Physical Power System Research Articles

On Cyber-Physical Coupling and Distributed Control in Smart Grids

This article focuses on characterizing the impact of communication on distributed control performance in smart grid systems. Using a cyber-physical model of the smart grid and utilizing electromechanical wave propagation in transmission systems, a holistic approach is proposed to unify cyber-physical coupling representation. This is facilitated by establishing an event-propagation paradigm relating measurements, distributed control and the physical power systems network characteristics. We investigate how power system characteristics impose limitations on distributed control performance using the proposed approach. The proposed approach is then used to derive fundamental communication delay limits for effective distributed control, and comparative analysis of distributed control performance is investigated for example distributed control scenarios.

Coordinated Defense of Distributed Denial of Service Attacks against the Multi-Area Load Frequency Control Services

With the application of information and communication technology (ICT), the modern power system has gradually been updated to a typical cyber physical power system (CPPS). The deployment of distributed measurement devices enriches the application range of power communication services. However, due to the easy accessibility of distributed devices, it also creates favorable conditions for distributed denial-of-service (DDoS) attacks. In this paper, we focus on the security performance and defense strategies of the CPPS against DDoS attacks. In order to construct a coordinated defense in the power and information space, the cyber-attack process with a complete data flow of the CPPS needs to be described precisely. Therefore, a co-simulation technology-based platform is utilized to coordinate various layers in the CPPS and provide a unified research tool for the attack-defense test. On this basis, OPNET is used to replicate DDoS attacks in the information layer. Then, through the load frequency control (LFC) service of a multi-area interconnected power system, the influence of delays resulting from attacks on the control effect of the power layer is analyzed. Finally, to cope with the attack effects in both layers, detection measures of information and recovery measures of power quality are coordinated to eliminate attack consequences. Therefore, the stable operation of power services can be enabled.

VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems

The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE’s hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.

Dynamic Data Injection Attack Detection of Cyber Physical Power Systems With Uncertainties

Understanding potential behaviors of attackers is of paramount importance for improving the cybersecurity of power systems. However, the attack behaviors in existing studies are often modeled statically on a single snapshot, which neglects the reality of a dynamically time-evolving power system. Accordingly, a dynamic cyber-attack model with local network information is proposed to characterize the typical data injection attack with the integration of potential dynamic behaviors of an attacker. The proposed model collaboratively alters the meter measurement in a stealthy way to illegally contaminate the system state, thus posing severe threats to cyber physical power systems. We then develop a novel anomaly detection countermeasure from the perspective of state estimation to effectively recognize the dynamic injection attack. In this countermeasure, an interval state forecasting method is proposed to approximate the possible largest variation bounds of each state variable based on a worst-case analysis considering the forecasting uncertainties of renewable energy sources, electric loads, and network parameter perturbations. In addition, the kernel quantile regression is introduced and implemented to formulate the uncertainties in renewable energy and electric load forecast as a series of confidence intervals. When any state variable falls outside its preforecasted intervals, the proposed countermeasure detects the anomaly and sets an alarm condition indicating the possibility of data contamination. Finally, the results from our extensive studies on several IEEE standard test systems have been presented to demonstrate the feasibility of the dynamic attack and the effectiveness of the detection countermeasure.

Security Assessment for Cyber Physical Distribution Power System Under Intrusion Attacks

A cyber physical distribution power system (CPDS) is a large and complex infrastructure that coordinates the cyber communication system and the physical distribution power system. Because of the increasingly advanced information communication technology, the development of cyber physical distribution power system has caused key cyber security issues related to system operation. This paper is focused on realizing a unified system attack modeling and security assessment of an active distribution power system. In this paper, first we present an overview of the system operation from the fusion system perspective. The significant effects of network intrusion attacks on operational security are evaluated. A new unified cyber physical network model is established using a limited stochastic Petri net graph theory that considers refined firewalls and password components. Then, a security effectiveness evaluation method is proposed to analyze channel throughput variation and system robustness. Overall CPDS security risk values are determined based on physical influence coefficients. Finally, simulations of an improved IEEE-33 bus distribution power system and security assessment under intrusion attacks are described. The research work could raise awareness of the cyber intrusion threats and provide the basis for security defense.

