VII. Future of Renewable Energy

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2 INCREMENTAL & DISRUPTIVE CHANGES IN TECHNOLOGY Incremental (evolutionary) changes in technology Continuous small changes Usually in established technologies Easy to predict (established trends) Quantitative methods can be used (REGRESSION, TREND ANALYSIS) Disruptive changes (revolutionary) in technology Sudden big changes Usually in emerging technologies Hard to predict (change in trends, even change in rule of the game ) Methods based on expert opinion are more suitable (USUALLY DELPHI AND SCENARIOS)

3 INCREMENTAL CHANGES Growth in functional capability Examples: Speed of a plane, strength of a material, duration of a battery, energy per area in PV, fuel consumption of a car... Market growth & substitution Increase in market share and volume, substitution rate of other technologies Diffusion into other markets Rate of spread into other industries (example: use of computers in entertainment, in automation..., use of gene technology in health, chemical production, environmental cleaning... Decline in market share substitution/replacement by other technologies

4 DISRUPTIVE CHANGES Examples: Photocopy Quartz watch CD PC Cellular phone Digital photography Gen technology? Nanotechnology? e-commerce? e-books MP3 player music downloads? IN ENERGY AREA? We try to predict upcoming of new technology

5 TREND ANALYSIS/EXTRAPOLATING Monitoring (At least watch, observe and interpret) Early warning, data collection for other techniques) Time series analysis (Use past to predict future) Short term, low uncertainty, incremental changes in technologies Delphi Survey(Ask someone who knows) Long term, high uncertainty, radical changes or shifts in technology Scenarios (Create multiple version of future and be prepared) Long term, high uncertainty, multiple factors & multiple possible futures

6 TIME SERIES ANALYSIS (TREND EXTRAPOLATING 1.Identify the parameters to be studied Which parameters? : Functional capabilities, market share, number of new products, substation rate How to decide? Suitability for purpose, suitability for both old and substituting technology, availability of data 2. Choose proper model/tools Considering objective, parameters to be studied, time frame, type of chance, available resources and expertise... (Smoothing), Regression, Product/technology life cycle, Growth curves, Substitution curve, 3. Fit the data into the models Calculate the constants in model equation, calculate statistical fitness 4. Use the model to project the future

7 TRENDS no trend linear Y y X x exponential logarithmic Y X Y X sinusoidal 2.50 y x There could be many more...

8 TRENDS 25.0 Parameter X Trend line 5.0 Deviation time (month) X increases linearly with time

9 TRENDS 20.0 Y X Which line shows the trend?

10 LEAST SQUARE REGRESSION y x We want to minimize the sum of errors, for practical reasons we minimize sum of square or error Model equation: y=a+bx Deviation of model (line) from the data (point) error

11 GROWTH CURVES Common patterns in many natural/social phenomena Used in many area in biology, ecology, technology, market etc. Used in various forms S-shaped logistic curve (Pearl curve) Fisher-Pry substitution curve Gompertz function Envelope curves The past data may be fit these curves and used to predict the future (We may have to smooth the data first) We can linearise the model and may use linear regression)

12 GROWTH CURVES Itroduction/ innovation Improvement Maturity Natural limits y Slow growth due to initial difficulties Fast growth due to growing strength Slow growth due to the approaching limits X: usually time (sometimes efforts) x Y: growth in functional capability (size, strength, speed etc), market share/volume, number of new product, number of innovation etc. For the special case of Fisher-Pry curve y=substitution rate

13 Example: pumpkin, yeast (Martino, Technological Forecasting for Decisionmaking, Elsevier, 1972)

14 Example: cow weight (Source: (

15 Example: (

16 Example: chemicals

17 Example:cars

18 Example: energy ww.eia.doe.gov/oiaf/ieo/index.html#highlights

19 Example: renewable energy

20 Example: wind energy

21 Example: wind energy

22 Example: wind RENEWABLE ENERGY, Market & Policy Trends in IEA Countries, OECD/IEA, 2004

23 Example: PV RENEWABLE ENERGY, Market & Policy Trends in IEA Countries, OECD/IEA, 2004

24 TECHNOLOGY/PRODUCT LIFE CYCLE

25 TECHNOLOGY/PRODUCT LIFE CYCLE Boyle, Renewable Energy, Oxford University Press (2004)

26 TECHNOLOGY/PRODUCT LIFE CYCLE

27 TECHNOLOGY/PRODUCT LIFE CYCLE Geoffrey A. Moore, Crossing the chasm, 1991 Trott, Innovation Management and New Product Development, 5th ed.,2013

28 TECHNOLOGY/PRODUCT LIFE CYCLE Geoffrey A. Moore, Crossing the chasm, 1991 Problem: Early majority need references and early adaptors do not make good references

