IEEE Power & Energy Magazine - January/February 2015 - 65

Uncertainties

Real-Time Restrictions

Network Model
Including Load/Feed-In
Secure Network State

Network-Related Remedials
Umbrella
Optimization Tool
Remedial Measure
Utilization

Redispatch Potential

figure 5. The Umbrella project optimization tool: inputs and results.

Dynamic Security Assessment
as additional information about uncertainties becomes available through enhanced forecasting techniques, this information
should be included in the system security assessment to enhance
decision-making processes. using stochastic optimization
methods, a probabilistic optimal power flow (opf) problem
formulation that explicitly accounts for forecast uncertainty
has been developed. the formulation lets the operator choose a
risk preference and defines the appropriate uncertainty margins
january/february 2015

needed to secure the system against fluctuations from renewable sources. these uncertainty margins can be applied to different types of opfs-in some cases, this can even be done
without increasing the complexity of the opf problem.

2,500

Comparison of Dynamic and Static Ratings

2,000
Line Rating (A)

difficult, especially when more than 100 gw of wind farm
(wf) capacity and 80 gw of photovoltaic (pV) capacity
are inserted in the grid. the itesla project proposes to make
full use of data-mining techniques to quantify the level of
uncertainty seen by the grid. It proposes the exclusive use
of existing data that are already shared among the european
tSos, such as day-ahead congestion forecast (DaCf) files,
of which there are 24 per day, and system snapshots. the
main idea is to build an "uncertainty database" reflecting the
difference between forecast and realized situations and then
to use it to predict the level of uncertainty for the coming
days. of course, the implementation is much more complicated than this, and the itesla project has had to develop new
techniques to address the spatial (there are more than 10,000
nodes at the european level) and time-related (there are distinct intrayear, intraweek, and intraday patterns) complexities of the problem, especially if one wants to capture and
mimic non-gaussian behaviors. the results, however, are
very promising (see figures 1 and 2).

1,500
1,000
500
0

0

20
40
60
80
Cumulative Frequency (%)

100

System 1-December
System 1-January
System 3-December
System 3-January
Static Line Rating
System 3-December-CP
System 3-January-CP

figure 6. A comparison of static and dynamic ratings.
ieee power & energy magazine

65



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - January/February 2015

IEEE Power & Energy Magazine - January/February 2015 - Cover1
IEEE Power & Energy Magazine - January/February 2015 - Cover2
IEEE Power & Energy Magazine - January/February 2015 - 1
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IEEE Power & Energy Magazine - January/February 2015 - Cover3
IEEE Power & Energy Magazine - January/February 2015 - Cover4
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