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7/28/2019 Setac Jannick Mors01-01
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Modelling of electricity in life cycle inventory andcarbon footprint schemes
comparisons and recommended approach
JannickH SchmidtCopenhagen 26th November 2012
http://people.plan.aau.dk/~jannick/
AssociateProfessor,PhD
SETAC Europe 18th LCA Case Study Symposium, Copenhagen, 26-28 Nov 2012
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Background
- Electricity club: www.lca-net.com/projects/electricity_in_lca/Who Project is recently initiated by 2.-0 LCA and DuPont
Tested by researchers and students at AAU
Currently, the project includes LCI data for:
BE Belgium
BR Brazil
CN China
CZ Czech Republic
DE Germany
DK Denmark
EU Europe
FI Finland
FR France
GLO World
IN - India
MX Mexico
MY Malaysia
SE - Sweden
US United States
2
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Background and purpose
- Electricity in LCABackground Electricity is a hot-spot in many LCAs
IPCC 4thAR: energy supply accounts for ~25% of global GHG emissions
System delimitation many influential choices:
Market delimitation: local, national, regional or global?
Time: historic, current or future?
Time: short term or long term?
Supply and demand: Marginal versus average?
By-products: substitution or allocation
Modelling of electricity is important
Purpose
Identify relevant modelling approach
Establish operational methodology
Compare different GHG schemes/standards3
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Methodology
- consequential vs. attributional modelling
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LCA = decision support (if not, it is not an LCA)
Purpose ofclca:
"what is the consequence of buying this product"
"what is the consequence of choosing A in stead of B"
"what is the consequence of implementing new technology"
Purpose ofalca:
"how has this product been produced" "analyse the current situation"
"historically tracking mass and energy flows"
What information is relevant for decision maker?
Irrelevant how products have been produced
Relevant how the product will be produced if we change the demand
(since future products are most often produced as existing, the existing
technology is a good starting point)
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Methodology
- modelling assumptions how it should be
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Market delimitation: local, national, regional or global? National
most countries have national energy plans
over time, most countries balance between national supply and demand
Time: historical or future? Future (based on historical/outlook data)
future: relevant, but uncertain data
historical: not relevant, but we have data
Supply and demand: Marginal versus average? Marginal Reflects consequece
Time: short-term or long-term? Long-term
LCA: long term changes => build marginal, not production marginal
By-products: substitution or allocation Substitution
substitution follows ISO, ILCD, GHG-protocol
substitution is causal based and preserves mass balances
substitution does not operate with non-existing processes
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Methodology- Operational method to identify marginal elec
Marginal supplier in regulated market:
regulation determine marginal
marginal can be determined based on difference between future and current
situation suppliers with fastest increase rate = marginal (ex constrained)
Marginal supplier in non-regulated market:
in increasing/constant market, competitive suppliers are the marginal ones =>
can be determined based on increase rate
suppliers with fastest increase rate = marginal (ex constrained)
Doesn't matter if market is regulated or not => same method
Approach: Increase rate indicates relative competiveness (or regulated increase)
Decrease indicate that supplier is constrained (regulatory or physical) or non-
competitive
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Electricity mix in different schemes/standards
- approaches
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clca scenarios in this presentation (ISO14044 compliant)
increase rate of suppliers used to predict marginal suppliers
ecoinvent v3
clca: average for specific year excluding constrained/old technologies
ILCD (situation A: micro level decision support)
clca/alca: market average excluding constrained suppliers (due to by-products)
GHG protocol regional average (?) and avoid allocation not clear
PAS2050
national market average for specific year + energy allocation
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Electricity mix scenarios: 4 clca + 1 alca
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1. clca (historical) 2000-2008 (ex constrained/least competitive)
2. clca (future 1) 2008-2020 (ex constrained/least competitive)
3. clca (future 2) 2008-2020 (ex constrained/least competitive)
4. clca (ecoinvent*) aver 2008 (ex old/constrained technologies)
5. alca aver 2008
Data: publically available energy statistics and outlooks/scenariosLimitations:
data do not distinguish condensation (pure electricity) from electricity co-
produced with heat (CHP)
all electricity data have been treated as pure electricity
*ecoinvent modelling only represents illustrative example
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Electricity mix scenarios: 4 clca + 1 alca
Danish Energy Agency (2011), Danish Energy Outlook 2011. http://www.ens.dk/Documents/Netboghandel%20-
%20publikationer/2011/Danish_Energy_Outlook_2011.pdf
European Commission (2010, p 12,105), National Renewable Energy Action Plans for Denmark, prepared by the Ministry of Climate and Energy,
Copenhagen. http://ec.europa.eu/energy/renewables/action_plan_en.htmIEA (2010), Electricity Information 2010, International Energy Agency (IEA), Organisation for Economic Co-operation and Development (OECD), Paris
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Baseline=2008
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Elec mixes for five scenarios
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Elec mixes for five scenarios
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Conclusions and recommendations Existing schemes/standards
operational acknowledged causal based approach missing
potentially misleading decision support
Operational method for identifying marginal electricity is established causal based
easy accessible data / global coverage (e.g. IEA + national data)
easy to update and to produce scenarios
Relative small difference between clca scenarios
Uncertainties & limitations
hard to find data on how much electricity is co-produced with heat
(constrained)
slowly changing markets => small changes in mix have relatively large effect
Recommended scenario
clca_future: most likely (optimistic) but uncertain data
clca_historical: underestimates new technologies, statistical data12