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 $N-1$ 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

Quantifying the Influence of Component Failure Probability on Cascading Blackout Risk

The risk of cascading blackouts greatly relies on failure probabilities of individual components in power grids. To quantify how component failure probabilities (CFP) influences blackout risk (BR), this paper proposes a sample-induced semi-analytic approach to characterize the relationship between CFP and BR. To this end, we first give a generic component failure probability function (CoFPF) to describe CFP with varying parameters or forms. Then the exact relationship between BR and CoFPFs is built on the abstract Markov-sequence model of cascading outages. Leveraging a set of samples generated by blackout simulations, we further establish a sample-induced semi-analytic mapping between the unbiased estimation of BR and CoFPFs. Finally, we derive an efficient algorithm that can directly calculate the unbiased estimation of BR when the CoFPFs change. Since no additional simulations are required, the algorithm is computationally scalable and efficient. Numerical experiments well confirm the theory and the algorithm

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 $n-2$ contingencies in a $2896$ 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

Can the Markovian influence graph simulate cascading resilience from historical outage data?

It is challenging to simulate the cascading line outages that can follow initial damage to the electric power transmission system from extreme events. Instead of model-based simulation, we propose using a Markovian influence graph driven by historical utility data to sample the cascades. The sampling method encompasses the rare, large cascades that contribute greatly to the blackout risk. This suggested new approach contributes a high-level simulation of cascading line outages that is driven by standard utility data

Analysis of the blackout risk reduction when segmenting large power systems using lines with controllable power flow

Large electrical transmission networks are susceptible to undergo very large blackouts due to cascading failures, with a very large associated economical cost. In this work we propose segmenting large power grids using controllable lines, such as high-voltage direct-current lines, to reduce the risk of blackouts. The method consists in modifying the power flowing through the lines interconnecting different zones during cascading failures in order to minimize the load shed. As a result, the segmented grids have a substantially lower risk of blackouts than the original network, with reductions up to 60% in some cases. The control method is shown to be specially efficient in reducing blackouts affecting more than one zone.DG and PC acknowledge funding from project PACSS RTI2018-093732-B-C22 and APASOS PID2021-122256NB-C22 of the MCIN/AEI/10.13039/501100011033/ and by EU through FEDER funds (A way to make Europe), from the Maria de Maeztu program MDM-2017-0711 of the MCIN/AEI/10.13039/501100011033/, and also from the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 957852, VPP4Islands). B.A.C. and J.M.R.-B. acknowledge access to Uranus, a supercomputer cluster located at Universidad Carlos III de Madrid (Spain) funded jointly by EU FEDER funds and by the Spanish Government via the National Research Project Nos. UNC313-4E-2361, ENE2009-12213-C03-03, ENE2012-33219, and ENE2012-31753. OGB was supported in part by FEDER/Ministerio de Ciencia, Innovacion y Universidades - Agencia Estatal de Investigación, Project RTI2018-095429-B-I00 and in part by FI-AGAUR Research Fellowship Program, Generalitat de Catalunya. The work of OGB is supported by the ICREA Academia program

Stochastic Model for Power Grid Dynamics

We introduce a stochastic model that describes the quasi-static dynamics of an electric transmission network under perturbations introduced by random load fluctuations, random removing of system components from service, random repair times for the failed components, and random response times to implement optimal system corrections for removing line overloads in a damaged or stressed transmission network. We use a linear approximation to the network flow equations and apply linear programming techniques that optimize the dispatching of generators and loads in order to eliminate the network overloads associated with a damaged system. We also provide a simple model for the operator's response to various contingency events that is not always optimal due to either failure of the state estimation system or due to the incorrect subjective assessment of the severity associated with these events. This further allows us to use a game theoretic framework for casting the optimization of the operator's response into the choice of the optimal strategy which minimizes the operating cost. We use a simple strategy space which is the degree of tolerance to line overloads and which is an automatic control (optimization) parameter that can be adjusted to trade off automatic load shed without propagating cascades versus reduced load shed and an increased risk of propagating cascades. The tolerance parameter is chosen to describes a smooth transition from a risk averse to a risk taken strategy...Comment: framework for a system-level analysis of the power grid from the viewpoint of complex network