the (im)possible mission of embracing parametric and ......8th annual iamc conference, potsdam, 16...
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Evelina Trutnevyte, ETH Zurich
8th Annual IAMC Conference, Potsdam, 16 November 2015
The (im)possible mission of embracing parametric and structural uncertainties in energy system models
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Motivation
u Bottom-up energy system models have been criticized for their structural assumption of cost optimization
u Practically no evidence exists, demonstrating whether cost optimization is a suitable proxy of the real-world transition or not
u This lack of evidence amplifies the tension between exploratory vs. predictive use of bottom-up models and contests the policy messages
u Aims: −Contribute to this debate with evidence from retrospective
modelling−Find ways to improve the bottom-up energy system models
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Rationale for cost optimization
Mathematics, engineering, management science:§ Well-established approach§ Minimize or maximize (Jeremy Bentham)
In energy systems modeling:uSocial planner’s approach
§ A single decision maker that maximizes the social welfare § But such a decision maker rarely exists
uPartial equilibrium argument§ The supply-demand equilibrium is reached when the total
surplus is maximized§ But partial equilibrium does not account for interactions
with the other sectors
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u Bounded rationality (Gigerenzer 2002; Simon 1957)
u Unmodeled objectives (Chang et al. 1982a, 1982b; DeCarolis 2011; Trutnevyte 2013)
u Complex system (Ottino 2004)
u and many more
Why the real-world transition may not be cost-optimal?
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§ Bottom-up, technology rich, perfect foresight, cost optimization model
§ Exploration of near-optimal scenarios (new!)
§ Monte Carlo technique to address parametric uncertainty
D-EXPANSE model Dynamic EXploration of PAtterns in Near-optimal energy ScEnarios
Sources: Trutnevyte, E. 2013 Applied EnergyTrutnevyte, E. 2013 Energy Strategy Reviews
Figure: blog.atrinternational.com
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Retrospective UK electricity system modeling, 1990-2014
u Timeframe: 25 years, 1990-2014 u Scope: electricity generation mix with exogenously given demandu Historical data:
§ Actual electricity demand data§ Actual plant retirement data§ Actual costs and technology characteristics (as precise as possible) § No GHG emission targets
i.e. I assume that in 1990 I “guessed” all the future parameters precisely, thus there is no parametric uncertainty
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Cost-optimal scenario
A large number of near-optimal
scenarios
Maximally-different
scenarios
Analyze patterns in a large number
of scenariosSlack•e.g. 20% on total system costs
Efficient random generation (Chang et al. 1982a,b)•1000 scenarios•The same set of assumptions!
Minimize total system costs• Supply-demand
constraints• Technology
constraints• Resource constraints• Costs• Deterministic run
D-EXPANSE procedure
Statistics and visualization techniques
888
Cost-optimal scenario vs. real-world transition
1990 1995 2000 2005 2010 1990 1995 2000 2005 2010
999
Cost-optimal scenario vs. real-world transition
16%
12%
Deviation=(Creal-world – Coptimal)/Coptimal
1990 1995 2000 2005 2010 1990 1995 2000 2005 2010
1010
Cost-optimal scenario
A large number of near-optimal
scenarios
Maximally-different
scenarios
Analyze patterns in a large number
of scenariosSlack•e.g. 20% on total system costs
Efficient random generation (Chang et al. 1982a,b)•1000 scenarios•The same set of assumptions!
