IEEE Power & Energy Magazine - May/June 2018 - 60

such simulation methods facilitate a comprehensive
analysis of the output parameters of wind power and Pv,
such as annual utilization hours, average output, guaranteed output, maximum output, output simultaneity factor,
and smoothing effect. they further serve as inputs for
power system operation simulation considering renewable
energy integration.

Big Data Thinking for Stability Analyses
Power system stability analysis is another area for which big
data analysis applications are under consideration. although
stability analysis is a very traditional topic in power system
analysis, big data technology brings new and innovative
insights and methods of calculation.
the analysis of power system transient stability is
based on differential algebraic equations (Dae), which
can be difficult to solve analytically and yield results that
can be difficult to interpret. Large interconnected power
systems add further difficulties to stability analysis due to
the high dimensionality, multiple time scales, strong nonlinearity, time-varying parameters, and real-time calculation requirements.

we can obtain the numerical postdisturbance trajectory of a large power grid by chronological simulations.
the simulation is based on the detailed Dae-based power
system dynamic models conducted by computers. Mathematically, such large Dae models can be analyzed numerically rather than analytically so that the stability/instability
judgment relies on statistical experience indices rather
than theoretical criteria. the challenges of using experience indices in judging stability include avoiding mistakenly judging a stable trajectory as an unstable one-but
then the threshold of the oscillation has to be set quite
large. simulations over a long interval can avoid false positives but largely increase the cost of the computational
burden. because stability experience indices can be determined only by numerical statistics analysis, we can neither
obtain the necessary/sufficient condition for stability nor
quantify the influences of various parameters on the system's stability performance. these challenges hinder the
study of the stability mechanism and the optimal stability
control decision.
in theory, analytical methods exist for calculating power
system transient power angle stability. the best known is the

(a)

(b)
120

160
PV Power Output (MW)

Wind Power Output (MW)

180
140
120
100
80
60
40
20
0

0 12 24 36 48 60 72 84 96 108 120 132 144 156
Time (h)
(c)

100
80
60
40
20
0

0 12 24 36 48 60 72 84 96 108 120 132 144 156
Time (h)
(d)

figure 5. A software program for the simulation and analysis of renewable energy outputs: (a) and (b) the basic user
interfaces and (c) and (d) the simulated wind and PV power outputs, respectively, of ten sites.
60

ieee power & energy magazine

may/june 2018



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2018

Contents
IEEE Power & Energy Magazine - May/June 2018 - Cover1
IEEE Power & Energy Magazine - May/June 2018 - Cover2
IEEE Power & Energy Magazine - May/June 2018 - Contents
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IEEE Power & Energy Magazine - May/June 2018 - Cover3
IEEE Power & Energy Magazine - May/June 2018 - Cover4
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