3 ENERGY DEMAND
3.1 E LECTRIC POWER DEMAND
Table 3.1.2: National electric power demand development in the investigation area in TWh/a.
2010 2020 2050 2010 2020 2050
Albania 4.6 4.7 7.3 Slovakia 28 28 29
Bosnia 10 11 18 Liechtenstein 0.4 0.4 0.2
Serbia 37 37 49 Luxembourg 8.8 10 11
Macedonia 7.2 7.5 11 Malta 2.7 2.9 2.3
Moldova 6.5 7.1 9 Netherlands 120 131 116
Austria 64 66 49 Norway 130 133 112
Belgium 91 93 67 Poland 142 153 191
Bulgaria 32 28 27 Portugal 47 54 62
Cyprus 4.0 4.7 4.9 Romania 53 58 96
Czech Rep. 62 60 52 Slovenia 12 12 9
Denmark 44 49 51 Spain 258 299 320
Ireland 30 35 34 Sweden 155 161 154
Estonia 8.4 9.0 11.2 Switzerland 63 64 39
Finland 83 84 76 Turkey 149 206 494
France 507 542 426 UK 431 477 451
Germany 605 640 549 Ukraine 170 184 229
Greece 56 62 62 Belarus 39 42 52
Croatia 15 16 20 Algeria 41 81 249
Hungary 39 40 44 Morocco 27 57 235
Italy 344 373 311 Tunisia 15 24 66
Lithuania 11 12 15 Libya 23 27 44
Latvia 7.7 8.3 10 Egypt 103 172 631
Total Area 4085 4568 5497
Figure 3.1.1: Electric power demand development in the investigation area in TWh/a.
3.1.2 Temporal resolution
The annual electric power demand given for each country was temporally disaggregated with time curves generated by normalising year 2006 load data. ‘Load’ is referred to by the European Network of Transmission System Operators for Electricity (ENTSO-E) as the
0.0 1000.0 2000.0 3000.0 4000.0 5000.0 6000.0
2010 2020 2030 2040 2050
TWh.
Albania Bosnia Serbia Macedonia Austria Belgium Bulgaria Cyprus Czech Republic Denmark Ireland Estonia Finland France Germany Greece Croatia Hungary Italy Lithuania Latvia Slovakia Liechtenstein Luxembourg Malta Netherlands Norway Poland Portugal Romania Slovenia Spain Sweden Switzerland United Kingdom Turkey Ukraine Moldova Belarus Algeria
Morocco Tunisia Libya Egypt
‘hourly average active power absorbed by all installations connected to the transmission network or to the distribution network’ (ENTSO-E 2010). It includes transmission losses and it excludes the consumption for pumped storage and the consumption of power generating auxiliaries.
Table 3.1.3: Sources of load data for the generation of normalised time curves.
Load data source Backup time
curve Load data source Backup time curve Albania Macedonia Slovakia UCTE (ENTSO-E)1)
Bosnia UCTE (ENTSO-E)1) Liechtenstein Switzerland Serbia UCTE (ENTSO-E)1) Luxembourg UCTE (ENTSO-E)1)
Macedonia UCTE (ENTSO-E)1) Malta Greece Moldova Poland Netherlands UCTE (ENTSO-E)1)
Austria UCTE (ENTSO-E)1) Norway
NORD POOL (NORD_POOL_ASA
2007) Belgium UCTE (ENTSO-E)1) Poland UCTE (ENTSO-E)1) Bulgaria UCTE (ENTSO-E)1) Portugal UCTE (ENTSO-E)1)
Cyprus Greece Romania UCTE (ENTSO-E)1) Czech Rep. UCTE (ENTSO-E)1) Slovenia UCTE (ENTSO-E)1)
Denmark
NORD POOL (NORD_POOL_ASA
2007)
Spain UCTE (ENTSO-E)1)
Ireland EIRGRID (EIRGRID
2007) Sweden
NORD POOL (NORD_POOL_ASA
2007) Estonia Eesti Energia
(Eesti_Energia 2007) Switzerland UCTE (ENTSO-E)1) Finland
NORD POOL (NORD_POOL_ASA
2007)
Turkey Greece
France UCTE (ENTSO-E)1) UK UCTE (ENTSO-E)1)
Germany UCTE (ENTSO-E)1) Ukraine2) Poland Greece UCTE (ENTSO-E)1) Belarus Poland Croatia UCTE (ENTSO-E)1) Algeria MEM Algeria (MEM
2007) Hungary UCTE (ENTSO-E)1) Morocco
World Bank (Eichhammer, Ragwitz et al. 2005) Italy UCTE (ENTSO-E)1) Tunisia STEG (STEG 2007) Lithuania Estonia Libya GEC (GEC 2007) Latvia Estonia Egypt EEHC (EEHC 2005)
1) ‘Union for the Coordination of the Transmission of Electricity’ (UCTE), now called ‘European Network of Transmission System Operators for Electricity’ (ENTSO-E), (UCTE 2007)
2) Load data available from UCTE only for Burshtyn Island
2006 is the first year for which comprehensive hourly load data were published by most of the European transmission system operators. Before 2006, hourly load data were provided for every 3rd Wednesday and for the following Saturday and Sunday in a month. These data were used as representatives for all working days and weekend days in a month in many studies that needed high temporal resolution load data. Here, the continuous real-time load data available for 2006 were used. This improved data base enables the automatic consideration of correlations between load and weather-dependent renewable energy availability which were not directly taken into account when using the previous representative load data.
