PROFILINGBLACKOUTS

Cascading failures in electrical networks: stochastic analysis and distributed prevention methods

Coordinatore	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE    more  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Mr. Nome: Shaun Cognome: Power Email: send email Telefono: 442076000000 Fax: 442076000000
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

#	participant	country	role	EC contrib. [€]
1	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Mr. Nome: Shaun Cognome: Power Email: send email Telefono: 442076000000 Fax: 442076000000	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.