Special consideration must be given to any Resource which is required by a Service whose demand is positive in the first year of the model run. You have to specify whether the Resource is being installed for the first time, or whether it has already been in use for some years; in the latter case, STEM will simulate pre-run installation. Some or all of the units of the Resource, present in the first year, will act as if they were installed up to a Physical Lifetime previously. There will be a variety of ages and only those considered to be brand new will contribute to the Incremental Units result in the first year.
Note: Throughout this section, ‘first year’ means year zero if the input Include Year Zero =
Yes, otherwise year one.
Side effects
An Installation Expenditure will only be generated for those units which are brand new in the first year, but Depreciation will arise from all ages up to the Financial Lifetime of the Resource. The Written-Down Value of the Resource in the first year will reflect the depreciation which has occurred during the pre-run period.
Max. Age of Installed Units
Limits the age of pre-run installed Resources to a certain age. For example, you might want to model a network where a Resource with a Physical Lifetime of ten years was installed five years before the model run begins, and will therefore need to be replaced five years into the model run. The default of –1 is interpreted as Physical Lifetime minus 1, which means that, in general, Resource units of all possible ages are present when the model starts.
Note: Be careful to enter a value of 0 if you are modelling a totally new network; or alternatively, to set the global input Pre-Run Installation = No.
Pre-Run Installation (global choice)
It can be useful, on occasion, to inhibit the pre-run installation algorithm altogether (either because you are modelling a totally new network, or simply for debugging purposes) without having to set the input Max. Age of Installed Units =
0
for every Resource. To this end, a global input, Pre-Run Installation, has been added to the global Other Details dialog, governing the installation of equipment in the first period of the model run, and taking one of the following values:
Yes: Resources present in the first year are installed with a range of different ages according to the respective Installation Profiles.
No: all equipment is installed brand new.
Installation Profile
A time series which controls the relative installation in the years up to and including the first year. By default, this is a constant of 1 which generates as near as possible an even installation, given that the number of units to install may not be divisible by the lifetime.
Normalise Profile
By default, the Installation Profile input for a Resource is ‘normalised’, i.e., scaled to match the actual number of units required in the first year. Although this is very convenient for specifying a general growth trend, it is convoluted for situations where specific numbers of units are known to have been installed in a range of pre-run years, when the profile would then have to be tailored to match the actual installation in the first year.
Therefore a new input, Normalise Profile, has been added to the Other Details dialog for each Resource, governing the interpretation of the Installation Profile input and taking one of the following values:
Yes: the Installation Profile is scaled to match the actual number of units required in the first period of the model run
No: the Installation Profile stipulates specific numbers of units to be installed for each of the pre-run years up to and including the first period of the model run. If this total installation is less than is actually required, the deficit is installed brand new in order to honour the historical data, regarding the value given for the first period as a minimum. However, if this total
exceeds
what is required, the excess is simply installed as slack – see also 10.3.25 Resource deployment.
Example
There are two common ways in which you might define the installation profile, as follows.
Historical data
If you have detailed information about the numbers of units installed in previous years, you can enter these values as an Interpolated Series.
For example, if the network initially contains 100 units, of which 40 are one year old, 25 are two years old and 35 are four years old, you would enter the Interpolated Series as:
|
Year
|
–5
|
–4
|
–3
|
–2
|
–1
|
0
|
|
Value
|
0
|
35
|
0
|
25
|
40
|
0
|
Note: Zero values are defined in years 0 and –5 to prevents the values for years –1 and –4 being extrapolated.
You can also enter relative proportions for the relevant years. STEM will then install units of the appropriate ages to match this profile as closely as possible.
Exponential growth
Alternatively, you may wish to specify that the number of units of a Resource has been growing at a fixed rate, say 15%. In this case, you can simply specify the Installation Profile as an Exponential Growth, with Base = 1 and Multiplier = 1.15.
Rounding
When the input Normalise Profile = Yes, the Installation Profile input represents an idealised incremental demand for the pre-run years. Clearly this may not match the actual installation, especially in a case where only a few units are to be installed in year zero; e.g., only seven units of a Resource which has a ten-year Physical Lifetime.
In this case, STEM calculates a cumulative profile from the Installation Profile input, and scales this to match the actual total number of installed units which must be installed in year zero to meet Service demand. This defines a kind of schedule for the pre-run installation. With a constant Installation Profile, the schedule would be as follows:
|
Year
|
–10
|
–9
|
–8
|
–7
|
–6
|
–5
|
–4
|
–3
|
–2
|
–1
|
0
|
|
Cumulative Units
|
0.0
|
0.7
|
1.4
|
2.1
|
2.8
|
3.5
|
4.2
|
4.9
|
5.6
|
6.3
|
7.0
|
Armed with this idealised, cumulative schedule, STEM then starts with the oldest age (–9, in this case), and installs enough units to match or just exceed the schedule, and then works up through the ages to the brand new units installed in year zero. Thus the actual installation pattern will be as follows:
|
Year
|
–10
|
–9
|
–8
|
–7
|
–6
|
–5
|
–4
|
–3
|
–2
|
–1
|
0
|
|
Cumulative Units
|
0.0
|
0.7
|
1.4
|
2.1
|
2.8
|
3.5
|
4.2
|
4.9
|
5.6
|
6.3
|
7.0
|
|
Cumulative Actual
|
0.0
|
1.0
|
2.0
|
3.0
|
3.0
|
4.0
|
5.0
|
5.0
|
6.0
|
7.0
|
7.0
|
|
Incremental
|
0.0
|
1.0
|
1.0
|
1.0
|
0.0
|
1.0
|
1.0
|
0.0
|
1.0
|
1.0
|
0.0
|
Clearly this algorithm has a slight tendency to install older equipment than new, but it is only designed as an approximate model for high numbers of units relative to the lifetime. In this case, if there is a precise historical pattern to match, the best approach is to enter the actual numbers with an Interpolated Series, as described above.