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Real-Time Carbon Accounting Method for the European Electricity Markets
Bo Tranberga,b,c,∗
, Olivier Corradid, Bruno Lajoied, Thomas Gibone, Iain Staffellf, Gorm Bruun Andresenb
aEnto Labs ApS, Inge Lehmanns Gade 10, 6., 8000 Aarhus C, Denmark
bDepartment of Engineering, Aarhus University, Inge Lehmanns Gade 10, 8000 Aarhus C, Denmark
cDanske Commodities, Værkmestergade 3, 8000 Aarhus C, Denmark
dTomorrow, TMROW IVS, tmrow.com, Godthåbsvej 61 B, 3. th., 2000 Frederiksberg
eLuxembourg Institute of Science and Technology, 5 Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette, Luxembourg
fCentre for Environmental Policy, Imperial College London, London, UK
Abstract
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires
accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time
consumption-based accounting approach based on flow tracing. This method traces power flows from producer to consumer thereby
representing 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 differences
between production and consumption intensities. This emphasizes the importance of considering cross-border flows for increased
transparency 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 electricity
generation has been generated from fossil fuel [1]. As a re-
sult, electricity and heat production account for 25% of global
greenhouse gas (GHG) emissions [2]. Furthermore, electric-
ity demand is widely expected to rise because of electrification
of vehicles [3]. These facts highlight the importance of an ac-
curate and transparent 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 2
denotes 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 to
trace 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-based
method [4,7]. Grid average factors are averaged over time and
therefore not specific to the time of consumption due to limited
availability 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, which
do not affect the amount of renewable electricity being gener-
ated, and therefore fail to provide accurate information in GHG
reports. 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 method
∗Corresponding author: bo@entolabs.co
is applied to hourly market data for 28 areas within Europe. We
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 models
of carbon accounting [8,9,10]. The approach advances beyond
[11], where a similar flow tracing methodology is used to create
a consumption-based carbon allocation between six Chinese re-
gions. However, the data for that study was limited to annual
aggregates and different generation technologies were also ag-
gregated. We apply the method to real-time system data, includ-
ing the possibility of distinguishing between different genera-
tion technologies, providing a real-time CO2signal for all ac-
tors involved. This increases the overall transparency and credi-
bility of emission accounting related to electricity consumption,
which is of high importance [12]. To investigate the impact of
the new consumption-based accounting method we compare it
with the straightforward production-based method (i.e. looking
at the real-time generation mix within each area). For discus-
sions on the shift from production-based to consumption-based
accounting and the idea of sharing the responsibility between
producer and consumer, we refer to [13,14].
2. Methods
2.1. Data
The method is applied to data from the electricityMap
database [15], which collects real-time data from electricity
generation 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 area
can be found on the project’s webpage [16]. Figure 1shows the
Preprint submitted to Energy Strategy Reviews May 16, 2019
arXiv:1812.06679v3 [physics.soc-ph] 15 May 2019
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 is
proportional to the magnitude of the flow on each line. Power flows to and
from neighboring countries, e.g. Switzerland, are included when available, and
these areas are shown in gray. The cascade of power flows from German wind
and Polish coal are highlighted with blue and brown arrows, respectively.
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).
28 areas and the 47 interconnectors considered. Power flows
to and from neighboring areas, e.g. Switzerland, are included
when available. The black arrows show a snapshot of hourly
power flows between the areas. In the results, we aggregate the
two price areas of Denmark and, thus, compare 27 countries.
The top panel of Figure 2shows stacked daily-average pro-
duction for each technology for Austria. The bottom panel
shows 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 representing
the full year 2017.
Carbon emission intensities are derived from the ecoinvent
3.4 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 used
for the production and consumption-based carbon allocation in
this study. Operational emissions include all emissions occur-
Table 1: CO2equivalent operation intensity per technology averaged across
countries. The dashed line indicates the split between non-fossil and fossil
technologies. For details, see Table 1–3 in the supplementary material.
Technology Intensity [kgCO2eq/MWh]
solar 0.00410
geothermal 0.00664
wind 0.141
nuclear 10.3
hydro 16.2
biomass 50.9
gas 583
unknown 927
oil 1033
coal 1167
ring over the fuel chain (from extraction to supply at plant)
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 emissions
are strictly from maintenance operations.