29 TECHNOLOGY/PRODUCT LIFE CYCLE

30 TECHNOLOGY/PRODUCT LIFE CYCLE ogers-bell-curve/#axzz3iqga2njh

31 RAYMOND PEARL MODEL RP Model is used to model the market penetration of a technology It suggests that the fraction of market penetration (f), is changed with time (t) according to the following curve f t

32 RAYMOND PEARL MODEL Justification of RP model: If f is the fraction of market penetration, the growth of f is df/dt: df dt bf ( 1 f ) (A) If f small (initially) slow growth integrate If (1-f) small (f is large maturity) slow growth Fast growth for the intermediate values of f and (1-f). b and c are constants (depend on technology/industry) f 1 1 cexp( bt) Usually linearised form is used (B) ln 1 f f ln( c) bt (C)

33 FISHER PRY MODEL Special case of growth model Used to model the substitution of a technology by a new one Penetration of a technology into a market is technology diffusion New technology diffuses by substituting an existing technology in an existing market or it creates its own market Early applications of new technologies are usually substitutions of an older technology Substitution occurs with difficulties (problems in new technology, improvement in old technology etc) and it takes times (sometimes it fails) For success, new technology should offer some additional benefits (functions, quality, cost etc..) compare to existing one

34 FISHER PRY MODEL Fisher-Pry model is model for substitution The same basic equation: df dt bf ( 1 f But the meaning of f is different: ) If technology A is substituting technology B, f is fraction of B that substituted by A, and 1-f is fraction of B remained The form of solution commonly used: 1 f ln 2a( t0 t) f a is a constant, t 0 is time for f=0.5

35 See Telektronikk 4, 2004 for more examples

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37 CHE ETM VII. 4. Technology Future of Evolution Renewable & Forecasting Energy

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40 DELPHI PROCESS Developed by Rand Corp (1950s) A special type of survey based on anonymity, iteration, controlled feedback and statistical evaluation Carried out with the participation of a group of geographically dispersed experts under the coordination of a facilitator /coordinator (team) Consists of series of questionnaire filled by the participants in round One of the most often used technique in technology forecasting

41 DELPHI PROCESS Starts with a question about the possible future developments and predicted dates in a technology or list of possible developments and dates (prepared with a preliminary work) to choose from. There may be other related questions in addition to date (for example, significance, impacts etc.) The responses are collected and statistically evaluated by the coordinator and the results (and median and range of the dates, comments of the participants etc) are fed to the participant for the next round to revise their response or answer the new round. Continued until the desired outcomes are achieved.

42 EURENDEL: An energy Delphi by EU Objective: o provide advice on energy R&D priorities, based on sound expert knowledge (Energy Future, EU Directorate-General for Research, Sustainable Energy Systems, 2006)

43 EURENDEL: An energy Delphi by EU

44 EURENDEL: An energy Delphi by EU

45 EURENDEL: An energy Delphi by EU

46 SCENARIOS If there are some significant uncertainties there are more than one possible future and we do not know which one will be real we describe/predict all possible future (write scenarios) and we prepare for all But the scenarios should be Based on major uncertainties They should not to exclude or disprove each other They should be internally consistent They should be focused in scope and time period They should be for long term They should be developed by an expert group

47 Uncertain Certain SCENARIOS Common Procedure: 1. Identify all the factor effecting future 2. Evaluate them in terms of importance and uncertainty 3. Get ready for major trends and write scenarios around the major uncertainties 4. Sometimes as usual, worst and best scenarios are formed Unimportant Important Major trends - get prepared Major uncertainities: Construct scenarios

48 SCENARIOS Future of Energy Shell Scenarios

49 SCENARIOS Given the recent developments such as economic volotality, political instability, progress in technology, surging in energy, changes in demographics etc, any plausible outlooks will be messy and patchy. Nevertheless, we have found that a number of new lenses can help us view familiar landscapes from fresh angles so that we can focus and clarify possible futures. Paradox and Pathway lenses help us zoom in on trends and drivers in detail, while our Panoramic scenarios highlight broader patterns in possible future landscapes.

50 SCENARIOS

51 SCENARIOS

52 SCENARIOS

53 (Energy Future, EU Directorate-General for Research, Sustainable Energy Systems, 2006)

54 Scenario development in China's electricity sector P.A. Steenhof, W. Fulton / Technological Forecasting & Social Change 74 (2007) 779

55 Scenario development in China's electricity sector P.A. Steenhof, W. Fulton / Technological Forecasting & Social Change 74 (2007) 779

56 Scenario development in China's electricity sector P.A. Steenhof, W. Fulton / Technological Forecasting & Social Change 74 (2007) 779

57 Scenario development in China's electricity sector as current conservative optimistic P.A. Steenhof, W. Fulton / Technological Forecasting & Social Change 74 (2007) 779