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AES-dagarna Katrineholm, 6-7 May 2009
Leo Schrattenholzer In Memoriam
Technology Learning for Energy Technology
Policy Clas-Otto Wene
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First compilation for energy technologies Renewabl
es, fossil, nuclear, energy efficiency Industry
level
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Technology Learning measured by Experience
CurveThree decades, four orders of magnitude
and a deployment roller-coaster
Price const (Cum. Ship)-E Learning Rate 1
2-E
Learning Rate 20
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Technology Learning Measurement and Energy
Policy

• Technology Learning deploying technologies in
  competitive markets increases skills and
  stimulates private RD, leading to cost
  reductions and improved technical performance.

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How to design Deployment Programmes
stimulatingindustry internal processes at low
cost to tax payers?
A
Cost
Niche Markets for the Challenger
B
Cumulative Sales

• Special efforts to create niche markets
  (labelling, feed-in tariffs)?
• Is the niche market curve flat enough?
• Contributions from industry in A to have the
  benefits in B?

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Using Niche Markets to stimulate Learning
Investmentsfrom private sources (Example Japan
Residential PV Systems, IEA (2000))
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Examples of regulation stimulating Technology
Learning and measured by Experience
Curves (Wene, 2008a)
Germany 1992-2000 Coated Glass for Selective
Windows (Data from Blessing 2002)
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The Technology Learning System and the Energy
Systemare coupled to each other
Energy System
Technology Learning System (manufacturing ind.)
Structural coupling interlocked history of
structural transformation, selecting each
others trajectories (Varela, 1979)
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Modelling experiment showing effective but
alternative paths (Results from Genie model 1997)
The structural coupling between ETLSs and energy
system expressed in Experience Curves have
created two very different Least-Cost solutions
from identical starting points and assumptions
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Critical assessment of Experience/Learning
Curves High-level Reports positive but important
caveats

• IEA Energy Technology Perspectives ? Key
  phenomenon for determining future cost of
  renewable ? State-of-the-art does not permit
  reliable extrapolations
• UK Stern Report ? Can be used to justify
  deployment support ? Very different learning
  rates from causes uncertain

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Theory Logic of the argument
? Operational closure The technology learning
system is an operationally closed system.
? Fundamental Cybernetic Theorem All
operationally closed systems develop
Eigenbehaviour (von Förster, Varela)
? Operators Define operators working on the
internal state function and compatible with
the ECLC equation
? Eigenvalues Use the operators to calculate
eigenvalues for the system
? Experience Parameter Interpret the eigenvalues
in terms of the experience parameter in the
ECLC equation
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A formal view of the present theory
k
CSRL
0
Lim
0
C
n 0, 1, 2,
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Result from the Theory

• Value of E and Learning Rates Eigenvalue
  analysis provides E(n) 1/(2n1)p
  for n 0, 1, 2, 3, LR(n) 20, 7, 4,
  for n 0, 1, 2

Theory reformulates the research question From
Why is the learning rate X? toWhy are not
all learning rates 20?
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Frequency distribution of Learning Rates 108
cases from individual firms and by cost
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Comparison theoretical and measured
distribution 108 measurements in individual
firms and by cost
Emean (DT) 0.3110Etheory (0) 0.3183
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Comparison theoretical and measured
distribution 42 Energy technologies on industry
level and by price
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Developing the cybernetic approach withinthe AES
project

• Closure and Eigenvalue - Matrix
  formulation to include double closure -
  Phenomena of radical innovation, technology
  drift, grafted technologies, compound
  systems, dispersion
• Modelling the Technology Learning System -
  Feasibility of using Beers Viable System Model
• Applications - Cooperation to apply the
  theoretic approach to a few key
  technologies (renewables and energy
  efficiency)

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Thank you!
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Effect of Radical InnovationResetting the
cumulative sales (resetting feedback)