The operations intensity per technology averaged over all
countries is summarized in Table 1. The dashed line indicates
the split between non-fossil and fossil technologies. For details
on country-specific values, see Table 1–3 in the supplementary
material.
2.2. Carbon emission allocation
The consumption-based accounting method proposed in this
case study builds on flow tracing techniques. Flow tracing was
originally introduced as a method for transmission loss alloca-
tion and grid usage fees [18,19]. It follows power flows on
the transmission network mapping the paths between the loca-
tion of generation and the location of consumption. It works in
such a way that each technology for each country is assigned
a unique color mathematically. This is a mathematical abstrac-
tion since it is not physically possible to color power flows. For
each hour local production and imported flows are assumed to
mix evenly at each node in the transmission network (see Fig-
ure 1) and determine the color mix of the power serving the
demand and the exported flows. As an example, the colored
arrows in Figure 1show the cascade of power flows resulting
from 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 total
power flow (in black) is accounted for. A threshold has been
applied such that the technology specific flows are only shown
if they account for at least 2% of the total power flow for each
interconnector.
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 method
has been applied to various aspects of power system models
to allocate transmission network usage [22,23], a generaliza-
tion that allows associating power flows on the grid to specific
regions or generation technologies [24], creating a flow-based
nodal levelized cost of electricity [25], and analyzing the usage
of different storage technologies [26].
2
Figure 3: Comparison of average hourly production and consumption intensity
as a function of the share of non-fossil generation in the country’s generation
mix. Size of circles are proportional to mean generation and mean consumption
for 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 or
net exporters and neither are able to distinguish between dif-
ferent generation technologies. Those simplifications are not
necessary in our approach as we can deal with both imports,
exports, consumption and generation simultaneously at every
node while also distinguishing between different generation
technologies. Additionally, Figure 1exhibits 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 as
they occur naturally in the transmission system at the area level
[27].
Flow tracing methods are almost unanimously applied to
simulation data – typically with high shares of renewable en-
ergy. In this case study, we apply the flow tracing method to
hourly time series from the electricityMap [16]. From this we
are able to map the power flows between exporting and im-
porting countries for each type of generation technology for
every hour of the time series. Applying country-specific aver-
age carbon emission intensity per generation technology to this
mapping, we construct a consumption-based carbon accounting
method. 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 generation
within each country.
3. Results
Figure 3shows a comparison of average production and con-
sumption intensity as a function of the share of non-fossil gen-
Figure 4: Average hourly consumption intensity per consumed MWh per coun-
try (stacked bar) split in contributions from local generation and imports. The
countries 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 is
proportional to the average hourly generation and consumption
in MWh, respectively. A vertical gray line connects the pro-
duction and consumption intensity corresponding to the same
country. We see a decline in intensity with increasing share of
non-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 in
Table 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 on
imports of large amounts of coal power especially from Poland
(PL) and Czech Republic (CZ). Denmark (DK) is an extreme
example of the opposite case, having a high share of coal and
gas power and importing large amounts of hydro and nuclear
power from Norway (NO) and Sweden (SE).
While this figure only shows average values, Figure 7 in the
supplementary material highlights the interval of hourly varia-
tion of production and consumption intensity per country. This
interval is high for all countries except the ones with very high
non-fossil share (FR, SE, NO).
From a national perspective, it is important to know the
source electricity that is being imported, and whether it in-
creases a country’s reliance on high-carbon, insecure, or oth-
erwise undesirable sources of generation.
Figure 4shows 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 shows
how much of a particular country’s consumption intensity is
caused by the local generation mix compared with the genera-
tion mix of imported power. We see that for many countries it
is important to be able to distinguish between local generation
and imports since the imports make a substantial contribution
to the country’s consumption-based emission. In cases with a
large difference between the intensity of local power production
and the imported power, imports have a high impact. As men-
3
tioned in an earlier example, this is the case for both Austria
and Slovakia. For details on the average intensity of imports
and exports between the countries, see Figure 9 and Table 5 in
the supplementary material.
