 32.
    Richter, J.: DIMENSION—A Dispatch and Investment Model for European Electricity Markets. Working Paper (Institute of Energy Economics at the University of Cologne) (2011)Google Scholar
  33. 33.
    Roques, F.A., Nuttall, W.J., Newbery, D.M., de Neufville, R., Connors, S.: Nuclear power: a hedge against uncertain gas and carbon prices? Energy J. 27, 1–24 (2006)CrossRefGoogle Scholar
  34. 34.
    Stoft, S.: Power System Economics—Designing Markets for Electricity. Wiley, IEEE (2002)Google Scholar
  35. 35.
    Sun, N., Ellersdorfer, I., Swider, D.J.: Model-based long-term electrictity gerneration system planning under uncertainty. In: Int. Conference on Electric Utility Deregualtion and Re-structuring and Power Technologies (DRPT 2008) (2008)Google Scholar
  36. 36.
    Swider, D.J., Weber, C.: The costs of wind’s intermittency in Germany. Eur. Trans. Electr. Power 17, 151–172 (2006)CrossRefGoogle Scholar
  37. 37.
    Weigt, H.: Germanys wind energy: the potential for fossil capacity replacement and cost saving. Appl. Energy 86, 1857–1863 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michaela Fürsch
    • 1
  • Stephan Nagl
    • 1
  • Dietmar Lindenberger
    • 1
  1. 1.Institute of Energy Economics at the University of CologneCologneGermany

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