Modelling and Analysis of Smart Grid: A Stochastic Model Checking Case Study

Yüksel, Ender; Zhu, Huibiao; Nielson, Hanne Riis; Huang, H. Howie; Nielson, Flemming

doi:10.1109/tase.2012.44

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Model checking has been applied to analyse complex system in recent times. [34,35] show the application of model checking to smart grids using different model checking tools. Model checking has also been used for finding errors in cloud applications [36].…”

Section: Related Workmentioning

confidence: 99%

Control-based event-driven bandwidth allocation scheme for video-surveillance systems

Seetanadi

Årzén

Maggio

2021

Cyber-Physical Systems

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Modern computer systems consist of large number of entities connected through a shared resource. One such system is a video surveillance network consisting of a set of cameras and a network manager. The surveillance cameras capture a stream of images, encode them with a quality factor and transmit them to the manager over a shared constrained network. The central manager allocates bandwidth to the cameras in a fair manner using a threshold based gametheoretic approach. The presence of these multiple control loops that interact with each other leads to complexity in providing performance and safety guarantees. Our previous work explored performance of the eventbased manager using model checking to verify relevant properties of linear models. In this paper we build on our previous work by verifying complex camera models that capture uncertainties during image capture. We model the uncertainties using probabilistic Markov Decision Processes (MDPs) and verify relevant properties of the system. We also evaluate system performance for different system parameters with varying triggering thresholds, showing the advantage of model checking for safe and informed parameter selection. Finally, we evaluate the effect of varying thresholds on manager interventions by capturing images on a commercial off-the-shelf (COTS) camera test-bed.

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Section: Related Workmentioning

confidence: 99%

Control-based event-driven bandwidth allocation scheme for video-surveillance systems

Seetanadi

Årzén

Maggio

2021

Cyber-Physical Systems

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“…There exist other works adapting statistical model checking techniques to smart grids, see, e.g., [30], [31], which however are based on sequential (non-parallel) algorithms. In a smart grid context, usage of exhaustive (instead of statistical) methods for safety analysis has been studied, e.g., in [32], [33], [34]. All such works are plagued by the state explosion problem.…”

Section: Scalability Analysismentioning

confidence: 99%

Parallel Statistical Model Checking for Safety Verification in Smart Grids

Mancini

Mari

Melatti

et al. 2018

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable timedependent tariffs) management of the electric grid by avoiding demand peaks. Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks. In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network.

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“…By introducing multi-agent system in microgrid power systems and using different kinds of communication between the agents, the possibility of occurring cyber attacks is increased and investigated in literatures [49][50][51][52]. Different control approaches and defense strategies have been proposed in order to increase the resiliency of the system against these contingencies which usually happen on the measurement signals of the power system [53][54][55][56].…”

Section: E Multi Agent System and Cyber Physical Systemmentioning

confidence: 99%

“…The resulting system size N(t), t>=0, is a Markov process in continuous time which evolves as follows. Start from N(0)=n_0[52]. Wait an exponential time with intensity lambda+mu (intensity lambda if n_0=0), then charge with probability lambda/(lambda+mu) and discharge with probability mu/(lambda+mu).In this study, behavior of 500 PHEVs has been modeled by M/M/1 model with lambda of 1.5 and mu of 0.8.…”

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confidence: 99%

Modeling and Controlling a Hybrid Multi-Agent based Microgrid in Presence of Different Physical and Cyber Components

Keshtkar¹

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Modelling and Analysis of Smart Grid: A Stochastic Model Checking Case Study

Cited by 7 publications

References 7 publications

Control-based event-driven bandwidth allocation scheme for video-surveillance systems

Control-based event-driven bandwidth allocation scheme for video-surveillance systems

Parallel Statistical Model Checking for Safety Verification in Smart Grids

Modeling and Controlling a Hybrid Multi-Agent based Microgrid in Presence of Different Physical and Cyber Components

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