Real-Time Carbon Accounting Method for the European Electricity Markets

Bo Tranberga,b,∗, Olivier Corradic, Bruno Lajoiec, Thomas Gibond, Iain Staffelle, Gorm Bruun Andresena

aDepartment of Engineering, Aarhus University, Inge Lehmanns Gade 10, 8000 Aarhus C, DenmarkbDanske Commodities, Værkmestergade 3, 8000 Aarhus C, Denmark

cTomorrow, TMROW IVS, tmrow.com, Godthåbsvej 61 B, 3. th., 2000 FrederiksbergdLuxembourg Institute of Science and Technology, 5 Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette, Luxembourg

eCentre for Environmental Policy, Imperial College London, London, UK

Abstract

Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requiresaccurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-timeconsumption-based accounting approach based on flow tracing. This method traces power flows from producer to consumer therebyrepresenting the underlying physics of the electricity system, in contrast to the traditional input-output models of carbon account-ing. With this method we explore the hourly structure of electricity trade across Europe in 2017, and find substantial differencesbetween production and consumption intensities. This emphasizes the importance of considering cross-border flows for increasedtransparency regarding carbon emission accounting of electricity.

Keywords: carbon accounting, carbon emission, carbon intensity, flow tracing

1. Introduction

For several decades, more than 80% of the global electricitygeneration originates from fossil fuel [1]. As a result, electric-ity and heat production account for 25% of global greenhousegas (GHG) emissions [2]. Furthermore, electricity demand iswidely expected to rise because of electrification of vehicles[3]. These facts highlight the importance of an accurate andtransparent carbon emission accounting system for electricity.

Reducing emissions related to electricity consumption re-quires accurate measurements readily available to consumers,regulators and investors [4]. In the GHG protocol [5], “Scope 2denotes the point-of-generation emissions from purchased elec-tricity (or other forms of energy)” [4]. A major challenge re-garding Scope 2 emissions is the fact that it is not possible totrace electricity from a specific generator to a specific consumer[6, 7]. This has lead to the use of two different accounting meth-ods: the of grid average emission factors or the market basedmethod [4, 7]. Grid average factors are averaged over time andtherefore not specific to the time of consumption due to limitedavailability of emission factors with high temporal resolution.The market based method entails purchasing contractual emis-sion factors in the form of different types of certificates, whichdo not affect the amount of renewable electricity being gener-ated, and therefore fail to provide accurate information in GHGreports. For a detailed criticism of both approaches, see [4].

In this case study, we propose a new method for real-time car-bon accounting based on flow tracing techniques. This methodis applied to hourly market data for 28 areas within Europe. We

∗Corresponding author: bo@eng.au.dk

use this method to introduce a new consumption-based account-ing method that represents the underlying physics of the elec-tricity system in contrast to the traditional input-output modelsof carbon accounting [8, 9, 10]. The approach advances beyond[11], where a similar flow tracing methodology is used to createa consumption-based carbon allocation between six Chinese re-gions. However, the data for that study was limited to annualaggregates and different generation technologies were also ag-gregated. We apply the method to real-time system data, in-cluding the possibility of distinguishing between different gen-eration technologies, providing real-time signals for all actorsinvolved. This increases the overall transparency and credibil-ity of emission accounting related to electricity consumption,which is of high importance [12]. To investigate the impact ofthe new consumption-based accounting method we compare itwith the straightforward production-based method (i.e. lookingat the real-time generation mix within each area). For discus-sions on the shift from production-based to consumption-basedaccounting and the idea of sharing the responsibility betweenproducer and consumer, we refer to [13, 14].

2. Methods

2.1. Data

The method is applied to data from the electricityMapdatabase [15], which collects real-time data from electricitygeneration and imports/exports around the world. The Euro-pean dataset, consisting of 28 areas, is used with hourly reso-lution for the year 2017. Data sources for each individual areacan be found on the project’s webpage [16]. Figure 1 shows the28 areas and the 47 interconnectors considered. Power flows

Preprint submitted to Energy Strategy Reviews December 24, 2018

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BE

BG

CZ

DE

DK1DK2

EE

ES

FI

FR

GB

GR

HU

IE

IT

LT

LV

ME

NL

NO

PL

PT

RO

RS

SE

SI

SK

Figure 1: The 28 areas considered in this case study, and the power flows be-tween them for the first hour of January 1, 2017. The width of the arrows isproportional to the magnitude of the flow on each line. Power flows to andfrom neighboring countries, e.g. Switzerland, are included when available, andthese areas are shown in gray. The cascade of power flows from German windand Polish coal are highlighted with blue and brown arrows, respectively.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan0

2

4

6

8

Prod

uctio

n [G

Wh]

hydrogas

biomasscoal

geothermalwind

oilnuclear

solarunknown

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan42024

Powe

r [GW

h]

Power balance Export Import

Figure 2: Daily-average stacked power production for each technology for Aus-tria during 2017 (top) as well as exports, imports and power balance (bottom).

to and from neighboring areas, e.g. Switzerland, are includedwhen available. The black arrows show a snapshot of hourlypower flows between the areas. In the results, we aggregate thetwo price areas of Denmark and, thus, compare 27 countries.

The top panel of Figure 2 shows stacked daily-average pro-duction for each technology for Austria. The bottom panelshows daily-average exports and imports. The black line repre-sents the sum of the hourly exports and imports showing Aus-tria’s net import/export position. The daily averages in this fig-ure are based on the full 8760 hours in the dataset representingthe full year 2017.