Modelling Cyber Attacks on Electricity Market Using Mathematical Programming With Equilibrium Constraints

With the development of communication infrastructure in smart grids, cyber security reinforcement has become one of the most challenging issues for power system operators. In this paper, an attacker is considered a participant in the virtual bidding procedure in the day-ahead (DA) and real-time (RT) electricity markets to maximize its profit. The cyber attacker attempts to identify the optimal power system measurements to attack along with the false data injected into measurement devices. Towards the maximum profit, the attacker needs to specify the relation between manipulated meters, virtual power traded in the markets, and electricity prices. Meanwhile, to avoid being detected by the system operator, the attacker considers the physical power system constraints existing in the DA and RT markets. Then, a bi-level optimization model is presented which combines the real electricity market state variables with the attacker decision-variables. Using the mathematical problem with equilibrium constraints, the presented bi-level model is converted into a single level optimization problem and the optimal decision variables for the attacker are obtained. Finally, simulation results are provided to demonstrate the performance of the attacker, which also provides insights for security improvement.

Data-Centric Hierarchical Distributed Model Predictive Control for Smart Grid Energy Management

The smart grid energy management with variable renewable energy resources presents many challenges to the grid operation. An optimized solution to manage the available resources is necessary to achieve reliable operation. This paper presents the hierarchical distributed model predictive control (HDMPC) to solve the energy management problem in the multitime frame and multilayer optimization strategy. The HDMPC combines the concept of enabling the optimization over long time-horizon for a centralized supervisory management (SM) layer and another short time-horizon during high-power variability for a distributed coordination management (CM) layer. The information exchange and interoperability between different layers are provided through the data-centric communication approach. The SM (upper layer) works to present the grid operator with certain operational plans and gives the guidelines to the CM (lower layer). The CM has the responsibility to coordinate the relationship between the centralized optimization objectives and the physical power system layer. The proposed HDMPC control was verified both numerically and experimentally. The obtained simulation results show that the control strategy proposed here is successful and combines the benefits of both the centralized and distributed control for a global solution of the grid operation problem. The experimental results demonstrate the feasibility of the real-time implementation of the proposed system for deployment to control future smart grid assets.

Source-Load Coordinated Reserve Allocation Strategy Considering Cyber-Attack Risks

With the developing cyber physical power systems and emerging threat of cyber-attacks, the traditional power services are faced with higher risks of being compromised, as vulnerabilities in cyber infrastructure can be exploited to cause physical damage. Therefore, adjustments need to be made in current control scheme design methods to mitigate the impact of potential attacks on service quality. In this paper, focusing on the service of coordinated source-load participation in primary frequency regulation, a vulnerability analysis is performed with modelling the attack intrusion process, and the risk assessment of the service is made by further modelling the attack impacts on the service’s physical effects. On that basis, the traditional coordinated reserve allocation optimization model is modified and the allocation scheme is corrected according to the cyber-attack impacts. The proposed correction methods are validated through a case study, showing effectiveness in defending against the cyber-attack impacts.

Advancing cyber–physical sustainability through integrated analysis of smart power systems: A case study on electric vehicles

Satisfying the growing energy demand, power systems are required to increase their capacity and be able to distribute energy over wider geographical area. To maintain the reliability of such power systems, more dependence is placed on automating the process controlling the physical system. Such power systems are known as smart grids, where data is transmitted in real-time across the power grid facilitating automated actions. These smart systems have intrinsic vulnerabilities. A smarter power grid is more reliant on an ICT backbone. Such reliance renders the physical power system subjected to an ICT realm whose security depends on a set of metrics, and standards alien to those of classical power systems. The sustainability of a power system will depend on the secure and reliable operation of the new smart system. This paper proposes a comprehensive approach to solve the challenges enumerated above. The proposed approach sets the ground for new metrics of overall system sustainability, by dissecting the overall smart grid into three layers: the physical system, the SCADA system, and the ICT infrastructure. The proposed methodology is tested on the case of electric vehicles, where cyber intrusion scenarios are studied in light of their effect on the physical layer.

Self-organized cyber physical power system blockchain architecture and protocol

The state parameters of electrical equipment and power system control parameters are the key data to realize the precise control and cooperative autonomy of a cyber physical power system. The trustworthiness of the data is the primary condition to guarantee the electric power system’s safe and reliable operation. In the traditional centralized data acquisition and management architecture, the security and trustworthiness completely depend on the central main server. A small mistake in the main server will result in data loss, which is irreversible and fatal. Blockchain technique combines the dispersed data with mutual backup and retrieval mechanism, which guarantees that the data cannot be tampered with and forged privately. Based on the blockchain technology, this article proposes a novel data trustworthy acquisition model with high credibility, applied to a self-organized cyber physical power system. In order to overcome the long time-consuming process of building traditional blockchains, a new on-demand data transmission routing algorithm with M/M/1/k queuing model is proposed in this article. Different scales of IEEE standard bus systems are employed as experimental examples to evaluate the algorithm’s performance. The results show that the proposed algorithm can effectively shorten the time consumption of blockchain construction and realize the data trustworthiness of cyber physical power system.

