Advanced data-driven anomalies detection and diagnosis for cyber-physical energy systems

Currently, enhancing sustainability, and in particular reducing energy consumption, is a huge challenge for manufacturing enterprises. The vision of the fourth industrial revolution (so-called “industry 4.0”) is not only to optimize production and minimize costs, but also to reduce energy consumption and enhance product life-cycle management. To address this challenge, a multi-agent architecture aimed at elaborating predictive and reactive energy-efficient scheduling through collaboration between cyber physical production and energy systems is proposed in this paper. Smart, sustainable decision tools for cyber physical production systems (CPPS) and cyber physical energy systems (CPES) are proposed. The decision tools are data-driven, agent-based models with dynamic interaction. The main aim of agent behaviours in the cyber part of CPPS is to find a predictive and reactive energy-efficient schedule. The role of agents in CPES is to control the energy consumption of connected factories and switch between the different renewable energy sources. Dynamic mechanisms in CPPS and CPES are proposed to adjust the energy consumption of production systems based on the availability of the renewable energy. The proposed approach was validated on a physically distributed architecture using networked embedded systems and real-time data sharing from connected sensors in each cyber physical systems. A series of instances inspired from the literature were tested to assess the performance of the proposed method. The results prove the efficiency of the proposed approach in adapting the energy consumption of connected factories based on a real-time energy threshold.

Cyber-physical systems (CPS) are interconnected architectures that employ analog, digital, and communication resources for their interaction with the physical environment. CPS are the backbone of enterprise, industrial, and critical infrastructure. Thus, their vital importance makes them prominent targets for malicious attacks aiming to disrupt their operations. Attacks targeting cyber-physical energy systems (CPES), given their mission-critical nature, can have disastrous consequences. The security of CPES can be enhanced leveraging testbed capabilities to replicate power system operations, discover vulnerabilities, develop security countermeasures, and evaluate grid operation under fault-induced or maliciously constructed scenarios. In this paper, we provide a comprehensive overview of the CPS security landscape with emphasis on CPES. Specifically, we demonstrate a threat modeling methodology to accurately represent the CPS elements, their interdependencies, as well as the possible attack entry points and system vulnerabilities. Leveraging the threat model formulation, we present a CPS framework designed to delineate the hardware, software, and modeling resources required to simulate the CPS and construct high-fidelity models which can be used to evaluate the system's performance under adverse scenarios. The system performance is assessed using scenario-specific metrics, while risk assessment enables system vulnerability prioritization factoring the impact on the system operation. The overarching framework for modeling, simulating, assessing, and mitigating attacks in a CPS is illustrated using four representative attack scenarios targeting CPES. The key objective of this paper is to demonstrate a step-by-step process that can be used to enact in-depth cybersecurity analyses, thus leading to more resilient and secure CPS.

One of the core components of Energy 4.0 are Cyber-physical energy systems (CPES). CPES are intelligent systems that combine embedded systems, automation, and information and communication technologies into the power grid. These outcomes produce a smart grid that can provide end consumers with dependable, secure, and clean energy in real-time. One of the alleged properties of cyber-physical energy systems is self-organization and autonomy. When used in this context, the term “self-organization” refers to a complex system like the Cyber-Physical Energy System that can adjust to changes in its surroundings and complete tasks independently without human assistance.On the other hand, autonomous operations are carried out on CPES because of its complexity. Cyber-Physical Energy Systems may adapt to changes and learn on their own without human involvement when they operate autonomously. Disruptive technologies like artificial intelligence and machine learning will foster this capacity to learn and adapt to change. This paper thoroughly analyzed self-organization and autonomous operations in cyber-physical energy systems. Self-organization and autonomous operations, it was claimed, will help in overcoming the difficulties faced during operations in Cyber-Physical Systems. Additionally, as “management” is one of the key problems in energy systems, creating a self-organized and autonomous cyber-physical energy system would help offer dependable, clean energy in real time without requiring human intervention.

