Experience curves i the wind energy sector

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The Power of Learning Book preview
Kenniskracht, 30.1.2009, Amsterdam
Martin Junginger, Wilfried van Sark, Andrea
Ramirez Copernicus Institute, Utrecht
University With contributions reflections from
Andre Wakker (ECN-BS) and Erik ten Elshof
(Ministry of Economic Affairs)
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Presentation Overview

• Introduction and aims of the book The Power of
  learning
• Overview of methodological issues
• How can experience curves be used for
  energy-intensive bulk-chemical productions (and
  what can policy makers learn of that) the case
  of Ammonia (Andrea Ramirez)
• General policy implications and recommendations
  (Martin Junginger)
• Reflections from Andre Wakker Erik ten Elshof
  on policy relevance
• -gt Discussion

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Main basis for book

• PhD-Work performed in NWO-Novem sponsored
  programmes (a.o. Andrea Ramirez, Martin
  Junginger)
• Learning energy efficiency (sponsored by the
  Ministry of Economic Affairs)
• Technological learning in the energy sector
  (TLITES) (sponsored by the framework of the
  Netherlands Research Programme on Scientific
  Assessment and Policy Analysis for Climate Change
  (WAB))

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Main aims of the book

• To provide a comprehensive and up-to-date
  overview of experience curves studies for a host
  of energy supply- and demand-side technologies
• To discuss the applications and limits of
  experience curve approach
• To summarize and synthesis implications and
  recommendations for 1) policy makers, 2)
  scientists and 3) industry

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Structure of the book part 1

• Experience curve methodology and application
  including
• 1) General Introduction
• 2) General aspects and caveats of experience
  curve analysis
• 3) Putting experience curves in context links
  between technological development, market
  diffusion, learning mechanisms and systems
  innovation theory
• 4) The use of experience curves in energy models

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Structure of the book part 2

• Over 20 technology case studies, including
• Renewable energy supply, including onshore wind,
  offshore wind, PV, Concentrating solar thermal
  electricity technology biomass for electricity,
  heat biofuels
• Fossil energy supply, including gas combined
  cycle plants, pulverised coal-fired power plant
  (with without CCS) and nuclear power
• Energy demand technologies, including household
  appliances, lighting, space heating and cooling,
  and learning in the refinery sector, the
  production of bulk chemicals fertilizers

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Structure of the book part 3

• Synthesis and recommendations, including
• 1) Expected developments for selected
  technologies in terms of investment costs,
  production costs and avoided GHG emission costs
• 2) Methodological lessons and recommendations for
  scientists and modellers
• 3) Possibilities and limitations of experience
  curves for policy support on accelerating
  technological progress - lessons for policy
  makers and industry

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Author team and time planning

• (Co-) authors
• International panel of leading experts in the
  field of experience curves, e.g. Clas-Otto Wene,
  Lena Neij, Dolf Gielen, Ed Rubin, Martin Jakob
  and many (gt15) more.
• Time planning
• Contributions to book chapters during spring
  2009
• Finalization of the book by the end of 2009
• Publication by A-list publisher (almost) secured

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Methodology What is an experience curve?
Emperically observed many times With every
doubling of cumulative production
Source Harmon, IIASA, 2000
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Typical values of PRs
Source Dutton and Thomas, 1984
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Why is this relevant for policy makers? Learning
investments the cost of learning
Source IEA, 2000
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Energy supply technologies - overview
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Required CO2 price
2020
2010
2010
2020
and a CoE of 4 ct/kWh from fossil sources
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(No Transcript)
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Comparison of electricity generation generation
costs CCS options vs. onshore and offshore and
wind
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Biofuels for transportationBrazilian sugarcane
ethanol
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Experience curves for demand-side technologies
The case of ammonia production Over to Andrea...
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Overview findings Refinery Sector, Bulk
Chemicals, and Fertilizers
PE, PVC, Ammonia
Viscose rayon, Polyester, Cellophane

• 17 estimates based on 7 studies
• Average PR (77 10)

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Overview Energy Demand Technologies
Freezers (NL), Condensing (gas) boilers (NL,
Germany)
Magnetic FL ballasts, Magnetic CFLs, Freezers
(USA)

• 51 estimates based on 12 studies
• Average PR (85 9)

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Washing machines Energy efficiency
Data NL/World
1965
2007
Source Weiss et al. (2008)

• The share of A-label washing machine sales
  increased from 19 to 95 between 1998 and 2002
  in the Netherlands

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A Dutch technology the high efficiency
Condensing gas boilers
Boiler data NL/Europe
Based on Weiss et al. (2008)
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Summary of main findings

• The experience curve seems applicable for
  (almost) all energy technologies also efficient
  energy demand technologies (though they face
  several additional dilemmas compared to supply
  technologies)
• No structural trend was identified that PRs
  change over time or with increasing market
  diffusion
• Experience curve extrapolation holds clear
  advantages above only bottom up studies, but
  error/uncertainty margins have to be included

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Limits of experience curves

• Experience curve theory appears not to include
  the effects of increasing raw material costs, at
  least not on the short term
• Experience curve theory does also not include
  limitations due to geographical potential
  constraints
• Experience curves allow for projections for the
  development of production costs they do not
  forecast the development of market prices.

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Insights for Energy Policy

• The optimal distribution between RD and market
  support measures remains difficult to determine
• Policy seems not able to bend- down the
  experience curve

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Insights for Energy Policy

• Over-stimulation of markets may increase demand
  drastically, which may result in stabilizing or
  increasing prices which are not captured by
  experience curve analysis
• Experience curves in combination with bottom-up
  cost estimates and market analysis can be a
  tool to assess the cost reduction potential over
  e.g. a period of 10-20 years, and support
  designing policy accordingly

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Experience curves for energy efficiency?

• Energy efficiency improvement trends can be
  largely explained through cost optimization
  drivers (energy costs major factor for ammonia
  ethanol production)
• But also for end-consumer products, autonomous
  improvements are found
• In this case, energy policy seems to be able to
  bend down the curve / enforce rapid
  learning/energy efficiency improvements
• More case studies needed though to confirm
  hypothesis

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Final summary / Stellingen

• Policy seems not able to bend- down the
  experience curve for production costs but it
  can accelerate the ride down the curve
• In the case of energy efficiency improvements,
  energy policy is able to bend down the curve /
  enforce rapid learning/energy efficiency
  improvements
• Policy makers have to realize that all
  technologies investigated have international
  learning systems a national policy focus seems
  only advisable for technologies in a niche-market
  phase and a high Dutch market potential / share

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Thank you for your attention!
Over to Andre and Erik..