Cyber-physical Energy Systems Research Articles

On the Feasibility of Market Manipulation and Energy Storage Arbitrage via Load-Altering Attacks

Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network.

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems.

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

A cyber physical energy system (CPES) involves a combination of processing, network, and physical processes. The smart grid plays a vital role in the CPES model where information technology (IT) can be related to the physical system. At the same time, the machine learning (ML) models find useful for the smart grids integrated into the CPES for effective decision making. Also, the smart grids using ML and deep learning (DL) models are anticipated to lessen the requirement of placing many power plants for electricity utilization. In this aspect, this study designs optimal multi-head attention based bidirectional long short term memory (OMHA-MBLSTM) technique for smart grid stability prediction in CPES. The proposed OMHA-MBLSTM technique involves three subprocesses such as pre-processing, prediction, and hyperparameter optimization. The OMHA-MBLSTM technique employs min-max normalization as a pre-processing step. Besides, the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES. At the same time, the moth swarm algorithm (MHA) is utilized for optimally modifying the hyperparameters involved in the MBLSTM model. To ensure the enhanced outcomes of the OMHA-MBLSTM technique, a series of simulations were carried out and the results are inspected under several aspects. The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

A New False Data Injection Attack Detection Model for Cyberattack Resilient Energy Forecasting

As power systems are gradually evolving into more efficient and intelligent cyber–physical energy systems with the large-scale penetration of renewable energies and information technology, they become increasingly reliant upon more accurate and complex forecasting. The accuracy and generalizability of the forecasting rest, to a great extent, upon the data quality, which is very susceptible to cyberattacks. False data injection (FDI) attacks constitute a class of cyberattacks that could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby deteriorating the forecasting performance causing catastrophic consequences in the power system. This article proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resiliency of energy forecasting systems. The proposed mechanism is based on cross-validation, least-squares, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</i> -score metric providing accurate detections with low computational cost and high scalability without utilizing either system’s models or parameters. The effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models. Experiments indicate that corrupted data injected into input, output, and input–output data is properly located and removed, whereby the accuracy and generalizability of the final forecasts are recovered.

Exploring Cyber-Physical Energy and Power System: Concepts, Applications, Challenges, and Simulation Approaches

With the advancement of data-acquisition systems, information technology, and network technologies, the energy and power system has entered the deep integration of the cyber and physical sides as known as cyber-physical energy and power system (CPEPS) leading to more attention from researchers and practitioners in industry. The feedback loops in which physical processes affect cyber parts and vice versa, therefore, can gain greater efficiency, resilience, and intelligence through the data exchange process between two layers. The concept of CPEPS has been existed for a long time and its applications are gradually increasing in the real world, however, there has been still many potential risks and challenges in the research and development process. Simulation method is a common and simple approach for the purpose of analyzing, evaluating, and considering solutions before actual implementation. For this purpose, this paper provides first the framework of CPEPS, and current applications and challenges that researchers are facing. Then, the next section is the overview of simulation methods with different software and tools in CPEPS research in order to provide researchers with an optimal approach to the ongoing and further research towards the transition of digitalization on energy and power system.

Virtualization for performance guarantees of state estimation in cyber-physical energy systems

The strong interdependence between power systems and information and communication technologies (ICT) makes cyber-physical energy systems susceptible to new disturbances. State estimation (SE) is a vital part of energy management systems, for several monitoring, management, and control services. Failure of SE service leads to loss of situational awareness, which in turn has a detrimental impact on the grid operation. Therefore, it is essential to maintain the performance of SE service. Modern technologies such as virtualization are key drivers to provide the flexibility to reallocate, reconfigure, and manage services as a countermeasure to mitigate the impact of disturbances. This paper introduces the virtualization of SE service as a potential approach to maintain its performance in the case of disturbances. Following a review of existing approaches for maintaining the performance of the SE service, a description of the proposed approach is provided. The benefits of virtualization of SE service are demonstrated via a simulation test platform with an ICT-enriched CIGRE MV benchmark grid.

