1,721,082 research outputs found
Innovative Services for Electric Mobility Based on Virtual Sensors and Petri Nets
No full textAbout 60% of emissions into the earth's atmosphere are produced by the transport sector, caused by exhaust gases from conventional internal combustion engines. An effective solution to this problem is electric mobility, which significantly reduces the rate of urban pollution. The use of electric vehicles (EVs) has to be encouraged and facilitated by new information and communication technology (ICT) tools. To help achieve this goal, this paper proposes innovative services for electric vehicle users aimed at improving travel and charging experience. The goal is to provide a smart service to allow drivers to find the most appropriate charging solutions during a trip based on information such as the vehicle's current position, battery type, state of charge, nearby charge point availability, and compatibility. In particular, the drivers are supported so that they can find and book the preferred charge option according to time availability and the final cost of the charge points (CPs). To this purpose, two virtual sensors (VSs) are designed, modeled and simulated in order to provide the users with an innovative service for smart CP searching and booking. In particular, the first VS is devoted to locate and find available CPs in a preferred area, whereas the second VS calculates the charging cost for the EV and supports the driver in the booking phase. A UML activity diagram describes VSs operations and cooperation, while a UML sequence diagram highlights data exchange between the VSs and other electromobility ecosystem actors (CP operator, EV manufacturer, etc.). Furthermore, two timed Petri Nets (TPNs) are designed to model the proposed VSs, functioning and interactions as discrete event systems. The Petri Nets are synchronized by a single larger TPN that is simulated in different use cases and scenarios to demonstrate the effectiveness of the proposed VSs
Real time identification of discrete event systems by Petri Nets
No full textThe paper defines the identification problem for discrete event systems as the problem of inferring a Petri Net (PN) model using the observation of the events and the available output vectors. The transition and place sets are assumed unknown and only an upper bound of the number of places is given. Hence, the identification problem is solved by an algorithm that stores in real-time the occurred events and the corresponding output vectors. An integer linear programming problem is defined and solved at each observation so that the PN system can be recursively identified. An example shows the flexibility and simplicity of the proposed techniqu
Identification of the Unobservable Behaviour of Industrial Automation Systems by Petri Nets
No full textA District Energy Management Based on Thermal Comfort Satisfaction and Real-Time Power Balancing
Full text linkThis paper presents a district energy management strategy devoted to monitor and control the district power consumption in a twofold human-centered perspective: the respect of user’s comfort preferences and the minimization of the power consumption and costs. The presented district energy management system forwards the power profile determined the day ahead to each building energy management system that, in turn, minimizes its real-time power consumption and costs (based on rewards and penalties), respecting the comfort preferences. Successively, the power is redistributed among the district buildings in order to minimize the penalties by applying two approaches: a centralized approach for public buildings and a distributed methodology for private buildings. Such optimization problems are formalized by defining some linear programming problems: two case studies are solved to show the applicability of the proposed management strategies
Smart District Energy Management with Cooperative Microgrids
Full text linkThis paper faces the energy management problem of cooperative microgrids in a smart energy district. In particular, the aim of the research work is to propose an innovative optimization model to solve the problem of energy management in a district composed of several microgrids, taking into account uncertainties of key parameters. In this context, the objective of the paper is threefold: i) maximize the use of energy purchased at the day-ahead market; ii) minimize the need of additional and expensive energy in real-time iii) optimize the integration of renewable energy sources (RES), energy storage systems (ESS) and electric vehicle (EV) batteries in the microgrid. To these goals, the District Energy Management System (DEMS), i.e. the central controller of the district, must balance the microgrids energy demand with the optimal integration of RES, ESS and the batteries of EVs that are seen as movable storage devices shared among buildings. Moreover, the energy surplus can be sold back to the main power grid. The DEMS problem is solved by two approaches. In the first approach, the energy demand, the RES production and the costs are known and a linear programming problem is formalized and solved by the DEMS. In addition, a second approach is proposed in order to address the parameters uncertainty and is formalized as a stochastic linear programming problem. The optimization problems solutions provide the optimal strategy to schedule the charging and discharging operations of the storage systems and the electric vehicle batteries. A simulation case study demonstrates the benefits of the proposed approaches for the smart district