Carbon emission intensities are derived from the ecoinvent3.4 database to construct an accurate average intensity per gen-eration technology per country decomposed in lifecycle, infras-tructure and operations [17]. The operations intensities are usedfor the production and consumption-based carbon allocation inthis study. Operational emissions include all emissions occur-ring over the fuel chain (from extraction to supply at plant)

Table 1: CO2 equivalent operation intensity per technology averaged acrosscountries. The dashed line indicates the split between non-fossil and fossiltechnologies. For details, see Table 1–3 in the supplementary material.

Technology Intensity [kgCO2eq/MWh]solar 0.00410geothermal 0.00664wind 0.141nuclear 10.3hydro 16.2biomass 50.9gas 583unknown 927oil 1033coal 1167

as well as direct emissions on site. For fossil fuels, opera-tional emissions are therefore higher than only direct combus-tion emissions. For solar, geothermal and wind, the emissionsare strictly from maintenance operations.

The operations intensity per technology averaged over allcountries is summarized in Table 1. The dashed line indicatesthe split between non-fossil and fossil technologies. For detailson country-specific values, see Table 1–3 in the supplementarymaterial.

2.2. Carbon emission allocationThe consumption-based accounting method proposed in this

case study builds on flow tracing techniques. Flow tracing wasoriginally introduced as a method for transmission loss alloca-tion and grid usage fees [18, 19]. It follows power flows onthe transmission network mapping the paths between the loca-tion of generation and the location of consumption. It works insuch a way that each technology for each country is assigneda unique color mathematically. This is a mathematical abstrac-tion since it is not physically possible to color power flows. Foreach hour local production and imported flows are assumed tomix evenly at each node in the transmission network (see Fig-ure 1) and determine the color mix of the power serving thedemand and the exported flows. As an example, the coloredarrows in Figure 1 show the cascade of power flows resultingfrom flow tracing of German wind power (light blue) and Pol-ish coal power (brown) for the first hour of January 1st, 2017.The size of the colored arrows shows how much of the totalpower flow (in black) is accounted for. A threshold has beenapplied such that the technology specific flows are only shownif they account for at least 2% of the total power flow for eachinterconnector.

Flow tracing has been proposed as the method for flow al-location in the Inter-Transmission System Operator Compensa-tion mechanism for transit flows [20, 21]. Recently, the methodhas been applied to various aspects of power system modelsto allocate transmission network usage [22, 23], a generaliza-tion that allows associating power flows on the grid to specificregions or generation technologies [24], creating a flow-basednodal levelized cost of electricity [25], and analyzing the usageof different storage technologies [26].

2

0 10 20 30 40 50 60 70 80 90 100Share of non-fossil production [%]

0

200

400

600

800

1000

Aver

age

inte

nsity

[kgC

O2eq

/MW

h]

ATBE

BGCZ

DE

DK

EE

ES

FI

FR

GB

GR

HUIE

IT

LT

LV

ME

NL

NO

PL

PTRO

RS

SE

SISK

Production intensityConsumption intensity

Figure 3: Comparison of average hourly production and consumption intensityas a function of the share of non-fossil generation in the country’s generationmix. Size of circles are proportional to mean generation and mean consumptionfor each country.

The challenge of cross-border power flows in relation to car-bon emission accounting has previously been studied in [6, 11].Both studies simplify nodes as being either net importers ornet exporters and neither are able to distinguish between dif-ferent generation technologies. Those simplifications are notnecessary in our approach as we can deal with both imports,exports, consumption and generation simultaneously at everynode while also distinguishing between different generationtechnologies. Additionally, Figure 1 exhibits loop flows. How-ever, these do not affect the validity of the flow tracing method-ology [11], and no effort has been made to eliminate them asthey occur naturally in the transmission system at the area level[27].

Flow tracing methods are almost unanimously applied tosimulation data – typically with high shares of renewable en-ergy. In this case study, we apply the flow tracing method tohourly time series from the electricityMap [16]. From this weare able to map the power flows between exporting and im-porting countries for each type of generation technology forevery hour of the time series. Applying country-specific aver-age carbon emission intensity per generation technology to thismapping, we construct a consumption-based carbon accountingmethod. For details on the mathematical definitions, see Sec-tion B in the supplementary material.

The production-based accounting method used for compari-son, is calculated as the carbon intensity from local generationwithin each country.

3. Results

Figure 3 shows a comparison of average production and con-sumption intensity as a function of the share of non-fossil gen-

NO SE FR FI LT BE DK AT ES SI LV GB IE RO HU SK PT IT DE BG CZ NL GR EE RS ME PL

0100200300400500600700800900

1000

Aver

age

inte

nsity

[kgC

O2eq

/MW

h]

Production intensity Import intensity

Figure 4: Average hourly consumption intensity per consumed MWh per coun-try (stacked bar) split in contributions from local generation and imports. Thecountries are sorted by average consumption intensity.

eration in each country’s generation mix. The consumption in-tensity is calculated using flow tracing. The size of the circles isproportional to the average hourly generation and consumptionin MWh, respectively. A vertical gray line connects the pro-duction and consumption intensity corresponding to the samecountry. We see a decline in intensity with increasing share ofnon-fossil generation. For high shares of non-fossil generation,the consumption intensity tends to be higher than the produc-tion intensity due to imports from countries with higher pro-duction intensity. The pattern is reversed for low shares of non-fossil generation. The values plotted in this figure are shown inTable 4 in the supplementary material.

