MURE Database and Simulation Tool MURE Database and Simulation Tool for Energy Efficiency Measuresfor Energy Efficiency Measures

EU and eceee expert seminar on measurement and EU and eceee expert seminar on measurement and verification in the European Commission’s Proposal for verification in the European Commission’s Proposal for a Directive on Energy Efficiency and Energy Servicesa Directive on Energy Efficiency and Energy Services

Brussels 21/09/2004 Brussels 21/09/2004 Wolfgang Eichhammer, Fraunhofer ISIWolfgang Eichhammer, Fraunhofer ISI

MURE : a brief reminder

A comprehensive database of RUE measures, for each EU member state, for the EU, and for all end-use sectors (Household, Transport, Industry and Tertiary)A simulation tool, allowing to build and run RUE scenarios to calculate potential costs and impacts associated to RUE policies and measureswww.mure2.com

Path of Analysis 2004

1

Packages of measures + associ-ated energy indicators

Analysis of measures with MURE simulation tool

Analysis of impacts specified in MURE measure descriptions

Analysis of impacts specified in MURE measure descriptions

Analysis of measures with MURE simulation tool

Backcasting (1990-2000) Forecasting (2000-2010-2020)

Energy savings (ex-post) Energy savings (ex-ante)

ENERDA-TAFhG-ISI

ISIS FhG-ISI

FhG-ISI

Level 1

Level 1

Level 2

Level 2

Level 3

Level 3

Case 2 : Several measures on one target, follow-up indicator for package of measures

Measure

Target ImpactM1

M2

M3

T1 I1

Example : M1 : minimum efficiency standards for boilersM2 : thermal insulation standard (for building shell)M3 : subsidies for condensing boilersT1 : new housesI1 : emissions or specific consumption per dwelling

or m2

Example : M1 : minimum efficiency standards for boilersM2 : thermal insulation standard (for building shell)M3 : subsidies for condensing boilersT1 : new housesI1 : emissions or specific consumption per dwelling

or m2

P&Ms maps in the residential sector

Heating before EEAP (1990-1999) / after EEAP (2000-2004)Captive electricity White Goods before EEAP (1990-1999) / after EEAP (2000-2004)Captive electricity Brown Goods before EEAP (1990-1999) / after EEAP (2000-2004)Distributed renewables before EEAP (1990-1999) / after EEAP (2000-2004)

6 files per countryQuality of evaluation (1…3)

Level 1: Evaluation measure impact 1990-2000 with Odyssee Impact

Indicators (PJ) (EU-level)

PJ

8,67

50,91

22,83

12,667,72

2,76 2,32 2,62 3,16 3,16

40,97

20,89

0,28

19,80

0

10

20

30

40

50

60

70

80

90

100

Heatin

g

New d

welling

s

Existin

g dw

elling

s

Electri

c app

lianc

es -

white

good

s

Refrig

erat

ors

Freez

ers

Was

hing

mac

hines

Electri

c tum

ble d

riers

Combin

ed w

ashe

r drie

rs

Electri

c dish

washe

rs

Light

ing

Electri

c app

lianc

es -

brow

n go

ods TV

Other

s

Distrib

uted

rene

wables

/CHP

Solar t

herm

al PV

Distrib

uted

CHP

372 364

Evaluation measure impact 1990-2000 with Odyssee Impact Indicators

(Mt CO2) (EU-level)Mt CO2

0,61

5,67

2,541,41

0,860,31 0,26 0,29 0,35 0,35

2,99

1,46

0,03

1,50

0

2

4

6

8

10

12

14

16

18

20

Heatin

g

New d

welling

s

Existin

g dw

elling

s

Electri

c app

lianc

es -

white

good

s

Refrig

erat

ors

Freez

ers

Was

hing

mac

hines

Electri

c tum

ble d

riers

Combin

ed w

ashe

r drie

rs

Electri

c dish

washe

rs

Light

ing

Electri

c app

lianc

es -

brow

n go

ods TV

Other

s

Distrib

uted

rene

wables

/CHP

Solar t

herm

al PV

Distrib

uted

CHP

26.1 25.5

Level 2: Evaluation measure impact EU 1990-1999 with impacts specified in MURE

descriptions + semi-quantitative estimates (by instrument) (% of energy/CO2 savings)

0

5

10

15

20

25

30

35

40

45

50

legislative/normative legislative/information fiscal/tariffs financial information/education VA

(%)

Energy (%)

CO2 (%)

Objective of MURE simulations (Level 3)

To this end we define: The reference year, as the year from which starts the impact simulation exercise

The reference scenario, as the energy demand trend taking into account the main energy consumption drivers (i.e. the households growth rate) and the (residual) impact of the energy saving measures issued before the reference year

The policy scenario, as the energy demand development taking into account additional energy saving measures issued (or even planned) after the reference year.

To allow the User to simulate, starting from a given year, the impact of a given energy

Policy Scenario with respect to a Reference Scenario.

The Impact Evaluation of P&Ms

The impact evaluation can be carried out on both

Backcasting (1990-2000)and

Forecasting (2000 – 2025)exercises

Impact simulation methodology

General data set upMeasures analysis and parametrisationRun and discussion of resultsPossible further measures parametrisation and data calibration

Impact simulation methodology:Measures analysis and

parametrisation

Grouping the measures by homogeneous category (financial, fiscal, ordinances, informative, etc.) Sorting the measures by issuing date Setting of the simulation criteria (measures parameterisation):

Selection of the type of intervention to be simulated (insulation, boiler substitution, …)Simulation of the measure relative gain (% saving)Definition of the measures penetration rate (i.e. the rate of penetration in the involved dwelling stock)

MURE HOUSEHOLD CASE STUDIES: Back-casting Scenario Germany

Germany: Unitary Energy Consumption - Household Back-Casting Scenario

1,75

1,80

1,85

1,90

1,95

2,00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

toe

/dw

Reference Energy Labelling

Main Financial Policies Thermal Insulation Ordenance

Heating Installation Ordenance Policy

Observed

MURE HOUSEHOLD CASE STUDIES: Forecasting Scenario Germany

Germany: Total Energy Consumption Data - Forecast scenario

45,00

47,00

49,00

51,00

53,00

55,00

57,00

59,00

61,00

Mto

e

Reference New Labelling Scheme

Renewable energy source act + oth. Building Rehabilitation Prog.

New Building Codes

Advantages/disadvantages of the three evaluation paths

Evaluation with Odyssee impact indicators (Level 1): comprise still social factors/trends (i.e. yield often net effect between factors increasing energy consumption and policy measures) but have a full coverage of targets to be monitored. Link between indicator and measure impact sometimes weak depending on data availability

Bottom-up evaluation from MURE measure description + semi-quantitative estimates (Level 2): In general based on in-depth evaluation of measure hence more careful evaluation measure impact. Yields larger savings as social factors/trends are not often taken into account in the evaluations ("gross impact of measure"). Incomplete coverage although some of the gaps can be closed with fairly easy estimates of the impact derived from the measure descriptions. Overlaps between measures are not fully eliminated (in-depth work of ECN on measure overlap). Information based in general on national evaluations, hence on not fully comparable assumptions.

Systematic evaluation of measures with the MURE simulation tool (Level 3):provides evaluation of most types of measures based on a careful simulation of measure impacts. Homogeneous treatment of measures accross countries. Data requirements quite high (depending on the required precision)