For the North African countries only some load patterns for single days were available. In the context of the Trans-CSP study (Trieb 2006), load curves for the Arabian and North African countries were generated by interpolating between the few load curves available. Additional
information was taken from a temporally comprehensive load curve from Jordan: on Fridays - the official holidays in the Arabian world – the electric power demand is 10 % lower than on a working day. On Saturdays, the demand is 4 % lower and on Sundays it is 2 % lower than on a normal working day. This information was taken into account in the load curve generation for the islamic states in North Africa.
For some countries no hourly load data were available. In those cases the load patters of neighbouring countries were used as a proxy for the temporal disaggregation. The sources of base data for the load curve generation are listed in table 3.1.3.
Among other factors the temporal course of the electric load during a year depends on the weather and on the income situation of the inhabitants of a region. In countries in hot climates the electric load tends to be significantly higher in summer when air conditioning systems are used most, if people can afford them. In countries in cold regions more electric power is needed in the winter for cooking and for room and water heating.
Figure 3.1.2:
Standardised monthly average load for Germany, Norway, Algeria and for the total area investigated (all countries aggregated).
Figure 3.1.2 shows the standardised monthly average load for Germany, Norway, Algeria and of all countries in the investigated area. In Norway the load is clearly higher in winter and in Algeria the opposite is the case. The load pattern of all countries together is clearly smoother than is the load in Norway or Algeria, whereas Germany’s annual load pattern almost equals the load pattern of the total investigation area (red and dark blue lines).
Figure 3.1.3: Standardised hourly average load for Germany, Norway, Algeria and for the total area investigated (average of all countries).
Figure 3.1.3 shows the standardised hourly load pattern in the same countries and in all investigated countries together in one winter and one summer week. Again, the German load
0 0.03 0.06 0.09 0.12
1 2 3 4 5 6 7 8 9 10 11 12 month
standardized monthly load
All countries Germany Norway Algeria
Winter week
0 0.00004 0.00008 0.00012 0.00016 0.0002
193 213 233 253 273 293 313 333 353
hour
standardized hourly load
All countries Germany Norway Algeria
Summer week
0 0.00004 0.00008 0.00012 0.00016 0.0002
4562 4582 4602 4622 4642 4662 4682 4702 4722 hour
standardized hourly load
All countries Germany Norway Algeria
pattern almost equals the load pattern of the total area. While the two European countries show a clear reduction of the electric load on the weekend, the day with the lowest load in Algeria is the Friday.
3.1.3 Spatial resolution
The national load values were disaggregated spatially in order to allow for arbitrary choice of regions to be investigated. Since electricity consumption takes place mostly in urban areas, the land cover category ‘artificial surfaces and associated areas’ was chosen as the proxy parameter for the spatial disaggregation. The artificial surfaces in each raster cell of the investigation area were summed up nationally; then for each raster cell the share of artificial surfaces in the total national artificial surface was calculated. In each raster cell, this percentage was then multiplied by the national load. Figure 3.1.4 shows the distribution of the load in the year 2010 in dense urban centres (Paris, London and other English, Belgian, Dutch and German city regions), and in areas with sparse occurrence of artificial surfaces (e.g. in Northern and Eastern UK).
Figure 3.1.4:
Annual electricity demand in GWh/km2/a disaggregated with the proxy
parameter ‘artificial surfaces and associated areas’.
Extract: South-East UK, Northern France, Belgium and the
Netherlands.