Renewables in Future Power Systems

Implications of Technological Learning and Uncertainty

  • Fabian Wagner

Part of the Green Energy and Technology book series (GREEN)

Table of contents

  1. Front Matter
    Pages 1-14
  2. Fabian Wagner
    Pages 1-2
  3. Fabian Wagner
    Pages 21-42
  4. Fabian Wagner
    Pages 217-222
  5. Back Matter
    Pages 223-291

About this book


The book examines the future deployment of renewable power from a normative point of view. It identifies properties characterizing the cost-optimal transition towards a renewable power system and analyzes the key drivers behind this transition. Among those drivers, particular attention is paid to technological cost reductions and the implications of uncertainty. From a methodological perspective, the main contributions of this book relate to the field of endogenous learning and uncertainty in optimizing energy system models. The primary objective here is closing the gap between the strand of literature covering renewable potential analyses on the one side and energy system modeling with endogenous technological change on the other side. The models applied in this book demonstrate that fundamental changes must occur to transform today's power sector into a more sustainable one over the course of this century. Apart from its methodological contributions, this work is also intended to provide practically relevant insights regarding the long-term competitiveness of renewable power generation.



Cost Competitiveness Cost Reduction Deterministic Model Dynamic Model with Uncertainty Endogenous Learning Energy System Modeling Large-scale Power System Models Optimal Investment Strategy Stochastic Model Uncertain Learning Rates

Authors and affiliations

  • Fabian Wagner
    • 1
  1. 1.Faculty of Business Administration and EconomicsUniversity of Duisburg-Essen Campus EssenEssenGermany

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Energy Energy (R0)
  • Print ISBN 978-3-319-05779-8
  • Online ISBN 978-3-319-05780-4
  • Series Print ISSN 1865-3529
  • Series Online ISSN 1865-3537
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