Analysis of an Electric Vehicle Charging System Along a Highway

To reduce carbon emission, the transportation sector evolves toward replacing internal combustion vehicles by electric vehicles (EV). However, the limited driving ranges of EVs, their long recharge duration and the need of appropriate charging infrastructures require smart strategies to optimize the charging stops during a long trip. These challenges have generated a new area of studies that were mainly directed to extend the classical Vehicle Routing Problem (VRP) to a fleet of commercial EVs. In this paper, we propose a different point of view, by considering the interaction of private EVs with the related infrastructure, focusing on a highway trip. We consider a highway where charging stations are scattered along the road, and are equipped withmultiple chargers. Using Fluid Stochastic Petri Nets (FSPN), the paper compares different decision policies when to stop and recharge the battery to maximize the probability of a car to reach its destination and minimize the trip completion time

Evidence-Based Analysis of Cyber Attacks to Security Monitored Distributed Energy Resources

This work proposes an approach based on dynamic Bayesian networks to support the cybersecurity analysis of network-based controllers in distributed energy plants. We built a system model that exploits real world context information from both information and operational technology environments in the energy infrastructure, and we use it to demonstrate the value of security evidence for time-driven predictive and diagnostic analyses. The innovative contribution of this work is in the methodology capability of capturing the causal and temporal dependencies involved in the assessment of security threats, and in the introduction of security analytics supporting the configuration of anomaly detection platforms for digital energy infrastructures

Markovian Agent models with applications to wireless sensor networks

In recent years, a new versatile analytical technique has emerged whose main idea is to model a distributed system by means of interacting agents, so that each agent maintains its local properties but at the same time modies its behaviour according to the in uence of the interaction with the other agents. In this way, the analysis of each agent alone incorporates the eect of the interdependencies. In the present model each agent selects its actions based on the current state and is represented by a continuous time Markov chain (CTMC). We refer to this kind of agents as Markovian Agents (MA) [1{3] for which the innitesimal generator has a xed local component, that may depend on the geographical position of the MA, and a component that depends on the interactions with other MAs

Markovian Agent models for wireless sensor networks deployed in environmental protection

Wireless Sensor Networks (WSN) are distributed interacting systems formed by many similar tiny sensors communicating to gather information from the environment and transmit it to a base station. The present paper presents an analytical modeling and analysis technique based on Markovian Agents (MAs) and discusses a very complex scenario in which a WSN is deployed in a wide open area to monitor the outbreak of a fire and send a warning signal to a base station. The models is composed by four classes of MA modeling, respectively: the fire propagation, the high temperature front propagation, the sensor nodes and the sink; and four types of messages. It is shown that, even if the overall state space of the models is huge, nevertheless an analytical solution is feasible, by exploiting the locality of the interactions among MAs, based on a message passing mechanism combined with a perception function

Fire prevention by means of WSN: A preliminary propagation study using Interactive Markovian Agents

none3INFD. Cerotti;M. Gribaudo;A. BobbioD., Cerotti; Gribaudo, Marco; A., Bobbi

Presenting Dynamic Markovian Agents with a Road Tunnel Application

The paper discusses a Dynamic Markovian Agent Model obtained by adding mobility to a recently introduced new formalism suitable for the analysis of large scale systems, composed by a population of interacting entities, called Markovian Agents (MA). The differential equations describing the evolution of the MA density in time and space are derived, and their numerical solution is briefly sketched. An application to the analysis of the flow of vehicles in a road tunnel is discussed, together with the evaluation of the probability of collision against a fixed obstacle

A New Quantitative Analytical Framework for Large-Scale Distributed Interacting Systems

A Markovian Agent Model (MAM) is a stochastic model that provides a flexible, powerful and scalable way for an- alyzing complex systems of distributed interacting objects. The constituting bricks of a MAM are the Markovian Agents (MA) represented by a finite state continuous time Markov chain (CTMC) whose infinitesimal generator is composed by a fixed component (the local behaviour) and an induced component that depends on the interaction with the other MAs. An additional innovative aspect is that the single MA keeps track of its position so that the overall MAM model is spatial dependent. MAMs are expressed with analytical formulas suited for numerical solution. Extensive applications in different domains have shown the effectiveness of the approach. In the present paper, we propose an example that illustrates how the MAM technique can cope with extremely large state spaces