lecture: prospective environmental assessments prospective environmental assessments. ... design and...

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Upscaling and Learning

22.03.2017Stefanie Hellweg 1

Lecture:

Prospective Environmental Assessments

||www.ifu.ethz.ch/ESD 22.03.2017 2

Statement of the problem

Key question: environmental impact /kWh electricity?

1980 2012

20xx

1700

Prospective Environmental Assessment: Upscaling and Learning

• Technical and technology developments:

• Different sizes, changes of materials, design and production changes, supply chain

changes, acceptancy, regulations, etc

• General Life Cycle Assessment aspects:

• Usually only few LCA studies per size, data not harmonized, no method to include

technical & technology developments in LCA


What can change?

• Increase output capacity• Increase in efficiency• Change in utilities• Supplier changes• Maturization of used technology• Modified legal requirements• Process optimization• Technical development• By-product markets• Innovation• Change of background systems



Definitions

• Scaling: changes resulting from an increased output

• Learning: Process of acquiring modifications in existing knowledge, skills, habits, or tendencies (Britannica Concise Encyclopedia)

• Experience effects are defined as a combination of learning and scaling mechanisms



Scaling



Scaling: estimation



Cost scaling: examples



Cost scaling: example of coal combustion



Types of scaling

• Upsizing (up-scaling): increasing the size of an individual product, for instance upsizing a small engine to a large engine.

• Economies-of-scale: increasing plant capacity to produce large quantities.

• Economies-of-scope: synergies because of production of different products in the same company (joint use of production facilities, marketing, administration; by-products)



Learning curve concept

Concept idea:

• the time required to perform a task decreases as a worker gains experience

• time decreases when cumulative output doubles

Wright (1936): Labor costs in airframe manufacturing decline at a constant percentage with every

doubling of cumulative production



Types of learning

• Learning-by-Searching: learning by invention, research and development (R&D) and demonstration on a laboratory or pilot plant scale.

• Learning-by-Doing: learning during volume production, based on the total cumulative production.

• Learning-by-Using: learning after the product is introduced to the market, based on for instance user feedback.

• Learning-by-Interacting: learning during the diffusion of the technology for instance through a network between academia, industry etc.


||www.ifu.ethz.ch/ESD 22.03.2017 12Prospective Environmental Assessment: Upscaling and Learning


Experience curve

BCG: Boston Consulting Group


experience index


Electricity generation (1980-1995)


||www.ifu.ethz.ch/ESD 22.03.2017 15Prospective Environmental Assessment: Upscaling and Learning

Experience curve for PV modules

International Renewable Energy Agency, RENEWABLE ENERGY TECHNOLOGIES: COST ANALYSIS SERIES,

https://www.irena.org/DocumentDownloads/Publications/RE_Technologies_Cost_Analysis-SOLAR_PV.pdf, downloaded 2016

https://www.irena.org/DocumentDownloads/Publications/RE_Technologies_Cost_Analysis-SOLAR_PV.pdf


Technology structural change



Break-even PV



Experience not included



Experience included



Experience curve concept (costs) -

achievements

• Integration of curves into energy models has made it easier to integrate technology change into energy-system analysis and scenario planning

• Illustrate the approximate rate of cost reduction for different types of energy technologies

• Curves illustrate the need for an initial market in order to cut costs



Experience curve concept (costs) - drawbacks

• Driving forces of the cost reductions are not known – aggregated approach

• Empirical learning curves may masks underlying dynamics

• Limited usefulness for extrapolations



Environmental scaling and learning

• Efficiency changes

• Less material per product

• Higher performances

• Product life time

• Use of by-products

• Waste scenarios

• Changes in background systems



Methods for environmental scaling

1. Modelling based on empirical data

2. Engineering based quantifications

3. Environmental Impact Growth Laws (EIGL)



Methods for environmental scaling

1. Determining experience effects:

• Empirically fitting regression lines

• large dataset required

• no distinction between scaling and learning

• easy modelling: logY = loga + b logX; ordinary least-squares regression (OLS)

2. Engineering based models

• knowledge about physical relationships

• theoretical scaling; L α A1/2 α V1/3 α M1/3; e.g. swept area of rotor blades A = ¼

