The smart grid simulation framework: model-driven engineering applied to cyber-physical systems

International audienceSmart grids are complex systems for which simulation offers a practical way to evaluate and compare multiple solutions before deployment. However, the simulation of a Smart Grid requires the development of heterogeneous models corresponding to electrical, information processing, and telecommunication behaviors. These heterogeneous models must be linked and analyzed together in order to detect the influences on one another and identify emerging behaviors. We apply model-driven engineering to such cyber-physical systems combining physical and digital components and propose SGridSF, the Smart Grid Simulation Framework, which automates tasks in order to ensure consistency between different simulation models. This framework consists mainly of a domain specific language for modeling a cosimulation unit, called CosiML for Cosimulation Modeling Language, a domain specific language for modeling the functional architecture of a Smart Grid, called SGridML for Smart Grid Modeling Language, and a tool implementing different transformation rules to generate the files and scripts for executing a cosimulation. Finally, we illustrate the use of SGridSF on the real use case of an islanded grid implementing diesel and renewable sources, battery storage and intelligent control of the production. We show the sequencing of automatic generation tasks that minimizes the effort and the risk of error at each iteration of the process

Automated Validation of State-Based Client-Centric Isolation with TLA ⁺

Clear consistency guarantees on data are paramount for the design and implementation of distributed systems. When implementing distributed applications, developers require approaches to verify the data consistency guarantees of an implementation choice. Crooks et al. define a state-based and client-centric model of database isolation. This paper formalizes this state-based model in, reproduces their examples and shows how to model check runtime traces and algorithms with this formalization. The formalized model in enables semi-automatic model checking for different implementation alternatives for transactional operations and allows checking of conformance to isolation levels. We reproduce examples of the original paper and confirm the isolation guarantees of the combination of the well-known 2-phase locking and 2-phase commit algorithms. Using model checking this formalization can also help finding bugs in incorrect specifications. This improves feasibility of automated checking of isolation guarantees in synthesized synchronization implementations and it provides an environment for experimenting with new designs.</p

Integrating AADL and FMI to Extend Virtual Integration Capability

Virtual Integration Capability is paramount to perform early validation of Cyber Physical Systems. The objective is to guide the systems engineer so as to ensure that the system under design meets multiple criteria through high-fidelity simulation. In this paper, we present an integration scheme that leverages the FMI (Functional Mock-Up interface) standard and the AADL architecture description language. Their combination allows for validation of systems combining embedded platform captured by the AADL, and FMI components that represent physical elements, either mechanical parts, or the environment. We present one approach, and demonstrator case studies

Features of integrated model-based co-modelling and co-simulation technology

Given the considerable ongoing research interest in collaborative multidisciplinary modelling and co-simulation, it is worth considering the features of model-based techniques and tools that deliver benefits to cyber-physical systems developers. The European project “Integrated Tool Chain for Model-based Design of Cyber-Physical Systems” (INTO-CPS) has developed a well-founded tool chain for CPS design, based on the Functional Mock-up Interface standard, and supported by methodological guidance. The focus of the project has been on the delivery of a sound foundation, an open chain of compatible and usable tools, and a set of accessible guidelines that help users adapt the technology to their development needs

CoSim20: An Integrated Development Environment for Accurate and Efficient Distributed Co-Simulations

International audienceThe development of Cyber-Physical Systems involves several disciplines and stakeholders, which use heterogeneous models and formalisms to specify the system and make early validation and verification. In order to understand the behaviour emerging from the heterogeneous models, a collaborative simulation (co-simulation) can be used. To make it happen, the system engineer must define a correct coordination of the different executable models, which can be distributed over different enterprises. This is an important but difficult (and error prone) task that can not be done without information about the behavioral semantics of each model. In this paper, we introduce an integrated development environment which allows 1) to import different executable models (named simulation units), 2) to graphically connect them with rich connectors and 3) to generate a dedicated, accurate and efficient distributed co-simulation. The framework is based on Eclipse EMF for the modeling part and on ∅MQ for the deployment. It is named CoSim20