The fourth industrial revolution made modern technologies utilized in production control systems possible. Cyber-Physical Energy Systems are one way that Cyber-Physical Systems are applied to energy systems. These systems are intelligent due to the integration of information and communication technology into the electrical grid. This study examines the security and auditability of cyber-physical energy systems. Due to the vulnerability of CPES to cyberattacks, it is necessary to build security countermeasures to lessen the frequency of attacks carried out by nefarious attackers. However, one of the points of attack is the utilities' financial reports. The idea of auditability is crucial if we want to prevent these attacks on utilities' financial reports. The capacity of an auditor to obtain correct results while examining a company's financial reporting is known as auditability. An excellent audit depends on the auditor's skill, the organization's flawless documentation, the clarity of its effective reporting, and if administrators make key papers available to the auditor. In conclusion, auditability and security are crucial in Cyber-Physical Energy Systems for safe energy consumption and utilities financial reporting.

There are many challenges and concerns about the impact of integrating more and more wind power farms (WPFs) into the power grid. A WPF can be considered as a cyber-physical energy system (CP-ES) due to the coupling between the physical power system and the cyber communication network. While the information and communication technology (ICT) infrastructure is the key concept to support a reliable operation, real-time monitoring and control of large-scale wind farms, it has been less addressed and rarely discussed. This work aims to design the ICT network architecture for a cyber-physical wind energy system (CP-WES) which consists of wind turbines, meteorological masts, substation, and a local control center. We consider different applications: operation data (analogue measurements & status information) from wind turbines, meteorological data from the meteorological towers, and protection & control data from intelligent electronic devices (IEDs). A real wind farm project (Zafarana-1, Egypt) has been considered as a case study. The proposed ICT network architecture is modeled and evaluated using OPNET Modeler. Network topology, link capacity, and end-to-end delay are three critical parameters investigated in this work. Our network model is validated by analyzing the simulation results.

Robust inter-reliant resilience of cyber-physical smart grids

Automation and the digital transformation have become important factors in the energy sector, as modern energy systems increasingly rely on communication and information technology to combine smart controls with hardware infrastructure. With the emergence of cyber-physical systems (CPS) as a trans-disciplinary field, such modern energy systems can be classified as cyber-physical energy systems (CPES), integrating the related research and development within a broader scope. An important aspect of the research and development related to CPS is to bridge the gap between the traditional engineering domains and computer science. This is especially true for CPES, where the related engineering domains have in the past come up with proven and reliable methods for designing even large and complex systems. However, existing modeling and simulation tools still struggle to cover all aspects of CPES. Hence, a combination of universal modeling languages and established, domain-specific tools (such as grid simulators and telecommunication simulators) is necessary. New methods, tools and algorithms are needed that are compact, computationally inexpensive, potentially self-organizing and intrinsically stable if applied to real energy systems.

In the context of the continuous deepening of energy Internet construction, the coupling degree between information systems and energy supply systems is deepening. In view of the problems that the energy supply reliability of the energy system does not take into account the fault of the information system, and the influence of the fault of the information component on the reliability of the system is not quantitatively analyzed. This paper proposed a cyber physical energy system reliability impact analysis method considering multivariate information disturbance. Firstly, the cyber physical energy system was taken as the research object, and the typical architecture of the cyber physical energy system was introduced. Secondly, the state model of the key equipment in the cyber physical energy system and the static connection and dynamic transmission model of the system were constructed. Besides, the operational characteristics and reliability evaluation indicators were put forward. Finally, the effectiveness and practicability of the proposed evaluation methods were verified by constructing practical cases, and the factors that affecting reliability were further analyzed.

This paper describes the application of Real-Time Physical Systems (RTPS) as a novel approach to model the physical process of Cyber-Physical Systems (CPS), with specific focus on Cyber-Physical Energy Systems (CPES). The proposed approach is based on the real-time scheduling theory which is nowadays developed to manage concurrent computing tasks on processing platforms. Therefore, the physical process is modeled in terms of real-time parameters and timing constraints, so that real-time scheduling algorithms can be applied to manage the timely allocation of resources. The advantage is to leverage the strong mathematical background of real-time systems in order to achieve predictability and timing correctness on the physical process behind the considered CPS. The paper provides an introduction to the possible application of RTPS to energy systems. The analogy between real-time computing systems and energy systems is presented; moreover, the relationship between RTPS and related research fields is traced. Finally, the introduced techniques are proposed to optimize the peak load of power consumption in electric power systems. This method is suitable for systems spanning from small networks to smart grids.

