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The secure operation of the transmission grid is of primary importance for any power system operator. However, the introduction of new technologies, market deregulation, and increasing levels of interconnectivity have made the analysis and assessment of power system security both more challenging and essential than ever. In this context, data-driven-based methodologies are being increasingly employed to classify and anticipate insecure future states, and make inferences on potential triggers of undesired operational conditions. This paper provides a comprehensive and systematic review of this fast-moving research area and covers data-driven-based methodologies deployed in both static and dynamic security assessment. Particular attention is paid to recent trends, such as the use of spatiotemporal feature selection algorithms and the increasing research activity in short-term voltage stability and frequency stability, which are not yet widely assessed as a collective in the existing literature.

As wind power is volatile and intermittent, an increase in the proportion of wind power generation may affect the security of power systems. Accurate wind power generation and ramp forecasting are crucial for reducing operating costs and maintaining the power balance. This study proposed an enhanced performance evaluation metric for wind power ramp event forecasting, and analyzed the forecasting results using this metric. Experimental ramp event forecasting was conducted on wind farms located in Jeju, South Korea by employing an indirect forecasting method for wind power output forecasting. Considering the evaluation of forecasting performance, the accuracy (ACC) was observed to be 0.86 for Case #1 and 0.90 for Case #2. The results highlight the advantages of the proposed metric over the widely used confusion matrix for performance evaluation, which has more detailed and visually based analytical capabilities. The simulation demonstrates that the proposed approach is poised to make a noteworthy contribution in the advancement of tool development, enabling real-time curtailment forecasting and facilitating well-informed decision-making in high wind power scenarios.

The National Electricity Market (NEM) in Australia was suspended during June 15-23, 2022, with a primary attribution to the lack of available generation capacity. This incident is noteworthy because it was the first market suspension in NEM's history and took place in a major energy exporting country. In this letter, we review the outline and impacts of the incident. From the perspectives of market regulation, electricity supply, and electricity demand, we identify three underlying causes of the market suspension and offer four recommendations for the market mechanism evolution to ensure power supply security.

In a smart grid, state estimation (SE) is a very important component of energy management system. Its main functions include system SE and detection of cyber anomalies. Recently, it has been shown that conventional SE techniques are vulnerable to false data injection (FDI) attack, which is a sophisticated new class of attacks on data integrity in smart grid. The main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model, which is different from the traditional weighted least square based SE model. This SE model has a number of unique advantages compared with traditional SE models. First, the prediction technique can better maintain the inherent temporal correlations among consecutive measurement vectors. Second, the proposed SE model can learn the actual power system states. Finally, this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data detectors. The proposed FDI attack detection technique is evaluated on a number of standard bus systems. The performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-of-the-art techniques. Experimental results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.

Under future low-carbon scenarios, it will be crucial to boost the rapidity in the delivery of frequency response services, especially those provided by conventional generators. This paper proposes a novel framework for the scheduling of frequency response services that incentivizes a rapid response from different system assets, including conventional generators. Differently from other approaches which envisage a fixed delivery time for frequency response services, the proposed methodology implements time-varying delivery intervals, which coincide with the time of frequency nadir occurrence. A relevant set of linearized constraints is embedded in a typical power system scheduling model. Results indicate that the proposed methodology reduces the unnecessary overscheduling associated with traditional methodologies, leading to significant operational cost savings. [ABSTRACT FROM AUTHOR]

Direct current (dc) systems have an increasingly important role in electrical systems, especially photovoltaic (PV) solar power plants and battery storage systems as they are essential for the development of more sustainable, efficient, and reliable electrical systems. PV panels and batteries are generally composed of multiple series-parallel cells. This fact makes their fault diagnosis challenging, especially against ground faults as these systems are generally ungrounded. In this article, a ground fault location method for dc sources with multiple series-parallel cells is proposed. It estimates the faulty branch, the faulty cell location inside this branch and its fault resistance. The method is based on the output current measurement of each branch and on the voltage measurement on a grounding resistor, which is switched between three positions: the positive, the midpoint and the negative terminals of the protected dc system. To validate it, several computer simulations and experimental tests on a battery pack have been carried out. The results obtained proves an excellent performance for faulty cell location and fault resistance estimation, although further investigations are required to better discern the faulty branch. [ABSTRACT FROM AUTHOR]

Wind farm industrial control systems (ICSs) play an important role in the operation of wind turbines (WTs). To increase revenues, a corrupt wind power producer tends to compromise the ICSs of the opponents’ wind farms by launching false data injection (FDI) attacks. In this article, we investigate a novel profit-oriented FDI (POFDI) attack against wind farms and study corresponding attack detection schemes. Specifically, we proposed a potential FDI attack model against ICSs of wind farms considering the power distribution of wind farms. Based on the characteristics of the proposed attack model, a profit-oriented attack strategy is discussed as a composition of attack detection. Considering the characteristics of the compromised data and those of normal data, we develop countermeasures to detect the POFDI attack, through the relationship between the characteristics of WTs in wind farms. A relation-based detection network is used to evaluate the similarity between the data to be tested and the historical data. Simulation results show the impact of the proposed attack strategy and the effectiveness of the attack detection scheme. [ABSTRACT FROM AUTHOR]

