463 research outputs found
Changes in Cascading Failure Risk with Generator Dispatch Method and System Load Level
Industry reliability rules increasingly require utilities to study and
mitigate cascading failure risk in their system. Motivated by this, this paper
describes how cascading failure risk, in terms of expected blackout size,
varies with power system load level and pre-contingency dispatch. We used Monte
Carlo sampling of random branch outages to generate contingencies, and a model
of cascading failure to estimate blackout sizes. The risk associated with
different blackout sizes was separately estimated in order to separate small,
medium, and large blackout risk. Results from secure models of the IEEE
RTS case and a 2383 bus case indicate that blackout risk does not always
increase with load level monotonically, particularly for large blackout risk.
The results also show that risk is highly dependent on the method used for
generator dispatch. Minimum cost methods of dispatch can result in larger long
distance power transfers, which can increase cascading failure risk.Comment: Submitted to Transmission and Distribution Conference and Exposition
(T&D), 2014 IEEE PE
Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology
In a cascading power transmission outage, component outages propagate
non-locally, after one component outages, the next failure may be very distant,
both topologically and geographically. As a result, simple models of
topological contagion do not accurately represent the propagation of cascades
in power systems. However, cascading power outages do follow patterns, some of
which are useful in understanding and reducing blackout risk. This paper
describes a method by which the data from many cascading failure simulations
can be transformed into a graph-based model of influences that provides
actionable information about the many ways that cascades propagate in a
particular system. The resulting "influence graph" model is Markovian, in that
component outage probabilities depend only on the outages that occurred in the
prior generation. To validate the model we compare the distribution of cascade
sizes resulting from contingencies in a branch test case to
cascade sizes in the influence graph. The two distributions are remarkably
similar. In addition, we derive an equation with which one can quickly identify
modifications to the proposed system that will substantially reduce cascade
propagation. With this equation one can quickly identify critical components
that can be improved to substantially reduce the risk of large cascading
blackouts.Comment: Accepted for publication at the IEEE Transactions on Power System
Calculation of the Autocorrelation Function of the Stochastic Single Machine Infinite Bus System
Critical slowing down (CSD) is the phenomenon in which a system recovers more
slowly from small perturbations. CSD, as evidenced by increasing signal
variance and autocorrelation, has been observed in many dynamical systems
approaching a critical transition, and thus can be a useful signal of proximity
to transition. In this paper, we derive autocorrelation functions for the state
variables of a stochastic single machine infinite bus system (SMIB). The
results show that both autocorrelation and variance increase as this system
approaches a saddle-node bifurcation. The autocorrelation functions help to
explain why CSD can be used as an indicator of proximity to criticality in
power systems revealing, for example, how nonlinearity in the SMIB system
causes these signs to appear.Comment: Accepted for publication/presentation in Proc. North American Power
Symposium, 201
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