Set-theoretic detection of data corruption attacks on cyber physical power systems

This paper addresses a set-theoretic method for the detection of data corruption cyber-attacks on the load frequency control loop of a networked power system. The system consists of several interconnected control areas forming a power grid. Based on the overall discrete-time network dynamics, a convex and compact polyhedral robust invariant set is extracted and is used as a set-induced anomaly detector. If the state vector exits the invariant set, then an alarm will be activated, and the potential threat is considered disclosed. The attack scenario used to assess the efficiency of the proposed anomaly detector concerns corrupted frequency sensor measurements transmitted to the automatic generation control unit of a compromised control area. Simulation studies highlight the ability of a set-theoretic approach to disclose persistent and intermittent attack patterns even when they occur at the same time with changes in the power load demand.

A Frequency Control Model for Cyber Physical Power System Considering Demand Response Strategy

Due to the increase of power transmission capacity, once an active power vacancy happens, power systems will face a serious frequency instability problem. With the development of communication technology in cyber physical power system (CPPS), demand response (DR) resources can take the initiative to reduce electricity utilization in order to tackle the system under-frequency challenge. This paper presented a frequency control model for CPPS considering DR strategy to rapidly forecast the effects of DR sources participating in frequency control. The model was verified through a case study as well.

Permit banking in emission trading: Competition, arbitrage and linkage

Several existing or proposed climate policies have considered bankable permits in a cap-and-trade (C&T) program that covers beyond a single sector, e.g., electric power, or allows the program to link to external C&T programs in other regions. This paper develops a model of permit banking under imperfect competition and imperfect inter-temporal arbitrage, in which the firms in one dominant sector can exert market power in both product and permit markets, while those in other sectors or linked programs are perfectly competitive. A simple analytical model is developed to generate contestable hypothesis. We further extend the model to account for the physical power system, institutional rules and market conditions, and then apply it to the Pennsylvania-Jersey-Maryland (PJM) market. We show that if the dominant firm has market power, then the permit price rises at a higher rate than the discount rate, contrary to perfectly competitive permit market, where the permit price rises at the discount rate following the classic Hotelling's rule. Furthermore, under a declining emissions cap system with the permits front-loaded in early time periods, the dominant firm has an incentive to suppress the permit prices (monopsony) when buying the permits in early periods, and then inflate the permit prices (monopoly) when selling them in later periods. Numerical results of the PJM case are consistent with the analytical conclusion.

CIM-Compliant Power System Dynamic Model-to-Model Transformation and Modelica Simulation

European regulations on information exchange have put new requirements on analysis tools, the main one being the adoption of the International Electrotechnical Commission common information model (CIM) that may help interoperability across applications. This paper proposes the use of model-driven software engineering methods to meet these new requirements. Specifically, this paper shows how to apply model-to-model (M2M) transformations. The M2M method presented herein allows us to work directly with the information and mathematical description and computer implementation of dynamic models, independent from specific analysis tools. The M2M method proposed requires the development of a mapping between CIM/unified modeling language and the Modelica language, which allows us to derive Modelica models of physical power systems for dynamic simulations.

Cascading Failure Analysis of Cyber Physical Power System With Multiple Interdependency and Control Threshold

The traditional infrastructure in power system is undergoing a transition to the Smart Grid, in which the communication network and power grid will be integrated into a cyber-physical power system (CPPS). Although the traditional topological analysis reveals the mechanism of cascading failure between two networks, it ignores the control redundancy and standby lines from communication network to power grid. The robustness analysis in CPPS requires a more comprehensive model to analyze failure behavior in reality. Here, we propose a cascading failure model with one-to-multiple interdependency and a relevant theoretical framework to analyze CPPS cascading failure. In consideration of real CPPS, in the proposed model we introduce two robustness factors, the number of dependent links and control threshold, which can better describe the control function from communication nodes to power nodes. The remaining fraction under different initial attacking on high voltage transmission network, small world network, double star network, and the different topological combination of CPPS are analyzed. The results show that the proposed model and robustness factors can better reveal the robustness and the mechanism of two networks in cascading failure.

