LCA of electricity generation technologiesUNECE modelling activities – Carbon neutrality project

19.05.2021

Context of life cycle assessment task

Starting point: UNEP IRP report “Green Energy Choices”

Life cycle assessment (LCA) of electricity production technologiesCoal, natural gas, with and without CCSHydropowerWind powerConcentrating solar powerPhotovoltaic powerGeothermal power

Impact assessment over 2010-2050 period

Two IEA scenarios (Baseline, Blue Map) and 9 world regions

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Context of LCA task

Starting point: UNEP IRP report “Green Energy Choices”

LimitationsAbsence of state-of-the-art nuclear power and biomass==> need for expertise on these technologies

Optimistic efficiencies?Limited consideration of methane leakage in fossil fuel extractionNo direct emissions in hydropowerNo consistent end-of-life treatment consideration across technologiesEnergy scenarios outdated: use REMIND? MESSAGE? …?

Update welcome!Most data is 10 yearsAdd newer technologies (namely small modular reactors)

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Life cycle assessment

A method and tool for attributing environmental impacts to products and servicesConsidering impacts over the life cycleProduction, use, end-of-life

Considering impacts upstream in supply chainsResource extraction, transport, etc.

And typically:Considering hundreds of emitted substances and extracted resourcesConsidering a range of impact typesHuman health, ecosystem health, natural resource use

Definition

Figure: Adapted from Hellweg & Mila i Canals (2014)

Holistic

Multicriteria

Biosphere

Life cycle assessment

Obtained from UNEP report and ecoinvent database

Life cycle inventories are a listof physical inputs and output to processes that interact with each other

Final output is 1 kWh delivered to the grid

Flows are characterized by name/amount/quantity/region, and are attached to a technological description with a list of exchanges with the environment (resources, emissions)

Data

Technologies

Full list

PhotovoltaicsPolycrystalline silicon, ground-/roof-mountedCIGS, ground-/roof-mountedCdTe , ground-/roof-mounted

CSPTroughTower

CoalExisting PC, with and without CCSIntegrated gasification CC, with and without CCSCoal SCPC, with and without CCS

GasNGCC, with and without CCS

Hydropower660 and 360 MW designs

WindOnshoreOffshore, concrete and steel foundation

Nuclear powerBoiling water reactorPressure water reactorSMR

Update (practically) completedWork in progress Status

REgions

Why regionalizing?

Data representativenessElectricity mixes can be systematically adapted to region, year, and a given scenario (with REMIND “Base SSP2” as baseline), as well as a few other processes (cement…)

Adapting load factors to regional climate conditionsSolar irradiationWind regimes

REMIND regions CodeCanada, Australia & New Zealand CAZChina CHAEuropean Union EURIndia INDJapan JPNLatin America LAMMiddle East and NorthAfrica MEANon-EU member states NEUOther Asia OASReforming countries REFSub Saharan Africa SSAUnited States USA

WIND power

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RegionCapacity factor

On-shoreCapacity factor

Off-shore

CAZ 29.16% 30.50%*

CHA 22.67% 22.68%

EUR 22.83% 36.18%

IND 17.80% 30.50%*

JPN 25.00% 30.00%

LAM 36.05% 30.50%*

MEA 29.56% 30.50%*

NEU 26.15% 31.43%

OAS 22.67% 22.68%**

REF 26.18% 30.50%*

SSA 29.16% 30.50%*

USA 33.35% 40.00%

IRENA. (2021). Query Tool. IRENA International Renewable Energy Agency. https://www.irena.org/Statistics/Download-Data.

* Numbers not available. Average value is taken** Numbers not available. Value for China is taken

Wind power

Main parametersinfluencing capacityfactorWind resources of the location

Turbine type and balance-of-plant technology used

Size of turbine

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Wind power

Caduff, M., Huijbregts, M. A., Althaus, H. J., Koehler, A., & Hellweg, S. (2012). Wind power electricity: the bigger the turbine, the greener the electricity?. Environmental science & technology, 46(9), 4725-4733.

Main parameters influencing capacity factorWind resources of the location

Turbine type and balance-of-plant technology used

Size of turbine

Photovoltaics

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Region Capacity factor kWh/m2/year Reference location

CAZ 13.40% 2648 Australia (-32.594,137.856)

CHA 11.61% 2300 China (41.507, 108.588)

EUR 12.43% 2320 Spain (37.442,-6.25)

IND 12.92% 1637 India (27.601,72.224)

JPN 12.89% 1298 Japan(33.22,131.63)

LAM 16.87% 3438 Chile (-22.771,-69.479)

MEA 15.09% 2471 Morocco (30.218,-9.149)

NEU 10.55% 936 Denmark(57.05,9.9)

OAS 15.68% 1412 Thailand (14.334,99.709)

REF 9.58% 1459 Russia(47.21,45.54)

SSA 11.19% 2461South Africa

(31.631,38.874)

USA 18.03% 2817 USA (35.017,-117.333)

1 IRENA. (2021). Query Tool. IRENA International Renewable Energy Agency. https://www.irena.org/Statistics/Download-Data. 2 National Renewable Energy Laboratory (NREL). (2021). What Is the NSRDB? National Solar Radiation Database. https://nsrdb.nrel.gov/.