Due to the potentially severe consequences of coordinated cyber-physical attacks (CCPA), the design of defenses has gained significant attention. A popular approach is to eliminate the existence of attacks by either securing existing sensors or deploying secured PMUs. In this work, we improve this approach by lowering the defense target from eliminating attacks to preventing outages and reducing the required number of PMUs. To this end, we formulate the problem of PMU Placement for Outage Prevention (PPOP) under DC power flow model as a tri-level non-linear optimization problem and transform it into a bi-level mixed-integer linear programming (MILP) problem. Then, we propose an alternating optimization framework to solve PPOP by iteratively adding constraints, for which we develop two constraint generation algorithms. In addition, for large-scale grids, we propose a polynomial-time heuristic algorithm to obtain suboptimal solutions. Next, we extend our solution to achieve the defense goal under AC power flow model. Finally, we evaluate our algorithm on IEEE 30-bus, 57-bus, 118-bus, and 300-bus systems, which demonstrates the potential of the proposed approach in greatly reducing the required number of PMUs. [ABSTRACT FROM AUTHOR]

False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors’ knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this article to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a graph neural network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern ac power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN-based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in $F$ 1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively. [ABSTRACT FROM AUTHOR]

With the increasing risks of malicious cyber-physical attacks and natural disasters, it is urged for power grids to improve the resilience performance against such high-impact, low-frequency events. The critical component identification is an important problem for the effective planning, operation and asset management of power systems. Thus, it is highly necessary and beneficial to develop a resilience oriented critical component identification method for defending power systems against extreme events. In this paper, a tri-level optimization model is developed to identify the critical components while the coordinated cyber-physical attacks on power systems are formulated considering the resources of attackers, the optimal attack strategy, and the actions of the system operator to enhance the system resilience against potential attacks. Meanwhile, a distributionally robust optimization (DRO) based model is proposed to consider the impacts of distribution uncertainty of the resource or ability of the malicious attackers in the criticality identification framework. A constraint-generation based algorithm is developed to solve the overall multi-level DRO model with a master and sub-problem scheme. Further, a criticality index is proposed to quantify the importance of key components to the system resilience against malicious cyber-physical attacks. In order to validate the proposed method, case studies were conducted on the IEEE reliability test system (RTS-79). The obtained results show that the proposed critical component identification method can provide effective and robust importance evaluation of system components to bolster the grid resilience against potential cyber-physical threats. [ABSTRACT FROM AUTHOR]

This letter presents an exact and an approximate power flow method to quickly compute line flows after $k$ -line removal from the power network. The first method uses sparse perturbation matrices consisting of perturbation on the admittance of $k$ -lines in a closed-form solution to directly compute the change in power flow on the lines without requiring precomputation of line outage distribution factor (LODF), while the second method utilizes power flow sensitivities computed using the array of non-zero numbers stored from the matrix multiplication terms of the first-order sensitivity function corresponding to the single line removal cases in a Taylor series approximation for power flow computation. [ABSTRACT FROM AUTHOR]

Data communication security between power systems, which has become a chronic problem in the classical network, is characterized by the application of information communication technology (ICT) in modern network models. Nowadays, the electricity network is spread over very large areas, and the load taking, and load shedding instructions are transmitted one-way and unsafe in the current structure. However, utilities in the smart grid are developing new methodologies to secure the communication infrastructure. This article presents a design that will make the current grid model more reliable and provide a secure data communication in accordance with the modern grid infrastructure. This design provides auxiliary communication codes such as chaotic codes embedded in the communication instructions between power systems, which are generation balance instructions sent to the power plants by the load dispatch center to achieve frequency balance. Used as a case study, one-day instructions are transmitted and encoded four times a day, which is the amount of capacity that only the receiver and transmitter can understand with two-sided encryption. The necessary reserve capacity, chaotic encryption and suitability for real-time applications were evaluated using experimentally obtained power system data for frequency control, which is very important in terms of sustainability in energy and economy against cyber-attacks, and very accurate results were obtained.

Developing tools that help us understand and analyze the effects of cyber attacks on physical assets is necessary in order to detect and prevent harmful consequences of integrating Information and Communication Technologies (ICTs). In this paper, we review existing technologies for developing a fully virtualized cyber-physical testbed for cyber and physical data acquisition and machine learning anomaly detection. We present a testbed that uses network emulation and real industrial communication protocols to emulate the interactions of ICTs inside a wind-powered system. We use the testbed to simulate malicious cyber attacks, their effect on the physical system, and detection mechanisms for such disturbances using anomaly detection. The advantages of the presented virtualized testbed are: 1) integration of real industrial protocols, network analysis tools, and industry-leading data-engineering and machine learning tools; 2) enables a holistic analysis of cyber-physical systems by acquiring and analyzing simultaneously cyber and physical data; 3) cost effective solution for prototyping and testing that can run in a single laptop. The testbed combines unique technologies that enable testing of entire data-driven pipelines, including data acquisition, data management, analysis, and storage, emulating how they would run in a real system. We show how the presented approach can be used to analyze and profile both cyber and physical behavior. The experiments show the capabilities of the presented approach by demonstrating the successful detection of a malicious insertion command through observing anomalous behavior in both cyber and physical data.

