Global Sensitivity Analysis of an Energy- economy Model of the - - PowerPoint PPT Presentation

Global Sensitivity Analysis of an Energy- economy Model of the Residential economy Model of the Residential Building Sector

• F. Branger, L.-G. Giraudet,
• C. Guivarch, P. Quirion (CIRED)

International BE4 Workshop – London – April 20, 2015

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

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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

Res Res-

• IRF: Scope

IRF: 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 A 5

Res Res-

• IRF’s Main Innovations

IRF’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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Intensity Intensity of

• f Retrofits

Retrofits

, , , i f i f i h h i

LCC PR LCC

ν ν − − >

= ∑

Heterogeneous discount rates across landlords and tenants

, , , i f i f f i f

LCC CINV CENER IC = + +

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Subject to endogenous decrease (learning-by-doing) Subject to endogenous decrease (peer effects)

Number of Retrofits Number of Retrofits

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Captures heterogeneity in preferences for heating (e.g. sensitiveness to cold)

Utilization Utilization Adjustments Adjustments

Data: EDF R&D (see Cayre et al

Elasticity -0.5

t al., 2011, ECEEE Proceedings) 9

Insights into French Policy Insights into French Policy

France’s Target -38%

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€200/tCO2 in 2010!!!

Quantifying Uncertainty: Monte-Carlo Analysis Monte-Carlo Analysis

Protocol Protocol

Randomly pick parameters parameters

Latin Hypercube Sampling

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Overall Uncertainty Overall Uncertainty

25% around the median value

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Characterizing Uncertainty: the Morris Method the Morris Method

Methods of Sensitivity Analysis Methods 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: Design The Morris Method: Design

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k+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 Diagram Results: Morris Diagram

Measure of interaction

Most interacting Most influential

Measure of influence

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Parameters Ranking Parameters Ranking

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Important Parameters: Comment Important 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 introduced

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Discussion Discussion

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 distributions distributions

• 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

REFERENCES REFERENCES

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.

Reference savings: -37%

Potential for energy conservation in French dwellings Potential for energy conservation in French dwellings

« Rebound » gap: -10% « Private efficiency » gap: -4% « Social efficiency » gap: -8% 22

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