Survivability models for the assessment of smart grid distribution automation network designs

Avritzer, Alberto; Suresh, Sindhu; Menasché, Daniel Sadoc; Leão, Rosa M. M.; Silva, Edmundo de Souza e; Diniz, Morganna Carmem; Trivedi, Kishor S.; Happe, Jens; Koziolek, Anne

doi:10.1145/2479871.2479905

Cited by 25 publications

(18 citation statements)

References 19 publications

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“…Our recently introduced approach [8,46] targets assessment of transient properties of the power systems accounting for the implications of electro-mechanical and computer-based strategies to address failures in an integrated manner. In this approach, we quantify the effect of FDIR behaviour and demand/response functionality on survivability metrics, based on extended SAIDI metrics.…”

Section: A Phased-recovery Modelmentioning

confidence: 99%

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Survivability Evaluation of Gas, Water and Electricity Infrastructures

Avritzer

Carnevali

Ghasemieh

et al. 2015

Electronic Notes in Theoretical Computer Science

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The infrastructures used in cities to supply power, water and gas are consistently becoming more automated. As society depends critically on these cyber-physical infrastructures, their survivability assessment deserves more attention. In this overview, we first touch upon a taxonomy on survivability of cyber-physical infrastructures, before we focus on three classes of infrastructures (gas, water and electricity) and discuss recent modelling and evaluation approaches and challenges.

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Section: A Phased-recovery Modelmentioning

confidence: 99%

“…Figure 7 (cf. [8]) shows the expected accumulated energy not supplied (EAENS) by time t, for two cases, namely the case when demand-response is not enabled (left graphs; r = 0) and demand-response being enabled (right graph; r = 1). Using our method, changes in different parts of the system, i.e., due to investments, can be assessed.…”

Section: Case Studymentioning

confidence: 99%

Survivability Evaluation of Gas, Water and Electricity Infrastructures

Avritzer

Carnevali

Ghasemieh

et al. 2015

Electronic Notes in Theoretical Computer Science

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“…To predict the survivability of a distribution circuit as described in the previous subsection, we build upon a holistic survivability analysis model [3]. It takes into account [3] Parameter Description p probability that communication works after failure q probability that backup power suffices to supply isolated sections r probability that demand response is effective after failure ENS {h s energy not supplied per hour for model state s γ communication repair rate in events/hour the topology of the distribution circuit, the communication availability, and the demand response capabilities.…”

Section: B Survivability Modelmentioning

confidence: 99%

“…It takes into account [3] Parameter Description p probability that communication works after failure q probability that backup power suffices to supply isolated sections r probability that demand response is effective after failure ENS {h s energy not supplied per hour for model state s γ communication repair rate in events/hour the topology of the distribution circuit, the communication availability, and the demand response capabilities. Table I shows the input parameters of the survivability model.…”

Section: B Survivability Modelmentioning

confidence: 99%

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A common analysis framework for smart distribution networks applied to survivability analysis of distribution automation

Koziolek¹,

Happe²,

Avritzer³

et al. 2012

2012 First International Workshop on Software Engineering Challenges for the Smart Grid (SE-SmartGrids)

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Smart distribution networks shall improve the efficiency and reliability of power distribution by intelligently managing the available power and requested load. Such intelligent power networks pose challenges for information and communication technology (ICT). Their design requires a holistic assessment of traditional power system topology and ICT architecture. Existing analysis approaches focus on analyzing the power networks components separately. For example, communication simulation provides failure data for communication links, while power analysis makes predictions about the stability of the traditional power grid. However, these insights are not combined to provide a basis for design decisions for future smart distribution networks. In this paper, we describe a common model-driven analysis framework for smart distribution networks based on the Common Information Model (CIM). This framework provides scalable analysis of large smart distribution networks by supporting analyses on different levels of abstraction. Furthermore, we apply our framework to holistic survivability analysis. We map the CIM on a survivability model to enable assessing design options with respect to the achieved survivability improvement. We demonstrate our approach by applying the mapping transformation in a case study based on a real distribution circuit. We conclude by evaluating the survivability impact of three investment options. Abstract-Smart distribution networks shall improve the efficiency and reliability of power distribution by intelligently managing the available power and requested load. Such intelligent power networks pose challenges for information and communication technology (ICT). Their design requires a holistic assessment of traditional power system topology and ICT architecture. Existing analysis approaches focus on analyzing the power networks components separately. For example, communication simulation provides failure data for communication links, while power analysis makes predictions about the stability of the traditional power grid. However, these insights are not combined to provide a basis for design decisions for future smart distribution networks.In this paper, we describe a common model-driven analysis framework for smart distribution networks based on the Common Information Model (CIM). This framework provides scalable analysis of large smart distribution networks by supporting analyses on different levels of abstraction. Furthermore, we apply our framework to holistic survivability analysis. We map the CIM on a survivability model to enable assessing design options with respect to the achieved survivability improvement.We demonstrate our approach by applying the mapping transformation in a case study based on a real distribution circuit. We conclude by evaluating the survivability impact of three investment options.

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Assessing survivability of smart grid distribution network designs accounting for multiple failures

Menasché

Avritzer

Suresh

et al. 2014

Concurrency and Computation

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distribution automation (DA). The automation of the smart grid brings novel challenges to the power grid engineers, such as the assessment of the tradeoffs involved to accurately engineer the power distribution reliability.The introduction of automation (intelligence) to power distribution networks has created a need for the holistic assessment of the distribution network. The distribution network reliability is a function of the correct operation of several architecture artifacts such as electrical power components, telecommunications, distribution network topology, failure detection isolation and restoration, demand response, and distributed generation (DG) and storage. The automation of power distribution requires a more integrated perspective across these domains. However, in the current mode of operation, they are still being engineered separately.Traditionally, the reliability of power systems has been quantified using average metrics, such as the system average interruption duration index (SAIDI). SAIDI is used by public service commissions in the USA to assess utilities' compliance with the commission rules. It was developed to track manual restoration times, and according to standard 166-1998, the median value for North American utilities is roughly one and a half hours. In smart grid networks, power failure and restoration events will have a finer level of granularity, because of the deployment of reclosers, which isolate faulty sections, and demand side management system activities, such as distributed generators and demand response application systems. Therefore, there is a need to extend the SAIDI metric and to develop new models and tools for the accurate computation of customer interruption indexes after power failure events occur, even if the occurrence of such events is rare. The survivability of a missioncritical application is the ability of the system to continue functioning during and after a failure or disturbance [1].In [2], we presented a proposal for a common analysis framework to support the survivability analysis of DA using extensions of the International Electrotechnical Commission-standardized common information model. The paper presented a case study of the application of the proposed method to the survivability analysis of a simple DA network that was derived from a real power distribution network. In [3], we have evaluated the impact of available active and reactive power supply after a section failure on the distributed automation survivability metric, and we derived closed-form expressions for certain survivability-related metrics. In [2][3][4], the survivability model accounted for single failures.In this paper, we present an analytical model to assess the survivability of distributed automation power grids and to predict SAIDI and related metrics as a function of different system parameters related to communications, DG, demand response, and other smart grid features, accounting for multiple failures. We use a performability model to capture how the system recovers from a fail...

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Survivability models for the assessment of smart grid distribution automation network designs

Cited by 25 publications

References 19 publications

Survivability Evaluation of Gas, Water and Electricity Infrastructures

Survivability Evaluation of Gas, Water and Electricity Infrastructures

A common analysis framework for smart distribution networks applied to survivability analysis of distribution automation

Assessing survivability of smart grid distribution network designs accounting for multiple failures

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