π D2

• only size, upper boundary for experience effects

3. Similarities:

• between different products

• between different disciplines such as economics

• Harmonization of goal & scope definitions necessary

• Parameterization of life cycle inventory parameters

• Calculation of life cycle assessment impacts

• Interpretation



Experience curve

- Dependent parameter: e.g. costs, LCI parameters, environmental impact

- For energy production systems Y: cumulative power production

- Commonly for a technology or sector and geographic location

Y = a X b

Y2 = Y1 (X2/X1)b

logY2 = logY1 + b log(X2/X1)

b: experience index

X: parameter defining size

Y: dependent parameter

PR = 2b progress rate

LR = 1 – PR learning rate



Size scaling

Y = a X b

Y2 = Y1 (X2/X1)b

logY2 = logY1 + b log(X2/X1)

b: scaling factor

X: parameter defining size

Y: size-dependent parameter

- Size-dependent parameter: e.g. costs, LCI parameters, environmental impact

- For energy production systems Y: power output

- Individual product level



Scaling: example of heat pumps – mass versus

power

R² = 0.77

10

100

1000

1 10 100 1000

a) Brine/water heat pumps (M versus P)

R² = 0.62

10

100

1000

1 10 100 1000

b) Air/water heat pumps (M versus P)

R² = 0.79

10

100

1000

1 10 100 1000

c) Water/water heat pumps (M versus P)

Ma

ss M

(kg)

Ma

ss M

(kg)

Ma

ss M

(kg)

Power P (kW) Power P (kW)

n=508



Scaling: example of heat pumps – refrigerant

use

0,1

1

10

1 10 100

Brine/water

Air/water

Water/water

Pot.(Brine/water)

Pot.(Air/water)

Pot.(Water/water)

Refr

ige

ran

tR

F (

kg)

Power P (kW)


Caduff M et al.., Scaling Relationships in Life Cycle Assessment: The Case of Heat Production

from Biomass and Heat Pumps. Journal of Industrial Ecology 18 (3), 393–406, 2014


Scaling: example of heat pumps – coefficient of

performance (COP)C

OP

(-)

Power P (kW)

2,5

3

3,5

4

4,5

5

5,5

6

1 10 100

Brine/water

Air/water

Water/water


Caduff M et al.., Scaling

Relationships in Life Cycle

Assessment: The Case of Heat

Production from Biomass and Heat

Pumps. Journal of Industrial Ecology

18 (3), 393–406, 2014


Scaling: example of heat pumps - GWPBrine/water heat pump Air/water heat pump

Water/water heat pump

(▬) total impact

(---) input materials

(···) manufacturing and disposal

(-·-) transport

(-··) refrigerant


Caduff M et al.., 2014


Scaling: example of heat pumps - GWP


(▬) total impact

(---) energy input

(···) refrigerant

(―) heat pump production

(-··) bore hole

(-·-) transport

Air/water heat pumpBrine/water heat pump




Scaling: example of heat pumps - ODP


(▬) total impact

(---) energy input

(···) refrigerant

(―) heat pump

(-··) bore hole

Brine/water heat pump Air/water heat pump




Scaling: example of biomass furnaces

R² = 0.51

,10

,100

1,000

10,000

100,000

1 10 100 1000

d) Biomass log furnace (M versus P)

R² = 0.95

,10

,100

1,000

10,000

100,000

1 10 100 1000

e) Biomass pellet furnace (M versus P)

n=243

Power P (kW)Power P (kW)





Effic

ien

cy (

%)

70

75

80

85

90

95

1 10 100 1000

a) Biomass log furnaces: Efficiency versus power

70

75

80

85

90

95

100

1 10 100 1000

b) Biomass pellet furnaces: Efficiency versus power

Effic

ien

cy (

%)

Power P (kW)

Power P (kW)





Ele

ctr

icity

Pel(k

Wh

)

Ele

ctr

icity

Pel(k

Wh)

0,01

0,1

1

1 10 100 1000

a) Biomass log furnaces: electricity consumption versus power

0,01

0,1

1

1 10 100 1000

b) Biomass pellte furnaces: electricity consumption versus power

Power P (kW)

