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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

Abstract

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.

Keywords

Multi-stage stochastic programming Renewable energy  Power plant optimization 

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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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