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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

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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