4. Conclusion
We introduce a new method for consumption-based carbon
emission allocation based on flow tracing applied to a historical
sample of real-time system data from the electricityMap.
The method we propose demonstrates that consumption-
based accounting is more difficult than production-based due to
the added complexity of cross-border flows. However, with this
method we have found substantial differences between produc-
tion and consumption intensities for each country considered,
which follow a trend proportional to the share of non-fossil
generation technologies. It would be straightforward to sub-
sequently apply these results to attribute carbon emissions to
individual consumers like companies or households.
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 the
accuracy of this method due to data availability and the mathe-
matical abstraction of flow tracing, we believe that this method
provides the first step in a new direction for carbon emission
accounting of electricity.
This case study focuses on the European electricity system.
When additional sources of live system data become available
this approach could be extended to cover a wider geographical
area. Even for areas without significant import and export the
method could be applied within a single country provided that
local system data is available at high spatial resolution. An-
other interesting application of this method would be to include
additional sectors such as heating and transport as these are be-
coming electrified. This could lead to a real-time carbon emis-
sion signal for the entire energy system and potentially lay the
foundation for time-varying electricity taxes.
Acknowledgments
Gorm Bruun Andresen acknowledges the APPLAUS project
for 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 Sch¨
afer for
helpful discussions.
References
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4
Supplementary material to: Real-Time Carbon Accounting Method
for the European Electricity Markets
Bo Tranberga,b,c, Olivier Corradid, Bruno Lajoied, Thomas Gibone, Iain Staffellfand Gorm Bruun
Andresenb
aEnto Labs ApS, Inge Lehmanns Gade 10, 6., 8000 Aarhus C, Denmark
bDepartment of Engineering, Aarhus University, Inge Lehmanns Gade 10, 8000 Aarhus C, Denmark
cDanske Commodities, Værkmestergade 3, 8000 Aarhus C, Denmark
dTomorrow, TMROW IVS, tmrow.com, Godth˚
absvej 61 B, 3. th., 2000 Frederiksberg
eLuxembourg Institute of Science and Technology, 5 Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette,
Luxembourg
fCentre for Environmental Policy, Imperial College London, London, UK
May 15, 2019
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 the
EU28 we calculate technology-specific factors extracted from the high-voltage level (for most
technologies) and low-voltage level (for photovoltaic technologies), to generate their lifecycle
carbon intensities in grams of CO
2
equivalents per kilowatthour. Furthermore, we also dif-
ferentiate infrastructure-related impacts from operational impacts. This is done by grouping
life 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 of
the electricity mix directly from high-voltage technologies, while ”low-voltage mix” values
denote 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 photovoltaic
electricity into the grid. The high- and low-voltage GWP scores are extracted directly from
ecoinvent 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, calculated
from the areas for which the data exists, and weighted by their respective contribution to the
EU28 mix. When the production source is unknown we assume an intensity averaged over
the particular country’s intensity for gas, oil and coal.
Table 1–3 show the country-specific lifecycle, infrastructure, and operation intensities per
technology in units of g CO
2
eq./kWh. EU28 averages are also shown, in bold. The relation
between the three tables is such that lifecycle = infrastructure + operation. The operation
intensities in Table 3 are the basis for the production as well as consumption-based carbon
allocation in this study.
2
Table 1: Lifecycle CO
2
equivalent intensity per technology and country, in g CO
2
eq./kWh. Values in italic indicate that the
country-specific factor is not available, and was replaced by the European weighted average for that technology (shown in bold).