Some countries exhibit a huge difference between produc-tion and consumption intensity. An example of this is Slovakia(SK), which has a high share of nuclear power and Austria (AT),which has a high share of hydro power, but both rely heavily onimports of large amounts of coal power especially from Poland(PL) and Czech Republic (CZ). Denmark (DK) is an extremeexample of the opposite case, having a high share of coal andgas power and importing large amounts of hydro and nuclearpower from Norway (NO) and Sweden (SE).

While this figure only shows average values, Figure 7 in thesupplementary material highlights the interval of hourly varia-tion of production and consumption intensity per country. Thisinterval is high for all countries except the ones with very highnon-fossil share (FR, SE, NO).

From a national perspective, it is important to know thesource electricity that is being imported, and whether it in-creases a countrys reliance on high-carbon, insecure, or oth-erwise undesirable sources of generation.

Figure 4 shows the consumption-based intensity per country.The height of each bar corresponds to the consumption inten-sity for each country shown in Figure 3. This figure decom-poses the consumption intensity for each country and showshow much of a particular country’s consumption intensity iscaused by the local generation mix compared with the genera-tion mix of imported power. We see that for many countries itis important to be able to distinguish between local generationand imports since the imports make a substantial contributionto the country’s consumption-based emission. In cases with alarge difference between the intensity of local power productionand the imported power, imports have a high impact. As men-

3

tioned in an earlier example, this is the case for both Austriaand Slovakia. For details on the average intensity of importsand exports between the countries, see Figure 9 and Table 5 inthe supplementary material.

4. Conclusion

In this study we have introduced a new method forconsumption-based carbon emission allocation based on flowtracing applied to a historical sample of real-time system datafrom the electricityMap.

With this method we have found substantial differences be-tween production and consumption intensities for each countryconsidered, which follow a trend proportional to the share ofnon-fossil generation technologies.

The difference between production and consumption intensi-ties and the associated impact of imports on average consump-tion intensity emphasize the importance of including cross-border flows for increased transparency regarding carbon emis-sion accounting of electricity. While there are limitations to theaccuracy of this method due to data availability and the approx-imation of flow tracing, we believe that this method providesthe first step in a new direction for carbon emission accountingof electricity.

This case study focuses on the European electricity system.When additional sources of live system data become availablethis approach could be extended to cover a wider geographicalarea or a higher spatial resolution. Another interesting applica-tion of this method would be to include additional sectors suchas heating, since these are coupled through technologies likeheat pumps, resistive heaters, and power plants with cogenera-tion. This could lead to real-time carbon emission signals forthe entire energy system.

Acknowledgments

Gorm Bruun Andresen acknowledges the APPLAUS projectfor financial support. Iain Staffell acknowledges the Engineer-ing and Physical Sciences Research Council (EPSRC) for fund-ing via project EP/N005996/1. We thank Mirko Schafer forhelpful discussions.

References

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4

Supplementary material to: Real-Time Carbon Accounting Methodfor the European Electricity Markets

Bo Tranberga,b, Olivier Corradic, Bruno Lajoiec, Thomas Gibond, Iain Staffelle and Gorm BruunAndresena

aDepartment of Engineering, Aarhus University, Inge Lehmanns Gade 10, 8000 Aarhus C, DenmarkbDanske Commodities, Værkmestergade 3, 8000 Aarhus C, Denmark

cTomorrow, TMROW IVS, tmrow.com, Godthabsvej 61 B, 3. th., 2000 FrederiksbergdLuxembourg Institute of Science and Technology, 5 Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette,

LuxembourgeCentre for Environmental Policy, Imperial College London, London, UK

December 21, 2018

Contents

A Carbon intensities 2

B Flow tracing 6

C Additional results 8

1

A Carbon intensities

Carbon emission intensities are derived from the ecoinvent 3.4 database [1]. For each of theEU28 we calculate technology-specific factors extracted from the high-voltage level (for mosttechnologies) and low-voltage level (for photovoltaic technologies), to generate their lifecyclecarbon intensities in grams of CO2 equivalents per kilowatthour. Furthermore, we also dif-ferentiate infrastructure-related impacts from operational impacts. This is done by groupinglife cycle inventory inputs by unit, where the set {’meter’, ’meter-year’, ’unit’, ’kilometer’}are assumed to denote infrastructure processes, whereas the rest, that is, ’kilowatthour’,’tonne-kilometer’, etc., are accounted as operation and maintenance processes.

The values under ”high-voltage mix” denote the global warming potential (GWP) score ofthe electricity mix directly from high-voltage technologies, while ”low-voltage mix” valuesdenote the GWP score of electricity at the consumer level, i.e. after transformation and dis-tribution from high and medium-voltage (including losses), and integration of photovoltaicelectricity into the grid. The high- and low-voltage GWP scores are extracted directly fromecoinvent 3.4, here only shown for information, and never used in the calculations.

Not all technology-area pairs are available in the database, in case of missing information,values have been proxied by the EU28 average intensity for the given technology, calculatedfrom the areas for which the data exists, and weighted by their respective contribution to theEU28 mix. When the production source is unknown we assume an intensity averaged overthe particular country’s intensity for gas, oil and coal.

Table 1–3 show the country-specific lifecycle, infrastructure, and operation intensities pertechnology in units of g CO2 eq./kWh. EU28 averages are also shown, in bold. The relationbetween the three tables is such that lifecycle = infrastructure + operation. The operationintensities in Table 3 are the basis for the production as well as consumption-based carbonallocation in this study.

