Top-down methodologies to assess energy savings for the ESD Dr - - PowerPoint PPT Presentation

ECEEE-June 2009-La Colle sur Loup

Top-down methodologies to assess energy savings for the ESD

Dr Didier Bosseboeuf (ADEME) Dr Bruno Lapillonne (Enerdata) Nathalie Desbrosses (Enerdata)

ECEEE-June 2009-La Colle sur Loup

Agenda

§ Overview of top-down methods § Conclusion from case studies

ECEEE-June 2009-La Colle sur Loup

Overview of top-down methods

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Calculation of energy savings with top-down methods: definition

• Top-down methods rely on energy efficiency indicators calculated from

national statistics  energy savings are derived from variations of indicators.

• For instance, the energy savings of a given appliance (e.g. refrigerators)

are derived from the reduction in the average unit energy consumption of the appliance (kWh/year) ; a reduction of this unit consumption of 100 kWh/ year over 10 years will result in total savings equal to 100 GWh/year (assuming a stock of refrigerators of 1 million units)

• In some sectors or end-uses, the influence of factors that are not linked to

energy efficiency is removed (effect of structural changes in industry, of changes in the size of dwellings…)  case of ODYSSEE indicators

• Total energy savings are calculated as the sum of energy savings by sub-

sector or end-use (e.g. 30 sub-sectors for ODEX).

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• ESD Annex IV
• “Adjustments to be made for extraneous factors, such as degree-days,

structural changes, product mix, etc. to derive a measure that gives a fair indication of total energy efficiency improvement” (ESD Annex IV )

• This statement has led to divergent interpretations that can be summarised

as follows:

• In the minimalist viewpoint only the adjustments for extraneous factors

explicitly mentioned in the Annex IV should be made  ESD energy savings = ‘total’ top-down savings (where they can be calculated)?

• In the maximalist viewpoint, additional adjustments (etc..) should be

made so as to only measure additional energy savings linked to explicit energy efficiency improvement measures  ESD energy savings are calculated by removing from ‘total’ energy savings the energy savings linked to all “other factors” than energy efficiency improvement measures

Calculation of energy savings according to ESD with top-down methods (1/3)

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Calculation of energy savings according to ESD with top- down methods: possible corrections

Total energy savings calculated from variations

• f indicators. *

Price effect Autonomous progress Additional ESD energy savings Old/other policies Hidden stuctural effect

*already corrected for main structural effects

Maximalist viewpoint: only additional energy savings Minimalist interpretation of ESD energy savings = ‘total’ savings

ECEEE-June 2009-La Colle sur Loup

Conclusions on top-down case studies

ECEEE-June 2009-La Colle sur Loup

Case studies were classified according to the statistical indicator

used to calculate the energy savings Type of indicator Example

Market diffusion indicator of energy saving technology or practice Modal share for transport of goods or passengers; stock of solar water heaters ; share of cogeneration Specific energy consumption of an equipment New cars, electricity consumption per appliance (kWh/year) Unit energy consumption indicator

• f a sub-sector (e.g. electricity uses

in household, industry or services ) Electricity consumption per employee in service, heating fuel consumption per household (kWh/year) Total energy consumption Evaluation of the effects of energy taxation

Top-down case studies in EMEEES

ECEEE-June 2009-La Colle sur Loup

Indicators used to evaluate top-down energy savings in EMEEES case studies

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1 Building shell & heating (households) Heat consumption per m2 2 Household electricity uses Specific consumption (kWh/dwelling) 3 Specific white goods (refrigerators) Specific consumption (kWh/dwelling 4 Solar thermal collectors m2 installed 5 Building shell & heating in tertiary sector Energy use per employee/m2 6 Electricity end-uses in tertiary sector Electricity use per employee/m2 7 Industrial thermal energy use Energy use per output 8 Industrial electricity consumption Electricity use per output 9 Industrial CHP Share of electricity cogenerated 10 New cars Specific consumption (l/100 km) 11 Car, bus and truck stock improvement Specific consumption (l/100 km) 11 Modal shift in passenger transport Share of public transport 13 Modal shift in goods transport Share of rail & water transport 14 Energy taxation ODEX or final energy intensity

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Corrections to calculate additional energy savings in EMEEES case studies

• EMEEES has focussed on two possible corrections : autonomous

trend and market price;

• The project has outlined the pros and cons of doing such corrections

and proposed a method in case such corrections were decided;

• Simple econometric methods were used to quantify the impact of

trend and market prices, on purpose :

• in view of a possibility of harmonisation and the easiness of their

understanding  even such methods raised a lot of questions and alternatives for their concrete implementation.

• and taking into account data limitations for additional explanatory

variables (e.g. price/tax on cars, cost of equipment);

• Generally results of the econometric analysis were not very robust as

data series used often too short

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Ln ES = a + b T + c ln P + d ln A + e ln ES -1 + K

with:

ES : energy saving indicator ; b: trend, T: time, c : price elasticity, P: energy price (2 components: ex-tax (market) price and tax), d: elasticity to macro economic variable A (e.g. GDP) to capture the impact of business cycles

Methods to define corrections of total energy savings in EMEEES case studies

Too simple …or too complex?