Fault Detection by Labeled Petri Nets in Centralized and Distributed Approaches
No full textThis paper addresses the problem of online fault de- tection and diagnosis in discrete event systems modeled by labeled Petri nets and using Integer Linear Programming Problem (ILPP) solutions. In particular, unobservable (silent) transitions model faults and both observable and unobservable transitions model the nominal system behavior. Furthermore, observable transi- tions exhibit a kind of non determinism since several different transitions may share the same event label. This paper proposes two diagnosers that work in two different system settings. The first one is a centralized fault detection strategy: the diagnoser waits for an observable event and an algorithm defines and solves some ILPPs to decide whether the system behavior is normal or may exhibit some faults. In the second setting, the system consists of a set of interacting PN modules and each module is monitored by a diagnoser that has local information on the module structure. Moreover, each diagnoser observes and detects the faults of the module it is attached to and shares information in some of its places that are shared with other modules of the system. Some case studies show the two different approaches and point out the peculiarities of the proposed strategies
Real Time Identification of Discrete Event Systems using Petri Nets
No full textThe paper defines the identification problem for Discrete Event Systems (DES) as the problem of inferring a Petri Net (PN) model using
the observation of the events and the available output vectors, that correspond to the markings of the measurable places. Two cases are studied
considering different levels of the system knowledge. In the first case the place and transition sets are assumed known. Hence, an integer linear
programming problem is defined in order to determine a PN modelling the DES. In the second case the transition and place sets are assumed
unknown and only an upper bound of the number of places is given. Hence, the identification problem is solved by an identification algorithm
that observes in real time the occurred events and the corresponding output vectors. The integer linear programming problem is defined at each
observation so that the PN can be recursively identified. Some results and examples characterize the identified PN systems and show the flexibility
and simplicity of the proposed technique. Moreover, an application to the synthesis of supervisory control of PN systems via monitor places is
proposed
Fault Monitoring of Discrete Event Systems by First Order Hybrid Petri Nets
No full textFault monitoring is an essential requirement for
safety and reliability of dynamic systems. Motivated by the fact
that a large class of dynamic systems can be viewed as Discrete
Event Systems (DESs) at some level of abstraction, the paper
presents a novel event-based approach for DES on-line
monitoring, ensuring timely and accurate detection of system
failures. The monitor model is based on first order hybrid Petri
nets, i.e., Petri nets that make use of first order fluid
approximation. The proposed fault analysis technique relies on a
modular framework, so that elementary monitors can be
connected with other monitors to check more complex systems
while avoiding the state space explosion problem. In addition, the
presented monitor detects system faults as soon as possible,
before the maximum execution time assigned to each task. An
application to a simple manufacturing system enlightens the
simplicity and modularity of the technique
Fuzzy Multi-Objective Optimization for Network Design of Logistic and Production Systems
No full textGlobal competition has given rise to logistic and production systems (LPSs), that are distributed manufacturing systems integrating international logistics and information technologies with production. This paper builds upon an LPS network design model previously proposed by some of the authors. The recalled technique formulates and solves a multi-criteria optimization problem to select the partners in the different stages of the production chain and the links connecting them. In this paper, in order to rank the equally optimal Pareto solutions of such a problem, we propose to employ fuzzy multi-criteria optimization. Two fuzzification techniques and two different multi-criteria methods are considered. In addition, the methodology is illustrated by way of a case study. Moreover, a discussion on the different advantages and limitations of the proposed techniques is provide
A First Order Hybrid Petri Net Model for Supply Chain Management
No full textA supply chain (SC) is a network of independent manufacturing and logistics companies that perform the critical functions in the order fulfillment process. This paper proposes an effective and modular model to describe material, financial and information flow of SCs at the operational level based on first-order hybrid Petri nets (PNs), i.e., PNs that make use of first-order fluid approximation. The proposed formalism enables the SC designer to choose suitable production rates of facilities in order to optimize the chosen objective function. The optimal mode of operation is performed based on the state knowledge of the obtained linear discrete-time, time-varying state variable model in order to react to unpredictable events such as the blocking of a supply or an accident in a transportation facility. A case study is modeled in the proposed framework and is simulated under three different closed-loop control strategie
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