Environmental Engineering Reference
In-Depth Information
independently, the total resulting variation in the net load to be met by the thermal plant is
approximately a ' sum - of - squares ' addition of the components:
2
2
2
Total variability
of load on thermal
units
Total va
riability
of electricity
demand
Total variability
o
⎛
⎞
⎛
⎞
⎛
⎞
(3.2)
=
+
f variable
source
⎜
⎟
⎜
⎟
⎜
⎟
⎝
⎠
⎝
⎠
⎝
⎠
Thus, for example, when the average power variation of the added source equals that of
the demand itself, the total variability is not doubled but increased by 40%. This has some
important implications. The impact of fl uctuations in variable sources at low penetrations can
be taken to be practically zero; in other words this impact is just noise added to demand
fl uctuations.
Over longer timescales, the level of operating reserve required at any given time depends
on two key factors: uncertainties in demand prediction and the probability of loss of the
largest generation plant on the network. When wind power plant is introduced into the system,
an additional source of variation is added to the already variable nature of demand. To analyse
the additional variation caused by the wind plant it is important to appreciate that the require-
ment is that the entire system must be balanced instead of balancing each individual load or
resource. The operator has to ensure that the average system reliability is maintained at the
same level it would have been without the wind resource.
However, the crucial question is by how much does wind generation increase the balancing
uncertainties? Intuitively it is known that minute-to-minute fl uctuations in wind output are
largely uncorrelated to load. This implies that the additional uncertainty introduced by wind
power does not add
linearly
to the uncertainty of predicting the load. As for the issue of
variability dealt with above, it can be shown that when errors in predicting the output from
variable sources occurs independently of those in predicting demand, the combined error is
again a sum - of - squares addition [9 - 12] :
2
2
2
Average error in
predicting net load
on thermal units
Average error in
predicting electricity
demand
Average error in
predicting variable
input
⎛
⎞
⎛
⎞
⎛
⎞
=
+
(3.3)
⎜
⎟
⎜
⎟
⎜
⎟
⎝
⎠
⎝
⎠
⎝
⎠
Demand prediction techniques are constantly being refi ned but there will always be occa-
sions when unforeseen circumstances push up or depress the load. Equation (3.3) indicates
that for small penetrations of variable sources the prediction errors are lost among load fl uc-
tuations. However, since demand is fairly predictable, forecasting errors from substantial
penetration of wind will incur some penalty.
Analysis of the combined uncertainties of wind, demand and conventional generation
based on the sum-of-squares calculation of Equation (3.2) make use of the standard error in
predicting the generation/demand balance. On typical developed country networks, one hour
ahead, this averages at around 1% of the demand. For four hours ahead, this fi gure rises to
3%.