2

Tabl

e1:

Lif

ecyc

leC

O2

equ

ival

ent

inte

nsit

yp

erte

chno

logy

and

cou

ntry

,in

gC

O2

eq./

kWh.

Val

ues

init

alic

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icat

eth

atth

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ctor

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aila

ble,

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aced

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eigh

ted

aver

age

for

that

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gy(s

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ED

KEE

ESEU

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FRG

BG

RH

UIE

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LVM

EN

LN

OPL

PTR

OR

SSE

SISK

cate

gory

vari

ant

high

-vol

tage

mix

-12

518

860

973

165

443

210

3033

642

626

241

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198

040

051

346

955

152

042

661

615

.910

0036

039

885

221

.843

421

6

win

d-

17.8

16.2

19.5

19.4

20.0

13.8

19.8

14.2

16.8

23.0

15.6

16.8

15.1

13.6

13.7

19.7

13.3

18.2

16.8

16.3

14.4

16.5

13.7

25.3

16.8

16.2

16.8

16.8

nucl

ear

-12

.412

.012

.012

.011

.312

.412

.412

.112

.412

.512

.912

.412

.412

.012

.412

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.412

.412

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.012

.412

.412

.414

.212

.412

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81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

biom

ass

coge

nera

tion

53.8

53.8

56.8

53.8

53.8

53.8

56.8

53.8

53.9

53.8

53.8

53.8

53.9

53.8

53.8

53.8

56.8

56.8

53.9

53.8

53.9

53.8

53.8

53.8

53.9

53.8

53.8

53.8

hydr

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237

890

111

4096

561

761

754

661

761

777

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2061

785

161

510

4061

761

761

741

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2061

761

768

4

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9714

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9714

.751

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9751

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run-

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4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

4.42

coal

-98

611

2011

8011

9011

7011

6013

0012

1011

6010

8010

9011

4013

0014

1010

7011

5011

8011

6011

6010

3011

6011

6011

4011

4013

4011

8012

0011

60

coge

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tion

1220

1210

1250

1710

1170

1050

1210

1210

1210

1100

1210

1210

1560

1240

1210

1260

1210

1210

1210

998

1490

1160

1210

1240

1240

1370

1250

1530

gas

-61

447

274

669

753

351

351

349

251

383

958

852

168

275

046

253

251

351

351

346

540

751

344

161

551

351

310

9069

4

coge

nera

tion

529

503

936

840

351

455

423

173

475

530

671

475

173

648

173

496

629

599

475

450

523

542

475

686

810

555

436

652

oil

-11

6091

316

7010

6087

712

4011

8086

610

2044

795

313

2099

311

3091

910

6010

2010

2010

2010

2010

2010

2083

410

0010

2085

413

9096

0

coge

nera

tion

959

854

965

1520

680

965

873

935

935

952

770

935

1080

873

935

904

1530

935

935

1080

935

880

610

1260

935

837

873

1400

low

-vol

tage

mix

-32

323

967

579

465

739

392

136

944

624

454

.980

597

348

758

844

372

978

073

861

030

.510

3040

047

494

042

.344

745

8

sola

r-

107

112

77.9

118

110

94.6

94.6

71.4

94.6

94.6

90.9

94.6

76.3

94.6

94.6

81.3

109

94.6

94.6

109

94.6

94.6

69.3

88.1

82.8

110

83.0

90.7

3

Tabl

e2:

CO

2eq

uiv

alen

tin

tens

ity

per

tech

nolo

gyan

dco

unt

ry,e

mbo

die

din

infr

astr

uct

ure

,in

gC

O2

eq./

kWh.

Val

ues

init

alic

indi

cate

that

the

coun

try-

spec

ific

fact

oris

not

avai

labl

e,an

dw

asre

plac

edby

the

Euro

pean

wei

ghte

dav

erag

efo

rth

atte

chno

logy

(sho

wn

inbo

ld).

AT

BEBG

CZ

DE

DK

EEES

EU28

FIFR

GB

GR

HU

IEIT

LTLV

ME

NL

NO

PLPT

RO

RS

SESI

SK

cate

gory

vari

ant

high

-vol

tage

mix

-5.

483.

103.

102.

394.

497.

413.

645.

234.

043.

163.

021.

183.

602.

634.

176.

186.

173.

804.

042.

406.

552.

826.

665.

423.

364.

412.

322.

56

win

d-

17.6

16.1

19.4

19.2

19.8

13.7

19.6

14.0

16.7

22.8

15.5

16.7

15.0

13.5

13.6

19.5

13.2

18.0

16.7

16.2

14.2

16.4

13.6

25.2

16.7

16.1

16.7

16.7

nucl

ear

-2.

101.

931.

931.

931.

892.

102.

101.

952.

101.

992.

272.

102.

101.

932.

102.

102.

102.

102.

101.

932.

102.

102.

101.

862.

101.

961.

931.

93

geot

herm

al-

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

81.8

biom

ass

coge

nera

tion

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

3.40

hydr

opow

erpu

mpe

dst

orag

e6.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

52

rese

rvoi

r6.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

526.

52

run-

of-r

iver

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

4.39

coal

-1.

371.

582.

412.

462.

131.

962.

341.

691.

961.

871.

581.

552.

383.

011.

841.

572.

401.

961.

961.

461.

961.

961.

562.

452.

822.

402.

591.

96

coge

nera

tion

1.17

1.82

1.82

1.66

1.34

1.43

1.82

1.82

1.82

1.20

1.82

1.82

2.37

2.25

1.82

1.42

1.82

1.82

1.82

1.58

1.25

1.87

1.82

2.25

2.25

1.02

2.24

1.96

gas

-0.

