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AES-dagarna Katrineholm, 6-7 May 2009

AES-dagarna Katrineholm, 6-7 May 2009. Leo Schrattenholzer In Memoriam. Technology Learning for Energy Technology Policy Clas-Otto Wene. First compilation for energy technologies Renewables, fossil, nuclear, energy efficiency Industry level. 20%. 5%. Technology Learning System. $.

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AES-dagarna Katrineholm, 6-7 May 2009

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  1. AES-dagarna Katrineholm, 6-7 May 2009 Leo Schrattenholzer In Memoriam Technology Learning for Energy Technology Policy Clas-Otto Wene

  2. First compilation for energy technologies Renewables, fossil, nuclear, energy efficiency Industry level 20% 5%

  3. Technology Learning System $ Wp M 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%

  4. Technology Learning: Measurement and Energy Policy • Experience/learning curves:measures technology learning when technical properties remains same • Deployment Policy • No Learning without Market Action • Scenario Modelling • Path dependence leading toε/Ω solutions Cybernetic Theory Technology Learning as eigenbehaviour • Technology Learning: deploying technologies in competitive markets increases skills and stimulates private R&D, leading to cost reductions and improved technical performance.

  5. How to design Deployment Programmes stimulatingindustry internal processes at low cost to tax payers? Challenger Incumbent Cost-efficient Technology 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”?

  6. Niche Markets Using Niche Markets to stimulate Learning Investmentsfrom private sources (Example Japan Residential PV Systems, IEA (2000))

  7. Announce-ment 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)

  8. 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 other’s trajectories” (Varela, 1979)

  9. 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

  10. Empirical studies: ● Analyse and verify learning rates ● Features, Events, Processes (FEPs) causing technology learning • Theoretical basis:● Cybernetic Approach proposed - FEP do not explain learning rates● Modelling the technology production system 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

  11. 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 EC&LC equation ►Eigenvalues:Use the operators to calculate eigenvalues for the system ► Experience Parameter: Interpret the eigenvalues in terms of the experience parameter in the EC&LC equation

  12. i i (2n+1)π 0 = ∞ 1 1 0 1 k A formal view of the present theory k CSRL 0 Lim 0 C+ n = 0, 1, 2, …

  13. Result from the Theory • Value of E and Learning Rates: Eigenvalue analysis providesE(n) = 1/[(2n+1)π] 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%?” to“Why are not all learning rates 20%?”

  14. Frequency distribution of Learning Rates: 108 cases from individual firms and by cost Theory predictsLR0 = 20%

  15. Comparison theoretical and measured distribution: 108 measurements in individual firms and by cost Emean (DT) = 0.3110Etheory (0) = 0.3183

  16. Price/cost cycle(but only 25% of total cases at E≈0.10) • Higher modes of learning (75%) - Insufficient closure - External perturbations Comparison theoretical and measured distribution: 42 Energy technologies on industry level and by price

  17. 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 Beer’s Viable System Model • Applications- Cooperation to apply the theoretic approach to a few key technologies (renewables and energy efficiency)

  18. Thank you!

  19. Effect of Radical InnovationResetting the cumulative sales (resetting feedback)

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