Petri Nets for Smart Grids: The Story So Far

Since the energy domain is in a transformative shift towards sustainability, the integration of new technologies and smart systems into traditional power grids has emerged. As an effective approach, Petri Nets (PN) have been applied to model and analyze the complex dynamics in Smart Grid (SG) environments. However, we are currently missing an overview of types of PNs applied to different areas and problems related to SGs. Therefore, this paper proposes four fundamental research questions related to the application areas of PNs in SGs, PNs types, aspects modelled by PNs in the identified areas, and the validation methods in the evaluation. The answers to the research questions are derived from a comprehensive and interdisciplinary literature analysis. The results capture a valuable overview of PNs applications in the global energy landscape and can offer indications for future research directions

Methodologies synthesis

This deliverable deals with the modelling and analysis of interdependencies between critical infrastructures, focussing attention on two interdependent infrastructures studied in the context of CRUTIAL: the electric power infrastructure and the information infrastructures supporting management, control and maintenance functionality. The main objectives are: 1) investigate the main challenges to be addressed for the analysis and modelling of interdependencies, 2) review the modelling methodologies and tools that can be used to address these challenges and support the evaluation of the impact of interdependencies on the dependability and resilience of the service delivered to the users, and 3) present the preliminary directions investigated so far by the CRUTIAL consortium for describing and modelling interdependencies

Overview of electric energy distribution networks expansion planning

Planning of the electric distribution networks is complex and about upgrading the system to satisfy the demand and constraints with the best economic plan. The planning alternatives include the expansion of substations, installing new distributed generation (DG) facilities, upgrading distribution feeders, etc. In the modern networks, distribution planners must gain the confidence of the reversibility of the investment where renewable energy resources (RERs) inject clean and cost-effective electrical power to respond to the rising demand and satisfy environmental standards. This paper is an exhaustive review on the distribution network expansion planning (DEP) including the modelling of DEP (possible objective functions, problem constraints, different horizon time, and problem variables), optimization model (single/multi-objective), the expansion of distributed energy resources (DERs), problem uncertainties, etc. We discuss the requirements of integrated energy district master planning to avoid conflicts between the goal of independence of district planning on energy, e.g. heat and electricity, and that of dependencies on the local electric utilities regarding instant power balance and stability services. Finally, we describe the primary future R&D trends in the field of distribution network planning

List of requirements on formalisms and selection of appropriate tools

This deliverable reports on the activities for the set-up of the modelling environments for the evaluation activities of WP5. To this objective, it reports on the identified modelling peculiarities of the electric power infrastructure and the information infrastructures and of their interdependencies, recalls the tools that have been considered and concentrates on the tools that are, and will be, used in the project: DrawNET, DEEM and EPSys which have been developed before and during the project by the partners, and M\uf6bius and PRISM, developed respectively at the University of Illinois at Urbana Champaign and at the University of Birmingham (and recently at the University of Oxford)

Stochastic system of systems architecture for adaptive expansion of smart distribution grids

The incorporation of the reconfiguration into the expansion planning of smart distribution networks is addressed in this paper, in which the potential of distributed energy resources and demand response (DR) are modeled. The system of systems (SoS) architecture is employed to model the strategy of a distribution company (DISCO), a private investor (PI), and a DR provider (DRP). The SoS is an efficient modeling architecture to model the behavior of independent and autonomous systems with distinct objective functions who are able to share some data and work together. The aim of the DISCO is to upgrade the system with the optimal cost and reliability, whereas the PI and DRP want to maximize their profit. The DISCO should try to persuade the PI to install DGs (Distributed generations) by offering the guaranteed purchasing prices. Furthermore, the DRP is a market player who can negotiate with the DISCO to sign a contract to sell the purchased DR capacities from the customers. The uncertainties of the DISCO problem is handled by using the chance-constraint method, but the PI and DRP use the conditional value at risk method to model their uncertainties. Finally, to solve the proposed model, the multiobjective optimization algorithm is employed

On the use of probabilistic model-checking for the verification of prognostics applications

Prognostics aims to improve asset availability through intelligent maintenance actions. Up-to-date remaining useful life predictions enable the optimization of maintenance planning. Verification of prognostics techniques aims to analyze if the prognostics application meets the design requirements. Online prognostics applications depend on the data-gathering hardware architecture to perform correct prognostics predictions. Accordingly, when verifying prognostics requirements compliance, it is necessary to include the effect of hardware failures on prognostics predictions. In this paper we investigate the use of formal verification techniques for the integrated verification of prognostics applications including hardware and software components. Focusing on the probabilistic model-checking approach, a case study from the power industry shows the validity of the proposed framework