A Hybrid Model for Analysing Disturbance Propagation in Cyber–Physical Energy Systems

Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services.

Resilient Cyber-Physical Energy Systems Using Prior Information Based on Gaussian Process

The power grid infrastructure is a large-scale, heterogeneous, and complex cyber-physical system, which forms the lifeline of modern societies. The trend of tight coupling of physics, communication, and computation in cyber-physical energy systems (CPESs) is evident by the inclusion of numerous measurement sensors. This contributes to enhancing the monitoring and control functionalities of CPESs. At the same time, the occurrence of adverse effects constitutes a vital dimension of CPES operation. Increasing the resilience of critical energy systems is of key importance for safeguarding the national economy and security. This article considers the problem of optimal estimation with sensing measurements subject to arbitrary corruption resulting from adverse effects. Such signals can cause false situation awareness and/or trigger a sequence of cascading effects leading to an ultimate system failure. We formulate the problem as a constrained optimization with additional prior information posed as a set inclusion constraint on the measurement vector. It is shown that if the prior set satisfies certain conditions, the resulting recovery error bound is improved. The approach demonstrates enhancement of the CPES resiliency by using the Gaussian process as the basis of a prior generative probabilistic regression model using historical data. The validation of the resiliency mechanism using prior information is performed using the New York Independent System Operator grid data, demonstrating 100% successful state recovery for up to 60% of CPES sensor failures.

A clustering-based framework for searching vulnerabilities in the operation dynamics of Cyber-Physical Energy Systems

Being highly digitalized and interconnected through smart sensors in an advanced communication network, Cyber-Physical Energy Systems (CPESs) enhance the dynamic control under different operational conditions, but bring up concerns on cybersecurity failures due to deliberate cyber-attacks. In this study, we propose a clustering-based framework for searching the minimum combination of the most vulnerable communication channels (between sensors and the control center) that are susceptible to the operation dynamics of CPESs. We first adopt a mixed-integer linear programming (MILP) approach to select the extreme operational condition period clusters, taking into account the multiple sources of uncertainty influencing the system dynamic behavior. For each cluster, we inject undetectable false data vectors into different sets of communication channels, and test their effects on system functionalities by resorting to the Conditional Value-at-Risk (CVaR) measure. The minimum combination of vulnerabilities is identified, when its False Data Injection Attacks (FDIAs) start to deviate the system functionality from the normal range.We illustrate the proposed framework by application to a renewable energy system susceptible to primary renewable resources availability. Specifically, the IEEE 30-bus test system is taken as case study and considered under FDIA scenarios in weather-related extreme operation transients, when accidents can aggravate damages.

Authenticating source information of distribution synchrophasors at intra-state locations for cyber-physical resilient power networks

As power systems are gradually evolving into more efficient and intelligent cyber-physical energy systems with the large-scale penetration of renewable energies and information and communication technologies, they become increasingly reliant upon more accurate monitoring and fast control. Phasor Measurement Units (PMUs) collect high-precision Distribution Synchrophasors (DS) data regarding the system dynamics and provide real-time situational awareness for better monitoring and control of large-scale power grids. The accuracy and generalizability of the PMUs heavily rely upon the data quality of DS measurements, which is very susceptible to newly emerging “Source ID Mix” data spoofing attacks. Such attacks could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby jeopardizing most DS-based applications and even causing catastrophic power interruptions. This paper proposes a novel data-driven source authentication method to automatically identify the source information of DS collected from multiple intra-state locations and thus enhance the reliability and cybersecurity of power systems. The proposed method integrates Mathematical Morphological Decomposition (MMD) and Multi-Weighted Deep Stacking Forest (MLW-DSF) for providing accurate source authentication with a low computational cost which requires neither system’s models nor parameters. The effectiveness of the proposed method is corroborated through seven “state-of-the-art” data-driven models by using real-life DS measurements of power networks in Queensland state.© 2017 Elsevier Inc. All rights reserved.