916

0.92

70.

469

0.55

90.

818

0.72

10.

721

1.15

0.72

11.

821.

250.

354

1.08

0.96

20.

880

0.96

40.

721

0.72

10.

721

0.80

90.

704

0.72

11.

150.

386

0.72

10.

721

0.68

80.

878

coge

nera

tion

2.30

2.48

4.96

4.35

4.31

3.35

4.00

5.69

3.19

2.56

3.79

3.19

5.69

2.85

5.69

2.78

3.37

3.02

3.19

1.89

2.32

1.88

3.19

3.57

4.31

3.71

3.91

2.54

oil

-2.

642.

083.

752.

391.

992.

762.

651.

942.

271.

022.

142.

972.

192.

602.

062.

322.

272.

272.

272.

272.

272.

271.

892.

242.

271.

953.

122.

16

coge

nera

tion

2.19

1.94

2.17

3.42

1.54

2.15

1.96

2.08

2.08

2.18

1.73

2.08

2.37

1.96

2.08

1.98

3.42

2.08

2.08

2.53

2.08

1.98

1.39

2.89

2.08

1.91

1.96

3.14

low

-vol

tage

mix

-4.

542.

996.

187.

7113

.82.

972.

956.

766.

412.

993.

662.

9612

.12.

952.

9613

.04.

022.

972.

913.

012.

972.

973.

685.

882.

933.

033.

033.

44

sola

r-

107

112

77.9

118

110

94.6

94.6

71.4

94.6

94.6

90.9

94.6

76.3

94.6

94.6

81.3

109

94.6

94.6

109

94.6

94.6

69.2

88.1

82.7

110

83.0

90.7

4

Tabl

e3:

CO

2eq

uiva

lent

inte

nsity

per

tech

nolo

gyan

dco

untr

y,em

bodi

edin

oper

atio

ns,i

ng

CO

2eq

./kW

h.V

alue

sin

italic

indi

cate

that

the

coun

try-

spec

ific

fact

oris

nota

vaila

ble,

and

was

repl

aced

byth

eEu

rope

anw

eigh

ted

aver

age

for

that

tech

nolo

gy(s

how

nin

bold

).A

TBE

BGC

ZD

ED

KEE

ESEU

28FI

FRG

BG

RH

UIE

ITLT

LVM

EN

LN

OPL

PTR

OR

SSE

SISK

cate

gory

vari

ant

high

-vol

tage

mix

-11

918

560

672

964

942

510

3033

142

225

938

.880

097

639

750

946

354

551

642

261

49.

3899

835

439

284

817

.443

221

4

win

d-

0.14

90.

156

0.14

90.

166

0.16

50.

126

0.16

50.

122

0.14

20.

156

0.13

30.

142

0.12

10.

114

0.11

60.

161

0.11

00.

159

0.14

20.

133

0.12

00.

140

0.11

70.

192

0.14

20.

141

0.14

20.

142

nucl

ear

-10

.310

.110

.110

.19.

3710

.310

.310

.210

.310

.510

.610

.310

.310

.110

.310

.310

.310

.310

.310

.110

.310

.310

.312

.310

.310

.310

.110

.1

geot

herm

al-

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

0.00

664

biom

ass

coge

nera

tion

50.4

50.4

53.4

50.4

50.4

50.4

53.4

50.4

50.5

50.4

50.4

50.4

50.5

50.4

50.4

50.4

53.4

53.4

50.5

50.4

50.5

50.4

50.4

50.4

50.5

50.4

50.4

50.4

hydr

opow

erpu

mpe

dst

orag

e44

537

289

411

4095

861

161

153

961

161

170

.861

114

1061

184

560

810

3061

161

161

135

.214

1058

262

212

1061

161

067

8

rese

rvoi

r0.

445

8.13

8.13

44.8

44.8

8.13

8.13

44.8

8.13

44.8

0.44

58.

138.

138.

138.

130.

445

8.13

8.13

8.13

8.13

0.44

58.

1344

.88.

130.

445

44.8

8.13

44.8

run-

of-r

iver

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

0.02

530.

0253

coal

-98

411

2011

8011

8011

6011

6013

0012

1011

6010

8010

9011

4013

0014

0010

7011

5011

7011

6011

6010

3011

6011

6011

4011

4013

4011

7011

9011

60

coge

nera

tion

1220

1210

1250

1710

1160

1050

1210

1210

1210

1100

1210

1210

1560

1230

1210

1260

1210

1210

1210

996

1490

1160

1210

1230

1230

1370

1240

1530

gas

-61

347

174

569

653

351

351

349

151

383

758

752

168

174

946

153

151

351

351

346

440

651

344

061

551

351

310

9069

4

coge

nera

tion

527

501

932

835

347

452

419

167

471

528

668

471

167

645

167

493

625

596

471

449

520

540

471

682

805

551

432

649

oil

-11

5091

116

6010

6087

512

4011

8086

410

1044

695

113

2099

011

3091

710

6010

1010

1010

1010

1010

1010

1083

299

710

1085

213

8095

8

coge

nera

tion

957

852

962

1520

678

963

871

933

933

949

768

933

1070

871

933

902

1530

933

933

1070

933

878

609

1250

933

835

871

1390

low

-vol

tage

mix

-31

923

666

978

664

339

091

836

244

024

151

.280

296

148

458

543

072

577

773

560

727

.510

3039

646

893

739

.344

445

5

sola

r-

0.00

580

0.00

502

0.00

423

0.00

642

0.00

448

0.00

349

0.00

349

0.00

234

0.00

349

0.00

349

0.00

370

0.00

349

0.00

415

0.00

349

0.00

349

0.00

166

0.00

591

0.00

349

0.00

349

0.00

591

0.00

349

0.00

349

0.00

185

0.00

478

0.00

445

0.00

565

0.00

453

0.00

493

5

B Flow tracing

B.1 Formulation

Nomenclature

α set of all generation/storage technologies.

Ln nodal load.

Fn→k nodal outflow to direct neighbors.

Fm→n nodal inflow from direct neighbors.

Gn,α nodal generation for all technologies.

S+n,α storage discharge for each storage technology α at node n.

S−n sum of storage charging at node n.

qn,α nodal colormix.

The nodal color mix refers to the mixing of electricity at each node from different technologiesand countries of origin, where each technology for each country has been assigned a uniquecolor [2]. Note that this is an assumption, analogous to the mixing of water flows in pipes,used to approximate the mixing of power flows at nodes in the transmission system.

Figure 1 shows a sketch of the flow tracing implementation. For every hour all imports,generation, and storage discharge are mixed equally in the node, which then determinesthe color mix of the exports and the power serving the local load. We do not keep track ofthe color mix flowing into storage, but track which storage type the power originated fromwhen the storages are discharging. This mixing approach is called average participation orproportional sharing in the literature which was also proposed initially in [3]. For a discussionof different allocation methods, see [4]. For comprehensive reviews, see [5, 6].

The sketch in Figure 1 describes the nodal power balance

Ln + S−n + ∑k

Fn→k = ∑α

(Gn,α + S+

n,α)+ ∑

mFm→n, (1)

where the left-hand side and the right-hand side account for the flows out of and into anode, respectively. In this, and following equations, there is an implicit time index as theflow tracing is performed for every hour. We include nodal color mixes in the nodal powerbalance

qn,α

(Ln + S−n + ∑

kFn→k

)= Gn,α + S+

n,α + ∑m

qm,αFm→n, (2)

which is now an equation per country n per technology type α. Rearranging (2) we can writea matrix formula describing a unique solution for the nodal power mix qn,α according to [7]:

∑m

[δn,m

(Lm + S−m + ∑

kFm→k

)− Fm→n

]qm,α = Gn,α + S+

n,α. (3)

6

Figure 1: Sketch of flow tracing methodology.

Here qm,α is the hourly nodal color mix for node m split into components for every technologyfor every country. The α set allows us to track originating technology as well as originatingcountry e.g. we can trace who is consuming Danish wind power. Multiplying the nodalcolor mix with the nodal load and the carbon intensity of the originating generation/storagetechnologies allows us to calculate consumption-based carbon intensity allocation.

B.2 Handling of missing data

As we are using raw data directly from the power system there will be occurrences of missingvalues. In case of missing data for production or imports/exports for a country the particularcountry is excluded from the flow tracing calculation for that specific hour.

Imports from countries not included in the topology are included (e.g. Switzerland), but donot have an effect on the nodal mix of the importer (they simply scale the color mix, but donot change the ratios). Exports to countries outside the considered topology are subtracted.

Figure 2 shows ∑α qn,α for every country for every hour. If (3) is perfectly balanced it shouldbe the case that ∑α qn,α = 1. Cases of partially missing data leads to ∑α qn,α 6= 1. This isusually caused by one country being excluded due to missing data (which explains theoccurrence of 0’s in Figure 2), which affects the nodal balance of neighboring countries. Seee.g. the effect of missing data for Ireland on Great Britain. We observe no cases of ∑α qn,α > 1.The missing data mostly occurs for small, satellite countries e.g. Ireland and Montenegro,which only have a small effect on the closest neighbors.

The total number of entries in Figure 2:

hours · nodes = 8760 · 28 = 245280 (4)

Of these there are 6367 occurrences of qn,α = 0 (due to missing data), which is only 2.6%.When the occurrences of 0 are subtracted there are 3742 occurrences where qn,α < .9999which is only 1.5%. The cases where 0 < qn,α < .9999 are all rather close to 1 (all except 3

7

are above .8 and most are above .9). The occurrences of 0 are predominantly for Ireland,Montenegro and Estonia, which are both small countries at the edge of the network.

0 1000 2000 3000 4000 5000 6000 7000 8000Hour

ATBEBGCZDE

DK-DK1DK-DK2

EEESFI

FRGBGRHUIEITLTLVMENLNOPLPTRORSSESI

SK

Coun

try

0.0

0.2

0.4

0.6

0.8

1.0

Colo

r mix

sum

Figure 2: Flow tracing consistency check. Dark blue means generation or import/exportdata is entirely missing for a country, lighter colors mean it is partially complete, and whitemeans fully complete data.

C Additional results

Figure 3 shows a comparison of hourly production intensity with hourly load for the full yearof 2017 for every country. The production intensity is calculated based on the productionwithin each country. The figure is split in two parts with large countries in the top panel andsmaller countries in the bottom panel. In the top panel we see that Norway, Sweden andFrance have low intensities regardless of the level of consumption, which is due to a highshare of hydro power in the Nordic countries and nuclear power in France. On the otherhand, Poland has very high intensity due to a high share of coal power generation.

Figure 4 shows the stacked average consumption intensity per kWh per hour in Austria forall of 2017. This figure does not tell anything about the amount of power being consumedby each technology.

