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0
18,000
14,000
16,000
12,000
10,000
6,000
2,000
8,000
4,000
GWh/d
2015
2020
2025
2030
2035
Green
Grey
Figure 6.1:
Evolution of total gas demand (Average day)
0
3,000,000
2,500,000
2,000,000
1,500,000
1,000,000
500,000
t CO
2
/d
2015
2020
2025
2030
2035
Green
Grey
Figure 6.2:
Evolution of the CO
2
emissions in the power
generation sector (Average day)
This means that the analysis rather focuses on the adequacy of gas infrastructures
and supplies to different Global Contexts which can only be influenced by global
equilibrium and political actions (e. g. setting an appropriate ETS scheme).
The difference in the evolution of gas demand between the Green and Grey Global
Contexts mostly arises from the power generation sector during the first five years of
the time horizon; then after the difference remains basically stable. It derives from
the fact that for 2015 gas, coal and CO
2
emission prices are set at the same level in
both scenarios in order to reflect the current situation. Beyond 2020 – 2025, the
Green scenario shows a gas demand level which is 16% higher than the Grey one
but in terms of CO
2
emissions it is 25% lower than the Grey scenario. This opposite
evolution of gas demand and CO
2
emissions illustrates the significant environmen-
tal advantage of using gas in the power generation sector.
6.2.2 EVOLUTION OF GAS SUPPLY
Over the TYNDP time horizon, Europe is likely to see major evolution in gas exports
from surrounding regions. At the same time the LNG market is likely to change due
to new available sources and hence this may lead to an evolution of its price in
relation to pipeline bounded gas.
The following graphs represent the evolution of each source in the gas supply mix
under:
\\
annual condition (Average day) with:
–
–
one pair of lines represents the minimum and maximum share of each
source (respectively when the source is the most expensive then the
cheapest) under the Low Infrastructure scenario
–
–
one pair of lines along the same approach under the High Infrastructure
scenario
\\
peak condition (1-day Design Case) with one bar graph for the maximization of
UGS (limited to a eighty percent deliverability) and another one for the minimi-
zation of UGS