AT BE BG CZ DE DK EE ES EU28 FI FR GB GR HU IE IT LT LV ME NL NO PL PT RO RS SE SI SK
category variant
high-voltage mix - 125 188 609 731 654 432 1030 336 426 262 41.9 801 980 400 513 469 551 520 426 616 15.9 1000 360 398 852 21.8 434 216
wind - 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
nuclear - 12.4 12.0 12.0 12.0 11.3 12.4 12.4 12.1 12.4 12.5 12.9 12.4 12.4 12.0 12.4 12.4 12.4 12.4 12.4 12.0 12.4 12.4 12.4 14.2 12.4 12.2 12.0 12.0
geothermal - 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
biomass cogeneration 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
hydropower pumped storage 452 378 901 1140 965 617 617 546 617 617 77.3 617 1420 617 851 615 1040 617 617 617 41.7 1420 588 629 1220 617 617 684
reservoir 6.97 14.7 14.7 51.4 51.4 14.7 14.7 51.4 14.7 51.4 6.97 14.7 14.7 14.7 14.7 6.97 14.7 14.7 14.7 14.7 6.97 14.7 51.4 14.7 6.97 51.4 14.7 51.4
run-of-river 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 - 986 1120 1180 1190 1170 1160 1300 1210 1160 1080 1090 1140 1300 1410 1070 1150 1180 1160 1160 1030 1160 1160 1140 1140 1340 1180 1200 1160
cogeneration 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 - 614 472 746 697 533 513 513 492 513 839 588 521 682 750 462 532 513 513 513 465 407 513 441 615 513 513 1090 694
cogeneration 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 - 1160 913 1670 1060 877 1240 1180 866 1020 447 953 1320 993 1130 919 1060 1020 1020 1020 1020 1020 1020 834 1000 1020 854 1390 960
cogeneration 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-voltage mix - 323 239 675 794 657 393 921 369 446 244 54.9 805 973 487 588 443 729 780 738 610 30.5 1030 400 474 940 42.3 447 458
solar - 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
Table 2: CO
2
equivalent intensity per technology and country, embodied in infrastructure, in g CO
2
eq./kWh. Values in italic
indicate that the country-specific factor is not available, and was replaced by the European weighted average for that technology
(shown in bold).
AT BE BG CZ DE DK EE ES EU28 FI FR GB GR HU IE IT LT LV ME NL NO PL PT RO RS SE SI SK
category variant
high-voltage mix - 5.48 3.10 3.10 2.39 4.49 7.41 3.64 5.23 4.04 3.16 3.02 1.18 3.60 2.63 4.17 6.18 6.17 3.80 4.04 2.40 6.55 2.82 6.66 5.42 3.36 4.41 2.32 2.56
wind - 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
nuclear - 2.10 1.93 1.93 1.93 1.89 2.10 2.10 1.95 2.10 1.99 2.27 2.10 2.10 1.93 2.10 2.10 2.10 2.10 2.10 1.93 2.10 2.10 2.10 1.86 2.10 1.96 1.93 1.93
geothermal - 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
biomass cogeneration 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
hydropower pumped storage 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52
reservoir 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52 6.52
run-of-river 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 - 1.37 1.58 2.41 2.46 2.13 1.96 2.34 1.69 1.96 1.87 1.58 1.55 2.38 3.01 1.84 1.57 2.40 1.96 1.96 1.46 1.96 1.96 1.56 2.45 2.82 2.40 2.59 1.96
cogeneration 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 0.927 0.469 0.559 0.818 0.721 0.721 1.15 0.721 1.82 1.25 0.354 1.08 0.962 0.880 0.964 0.721 0.721 0.721 0.809 0.704 0.721 1.15 0.386 0.721 0.721 0.688 0.878
cogeneration 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 - 2.64 2.08 3.75 2.39 1.99 2.76 2.65 1.94 2.27 1.02 2.14 2.97 2.19 2.60 2.06 2.32 2.27 2.27 2.27 2.27 2.27 2.27 1.89 2.24 2.27 1.95 3.12 2.16
cogeneration 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-voltage mix - 4.54 2.99 6.18 7.71 13.8 2.97 2.95 6.76 6.41 2.99 3.66 2.96 12.1 2.95 2.96 13.0 4.02 2.97 2.91 3.01 2.97 2.97 3.68 5.88 2.93 3.03 3.03 3.44
solar - 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
Table 3: CO
2
equivalent intensity per technology and country, embodied in operations, in g CO
2
eq./kWh. Values in italic indicate
that the country-specific factor is not available, and was replaced by the European weighted average for that technology (shown in
bold).
