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Figure 3.2: Plot to show how mean annual constraint costs vary with power system background year.
3.7.1 Power System Background Year
Assumed Power System Backgrounds
Section 3.6 stated that [69] details power system background data for 20 consecutive years. The estimate of peak demand level and installed generating capacities are the main differences between each of these years. This can have a large impact on the costs estimated. [69] also gives values for the installed transmission capacities for each boundary for each year, which includes scheduled reinforcements (increases to the installed capacity) for years 2 to 20.
Figure 3.2 illustrates how mean annual constraint costs vary with the projections made for each year of the dataset. As can be seen, the cost estimates vary greatly with year, with the lowest estimate coming in year 5 and their magnitude being just one fourteenth of those in year 20. This is the simplest way to illustrate how input can have a large impact on the output of the simulator.
5 10 15 20
0.0e+005.0e+071.0e+081.5e+08
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Mean Annual Constraint Costs (£)
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Mean Annual Constraint Costs (£)
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Figure 3.3: Plots to show how mean annual constraint costs vary with power system background year as the transmission capacity of particular boundaries is raised to infinity.
Increasing Transmission Capacity Between Zones
In the power system there are 6 connections (boundaries) between zones and the trans-mission capacity across each of these boundaries could be increased to decrease con-straint costs. Making 6 simultaneous decisions to optimise concon-straint costs is a large task, especially when considering additional uncertainty in the power system back-ground (as is the case for the examples that will be presented). However, just because a boundary can be reinforced (the transmission capacity can be increased) does not necessarily mean doing so will reduce constraint costs (as if the power flow across a boundary never exceeds the transfer capacity, increasing the capacity across that boundary will not affect constraint costs). Further, there are considerable costs asso-ciated with increasing the transmission capacity of a boundary, so if the reduction in constraint costs is small, the reinforcement would not be justified economically.
In order to get some initial information on which boundaries are most relevant to consider for reinforcement, it is first considered how mean annual constraint costs differ from Figure 3.2 if each boundary is treated in turn as if it had infinite transmission capacity (as much capacity as desired could be traded across that boundary). This will give some initial insight into which boundaries will give the greatest reduction in constraint costs if reinforced.
3.7. Variation of Mean Constraint Cost Estimates and Uncertainties of
Input Data 49
Figure 3.3 displays how mean annual constraint costs vary as the transmission capacity of each boundary is taken to be infinity. A plot which does not take any boundary to infinity is given as reference. As can be seen, not all boundaries have a relevant effect on costs at all times. B4 and B9 seem to have next to no effect at all times, meaning we are quite unconcerned with them as reinforcing them does not appear to reduce mean annual constraint costs.
In years 5 to 9 it can be seen that B15 has the largest reduction on costs, with increasing B15 also notably reducing costs in years 1 to 4. B8 on the other hand appears to have little to no effect in early years, but has the largest effect on estimated cost for years 15 onwards, with a particularly large effect in years 19 and 20.
The boundaries B6 and B7a are of particular interest. This is because whilst B15 is very relevant early and has no effect late on, and B8 has no effect in early years but is quite relevant for late years, B6 and B7a together have non negligible effects across all years. B6 is particularly interesting in year 5 and earlier and between years 14 and 17, with B7a having a particularly large reduction in costs between years 6 and 13.
These boundaries make for an especially interesting example, as they are adjacent boundaries. This means it would be reasonable to expect an interaction to exist, i.e.
to expect reinforcing both of these boundaries to result in a greater reduction in mean constraint costs than simply the sum of their individual reductions. Figure 3.4 displays how mean annual constraint costs vary when both the B6 and B7a boundaries are taken to infinite capacity.
This is interesting, as there is far more interaction than might have been expected.
Between the years 8 and 17, it appears that only either B6 or B7a give a substantial reduction of mean annual constraint costs with the other having next to no effect on costs. However, the combined effect of increasing both boundaries gives a much greater reduction in mean annual constraint costs in comparison to just reinforcing the one boundary that gave a cost reduction.
Overall, in this subsection it has been shown how mean constraint costs vary greatly with the system background year used. Further, which boundaries would give the most benefit when reinforced also changes year on year. In early years, B15 alone appears to have the largest effect, with B8 alone having the largest effect in later years. However,
5 10 15 20
0.0e+005.0e+071.0e+081.5e+08
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Mean Annual Constraint Costs (£)
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Figure 3.4: Plot to show how mean annual constraint costs vary with power system background year when considering taking the transmission capacity of the B6 and B7a boundaries to infinity.
the interaction between boundaries B6 and B7a seems to provide an interesting example across all years.
Explanations for These Observations
From the data, some explanation can be offered for these results. South of the B15 boundary lies the zone which is modelled as having a small proportion of snapshot demand (3.5%) whereas north of the B15 boundary lies a zone which has a large proportion of snapshot demand (41%). The zone south of the B15 boundary also has a quite a lot of high merit order generating capacity installed, such as wind and CCGT (i.e. gas), whereas the zone North of the B15 boundary has relatively little generating capacity installed. This means that there will often be a power flow from south to north across the B15 boundary, and reinforcing this boundary allows for more of the cheap capacity installed south of the B15 boundary to be used more often in the constrained schedule.
Between the years 8 and 11, a lot of high merit order capacity, such as off-shore wind and CCGT, are scheduled to be built in the zone immediately north of the B15 boundary.
3.7. Variation of Mean Constraint Cost Estimates and Uncertainties of
Input Data 51
This means that there will be a power flow across the B15 boundary less often, leading to the observed lack of benefit from reinforcing the B15 boundary in later years.
The reasons for the B8 boundary being more relevant in later years is less easy to tell directly from the data, as the boundaries north and south of it have reasonably high proportions of demand (21.1% and 18.5% of total system demand respectively) and have quite a large amount of generating capacity installed in all years. However, several GW of CCS (carbon capture and storage) CCGT and coal are scheduled to be built in the zone immediately north of the B8 boundary in years 14 and 15 which would coincide with the increased benefit from reinforcing the B8 boundary.
The reason the B6 and B7a boundaries are relevant for all 20 years is there is a lot of high merit order capacity (especially on and off-shore wind) in the three zones north of the B7a boundary (especially the zone directly north of the B7a boundary and the zone north directly of the B6 boundary) across all years. Further, the sum of the demand across all three zones is just 15.9% of the total system demand, meaning there will often be a power flow south across these boundaries. However, power can only flow south of the B7a boundary from north of the B6 boundary if both the B6 and B7a boundaries are sufficiently reinforced, which explains the interaction between the two.