IEEE Power & Energy Magazine - November/December 2021 - 84

drivers for regulation use [demand (blue/
red), wind (blue/red), solar (blue/red), and
net load (blue/red)]. The ability to see how
individual days react gives further information
about each weather pattern's impact on
regulation requirements.
By implementing the ACE* methodology,
the CAISO has seen 50-70% improvement
in meeting the actual regulation
requirement. This improvement was determined
by looking at how many times ACE*
is outside the regulation recommendation.
The CAISO continues to explore smarter
regulation procurement methodologies. In
2021, the CAISO plans to trial a methodology
that utilizes both historical statistical
information and probabilistic forecast information
to compare the accuracy of the current
methodology against the new options.
Accounting for Forecasting
Uncertainty in the Week-Ahead
Resource Assessment Process
The week-ahead resource assessment process
is used to ensure there is a sufficient
supply of electricity to meet the expected
demand in the week ahead. Several factors
influence this assessment, including plant
or equipment outages, network constraints,
and the expected level of demand.
Over the last decade, the east coast electricity
grid in Australia has had an exceptional
increase in the penetration of behindthe-meter
rooftop PV installations, with one
in three houses in some locations now having
rooftop systems. The grid's generation composition
has also seen significant change,
with the installation of more than 22 GW
of variable renewable energy capacity in a
grid with a peak load of 36 GW. These differences
have made the week-ahead resource
assessment process increasingly sensitive
to weather predictions.
The accuracy of the forecast inputs is
increasingly driven by the expected weather
conditions. For example, a large high-pressure
system over continental Australia typically
produces clear skies, light winds, and pleasant
temperatures. These conditions are ideal
for accurately predicting electricity demand as
well as wind and solar generation, and uncertainty
in the forecasts feeds into the weekahead
resource assessment.
Conversely, dynamic weather patterns,
such as tropical cyclones, can produce
84
ieee power & energy magazine
november/december 2021
Θ1
∗
Θ∗
Input
X1
Input
Xn
M1
Θ2
∗
M2
Θn
∗
Input
Xn
Mn
Forecasting Models for
Energy and Market Quantities
Human
in the
Loop
Decision
Making T
Optimization Model
for Trading Decisions
Input
Xn
Forecasting and Trading
Decision
d
Input
Xn
Artificial Neural Network
Model
Decision
d
Input
X1
Increase
Interpretability
figure 8. The future evolutions in the model chain thanks to machine learning techniques. (Source: Smart4RES project; used with permission.)

IEEE Power & Energy Magazine - November/December 2021

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - November/December 2021

Contents
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