Operational Range of Several Interface Algorithms for Different Power Hardware-In-The-Loop Setups

The importance of Power Hardware-in-the-Loop (PHIL) experiments is rising more and more over the last decade in the field of power system and components testing. Due to the bidirectional exchange between virtual and physical systems, a true-to-reality interface is essential; however, linking several dynamic systems, stability issues can challenge the experiments, the components under test, and the individuals performing the experiments. Over the time, several interface algorithms (IA) have been developed and analyzed, each having different advantages and disadvantages in view of combining virtual simulations with physical power systems. Finally, IA are very specific to the kind of PHIL experiment. This paper investigates the operational range of several IA for specific PHIL setups by calculations, simulations, and measurements. Therefore, a selection of the mainly used respectively optimized IA is mathematically described. The operational range is verified in a PHIL system testing environment. Furthermore, in order to study the influence of different PHIL setups, according to software and hardware impedance, different tests using linear and switching amplifiers are performed.

Modeling and Mitigating Impact of False Data Injection Attacks on Automatic Generation Control

This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack , consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.

Distributed intrusion detection based on hybrid gene expression programming and cloud computing in a cyber physical power system

With the increasingly widespread application of information and communication technology, the smart grid has gradually evolved into a cyber physical system characterised by deep integration between the information space and physical space. All manner of intrusion attacks on cyber physical power systems are growing more and more frequent. Timely and accurate detection and identification of these intrusions are essential for the effective control and protection of cyber physical power systems. For massive and high-dimensional intrusion behaviour data in cyber physical power systems, distributed intrusion detection based on hybrid gene expression programming and cloud (DID-HGEPCloud) computing is proposed. In the DID-HGEPCloud, attribution reduction with noise data based on rough set and a global intrusion model based on non-linear least squares are applied to improve the efficiency and accuracy of intrusion detection. At the same time, the MapReduce programming framework of cloud computing is adopted, and parallelisation of the model of the proposed algorithm is performed to enhance its ability to manage massive and high-dimensional data. Comparative experiments show that the algorithm proposed in this paper has obvious advantages in terms of false attack rate, DAR, and average time consumed. Furthermore, the proposed algorithm possesses excellent parallel performance.

Multifunctional cyber‐physical system testbed based on a source‐grid combined scheduling control simulation system

To establish a real closed-loop control centre testing environment for a cyber–physical power system, a cyber–physical testbed was established by integrating industry supervisory control and data acquisition with a source-grid co-simulation system in the laboratory at the North China Electric Power University. The testbed employs a multi-time scale interface and virtual remote terminal units to overcome the shortcomings of the real-time digital simulator. Physical simulation was performed with a source-grid co-simulation system based on a real-time digital simulator. Control system simulation was implemented using a commercial energy management system. The testbed was also integrated with a wide-area-network emulator to provide wide-area-network emulation and advanced attack simulation. An overview of the system and the key technologies used in the cyber–physical system testbed is provided in this study. In addition, two specific cyberattacks are simulated to demonstrate the capacity of the cyber–physical system testbed.

A chaos-oriented prediction and suppression model to enhance the security for cyber physical power systems

Smart grid has become a fully automated system with common use of electronic equipments to control, compute and communicate. This can be modeled as cyber physical power systems (CPPS) to analyze connections and interactions, and security is one of the key factors to find the inner unmanaged vulnerabilities. In some cases, voltage source inverters (VSI) with pulse width modulation (PWM) are sensitive to initial conditions, and this is a typical chaotic state. In this paper, we present a CPPS model with the improved chaos prediction and suppression methods to enhance the security with the abilities to avoid and eliminate chaos. The main process is “prediction–quantization–control”. First, an improved maximum velocity criterion method is used to predict the possibility of chaos in the parameter space of CPPS. Second, when a possibility of chaos is predicted, a sinusoidal wave frequency modulation is used to suppress chaos. To analyze the connections of the inner entities’ transmission, a security coefficient is introduced to obtain hazard perception and to express measurements quantitatively. Third, it has proposed a chaotic control algorithm based on a fuzzy model with closed-loop dynamic mechanism to solve the security coefficient effectively. The experiments prove the predicted result has a margin of error of plus or minus 10%, and the sinusoidal wave frequency modulation can avoid chaos on CPPS. Also, the results show that security coefficient increases by 13.89%, which means it is useful with sensitive parameters and other related disturbances. Overall, this CPPS model can eliminate potential chaos phenomena caused by inversion processes, and it can be used into smart grids to improve the security effectively.