ECEEE-June 2009-La Colle sur Loup Savings from measures and taxes (ESD° Savings from taxes

Methods to remove other factors from total

top-down energy savings - example (3/3)

Econometric estimates

Estimation of energy savings in year t (e.g. 2012)

Total energy savings Total savings minus trend and price savings

Step 1 Step 2 Step 3

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Correction of autonomous technological trends

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Pros

• Savings are occurring independently of policy facilitating measures
• With past trends, the 1% target will be more easily reached
• Autonomous trend for the stock of equipment due the replacement of
• ld less efficient equipment (e cars, cold appliances)

Cons • Difficulty to measure a real autonomous technological trend (any

trend will include other factors)

• Technological trend is not granted forever and always require some

policy inputs (e.g. case of new cars) to continue

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Specific consumption of new cars (l/ 100 km)

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Correction for autonomous technological trend (1): the different options

• National trend: -2,2%/year
• EU average trend: - 1.1%/year
• Average trend of countries with the lowest autonomous trend (“average

slower trend”) = > - 0.8%/year for diesel

Specific consumption of new diesel cars: case of a country with a more rapid trend than the EU average (France)

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Pros

Cons

EU average trend (weighted average) Simple Gives a too great importance to the trend in a few large countries Average trend of the 3 countries with the slowest trend Value assumed to be close to situation without policies and measures, i.e. close to autonomous trend Expert value Simple Difficult to agree on the value

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Correction of market (ex-tax) energy prices

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Pros

• Better reflect the specificity of the consumers price response of

each country

• Account implicitly for the actual price level

Cons

• If there is no harmonisation among EU countries, there is a risk
• f having very different evaluations of savings associated to the

same price variation

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• What value to be used for the price elasticity?

 National data if relevant (for the few countries where the values

• btained from statistical regression were significant)

 Or harmonised values, the same for all countries, by sector/end-use

Market prices (1):if corrections are made, how to concretely account for price effects?

Note: in red, our proposal

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Market prices (3):if harmonised price elasticity, what value to be used?

• An EU average value  not meaningful in most of the cases
• Calculation on the EU average (gives a too high importance to

large EU countries );

• Arithmetic average of countries with relevant price elasticities

(need a sample large enough of representative countries)

• Pooled average (calculation over all countries) no good results
• Expert judgement with a low and asymmetric price elasticity:
• Low: the lower the elasticity the lower the correction, and the

higher the ESD energy savings (e.g. 0,1 or 0,2 )

• Asymmetric: correction made only when prices increase (no

correction if prices decrease).

Note: in red, our proposal

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Impact of the price elasticity on the calculation of ESD savings: case of solar in Germany

Total savings

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ESD savings

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Conclusions on the possibilities of top- down calculation methods for the ESD

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Conclusions on the applicability possibilities of top-down calculation methods (provided data availability)

Name Robustness

• f results

Data MS TD-1 Space heat. households Yes most TD-2 Elec.uses (all appliances) No EU-15 TD-3 Main appliances. Yes EU-15 TD-4 Solar thermal Yes all TD-5 Space heat. Tertiary Yes few TD-6 Elec.general Tertiary No few TD-7 Heat general industry No all TD-8 Elec.general industry No all TD-9 CHP - industry No all TD-10 New cars Yes most TD-11 Vehicle stock Yes most TD-12 Modal shift persons Yes ? all TD-13 Modal shift goods Yes ? all TD-14 Tax on energy Yes? all

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Many countries still with important data gap  priority to get data from 2008 onwards for the selected indicator

Data availability by country

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Summary of the proposed approach Correction of autonomous trend

• Case of specific energy consumption indicators (e.g., appliances, cars):
• Set an EU default value for the autonomous technical progress,

based on regression analysis, and on the average of the three countries with the slowest trend

• Case of unit energy consumption indicators (e.g., electricity consumption in

industry, tertiary, households; heating and process fuel consumption in households, industry and tertiary) and diffusion indicators (e.g., solar water heaters, CHP industry, modal split in passenger and goods transport) :

• Do the regression analysis for each country,
• Identify plausible autonomous trends if possible

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Summary of the proposed approach Correction of market price

For all types of indicators:

• Set an EU default value for the price elasticity between 0.1 and 0.2
• Correct reference trend if market energy price is going up

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Beyond the paper

• TD methodology becomes a key option for the MS (so-called option 1)
• The proposed list of TD indicators by the Commission (prefered and

alternative indicators ) is workable taking into account the current data avaibility in the MS (ODYSSEE project)

• The debate becomes : do we prefer flexibility instead of having a

european harmonised methodology for monitoring a common indicative target on energy efficiency? Thank you for your attention didier.bosseboeuf@ademe.fr Bruno.lapillonne@enerdata.fr

www.ademe.fr