AT BE BG CZ DE DK EE ES EU28 FI FR GB GR HU IE IT LT LV ME NL NO PL PT RO RS SE SI SK
category variant
high-voltage mix - 119 185 606 729 649 425 1030 331 422 259 38.8 800 976 397 509 463 545 516 422 614 9.38 998 354 392 848 17.4 432 214
wind - 0.149 0.156 0.149 0.166 0.165 0.126 0.165 0.122 0.142 0.156 0.133 0.142 0.121 0.114 0.116 0.161 0.110 0.159 0.142 0.133 0.120 0.140 0.117 0.192 0.142 0.141 0.142 0.142
nuclear - 10.3 10.1 10.1 10.1 9.37 10.3 10.3 10.2 10.3 10.5 10.6 10.3 10.3 10.1 10.3 10.3 10.3 10.3 10.3 10.1 10.3 10.3 10.3 12.3 10.3 10.3 10.1 10.1
geothermal - 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664 0.00664
biomass cogeneration 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
hydropower pumped storage 445 372 894 1140 958 611 611 539 611 611 70.8 611 1410 611 845 608 1030 611 611 611 35.2 1410 582 622 1210 611 610 678
reservoir 0.445 8.13 8.13 44.8 44.8 8.13 8.13 44.8 8.13 44.8 0.445 8.13 8.13 8.13 8.13 0.445 8.13 8.13 8.13 8.13 0.445 8.13 44.8 8.13 0.445 44.8 8.13 44.8
run-of-river 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253 0.0253
coal - 984 1120 1180 1180 1160 1160 1300 1210 1160 1080 1090 1140 1300 1400 1070 1150 1170 1160 1160 1030 1160 1160 1140 1140 1340 1170 1190 1160
cogeneration 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 - 613 471 745 696 533 513 513 491 513 837 587 521 681 749 461 531 513 513 513 464 406 513 440 615 513 513 1090 694
cogeneration 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 - 1150 911 1660 1060 875 1240 1180 864 1010 446 951 1320 990 1130 917 1060 1010 1010 1010 1010 1010 1010 832 997 1010 852 1380 958
cogeneration 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-voltage mix - 319 236 669 786 643 390 918 362 440 241 51.2 802 961 484 585 430 725 777 735 607 27.5 1030 396 468 937 39.3 444 455
solar - 0.00580 0.00502 0.00423 0.00642 0.00448 0.00349 0.00349 0.00234 0.00349 0.00349 0.00370 0.00349 0.00415 0.00349 0.00349 0.00166 0.00591 0.00349 0.00349 0.00591 0.00349 0.00349 0.00185 0.00478 0.00445 0.00565 0.00453 0.00493
5
B Flow tracing
B.1 Formulation
Nomenclature
αset of all generation/storage technologies.
Lnnodal load.
Fn→knodal outflow to direct neighbors.
Fm→nnodal inflow from direct neighbors.
Gn,αnodal generation for all technologies.
S+
n,αstorage discharge for each storage technology αat node n.
S−
nsum of storage charging at node n.
qn,αnodal colormix.
The nodal color mix refers to the mixing of electricity at each node from different technologies
and countries of origin, where each technology for each country has been assigned a unique
color [
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 determines
the color mix of the exports and the power serving the local load. We do not keep track of
the color mix flowing into storage, but track which storage type the power originated from
when the storages are discharging. This mixing approach is called average participation or
proportional sharing in the literature which was also proposed initially in [
3
]. For a discussion
of 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=∑
αGn,α+S+
n,α+∑
m
Fm→n, (1)
where the left-hand side and the right-hand side account for the flows out of and into a
node, respectively. In this, and following equations, there is an implicit time index as the
flow tracing is performed for every hour. We include nodal color mixes in the nodal power
balance
qn,α Ln+S−
n+∑
k
Fn→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 write
a matrix formula describing a unique solution for the nodal power mix
qn,α
according to [
7
]:
∑
m"δn,m Lm+S−
m+∑
k
Fm→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 technology
for every country. The
α
set allows us to track originating technology as well as originating
country e.g. we can trace who is consuming Danish wind power. Multiplying the nodal
color mix with the nodal load and the carbon intensity of the originating generation/storage
technologies 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 missing
values. In case of missing data for production or imports/exports for a country the particular
country 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 do
not have an effect on the nodal mix of the importer (they simply scale the color mix, but do
not change the ratios). Exports to countries outside the considered topology are subtracted.