Figure 5 shows the total annual consumption intensity for Austria for 2017 based on flowtracing. From this figure we see that hydro is the technology providing most of the consumedpower, but that the intensity from this consumption is among the lowest of the technologies.On the other hand coal power is one of the smaller contributors to the consumed power, buthas the largest intensity.

Figure 6 shows average hourly production/consumption carbon intensity plotted as durationcurves for Austria and Denmark e.g. if a country runs on 100% coal the entire year the

8

0 20000 40000 60000 80000 100000Load [MW]

0

200

400

600

800

1000

1200

Prod

uctio

n in

tens

ity [k

gCO2

eq/M

Wh]

DEITESGBFRNOPLSE

0 2500 5000 7500 10000 12500 15000 17500 20000Load [MW]

0

200

400

600

800

1000

1200

Prod

uctio

n in

tens

ity [k

gCO2

eq/M

Wh]

ATBEBGCZDKEEFIGRHUIELTLVMENLPTRORSSISK

Figure 3: Comparison of hourly production intensity with hourly load for every country.

9

2017-02 2017-04 2017-06 2017-08 2017-10 2017-120

100

200

300

400

500

Inte

nsity

[kgC

O2eq

/MW

h]

hydrogas

biomasscoal

geothermalwind

oilnuclear

solarunknown

Figure 4: Hourly intensity per consumed unit of energy for Austria downsampled to dailyaverages.

0 1 2 3 4 5Power [MWh] 1e7

10 2

10 1

100

101

102

103

Inte

nsity

[kgC

O2eq

/MW

h]

Total annual consumption intensity for ATwindnuclearhydrogeothermalsolarbiomassgasoilunknowncoal

Figure 5: Total annual consumption intensity for Austria for 2017.

10

duration curve would be flat at that country’s operational intensity for coal as seen in Table 3.This figure shows that AT has a low production intensity, but a higher consumption intensitydue to imports. DK is relying on imports for a low consumption intensity since it has a highproduction intensity for approximately half of the year.

Figure 7 shows a comparison of average production (blue) and consumption (orange)intensity for each country. White dots mark the mean. The colored bars indicate 25%–75%quantiles and the gray bars 5%–95% quantiles. This is a summary of the duration curves forindividual countries as shown in Figure 6.

Figure 8 shows the difference between production and consumption intensity as functionof the share of non-fossil production of total production. Size of circles are proportionalto average production. A value above zero corresponds to the country having a higherconsumption intensity than production intensity. The figure shows a general trend that thehigher the share of non-fossil production the higher the consumption intensity is comparedto the production intensity. This can be explained by countries with high share of non-fossilproduction tend to import from countries with lower share of non-fossil production whichresults in the importing country’s consumption intensity being higher than its productionintensity.

Table 4 shows average production and consumption intensity per country. These values areplotted in Figure 3 in the article, they are also shown as the white markers in Figure 7, andthe difference for each country is shown in Figure 8.

Figure 9 shows average intensity per imported/exported unit of energy. When calculatingthe average imported/exported intensity between two countries only hours with actualtransfers have been used. A white entry means no data and only occurs for ME and RS. Thefigure should be read as NO exporting mostly low intensity hydro to all countries whereasEE and PL are exporting oil and coal to all countries. This figure doesn’t say anything aboutthe amount of energy being transferred e.g. most of the column for ME is based on data forvery few hours as ME is a small, poorly connected country. The values in Figure 9 are alsoshown in Table 5.

11

0 1000 2000 3000 4000 5000 6000 7000 8000Hours

0

200

400

600

800

1000

1200

Inte

nsity

[kgC

O2eq

/MW

h]

Intensity duration for AT

Generation Consumption

0 1000 2000 3000 4000 5000 6000 7000 8000Hours

0

200

400

600

800

1000

1200

Inte

nsity

[kgC

O2eq

/MW

h]

Intensity duration for DK

Generation Consumption

Figure 6: Average hourly production/consumption carbon intensity duration curves forAustria and Denmark.

AT BE BG CZ DE DK EE ES FI FR GB GR HU IE IT LT LV ME NL NO PL PT RO RS SE SI SK

0

200

400

600

800

1000

1200

Avg.

inte

nsity

[kgC

O2eq

/MW

h]

ProductionConsumption

Figure 7: Comparison of average production (blue) and consumption (orange) intensity.White dots mark the mean, colored bars 25%-75% quantiles and the gray bars 5%-95%quantiles.

12

0 10 20 30 40 50 60 70 80 90 100Share of non-fossil production [%]

40

20

0

20

40

60

80%

diff

eren

ce b

etwe

en a

vg. p

rod.

and

con

s. in

tens

ity

AT

BE

BG

CZ DE

DK

EEES FI

FRGBGR HUIE

IT

LT

LV

MENL

NO

PL PT

RO

RS

SESI

SK

Figure 8: Difference between production and consumption intensity as function of the shareof non-fossil production of total production. Size of circles are proportional to averageproduction.

AT BE BG CZ DE DK EE ES FI FR GB GR HU IE IT LT LV ME NL NO PL PT RO RS SE SI SK

Exporter

ATBEBGCZDEDKEEESFI

FRGBGRHUIEITLTLVMENLNOPLPTRORSSESI

SK

Impo

rter

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

Aver

age

inte

nsity

[kgC

O2eq

/MW

h]

Figure 9: Average imported/exported intensity. White cells indicating missing data. Thisfigure doesn’t say anything about the amount of energy being transferred e.g. most of thecolumn for ME is based on data for very few hours as ME is a small, poorly connectedcountry.