Power P (kW)




Scaling: example of biomass furnaces - GWP

Biomass log furnace Biomass pellet furnace

(▬) total impact

(---) input materials

(···) manufacturing and disposal

(-·-) transport

(-··) refrigerant




Scaling: example of biomass furnaces - GWP

Biomass log furnace Biomass pellet furnace

total

biomass

inputtotal

biomass

input




Example of wind turbines


Caduff, M.; Huijbregts, M. A. J.; Althaus, H.-J.; Koehler, A.; Hellweg, S., Wind Power Electricity: the bigger

the turbine, the greener the electricity? Environmental Science & Technology, 2012, 46(9), 4725-4733


Example of wind turbines – engineering based

scaling relationships

Parameter proportional to

Power, P D2 h3/7

Mrotor D3

Mnacelle D3

Mtower D2 h

Mfoundation D3

Melectronics&cables h

EI production Mcomponents

EI use D2 h3/7

EI disposal Mcomponents


Caduff M et al. 2012


Example of wind turbines – empirical data

Rated power*, P

[kW]

Tower height, h

[m]

Rotor diameter, D

[m]

Construction year of turbine

Calculated captured power at rotor‡, Pcaptured,

max

[kW]

Calculated energy generation, Pcal [MWh/a]

660 55 55 2001† 219 1715500 41.5 39 1996† 98 764850 60 52 n/a 203 1591 3000 80 90 2003† 689 5392 2000 67 78 n/a 480 3754 1650 80 80 2005 545 4261 30 22 12.5 1990 8 60150 30 23.8 1994 32 248 600 40 43 1996 117 915 800 50 50 2001 174 1361 600 35 44 1998 116 9041500 67 66 2000 344 2688

assuming a standard site with 5 m/s wind speed at10 m height; wind shear gradient of 1/7




Example of wind turbines – empirical

relationships

Relationship* log a (95% CI) b (95% CI) R2 n

Mtotal D2 h3/7 1.90 (1.48 – 2.31) 0.76 (0.67 – 0.87) 0.97 12

Mrotor D 0.30 (-0.50 – 1.09) 2.22 (1.80 – 2.73) 0.93 10

Mnacelle D 0.64 (-0.07 – 1.35) 2.19 (1.81 – 2.65) 0.95 10

Mtower D 1.70 (1.27 – 2.13) 1.82 (1.58 – 2.09) 0.97 10

Mtower D2h 1.34 (0.94 – 1.74) 0.68 (0.60 – 0.76) 0.98 10

Mfoundation D 1.44 (0.63 – 2.25) 1.58 (1.20 – 2.09) 0.84 12

Melectronics&cables h 2.88 (2.83 – 2.93) 0.32 (0.30 – 0.35) 0.98 12




Example of wind turbines – empirical data

(mass versus rotor diameter)




LCA of wind turbines

• System boundaries:

• Resource extraction, material manufacturing and processing, production

of the elements (nacelle, rotor, turbine, foundation, cables inside the

turbine, cables to the grid, and the electronic control box), transport,

turbine maintenance and disposal;

• Assembly of the turbine and the energy for decommissioning of the

turbine were not included.

• Electricity produced was calculated for a standard site

• Material masses were linked to material inventories from ecoinvent

• Standard transport distances assumed for materials, foundation, operating

materials (lucricating oil)

• Cables length was parametrized according to hub height plus a size

independent distance to the grid of 1000 m for all cases.

• LCIA: midpoint indicators from ReCiPe



Example of wind turbines – LCIA

impact category unit log a (95% CI) b (95% CI) R2

climate change kg CO2 eq/kWh -0.93

(-1.27 – -0.59)

-0.22

(-0.16 – -0.31)

0.77

freshwater

ecotoxicity

kg 1,4-DB

eq/kWh

-1.66

(-2.13 – -1.18)

-0.39

(-0.29 – -0.51)

0.84

urban land

occupation

m2a/kWh 0.58

(0.41 – 0.76)

-0.87

(-0.82 – -0.91)

0.995

metal depletion kg Fe eq/kWh -0.22

(-0.68 – 0.23)

-0.35

(-0.26 – -0.46)

