Global SensitivityAnalysisof an Energy- economy Model of ...€¦ · • Sensitivity analysis only...
Transcript of Global SensitivityAnalysisof an Energy- economy Model of ...€¦ · • Sensitivity analysis only...
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Global Sensitivity Analysis of an Energy-
economy Model of the Residentialeconomy Model of the Residential
Building Sector
F. Branger, L.-G. Giraudet,
C. Guivarch, P. Quirion (CIRED)
International BE4 Workshop – London – April 20, 2015
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Space
heating
Water
heating
Lighting
Insulation
Heating
system
Lamps
Appliances
Installers / Retailers / Manufacturers
Households Energy services Firms
Income class
Tenure type Lighting
Cooking
Electrical &
electronical
uses
etc.
Electricity
Natural gas
Fuel oil
Energy durables
Energy Suppliers / Distributors / Producers
Tenure type
Type of dwelling
etc.
Institutional framework 2
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�Uncertainty associated with modelling such a
complex system?
1. The Res-IRF model in a nutshell
2. Quantifying uncertainty: Monte-Carlo 2. Quantifying uncertainty: Monte-Carlo
analysis
3. Characterizing uncertainty: the Morris
Method
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Res-IRF in a nutshell
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ResRes--IRF: ScopeIRF: Scope
• Energy use covered– Space heating (2/3 of French household demand)
– Electricity, natural gas, fuel oil
• Energy efficiency improvements• Energy efficiency improvements– New constructions (standard/low energy/passive)
– Retrofitting of existing dwellings (including fuel switch)
G F E D C B A
G
F
E
D
C
B
A5
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ResRes--IRF’s Main InnovationsIRF’s Main Innovations
• All margins of energy use are endogenous
– Intensity of retrofits
– Number of retrofits
– Utilization adjustments (Rebound effect)– Utilization adjustments (Rebound effect)
• Some barriers to energy efficiency
– Static: split incentives (discount rates)
– Dynamic: learning-by-doing, information acceleration
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IntensityIntensity of of RetrofitsRetrofits
,,
,
i fi f
i hh i
LCCPR
LCC
ν
ν
−
−
>
=∑
Heterogeneous discount
rates across landlords and
tenants
, , ,i f i f f i fLCC CINV CENER IC= + +
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Subject to endogenous
decrease (learning-by-doing)
Subject to endogenous
decrease (peer effects)
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Number of RetrofitsNumber of Retrofits
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Captures heterogeneity in preferences for heating (e.g. sensitiveness to cold)
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UtilizationUtilization AdjustmentsAdjustments
Da
ta: E
DF
R&
D (se
eC
ayre
et a
l., 20
11
, EC
EE
E
Elasticity -0.5 et a
l., 20
11
, EC
EE
E P
roce
ed
ing
s)
9
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Insights into French PolicyInsights into French Policy
France’s Target -38%
10
€200/tCO2
in 2010!!!
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Quantifying Uncertainty:
Monte-Carlo AnalysisMonte-Carlo Analysis
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ProtocolProtocol
Randomly pick
parametersparameters
Latin Hypercube
Sampling
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Overall UncertaintyOverall Uncertainty
25% around the median value
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Characterizing Uncertainty:
the Morris Methodthe Morris Method
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Methods of Sensitivity AnalysisMethods of Sensitivity Analysis
Computational
cost Sobol
Morris (a.k.a.
Local analysis Global analysis
One-at-
a-time
Morris (a.k.a.
Elementary
effects)
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The Morris Method: DesignThe Morris Method: Design
3k+1 simulations
�k elementary effects
1
2
Parameters space
We repeat the operation
for r trajectories
�r*(k+1) simulations
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Results: Morris DiagramResults: Morris Diagram
Measure of interaction
Most interacting
Most influential
Measure of
influence
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Parameters RankingParameters Ranking
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Important Parameters: CommentImportant Parameters: Comment
• Energy price
Somewhat reassuring that the model is sensitive to its main input…but very uncertain parameter in practice!
• Initial retrofitting rate• Initial retrofitting rate
Illustrates that calibration is a critical step
• Rebound effect elasticity
Importance of behaviours
The model is more sensitive to how the different margins of energy use are
disaggregated than to how barriers to energy efficiency are introduced19
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DiscussionDiscussion
Overall, we were quite happy with the results. But…
• Even though all inputs are taken into account, analysis still dependent on the choice of the probability distributionsdistributions
• Sensitivity specific to one particular output (energy use)
• Sensitivity analysis only captures uncertainty about model quantities, not about model forms
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- Branger et al. (2015) Global sensitivity analysis of an energy-economy
model of the residential building sector, forthcoming, Envionmental
Modeling & Software
- Giraudet et al. (2011). Comparing and combining energy saving policies.
Will proposed residential sector policies meet french official targets?
Energy Journal, 32 (SI 1):213–242
REFERENCESREFERENCES
Energy Journal, 32 (SI 1):213–242
- Giraudet et al. (2012). Exploring the potential for energy conservation in
french households through hybrid modeling. Energy Economics, 34
(2):426–445.
- Morris, M. D. (1991). Factorial sampling plans for preliminary
computational experiments. Technometrics, 33(2):161–174.
- Saltelli et al (2008). Global Sensitivity Analysis: The Primer. John Wiley &
Sons.
- Van Asselt, M. B. A. and Rotmans, J. (2002). Uncertainty in integrated
assessment modelling. Climatic Change, 54(1):75–105.
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Reference savings: -37%
Potential for energy conservation in French dwellingsPotential for energy conservation in French dwellings
« Rebound » gap: -10%
« Private efficiency » gap: -4%
« Social efficiency » gap: -8%
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Giraudet, L.-G., Guivarch, C., Quirion, P., 2012. Exploring the potential for energy conservation in French
households through hybrid modeling. Energy Economics 34, 426–445. doi:10.1016/j.eneco.2011.07.010