Tools for modelling and simulating the Smart Grid

The Smart Grid (SG) is a Cyber-Physical System (CPS) considered a critical infrastructure divided into cyber (software) and physical (hardware) counterparts that complement each other. It is responsible for timely power provision wrapped by Information and Communication Technologies (ICT) for handling bi-directional energy flows in electric power grids. Enacting control and performance over the massive infrastructure of the SG requires convenient analysis methods. Modelling and simulation (M&S) is a performance evaluation technique used to study virtually any system by testing designs and artificially creating 'what-if' scenarios for system reasoning and advanced analysis. M&S avoids stressing the actual physical infrastructure and systems in production by addressing the problem in a purely computational perspective. Present work compiles a non-exhaustive list of tools for M&S of interest when tackling SG capabilities. Our contribution is to delineate available options for modellers when considering power systems in combination with ICT. We also show the auxiliary tools and details of most relevant solutions pointing out major features and combinations over the years

A PVS-Simulink Integrated Environment for Model-Based Analysis of Cyber-Physical Systems

This paper presents a methodology, with supporting tool, for formal modeling and analysis of software components in cyber-physical systems. Using our approach, developers can integrate a simulation of logic-based specifications of software components and Simulink models of continuous processes. The integrated simulation is useful to validate the characteristics of discrete system components early in the development process. The same logic-based specifications can also be formally verified using the Prototype Verification System (PVS), to gain additional confidence that the software design complies with specific safety requirements. Modeling patterns are defined for generating the logic-based specifications from the more familiar automata-based formalism. The ultimate aim of this work is to facilitate the introduction of formal verification technologies in the software development process of cyber-physical systems, which typically requires the integrated use of different formalisms and tools. A case study from the medical domain is used to illustrate the approach. A PVS model of a pacemaker is interfaced with a Simulink model of the human heart. The overall cyber-physical system is co-simulated to validate design requirements through exploration of relevant test scenarios. Formal verification with the PVS theorem prover is demonstrated for the pacemaker model for specific safety aspects of the pacemaker design

Modeling and Simulation Methodologies for Digital Twin in Industry 4.0

The concept of Industry 4.0 represents an innovative vision of what will be the factory of the future. The principles of this new paradigm are based on interoperability and data exchange between dierent industrial equipment. In this context, Cyber- Physical Systems (CPSs) cover one of the main roles in this revolution. The combination of models and the integration of real data coming from the field allows to obtain the virtual copy of the real plant, also called Digital Twin. The entire factory can be seen as a set of CPSs and the resulting system is also called Cyber-Physical Production System (CPPS). This CPPS represents the Digital Twin of the factory with which it would be possible analyze the real factory. The interoperability between the real industrial equipment and the Digital Twin allows to make predictions concerning the quality of the products. More in details, these analyses are related to the variability of production quality, prediction of the maintenance cycle, the accurate estimation of energy consumption and other extra-functional properties of the system. Several tools [2] allow to model a production line, considering dierent aspects of the factory (i.e. geometrical properties, the information flows etc.) However, these simulators do not provide natively any solution for the design integration of CPSs, making impossible to have precise analysis concerning the real factory. Furthermore, for the best of our knowledge, there are no solution regarding a clear integration of data coming from real equipment into CPS models that composes the entire production line. In this context, the goal of this thesis aims to define an unified methodology to design and simulate the Digital Twin of a plant, integrating data coming from real equipment. In detail, the presented methodologies focus mainly on: integration of heterogeneous models in production line simulators; Integration of heterogeneous models with ad-hoc simulation strategies; Multi-level simulation approach of CPS and integration of real data coming from sensors into models. All the presented contributions produce an environment that allows to perform simulation of the plant based not only on synthetic data, but also on real data coming from equipments