Application and Control Aware Communication Strategies for Transportation and Energy Cyber-Physical Systems

Cyber--Physical Systems (CPSs) are a generation of engineered systems in which computing, communication, and control components are tightly integrated. Some important application domains of CPS are transportation, energy, and medical systems. The dynamics of CPSs are complex, involving the stochastic nature of communication systems, discrete dynamics of computing systems, and continuous dynamics of control systems. The existence of communication between and among controllers of physical processes is one of the basic characteristics of CPSs. Under this situation, some fundamental questions are: 1) How does the network behavior (communication delay, packet loss, etc.) affect the stability of the system? 2) Under what conditions is a complex system stabilizable?;In cases where communication is a component of a control system, scalability of the system becomes a concern. Therefore, one of the first issues to consider is how information about a physical process should be communicated. For example, the timing for sampling and communication is one issue. The traditional approach is to sample the physical process periodically or at predetermined times. An alternative is to sample it when specific events occur. Event-based sampling requires continuous monitoring of the system to decide a sample needs to be communicated. The main contributions of this dissertation in energy cyber-physical system domain are designing and modeling of event-based (on-demand) communication mechanisms. We show that in the problem of tracking a dynamical system over a network, if message generation and communication have correlation with estimation error, the same performance as the periodic sampling and communication method can be reached using a significantly lower rate of data.;For more complex CPSs such as vehicle safety systems, additional considerations for the communication component are needed. Communication strategies that enable robust situational awareness are critical for the design of CPSs, in particular for transportation systems. In this dissertation, we utilize the recently introduced concept of model-based communication and propose a new communication strategy to address this need. Our approach to model behavior of remote vehicles mathematically is to describe the small-scale structure of the remote vehicle movement (e.g. braking, accelerating) by a set of dynamic models and represent the large-scale structure (e.g. free following, turning) by coupling these dynamic models together into a Markov chain. Assuming model-based communication approach, a novel stochastic model predictive method is proposed to achieve cruise control goals and investigate the effect of new methodology.;To evaluate the accuracy and robustness of a situational awareness methodology, it is essential to study the mutual effect of the components of a situational awareness subsystem, and their impact on the accuracy of situational awareness. The main components are estimation and networking processes. One possible approach in this task is to produce models that provide a clear view into the dynamics of these two components. These models should integrate continuous physical dynamics, expressed with ordinary differential equations, with the discrete behaviors of communication, expressed with finite automata or Markov chain. In this dissertation, a hybrid automata model is proposed to combine and model both networking and estimation components in a single framework and investigate their interactions.;In summary, contributions of this dissertation lie in designing and evaluating methods that utilize knowledge of the physical element of CPSs to optimize the behavior of communication subsystems. Employment of such methods yields significant overall system performance improvement without incurring additional communication deployment costs

Modelling and Analysis of Critical Infrastructure for Situational Awareness Applications

Critical infrastructure forms an interdependent network, where individual infrastructure sectors depend on the availability of others in order to function. In such environment, faults easily propagate through the interlinked systems causing cascading failures. In order to effectively respond to incidents at national scale, it is necessary to maintain situational awareness by creating a common operational picture over all infrastructure sectors. A suitable way of modelling critical infrastructure and the interdependencies is required for building a system capable of delivering the needed information for obtaining robust situational awareness. This thesis presents a model of critical infrastructure for national scale situational awareness applications, as well as analysis methods for estimating current and future infrastructure status. The model uses directed graphs in conjunction with finite state transducers to present dependencies and operational status of critical infrastructure systems. Analysis method utilising graph centrality measures was developed for quantifying both system specific and infrastructure wide impact of disruptions. Additionally, an entropy based analysis method was created for estimating operational status of infrastructure systems in situations, where current data is not available. The electric grid and mobile networks of a coastal area of Finland were modelled using the presented methods. Dataset of system failures observed during a storm, in conjunction with simulation tools were used to evaluate the suitability of the framework for situational awareness tasks. Results indicate, that the proposed modelling and analysis methods are suitable for real time situational awareness applications