Figure 2 shows
∑αqn,α
for every country for every hour. If
(3)
is perfectly balanced it should
be the case that
∑αqn,α=
1. Cases of partially missing data leads to
∑αqn,α6=
1. This is
usually caused by one country being excluded due to missing data (which explains the
occurrence of 0’s in Figure 2), which affects the nodal balance of neighboring countries. See
e.g. the effect of missing data for Ireland on Great Britain. We observe no cases of
∑αqn,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<
.9999
which 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.
Figure 2: Flow tracing consistency check. Dark blue means generation or import/export
data is entirely missing for a country, lighter colors mean it is partially complete, and white
means fully complete data.
C Additional results
Figure 3 shows a comparison of hourly production intensity with hourly load for the full year
of 2017 for every country. The production intensity is calculated based on the production
within each country. The figure is split in two parts with large countries in the top panel and
smaller countries in the bottom panel. In the top panel we see that Norway, Sweden and
France have low intensities regardless of the level of consumption, which is due to a high
share of hydro power in the Nordic countries and nuclear power in France. On the other
hand, 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 for
all of 2017. This figure does not tell anything about the amount of power being consumed
by each technology.
Figure 5 shows the total annual consumption intensity for Austria for 2017 based on flow
tracing. From this figure we see that hydro is the technology providing most of the consumed
power, 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, but
has the largest intensity.
Figure 6 shows average hourly production/consumption carbon intensity plotted as duration
curves for Austria and Denmark e.g. if a country runs on 100% coal the entire year the
8
Figure 3: Comparison of hourly production intensity with hourly load for every country.
9
Figure 4: Hourly intensity per consumed unit of energy for Austria downsampled to daily
averages.
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 intensity
due to imports. DK is relying on imports for a low consumption intensity since it has a high
production 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 for
individual countries as shown in Figure 6.
Figure 8 shows the difference between production and consumption intensity as function
of the share of non-fossil production of total production. Size of circles are proportional
to average production. A value above zero corresponds to the country having a higher
consumption intensity than production intensity. The figure shows a general trend that the
higher the share of non-fossil production the higher the consumption intensity is compared
to the production intensity. This can be explained by countries with high share of non-fossil
production tend to import from countries with lower share of non-fossil production which
results in the importing country’s consumption intensity being higher than its production
intensity.
Table 4 shows average production and consumption intensity per country. These values are
plotted in Figure 3 in the article, they are also shown as the white markers in Figure 7, and
the difference for each country is shown in Figure 8.
Figure 9 shows average intensity per imported/exported unit of energy. When calculating
the average imported/exported intensity between two countries only hours with actual
transfers have been used. A white entry means no data and only occurs for ME and RS. The
figure should be read as NO exporting mostly low intensity hydro to all countries whereas
EE and PL are exporting oil and coal to all countries. This figure doesn’t say anything about
the amount of energy being transferred e.g. most of the column for ME is based on data for
very few hours as ME is a small, poorly connected country. The values in Figure 9 are also
shown in Table 5.
11
Figure 6: Average hourly production/consumption carbon intensity duration curves for
Austria and Denmark.
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
Figure 8: Difference between production and consumption intensity as function of the share
of non-fossil production of total production. Size of circles are proportional to average
production.
Figure 9: Average imported/exported intensity. White cells indicating missing data. This
figure doesn’t say anything about the amount of energy being transferred e.g. most of the
column for ME is based on data for very few hours as ME is a small, poorly connected
country.
13
Table 4: Average production and consumption intensity for each country. These values are plotted in Figure 3 in the article. Units
are kgCO2eq/MWh.
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
Consumption 248 236 604 657 518 271 889 327 174 82 349 751 447 424 513 230 338 912 665 16 947 455 424 917 84 365 438
Production 136 193 616 648 528 442 990 340 191 76 346 763 439 428 537 187 241 943 734 11 994 467 424 973 82 374 277
14
Table 5: Average intensity of power imported and exported between countries. These values are plotted in Figure 9. Columns are
exporters and rows are importers. Units are kgCO2eq/MWh.
