PROFILINGBLACKOUTS
Cascading failures in electrical networks: stochastic analysis and distributed prevention methods
| Coordinatore | IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Organization address
address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD contact info |
| Nazionalità Coordinatore | United Kingdom [UK] |
| Totale costo | 208˙592 € |
| EC contributo | 208˙592 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2010-IEF |
| Funding Scheme | MC-IEF |
| Anno di inizio | 2011 |
| Periodo (anno-mese-giorno) | 2011-09-01 - 2013-08-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Organization address
address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD contact info |
UK (LONDON) | coordinator | 208˙592.80 |
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Obiettivo del progetto (Objective)
'Electricity networks are designed to cope with a set of credible contingencies, usually involving the loss of a single piece of equipment. However, the interconnectedness of the network makes it hard to predict what happens when an unexpected contingency occurs. Sometimes this leads to an overload in one of the lines, causing it to be taken out of service, which may trigger other overloads. In recent years, such cascading failures have lead to a number of large scale blackouts.
This project’s first aim is to get a deeper understanding of the fundamental vulnerability of electrical energy systems to experience large-scale cascading failures after the loss of very few components. This is done by generating large numbers of generic networks with given connectivity profiles that satisfy Kirchhoff’s circuit laws and are robust against ‘simple’ contingencies. For these networks, a risk profile is computed that indicates the frequency of outages as a function of their magnitude. These will give insights into the factors influencing a network’s vulnerability. Particular attention will be paid to connectivity, margins and the size distribution of generators.
Building on this knowledge, it will be investigated whether the nodes in the network can identify and share information on network stress with their neighbours and whether this information can be used to prevent large-scale cascades. For example, a node may shed some load preventively or exercise a demand reduction contract in order to locally decrease the stress on the network. A few of these schemes will be constructed and it will be investigated how this affects the risk profile. The results will be compared with each other and the business-as-usual scenario with an aim to inform the design of real-world protection schemes.'
Introduzione (Teaser)
EU-funded scientists have developed models and tools to improve our understanding of cascading power failures. To reduce the risk of such events, they proposed a novel control mechanism to balance supply and demand using intelligent home appliances.