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

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 energy system simulation tool: framework for network co-simulation, GridLAB-D and ns-3

A cyber-physical energy system consists of information and communication technology, advanced power electronic devices, energy sources, and smart appliances. A simulator is required for research and to further enhancements in the cyber-physical energy system. The integration of a power system and information and communication technology raises the problem of integration of time-driven activity and event-driven activity. The power system simulators are time-driven, while the network communication simulators are event-driven. The Pacific Northwest National Laboratory, Richland, Washington developed a tool named 'framework for network co-simulation (FNCS)' which co-simulates ns-3 (a network simulator) and GridLAB-D (a power distribution system simulator). This work details the enhancement of the FNCS application program (which is integrated into ns-3) to incorporate the functionalities, namely: 1) internal real-time price calculation; 2) day-ahead charging schedule of a battery. The enhancement has been used for the development of cyber-physical application programs, namely, internal real-time pricing and battery charging schedules.

Cyber-physical energy system simulation tool: framework for network co-simulation, GridLAB-D and ns-3

Cyber-physical energy system simulation tool: framework for network co-simulation, GridLAB-D and ns-3

A survey on policies, modelling and security of cyber‐physical systems in smart grids

AbstractA 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.

A COMPREHENSIVE ANALYSIS OF THREATS AND COUNTERMEASURES IN VIRTUALIZED CYBER-PHYSICAL ENERGY SYSTEMS

High integration of distributed energy resources raises the challenge of reliable operation to power system operators. In order to improve the ability to monitor and control power systems, automation systems are necessary. However, the operational flexibility of automation systems to deal with abnormal situations is currently limited. In this regard, virtualization as a key enabling technology to improve the operational flexibility in Cyber-Physical Energy Systems has been proposed. To this end, grid services (e.g. state estimation, coordinated voltage control), which are traditionally bound to hardware, are separated from underlying computation platforms. This provides modularity and isolation of grid services, which enables monitoring, managing and deploying them independently. However, virtualizing grid services, resulting in increased system complexity, can cause new security concerns. This paper analyzes virtualized Cyber-Physical Energy Systems from a security viewpoint and identifies associated security threats. Based on security requirements and potential solutions addressed by IEC 62351 security standard, a set of studies is conducted to analyze the effect of traditional security mechanisms, when applied to virtualized Cyber-Physical Energy Systems. Finally, possible software solutions and recommendations that protect the system against threats are proposed. Future research directions and open problems are stated at the end.

Multi-objective tradeoff optimization of predictive adaptive cruising control for autonomous electric buses: A cyber-physical-energy system approach

Recently, Cyber-Physical System (CPS) has served as a cutting-edge technology for next-generation industrial applications, and is developing rapidly and inspires many application domains. The autonomous electric bus (AEB) that integrates the communication, perception, and control within vehicle dynamics is a typical CPS. However, the energy management is ignored in the vehicle cyber-physical system. Thus, a novelty cyber-physical-energy system (CPES) approach with deep integration and interaction of the cyber system with physical system for the energy management used cruising control is imposed. Under the new CPES framework, the energy consumption and battery capacity degradation are optimized in different driving environment. Simulation results show that the tradeoff optimization control algorithm can keep battery health by optimizing motor operating mode with a slightly penalty on the energy consumption. The total system cost effective analysis shows that the battery service lifetime is improved by about 41.59% with the proposed method, and even with the slightly sacrifice of power consumption, the whole vehicle economy is improved by about 10.08%, compared with the strategy optimizing the power consumption only. Additionally, the equivalent driving distance is significantly extended up to 70.87% when compared to the case that only energy consumption is optimized. Besides, the AEB with CPES framework not only keeps the host vehicle within the safe distance with the preceding vehicle, but optimizes the motion planning as well. The results validate the feasibility and effectiveness of the CPES-based optimization framework, and demonstrate the advantages of the tradeoff optimization energy management strategy.

Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components

Cyber-Physical Energy Systems (CPESs) are energy systems which rely on cyber components for energy production, transmission and distribution control, and other functions. With the penetration of Renewable Energy Sources (RESs), CPESs are required to provide flexible operation (e.g., load-following, frequency regulation) to respond to any sudden imbalance of the power grid, due to the variability in power generation by RESs. This raises concerns on the reliability of CPESs traditionally used as base-load facilities, such as Nuclear Power Plants (NPPs), which were not designed for flexible operation, and more so, since traditionally only hardware components aging and stochastic failures have been considered for the reliability assessment, whereas the contribution of the degradation and aging of the cyber components of CPSs has been neglected. In this paper, we propose a multi-state model that integrates the hardware components stochastic failures with the aging of cyber components, and quantify the unreliability of CPES in load-following operations under normal/emergency conditions. To show the application of the reliability assessment model, we consider the case of the Control Rod System (CRS) of a NPP typically used for a base-load energy supply.

Application Prospect of Energy Cyber-Physical Systems in Operation Optimizing of Wet Flue Gas Desulfurization

Limestone-gypsum WFGD is the most widely used to control SO2 emission of coal-fired power plants in China. Although SO2 and a variety of pollutants can be efficiently removed by WFGD. The WFGD has great potential in controlling SO2 emissions and saving energy and reducing consumption due to the influence of operating conditions of coal-fired power plants after ultra-low emissions. In order to explore and analyse the feasibility of e-CPSs in operation optimizing of WFGD. The concept of e-CPSs is introduced systematically. Then the main building blocks of e-CPSs applied to WFGD are described in detail. The existing problems of WFGD are analysed. The operation optimizing method of WFGD based on e-CPSs is introduced. E-CPSs as a new more efficient and reliable intelligent control system. The prospect and benefit of its application in WFGD of coal-fired power plant are discussed in this paper.

Cyber-Physical Energy Systems Security: Threat Modeling, Risk Assessment, Resources, Metrics, and Case Studies

Cyber-physical systems (CPS) are interconnected architectures that employ analog and digital components as well as communication and computational resources for their operation and interaction with the physical environment. CPS constitute the backbone of enterprise (e.g., smart cities), industrial (e.g., smart manufacturing), and critical infrastructure (e.g., energy systems). Thus, their vital importance, interoperability, and plurality of computing devices make them prominent targets for malicious attacks aiming to disrupt their operations. Attacks targeting cyber-physical energy systems (CPES), given their mission-critical nature within the power grid infrastructure, can lead to disastrous consequences. The security of CPES can be enhanced by leveraging testbed capabilities in order to replicate and understand power systems operating conditions, discover vulnerabilities, develop security countermeasures, and evaluate grid operation under fault-induced or maliciously constructed scenarios. Adequately modeling and reproducing the behavior of CPS could be a challenging task. In this paper, we provide a comprehensive overview of the CPS security landscape with an 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 that 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 the 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.

Impact Analysis of Team Leader on Green Behaviors Based on Affective Events Theory in Cyber Physical Social Energy System

With the formation of the concept of Cyber Physical Social Energy System, social and human factors become more prominent and stay at an important position. The interaction between the energy system and the social system will have a huge impact on the development of the energy system. As one of the hot topics in the CPSES area, the influence of the transformational leadership on the team green behaviors has attracted extensive attention of the academics. By influencing the enterprise behaviors through the leadership behaviors, it can promote the development of the Power Energy Internet Platform Technology and accelerate the application of the technology. Based on the Affective Events Theory, this article discusses the influencing mechanism and key factors of the transformational leadership on the team green behaviors. A total of 3 821 employees and their supervisors from 457 work teams in 19 energy enterprises in the Pearl River Delta region were tracked. This article finds that the transformational leadership positively affects the team green behavior. The connectedness to nature plays a positive mediating role between the transformational leadership and the team green behavior. The work features has a negative regulating effect on the relationship between the transformational leadership and the connectedness to nature, while the team task performance has a positive regulating effect. This article expands the application scope of the AET theory, discusses details of the influence mechanism between the transformational leadership and the team green behaviors, and provides a direction for further theoretical exploration.