Value of security: modeling time-dependent phenomena and weather conditions

Ríos, Martínez; Kirschen, Daniel S.; Jayaweera, Dilan; Nedic, Dusko P.; Allan, R.N.

doi:10.1109/pes.2003.1270373

Cited by 22 publications

(32 citation statements)

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“…We now explore the load-dependence of power grids via a simple network model, with different topologies and loading conditions. There are several approaches to modeling the dynamics of a power grid, including examples of networks obeying circuit laws [9,[34][35][36], sometimes incorporating phase information [6,43], as well as more abstract models [37,38], alongside a large volume of literature on failures in complex networks in general (see e.g. [39][40][41][42]).…”

Section: Modelmentioning

confidence: 99%

Load dependence of power outage statistics

Biswas

Goehring

2019

EPL

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The size distributions of power outages are shown to depend on the stress, or the proximity of the load of an electrical grid to complete breakdown. Using the data for the U.S. between 2002-2017, we show that the outage statistics are dependent on the usage levels during different hours of the day and months of the year. At higher load, not only are more failures likely, but the distribution of failure sizes shifts, to favor larger events. At a finer spatial scale, different regions within the U.S. can be shown to respond differently in terms of the outage statistics to variations in the usage (load). The response, in turn, corresponds to the respective bias towards larger or smaller failures in those regions. We provide a simple model, using realistic grid topologies, which can nonetheless demonstrate biases as a function of the applied load, as in the data. Given sufficient data of small scale events, the method can be used to identify vulnerable regions in power grids prior to major blackouts.

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Section: Modelmentioning

confidence: 99%

Load dependence of power outage statistics

Biswas

Goehring

2019

EPL

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“…Alternatively, by using the cascading outage models, cascading outage data can be generated by computer simulations. These models are such as the hidden failure model [5], Manchester model [6], [7], CASCADE model [8], the collection of OPA models [9]- [15], dynamic and quasidynamic models [16]- [18], PRA model [19], sandpile model [20]. Analyzing these outage data using statistical tools provides a feasible way to understand the patterns in which outages propagate in a power grid.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Cascading Outages Using a Dynamic Interaction Graph-Based Optimal Power Flow Model

Chen

Sun

et al. 2019

IEEE Access

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Online decision support for effective mitigation actions against propagating cascading outages in a power grid still poses a big challenge to grid operators. This paper proposes an online mitigation strategy against cascading outages using an optimal power flow model based on a Dynamic Interaction Graph. For a given power grid, its interaction graph, also called an interaction model, was proposed in recent literature, which is composed of components and links that have large contributions to outage propagation. Differing from a conventional interaction graph, a Dynamic Interaction Graph can adaptively update its parameters with changes of grid topology and hence is more accurate in online identification of the most vulnerable transmission lines and likely outage propagation paths for mitigation. Further, the paper introduces an optimal power flow model based on the proposed Dynamic Interaction Graph for determining the optimal control strategy to maintain transfer margins of vulnerable transmission lines and effectively mitigate outage propagation. The numerical results on the IEEE 118-bus system demonstrate the effectiveness of the proposed models and associated control strategy.

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“…Early studies of the potential for wind generation often used Markov state analysis to predict probability distributions and possible time series for power generation. [3][4][5] These studies became increasingly robust as they were confirmed by actual wind farm data, and their approach has been built on by more recent work, often making use of Monte Carlo simulation to produce relevant time series. [6][7][8][9][10] The concept of generator persistence has proved to be important, as described by Holttinen and Hirvonen, 11 with particular reference to the Nordic power system.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the contribution of wind farms to distribution network reliability

et al. 2014

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The reliability of power supply to distribution network customers can be increased by embedded generation, including wind farms. The value of this increase in reliability needs to be evaluated, and national standards such as the Great Britain security of supply standard P2/6 seek to do so. This paper appraises the capacity credit evaluation methodology in P2/6 and outlines an alternative methodology to integrate generation with load more effectively, taking into account the topology, loading and reliability of the surrounding network. It concludes that under certain circumstances, the presence of embedded wind generation can allow the deferral of costly network reinforcement projects but that the time for which reinforcement can reasonably be deferred is a function not only of the generators themselves but also of the surrounding network. Copyright © 2014 John Wiley & Sons, Ltd.

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Value of security: modeling time-dependent phenomena and weather conditions

Cited by 22 publications

References 0 publications

Load dependence of power outage statistics

Load dependence of power outage statistics

Mitigation of Cascading Outages Using a Dynamic Interaction Graph-Based Optimal Power Flow Model

Quantifying the contribution of wind farms to distribution network reliability

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