0.83




Example of wind turbines – GWP/kWh




Example of wind turbines (scaling and learning)

– GWP/rotor




Results overview engines, heat pumps,

furnaces, turbines: M = aPb

Equipment b (95% CI)

Gasoline engine 0.77 (0.71-0.83)

Diesel engine 0.64 (0.61-0.68)

Marine engine 1.23 (1.14-1.33)

Generator 0.68 (0.63-0.72)

Steam boiler 0.87 (0.84-0.90)

Brine-water heat pump 0.60 (0.55-0.65)

Air-water heat pump 0.67 (0.59-0.76)

Water-water heat pump 0.55 (0.48-0.64)

Log furnace 0.66 (0.59-0.74)

Pellet furnace 0.78 (0.74-0.82)

Wind turbine 0.76 (0.67-0.87)




Synthesis Results: GWP = aPb

Equipment b (95% CI)

cradle-to-gate

kg CO2/unit

b (95% CI)

cradle-to-grave

kg CO2/kWh

Brine-water heat pump 0.61 (0.54-0.68) -0.17 (-0.15- -0.17)

Air-water heat pump 0.82 (0.64-1.08) -0.08 (-0.13- -0.05)

Water-water heat pump 0.73 (0.60-0.89) -0.12 (-0.13- -0.12)

Log furnace 0.66 (0.59-0.74) -0.15 (-0.14- -0.15)

Pellet furnace 0.78 (0.74-0.82) -0.01 (-0.01- -0.02)

Wind turbine 0.78 (0.69-0.84) -0.22 (-0.16- -0.31)

0.73 (0.56-0.90) -0.12 (-0.13 - -0.12)




What about the very early stage?

0,0

1,0

2,0

3,0

4,0

5,0

Layout1

Layout2

Layout3

Layout4

En

vir

on

men

tal

imp

act

/ o

utp

ut

Laboratory & pilot plant scale

Y = 3.67X-0.20

R² = 0.91

0,0

1,0

2,0

3,0

4,0

5,0

0,0 10,0 20,0 30,0Cumulative production

Commercial scale



Conclusions

• Environmental impacts per FU do not remain

constant; they often display a non-linear scaling

pattern which can be modeled as a power law, y =

a xb

• Learning: concerned with cumulative production over

time – not the manufacture of a single product/batch

at a particular moment in time

• To enable a fair comparison of technologies at

different development stages, effects of learning and

scaling should be considered.



Limitations of empirical experience curves

• Harmonization of published datasets can be difficult and time-intensive

• Large datasets not always available

• Black box approach

• Modelling of entire production chain

• Extrapolation to other technologies, size ranges debatable

• Linking effects during laboratory and pilot plant scale to effect during volume production represents a challenge



Recommendations for future study

• Modelling of further products, sectors and ranges

to allow modelling of entire supply chain

• More research on environmental experience

effects of laboratory and/or pilot plant scale size

to volume production

• Division of environmental experience effects into

scaling and learning



Thank you to Marloes Caduff for providing an initial set of slides, on

which the current lecture is based on (adapted and updated

version).

Further reading

• Caduff, M.; Huijbregts, M. A. J.; Althaus, H.-J.; Koehler, A.; Hellweg, S., Wind Power

Electricity: the bigger the turbine, the greener the electricity? Environmental Science &

Technology, 2012, 46(9), 4725-4733

• Caduff, M.; Huijbregts, M. A. J.; Althaus, H.-J.; Hendriks, A. J., Power-Law

Relationships for Estimating Mass, Fuel Consumption and Costs of Energy Conversion

Equipments. Environmental Science & Technology, 2011, 45(2), 751-754

• Hendriks, A. J.; Schipper, A.; Caduff, M.; Huijbregts, M. A. J., Size relationships of water

inflow into lakes: Empirical regressions suggest geometric scaling. Journal of

Hydrology, 2012, 414-415, 482-490

• Caduff, M.; Koehler, A.; Huijbregts, M. A. J.; Althaus, H.-J.; Hellweg, S., Scaling

Relationships in Life Cycle Assessment: The Case of Heat Production from Biomass

and Heat Pumps. Journal of Industrial Ecology 18 (3), 393–406, 2014


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