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
AT 136 232 503 546 469 360 974 322 160 73 417 580 325 428 533 211 245 808 787 15 853 385 344 714 68 316 255
BE 91 193 533 615 506 381 774 316 181 65 377 572 227 394 417 174 195 452 734 19 938 384 322 736 67 265 237
BG 164 281 616 662 572 438 1397 372 196 85 513 719 395 518 708 273 342 690 1019 27 1021 447 450 891 91 406 324
CZ 143 258 599 648 552 413 904 377 196 86 479 668 285 480 549 234 294 501 843 59 1021 454 386 704 90 312 274
DE 121 206 599 640 528 391 748 336 192 73 406 613 263 412 470 193 234 493 739 55 989 406 360 869 82 302 244
DK 71 206 590 631 489 402 1050 335 182 69 375 600 256 420 491 210 221 - 808 20 969 399 343 981 75 275 256
EE 70 200 601 681 505 370 990 381 170 69 383 656 286 430 490 191 182 - 817 21 1066 451 413 - 76 363 281
ES 149 193 582 651 508 374 727 340 191 68 365 617 287 433 481 179 226 44 737 55 1003 484 359 795 83 351 260
FI 126 279 552 599 448 347 408 382 191 90 483 655 284 437 503 227 262 - 649 62 892 467 369 894 77 268 237
FR 167 231 533 610 496 382 804 356 181 76 429 594 307 450 518 193 235 292 766 14 925 432 348 715 68 327 256
GB 94 203 556 628 517 387 1106 322 183 69 346 594 277 477 501 208 221 8 871 21 965 389 345 772 77 283 272
GR 149 246 668 698 591 429 847 359 199 80 487 763 394 462 593 219 276 698 859 24 1113 448 469 976 75 409 293
HU 174 280 717 758 644 475 961 427 212 90 534 792 439 529 656 244 297 861 939 25 1206 515 489 1022 83 457 317
IE 86 197 577 581 480 361 782 297 185 70 326 561 214 427 386 169 154 - 717 18 913 367 326 - 63 205 203
IT 126 214 579 617 526 387 892 350 170 73 405 630 336 432 537 192 229 710 797 13 983 423 392 806 68 349 263
LT 68 199 596 630 486 354 856 337 165 68 371 605 255 406 469 187 235 - 750 20 973 405 346 992 70 274 244
LV 69 194 540 615 456 329 854 346 156 67 363 610 267 403 468 173 240 - 733 21 944 412 375 - 69 326 255
ME 114 194 594 618 522 359 848 325 158 69 371 613 349 396 526 183 241 942 759 22 984 388 413 872 70 408 262
NL 85 177 523 567 466 348 855 294 166 62 356 531 233 386 416 177 193 416 734 19 868 353 308 759 66 247 226
NO 57 167 589 620 422 298 845 291 183 59 313 569 234 364 448 159 159 - 684 11 920 341 327 957 66 261 231
PL 94 203 595 635 529 385 857 330 171 69 396 604 262 417 473 188 225 470 770 20 994 396 348 858 70 291 244
PT 145 182 537 606 463 334 693 314 177 66 327 565 268 414 458 166 221 44 683 55 926 467 333 746 82 329 245
RO 138 229 593 616 520 370 842 356 170 76 424 652 363 431 565 201 258 673 778 23 971 425 424 849 72 376 268
RS 127 206 691 709 596 420 853 363 189 71 425 686 367 444 549 187 234 913 815 13 1134 436 458 973 72 435 283
SE 97 198 599 631 477 357 731 335 191 71 372 608 254 389 454 191 224 - 701 55 961 404 355 997 82 282 241
SI 226 379 592 685 587 470 1184 472 217 111 652 805 463 622 772 322 379 486 1049 33 1048 565 466 955 92 374 347
SK 140 254 588 635 540 402 820 371 191 85 474 658 280 467 533 226 286 492 806 59 1002 449 380 690 87 305 277
15
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