Environmental Engineering Reference
In-Depth Information
Figure 3.7 shows that there are periods during the day (e.g. 6 to 7 am) when the rate of
demand growth is considerable. To maintain system frequency, the injected power must
closely track the trajectory of the demand curve. Unfortunately, because of the sluggishness
of the thermal plant, this tracking cannot be done unless preparative action is taken some
hours before the event.
It may be concluded that there is an absolute necessity to carry out a
demand forecasting
activity in order to prepare and progressively load plant as required. Utilities have invested
considerable effort in forecasting the daily pattern of demand. Through years of experience
they have evolved sophisticated mathematical techniques to correlate demand to the aggregate
of the national habits and to other factors such as weather. All methods are essentially based
on the fact that demand exhibits regular patterns. Forecasting techniques adjust past demand
to present weather and other conditions. Meteorological data on temperature, wind speed,
humidity, cloud cover and visibility are used as variables because such factors have an
important bearing on heating and lighting demand. The art of load forecasting has been
refi ned to such an extent that estimates are rarely in error by more than
±
3% and on average
in the UK system they are accurate to within
1.3%.
Demand prediction techniques are constantly being refi ned but there will always be occa-
sions when unforeseen circumstances increase or depress the load. The average daily errors
in demand in a typical month on the English system are shown in Figure 3.8; during this
period the maximum error in prediction was just under 4% and on average it was less than
1.53%. The standard error during this month was 1.6% which, as the average demand was
about 32 GW, corresponds to about 300 MW. The fi gure shows that on 11 November the
forecast was adrift by 3.5%, representing a maximum error of over 1 GW.
±
Scheduling error, %
4
3
2
1
0
-1
-2
-3
-4
0
5
10
15
20
25
30
Day of month (November 1995)
Source: Electricity Pool
Standard deviation: 1.6%
Figure 3.8
Typical scheduling errors on the network in England and Wales. (Reproduced from
Milborrow, D., 'Wind power on the grid', in: Boyle, G. (ed.), Renewable electricity and the grid - the
challenge of variability, with permission of Earthscan, 2007)