REMIND regions CodeCanada, Australia & New Zealand CAZChina CHAEuropean Union EURIndia INDJapan JPNLatin America LAMMiddle East and NorthAfrica MEANon-EU member states NEUOther Asia OASReforming countries REFSub Saharan Africa SSAUnited States USA

CONCENTRATED SOLAR POWER

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Main parameters influencing the capacity factor:

Direct Normal Irradiation (DNI) and other meteorologicalparameters (e.g. latitude, wind, surface albedo)

Plant size

Technology (solar tower or parabolic through)

Storage

Year of construction

1 Johan Lilliestam, Richard Thonig, Chuncheng Zang, & Alina Gilmanova (2021). CSP.guru (Version 2021-01-01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.46130992 Chhatbar, K., & Meyer, R. (2011). The influence of meteorological parameters on the energy yield of solar thermal plants.3 National Renewable Energy Laboratory (NREL). (2021). What Is the NSRDB? National Solar Radiation Database. https://nsrdb.nrel.gov/.

RegionCapacity factor

Solar TowerCapacity factor

Parabolic ThroughReference

location

CAZ 55.00% 38.93%Australia

(-32.594,137.856)

CHA 49.26% 33.89%China (41.507,

108.588)

EUR 49.23% 36.95% Spain (37.442,-6.25)IND 36.23% 29.25% India (27.601,72.224)JPN 14.40% 20.60% Japan(33.22,131.63)LAM 70.95% 55.80% Chile (27.601,72.224)

MEA 55.78% 42.76%Morocco (30.218,-

9.149)NEU 14.40% 12.30% Denmark(57.05,9.9)

OAS 29.27% 28.23%Thailand

(14.334,99.709)REF 29.10% 23.70% Russia(47.21,45.54)

SSA 55.19% 41.97%South Africa

(31.631,38.874)

USA 60.41% 37.49% USA (35.017,-117.333)

CONCENTRATED SOLAR POWER

Some locations are technically not suitable for certain technologies

ExamplesThese regions do not reach the minimum of 1800 kWh/m2

They are modelled for consistency but are technically non-viable (in economic but also environmental terms)

1 Johan Lilliestam, Richard Thonig, Chuncheng Zang, & Alina Gilmanova (2021). CSP.guru (Version 2021-01-01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.46130992 Chhatbar, K., & Meyer, R. (2011). The influence of meteorological parameters on the energy yield of solar thermal plants.3 National Renewable Energy Laboratory (NREL). (2021). What Is the NSRDB? National Solar Radiation Database. https://nsrdb.nrel.gov/.

RegionCapacity factor

Solar TowerCapacity factor

Parabolic ThroughReference

location

CAZ 55.00% 38.93%Australia

(-32.594,137.856)

CHA 49.26% 33.89%China (41.507,

108.588)

EUR 49.23% 36.95% Spain (37.442,-6.25)IND 36.23% 29.25% India (27.601,72.224)JPN 14.40% 20.60% Japan(33.22,131.63)LAM 70.95% 55.80% Chile (27.601,72.224)

MEA 55.78% 42.76%Morocco (30.218,-

9.149)NEU 14.40% 12.30% Denmark(57.05,9.9)

OAS 29.27% 28.23%Thailand

(14.334,99.709)REF 29.10% 23.70% Russia(47.21,45.54)

SSA 55.19% 41.97%South Africa

(31.631,38.874)

USA 60.41% 37.49% USA (35.017,-117.333)

Concentrated Solar Power

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It is not possible to find a DNI-Capacity factor relation to regionalize the inventories

Preliminary results

CSP tower

REMIND regions CodeCanada, Australia & New Zealand CAZChina CHAEuropean Union EURIndia INDJapan JPNLatin America LAMMiddle East and NorthAfrica MEANon-EU member states NEUOther Asia OASReforming countries REFSub Saharan Africa SSAUnited States USA

1.1E-1 kg CO2-Eq 7.3E-5 kg N-Eq 3.4E-5 kg P-Eq 3.9E-4 mol H+-Eq 9.3E-4 mol N-Eq 4.7E-9 disease i. 1.4E-8 kg CFC-11.2.7E-4 kgNMVOC-. 1.2E-2 kg U235-Eq 4.8E-2 m3 water-. 1.7E+0 megajoule 9.0E+0 points

0.000.100.200.300.400.500.600.700.800.901.00

electricity production, CSP, tower, THEMIS

CHA EUR IND JPN LAM MEA NEU OAS REF SSA USA

Preliminary results

Comparing CSP, wind (offshore), and nuclear power

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Normalized results for EUR

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Normalized results for LAM

Preliminary results

Influence of regionalization

00.10.20.30.40.50.60.70.80.9

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CSP TOWER - Influence of regionalization (Background system and capacity factor)

EUR, using EUR Capacity factor EUR, using LAM Capacity factor LAM, using EUR Capacity factor LAM, using LAM Capacity factor

Difference attributable to a different electricity grid in

the background

Difference attributable to different climatic conditions

Hydropower-based regions are more water-intensive, which shows in any LCA

using such electricity

Next steps

Complete the life cycle inventory updatesFossil fuels (coal, gas – with and wo CCS)Ensure consistency across technologies (same scope, etc.)Apply regionalization to all technologiesValidate resultsCharacterize uncertainty (at least cover variability in time/region)Apply REMIND scenarios: “Base SSP2”, and one carbon-neutral scenarioPotentially coupling scenarios with MESSAGE?

First draft report

QuestionsThe model is fully-parameterizable – how useful would it be to adapt inventories to specific countries? (where technologies are most suitable)Any request/comment on technologies included/not included?Any other feedback welcome!

thomas.gibon@list.lu