13

Tabl

e4:

Ave

rage

prod

uctio

nan

dco

nsum

ptio

nin

tens

ityfo

rea

chco

untr

y.Th

ese

valu

esar

epl

otte

din

Figu

re3

inth

ear

ticle

.Uni

tsar

ekg

CO

2eq/

MW

h.

AT

BEBG

CZ

DE

DK

EEES

FIFR

GB

GR

HU

IEIT

LTLV

ME

NL

NO

PLPT

RO

RS

SESI

SK

Con

sum

ptio

n24

823

660

465

751

827

188

932

717

482

349

751

447

424

513

230

338

912

665

1694

745

542

491

784

365

438

Prod

ucti

on13

619

361

664

852

844

299

034

019

176

346

763

439

428

537

187

241

943

734

1199

446

742

497

382

374

277

14

Tabl

e5:

Ave

rage

inte

nsity

ofpo

wer

impo

rted

and

expo

rted

betw

een

coun

trie

s.Th

ese

valu

esar

epl

otte

din

Figu

re9.

Col

umns

are

expo

rter

san

dro

ws

are

impo

rter

s.U

nits

are

kgC

O2e

q/M

Wh.

AT

BEBG

CZ

DE

DK

EEES

FIFR

GB

GR

HU

IEIT

LTLV

ME

NL

NO

PLPT

RO

RS

SESI

SK

AT

136

232

503

546

469

360

974

322

160

7341

758

032

542

853

321

124

580

878

715

853

385

344

714

6831

625

5

BE91

193

533

615

506

381

774

316

181

6537

757

222

739

441

717

419

545

273

419

938

384

322

736

6726

523

7

BG16

428

161

666

257

243

813

9737

219

685

513

719

395

518

708

273

342

690

1019

2710

2144

745

089

191

406

324

CZ

143

258

599

648

552

413

904

377

196

8647

966

828

548

054

923

429

450

184

359

1021

454

386

704

9031

227

4

DE

121

206

599

640

528

391

748

336

192

7340

661

326

341

247

019

323

449

373

955

989

406

360

869

8230

224

4

DK

7120

659

063

148

940

210

5033

518

269

375

600

256

420

491

210

221

-80

820

969

399

343

981

7527

525

6

EE70

200

601

681

505

370

990

381

170

6938

365

628

643

049

019

118

2-

817

2110

6645

141

3-

7636

328

1

ES14

919

358

265

150

837

472

734

019

168

365

617

287

433

481

179

226

4473

755

1003

484

359

795

8335

126

0

FI12

627

955

259

944

834

740

838

219

190

483

655

284

437

503

227

262

-64

962

892

467

369

894

7726

823

7

FR16

723

153

361

049

638

280

435

618

176

429

594

307

450

518

193

235

292

766

1492

543

234

871

568

327

256

GB

9420

355

662

851

738

711

0632

218

369

346

594

277

477

501

208

221

887

121

965

389

345

772

7728

327

2

GR

149

246

668

698

591

429

847

359

199

8048

776

339

446

259

321

927

669

885

924

1113

448

469

976

7540

929

3

HU

174

280

717

758

644

475

961

427

212

9053

479

243

952

965

624

429

786

193

925

1206

515

489

1022

8345

731

7

IE86

197

577

581

480

361

782

297

185

7032

656

121

442

738

616

915

4-

717

1891

336

732

6-

6320

520

3

IT12

621

457

961

752

638

789

235

017

073

405

630

336

432

537

192

229

710

797

1398

342

339

280

668

349

263

LT68

199

596

630

486

354

856

337

165

6837

160

525

540

646

918

723

5-

750

2097

340

534

699

270

274

244

LV69

194

540

615

456

329

854

346

156

6736

361

026

740

346

817

324

0-

733

2194

441

237

5-

6932

625

5

ME

114

194

594

618

522

359

848

325

158

6937

161

334

939

652

618

324

194

275

922

984

388

413

872

7040

826

2

NL

8517

752

356

746

634

885

529

416

662

356

531

233

386

416

177

193

416

734

1986

835

330

875

966

247

226

NO

5716

758

962

042

229

884

529

118

359

313

569

234

364

448

159

159

-68

411

920

341

327

957

6626

123

1

PL94

203

595

635

529

385

857

330

171

6939

660

426

241

747

318

822

547

077

020

994

396

348

858

7029

124

4

PT14

518

253

760

646

333

469

331

417

766

327

565

268

414

458

166

221

4468

355

926

467

333

746

8232

924

5

RO

138

229

593

616

520

370

842

356

170

7642

465

236

343

156

520

125

867

377

823

971

425

424

849

7237

626

8

RS

127

206

691

709

596

420

853

363

189

7142

568

636

744

454

918

723

491

381

513

1134

436

458

973

7243

528

3

SE97

198

599

631

477

357

731

335

191

7137

260

825

438

945

419

122

4-

701

5596

140

435

599

782

282

241

SI22

637

959

268

558

747

011

8447

221

711

165

280

546

362

277

232

237

948

610

4933

1048

565

466

955

9237

434

7

SK14

025

458

863

554

040

282

037

119

185

474

658

280

467

533

226

286

492

806

5910

0244

938

069

087

305

277

15

References

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[7] J. Horsch, M. Schafer, S. Becker, S. Schramm, M. Greiner, Flow tracing as a tool set forthe analysis of networked large-scale renewable electricity systems, International Journalof Electrical Power & Energy Systems 96 (2018) 390 – 397. doi:10.1016/j.ijepes.2017.10.024.

16