Advances in Safety, Reliability and Risk Management 2011
DOI: 10.1201/b11433-352
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Complexity and vulnerability of Smartgrid systems

Abstract: for the Proceedings of the European Safety and Reliability Conference the author version, submitted here, was reducedInternational audienceIn this paper, we look at the complexity and related vulnerability characteristics of Smartgrids. Typical characteristics of complex systems, such as self-organization, emergence, chaotic behavior and evolution, are considered with respect to Smartgrids as future energy infrastructures. These characteristics are categorized as inherent, challenge-response, or acquired. This… Show more

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Cited by 9 publications
(5 citation statements)
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“…When the external pressures applied to a system exceed 'critical values' beyond which adaptive learning mechanisms are inefficient, the system is forced to evolve. In the absence of a central authority governing system changes, the evolutionary process resembles natural selection in biological systems resulting in the consequent disappearance of elements associated with low adaptive fitness (Kuznetsova et al 2011). These properties are possible when the IA of the complex system acquire some behavioural autonomy and the complex system is no longer controlled exclusively by a central authority.…”
Section: A2 Behavioural Featuresmentioning
confidence: 99%
“…When the external pressures applied to a system exceed 'critical values' beyond which adaptive learning mechanisms are inefficient, the system is forced to evolve. In the absence of a central authority governing system changes, the evolutionary process resembles natural selection in biological systems resulting in the consequent disappearance of elements associated with low adaptive fitness (Kuznetsova et al 2011). These properties are possible when the IA of the complex system acquire some behavioural autonomy and the complex system is no longer controlled exclusively by a central authority.…”
Section: A2 Behavioural Featuresmentioning
confidence: 99%
“…energy imbalance generated by wind power plant at time step t (kWh), & , 5 and & , prices for positive and negative imbalances, respectively, at time step t (€/kWh), ( 6 and ( performance ratio calculated over a simulation period of Ns hours by normalizing the imbalance cost by the actual expenses / revenues calculated in the case of perfect forecast (%), $ 7 and $ 8 constants denoting the average annual duration of high and normal wind conditions, respectively, over the time period $ (h), failure rates at high and normal wind conditions (occur./y), respectively, ; ( ) weight factor caused by severe weather, ; and ; weight factors for hourly and daily variations, respectively, .…”
Section: *mentioning
confidence: 99%
“…11 Various approaches for control and energy management of microgrids are reported in the literature. 12 In previous studies, 1316 an agent-based modeling approach is proposed to model microgrids and to analyze by simulation the interactions between individual intelligent decision-makers. Han et al 11 and Prodan and Zio 17,18 develop an optimization-based control approach.…”
Section: Introductionmentioning
confidence: 99%