D-EXPANSE procedure
Statistics and visualization techniques
Minimize total system costs• Supply-demand
constraints• Technology
constraints• Resource constraints• Costs• 500 Monte Carlo runs
111111
Cost-optimal scenario vs. real-world transition
1212
Cost-optimal scenario
A large number of near-optimal
scenarios
Maximally-different
scenarios
Analyze patterns in a large number
of scenariosSlack• e.g. 17% or 23% on total system costs
Efficient random generation (Chang et al. 1982a,b)• 500 scenarios• Deterministic run
Minimize total system costs• Supply-demand
constraints• Technology
constraints• Resource constraints• Costs• Deterministic run
D-EXPANSE procedure
Statistics and visualization techniques
1313
time
Assumptionsetno.1
cost-optimal1
13
1414
time
Assumptionsetno.1
cost-optimal1
near-optimalspace
14
Near-optimal scenarios in D-EXPANSE model
1515
time
Assumptionsetno.1
near-optimalscenarios
Near-optimal scenarios in D-EXPANSE model
Realworld
1616
Technology deployment (near-optimal scenarios in a deterministic run)
1717
Cost-optimal scenario
A large number of near-optimal
scenarios
Maximally-different
scenarios
Analyze patterns in a large number
of scenariosMinimize total system costs• Supply-demand
constraints• Technology
constraints• Resource constraints• Costs• 500 Monte Carlo runs
D-EXPANSE procedure
Slack• e.g. 17% or 23% on total system costs
Efficient random generation (Chang et al. 1982a,b)• 500 scenarios• 500 Monte Carlo runs è 250 500 scenarios
1818
time
Assumptionsetno.1
Assumptionsetno.2
near-optimalscenarios
18
Near-optimal scenarios in Monte Carlo runs
1919
time
Assumptionsetno.1
Assumptionsetno.2
near-optimalscenarios
near-optimalscenarios
19
Near-optimal scenarios in Monte Carlo runs
2020
time
Assumptionsetno.1
Assumptionsetno.2
near-optimalscenarios
near-optimalscenarios
20
Near-optimal scenarios in Monte Carlo runs
2121
Cost-optimal scenario
A large number of near-optimal
scenarios
Analyze patterns in a large number
of scenarios
Maximally-different
scenariosMinimize total system costs• Supply-demand
constraints• Technology
constraints• Resource constraints• Costs• 500 Monte Carlo runs
D-EXPANSE procedure
Slack• e.g. 17% or 23% on total system costs
Efficient random generation (Chang et al. 1982a,b)• 500 scenarios• 500 Monte Carlo runs è 250 500 scenarios
2222
Cumulative investment costs vs. total system costs (Monte Carlo runs)
2323
Cumulative greenhouse gas emissions (Monte Carlo runs)
2424
Further methods for analyzing patterns in a large number of scenarios
Evelina Trutnevyte, ETH Zurich/UCL, Switzerland/UKCeline Guivarch, CIRED, France
Rob Lempert, RAND, US
Environmental Modelling & SoftwareThematic Issue in early spring 2016
2525
Cost-optimal scenario
A large number of near-optimal
scenarios
Analyze patterns in a large number
of scenarios
Maximally-different
scenariosMinimize total system costs• Supply-demand
constraints• Technology
constraints• Resource constraints• Costs• 500 Monte Carlo runs
D-EXPANSE procedure
Slack• e.g. 17% or 23% on total system costs
Efficient random generation (Chang et al. 1982a,b)• 500 scenarios• 500 Monte Carlo runs è 250 500 scenarios
2626
! ! !!
! ! !!
! ! !!
! !!!!!!
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150Cost-optimal scenario
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150Real-world transition
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150One maximally-different solution
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150One maximally-different solution
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150One maximally-different solution
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150One maximally-different solution
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150One maximally-different solution
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150One maximally-different solution
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150One maximally-different solution
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150One maximally-different solution
Year1990 1995 2000 2005 2010
Tota
l ins
talle
d ca
paci
ty, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Dis
coun
ted
cost
s, b
nGBP
(199
0)
0
50
100
150One maximally-different solution
Year1990 1995 2000 2005 2010
Tota
l inst
alle
d ca
pacit
y, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Disc
ount
ed c
osts
, bnG
BP(1
990)
0
50
100
150Real-world transition
Hydro storage
Import
Waste
Biomass
Landfill
Wave & Tidal
Solar PV
Hydro RoR
Wind offshore
Wind onshore
Nuclear
Oil & other
Gas CCGT
Coal
Cumulative total costs
Cumulative investment costs
Year1990 1995 2000 2005 2010
Tota
l inst
alle
d ca
pacit
y, G
W
0
20
40
60
80
100
120
1990 1995 2000 2005 2010
Disc
ount
ed c
osts
, bnG
BP(1
990)
0
50
100
150Real-world transition
Hydro storage
Import
Waste
Biomass
Landfill
Wave & Tidal
Solar PV
Hydro RoR
Wind offshore
Wind onshore
Nuclear
Oil & other
Gas CCGT
Coal
Cumulative total costs
Cumulative investment costs
Maximally-different near-optimal scenarios
2727
time
Assumptionsetno.1
Assumptionsetno.2
near-optimalscenarios
near-optimalscenarios
27
Cost-optimal and near-optimal scenarios under parametric uncertainty
Realworld
2828
time
Assumptionsetno.1
Assumptionsetno.2
near-optimalscenarios
near-optimalscenarios
28
Realworld
“envelope of predictability”(Cornell et al., 2010)
Cost-optimal and near-optimal scenarios under parametric uncertainty
2929
The (im)possible mission of embracing parametric and structural uncertainties
u Cost optimization with perfect foresight does not necessarily approximate the real-world transition (9-23% deviation in 25 years)
u Near-optimal scenarios can “encapsulate” the real-world transition
u Analyze cost-optimal and near-optimal scenarios under parametric uncertainty
u Treat the findings as the “envelope of predictability” and learn to navigate it
30
Please get in touch with questions and comments!
Evelina Trutnevyte
Email: [email protected]: http://www.tdlab.usys.ethz.ch/research/rigorous.html