IEEE Power & Energy Magazine - November/December 2019 - 52

analysis to evaluate the system's robustness for accommodat-
ing this impending event.
A statistical probabilistic forecast of type 1 provides a
range of values, characterized as percentiles or the probability
of occurrence, that can help the operators gauge the need for
reserves in each forecasted scenario. ERCOT has develop-
ed an online tool to incorporate probabilistic wind forecasts
into the real-time decision process, as shown in Figure 8. The
probabilistic wind forecast is delivered for 50th, 90th, 95th,
and 98th percentiles for the next 6 h. Operators can select one
of these wind forecasts in the online tool. For example, the
green line represents the load forecast, while the blue line
represents the online generation capacity when considering
the 50th-percentile wind forecast. The online generation
capacity with a 90th-percentile wind forecast is given in

red. The reserves [regulation-up (orange) and nonspinning
(purple)] are overlaid on the red line, which represents addi-
tional generation capacity to be deployed if needed. In this
example, after 18:00, a greater-than-10% chance exists for the
forecasted load to be higher than the predicted online gen-
eration capacity. Because reserves are procured, the system
maintains sufficient generation capacity to serve the load.

Use of Probabilistic Forecasting for Extreme
Events, Such as High-Speed Shutdown Risk
Assessment: An Irish Case
High-speed shutdown (HSSD) events, caused by wind speeds
near 25 m/s, occur predominantly in coastal or mountainous
areas but seldom cover a large portion of a given jurisdiction.
In some regions, if wind power is producing at full capacity, the

00:00 CDT

00:00 CDT

375 MW

500 MW

00:00 CDT

750 MW

0
10
20
30
40
50
60
70
80
90
100
22:00 CDT

02:00 CDT

1,000 MW

100
90
80
70
60
50
40
30
20
10
0
22:00 CDT

02:00 CDT
750 MW

1,000 MW

00:00 CDT

1,500 MW

0
10
20
30
40
50
60
70
80
90
100
22:00 CDT

02:00 CDT

2,000 MW

(%)

(%)

500 MW

180-min Probability

02:00 CDT

0
10
20
30
40
50
60
70
80
90
100
22:00 CDT

(%)

(%)

60-min Probability

250 MW

100
90
80
70
60
50
40
30
20
10
0
22:00 CDT

Down

(%)

(%)

15-min Probability

Up
100
90
80
70
60
50
40
30
20
10
0
22:00 CDT

00:00 CDT
1,000 MW

02:00 CDT
1,500 MW

2,000 MW

00:00 CDT

3,000 MW

02:00 CDT
4,000 MW

figure 7. A display of forecasts from the ELRAS. CDT: Central Daily Time.
52

ieee power & energy magazine

november/december 2019



IEEE Power & Energy Magazine - November/December 2019

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - November/December 2019

Contents
IEEE Power & Energy Magazine - November/December 2019 - Cover1
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IEEE Power & Energy Magazine - November/December 2019 - Cover3
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