1

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

22

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

33

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

44

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?

55

§ 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

66

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

77

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: tevelina@ethz.chGroup: http://www.tdlab.usys.ethz.ch/research/rigorous.html