Explainability can help cyber-physical systems alleviating risk in automating decisions that are affecting our life. Building an explainable cyber-physical system requires deriving explanations from system events and causality between the system elements. Cyber-physical energy systems such as smart grids involve cyber and physical aspects of energy systems and other elements, namely social and economic. Moreover, a smart-grid scale can range from a small village to a large region across countries. Therefore, integrating these varieties of data and knowledge is a fundamental challenge to build an explainable cyber-physical energy system. This paper aims to use knowledge graph based framework to solve this challenge. The framework consists of an ontology to model and link data from various sources and graph-based algorithm to derive explanations from the events. A simulated demand response scenario covering the above aspects further demonstrates the applicability of this framework.

Recently, cyber physical system (CPS) has gained significant attention which mainly depends upon an effective collaboration with computation and physical components. The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems (CPES). At the same time, the rising ubiquity of wireless sensor networks (WSN) in several application areas makes it a vital part of the design of CPES. Since security and energy efficiency are the major challenging issues in CPES, this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms (EASCPS-MA). The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection. The EASCPS-MA technique encompasses two main stages namely improved fruit fly optimization algorithm (IFFOA) based clustering and optimal deep stacked autoencoder (OSAE) based intrusion detection. Besides, the optimal selection of stacked autoencoder (SAE) parameters takes place using root mean square propagation (RMSProp) model. The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects. The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

Cyber-Physical Systems are becoming more autonomous, interconnected, complex and adaptive, and are expected to operate in highly dynamic environments. This is especially challenging for energy ecosystems that are increasingly difficult to control and maintain as the number of participating manufacturers and users grows. Digital Twins help analyze and predict these systems in the form of digital reflections that operate in parallel with the physical system. In this paper, we use Machine Learning to improve the predictive power of Digital Twins for Cyber-Physical Energy Systems. Specifically, we use a Temporal Convolutional Neural Network model to learn the temporal patterns in the system and predict its responsiveness to specific power setpoint instructions. Real-life data from ten batteries were used to predict the behavior over time. Compared to the baseline model that uses the prior probability of response and the average response rate within the configured time window, the model predicts the batteries’ responsiveness more accurately. The more temporal information is used as input for prediction, the better the model performs in both precision and recall. The results show that this compensates for the lack of information when fewer metrics are used. The use of Machine Learning for Digital Twins can help maintain a heterogeneous energy ecosystem, while minimizing the need to acquire or disclose detailed information.

The current study is a Systematic Literature Review (SLR) that investigates how digitization reforms the traditional complex systems approaches: engaging the public in Cyber-Physical Systems (CPS) evolves the paradigm in Cyber-Physical-Social Systems (CPSS). The scope of the study is Energy Systems and the main motive is to survey the Digital Twin (DT) as a digitization tool for integrating society in energy CPS. Currently, Cyber-Physical-Social Energy Systems (CPSES) are configured by means of involving end-users in the Energy ecosystem. The SLR discloses the significant application of DT in forecasting energy demand and consumption patterns by end-users, along with dispatchable generation scheduling out of fluctuating renewable energy sources on demand. The secondary outcome of the study emphasizes the main features of CPSES and the role of DT in supporting them.

A more reliable, efficient, and resilient smart grid depends on the applications of advanced information and communication technologies to support new functions and controls. The critical infrastructure of a smart grid consists of some major components such as monitoring, controls, communication protocol and software. The cyber‐physical system (CPS), which integrates these components, is an important enabler for the expected transition of the energy system driven by decarbonization, digitalization and decentralization. This paper provides an overview of the policy drivers and barriers for the implementation of CPS in power systems. With the vast deployment of distributed energy resources (DERs), there is increasing demand to model the hardware, software and their interactions in the smart grid environment. This paper reviews the modelling and applications of intelligent CPS for decentralized energy systems. The integration of DERs and the supportive infrastructure make modern power systems more vulnerable and less reliable to external threats such as terrorist intrusion. There are growing concerns over the risk of cyber‐attacks in mart grids. This paper surveys the latest progress on critical infrastructure identification and protection, as well as risk assessment and mitigation methods for cyber‐attacks. Finally, some advanced issues in cyber‐physical energy systems are addressed.

Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems.