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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
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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
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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
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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
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Scaling
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Scaling: estimation
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Cost scaling: examples
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Cost scaling: example of coal combustion
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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)
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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
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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.
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Experience curve
BCG: Boston Consulting Group
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experience index
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Electricity generation (1980-1995)
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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
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Technology structural change
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Break-even PV
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Experience not included
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Experience included
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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
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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
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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
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Methods for environmental scaling
1. Modelling based on empirical data
2. Engineering based quantifications
3. Environmental Impact Growth Laws (EIGL)
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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
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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
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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
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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
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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)
Prospective Environmental Assessment: Upscaling and Learning
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
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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
Prospective Environmental Assessment: Upscaling and Learning
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
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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
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Scaling: example of heat pumps - GWP
Water/water heat pump
(▬) total impact
(---) energy input
(···) refrigerant
(―) heat pump production
(-··) bore hole
(-·-) transport
Air/water heat pumpBrine/water heat pump
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Scaling: example of heat pumps - ODP
Water/water heat pump
(▬) total impact
(---) energy input
(···) refrigerant
(―) heat pump
(-··) bore hole
Brine/water heat pump Air/water heat pump
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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)
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Scaling: example of biomass furnaces
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)
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Scaling: example of biomass furnaces
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)
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Scaling: example of biomass furnaces - GWP
Biomass log furnace Biomass pellet furnace
(▬) total impact
(---) input materials
(···) manufacturing and disposal
(-·-) transport
(-··) refrigerant
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Scaling: example of biomass furnaces - GWP
Biomass log furnace Biomass pellet furnace
total
biomass
inputtotal
biomass
input
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Example of wind turbines
Prospective Environmental Assessment: Upscaling and Learning
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
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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
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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
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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
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Example of wind turbines – empirical data
(mass versus rotor diameter)
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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
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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
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Example of wind turbines – GWP/kWh
Prospective Environmental Assessment: Upscaling and Learning
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Example of wind turbines (scaling and learning)
– GWP/rotor
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Example of wind turbines
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Example of wind turbines
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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)
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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)
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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
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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.
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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
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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
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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
Prospective Environmental Assessment: Upscaling and Learning