False data injection attacks (FDIA) are a main category of cyber-attacks threatening the security of power systems. Contrary to the detection of these attacks, less attention has been paid to identifying the attacked units of the grid. To this end, this work jointly studies detecting and localizing the stealth FDIA in power grids. Exploiting the inherent graph topology of power systems as well as the spatial correlations of measurement data, this paper proposes an approach based on the graph neural network (GNN) to identify the presence and location of the FDIA. The proposed approach leverages the auto-regressive moving average (ARMA) type graph filters (GFs) which can better adapt to sharp changes in the spectral domain due to their rational type filter composition compared to the polynomial type GFs such as Chebyshev. To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems. Extensive simulations and visualizations show that the proposed approach outperforms the available methods in both detection and localization of FDIA for different IEEE test systems. Thus, the targeted areas can be identified and preventive actions can be taken before the attack impacts the grid. [ABSTRACT FROM AUTHOR]

Due to the development of cyber-physical systems for modernizing power grids, vulnerability assessment has become an emerging focus in power system security studies. With the increasing deployment of cyber-enabled technologies in power systems, modern power system is prevalently exposed to a wide gamut of cybersecurity threats. Thus, there is an urgent need to develop effective cyber risk management mechanisms to mitigate the growing cyberthreats. Recently cyber insurance is emerging as a promising financial instrument for cyber risk management of critical infrastructures such as power grids. In this paper, a new cyber-insurance design framework is proposed to hedge against the risk of massive monetary losses due to potential cyberthreats. Traditionally, insurance companies serve as third-party risk-bearers offering aggregate design of the insurance policy which may stipulate high premiums. However, unusual loss patterns may still lead to excess financial risk for insurance companies. In this paper, coalitional insurance is introduced as a promising alternative or supplement to the traditional insurance plans provided by insurance companies. Under the proposed cyber-insurance model, several transmission operators form an insurance coalition, where the coalitional premiums are derived considering system vulnerabilities and loss distributions. The indemnity which covers the loss of TOs complies with the budget sufficiency. Overall, this study proposes a novel coalitional platform based cyber-insurance design that estimates the insurance premiums via cybersecurity modeling and reliability implication analysis. [ABSTRACT FROM AUTHOR]

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional approaches for security assessment are often limited by their scope and/or speed, resulting in missing of critical contingencies that could lead to cascading failures. This paper proposes a two-component methodology to enhance power system security. The first component combines an efficient algorithm to detect cut-set saturation (called the feasibility test (FT) algorithm) with real-time contingency analysis (RTCA) to create an integrated corrective action (iCA), whose goal is to secure the system against cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) to secure the system against post-contingency cut-set saturation. The first component is more comprehensive, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. The results obtained by analyzing different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology successfully enhances the scope and speed of power system security assessment during multiple outages. [ABSTRACT FROM AUTHOR]

Optimal placement of flexible AC transmission systems (FACTS) devices and the cyber-security of associated data exchange are crucial for the controllability of wide area power networks. The placement of FACTS devices is studied in this paper from a novel graph theoretic perspective, which unlike the existing approaches, purely relies on topological characteristics of the underlying physical graphs of power networks. To this end, the maximum matching principle (MMP) is used to find the set of required FACTS devices for the grid controllability. In addition, the cyber-security of the most critical data related to the FACTS controllers is guaranteed by introducing the concept of moderated-k-security where k is a measure of data obscurity from the adversary perspective. The idea of moderated-k-symmetry is proposed to facilitate the arrangement of the published cyber graph based on a permutation of nodes within the symmetry group of the grid, called generator of automorphism. It is then verified that the published cyber-graph can significantly obscure the data exchange over the cyber graph for adversaries. Finally, a similarity is observed and demonstrated between the set of critical nodes attained from the symmetry analysis and the solution of the FACTS devices placement that further highlights the importance of symmetry for the analysis and design of complex power networks. Detailed simulations are applied to three power networks and analyzed to demonstrate the performance and eligibility of the proposed methods and results.

Cyber-attacks on a cyber-physical power system could lead to significant data failure, false data injection and cascading failure of physical power system components. This paper proposes an advanced approach based on a ternary Markovian model of cyber-physical components interactions to capture the subsystem layers' interactions of the cyber-physical power system and to quantify the interdependency impacts on physical power system security. The approach models cyber-physical interactive operation based on interactions and characteristics of three subsystem layers of the system with the presence of random and unforeseen contingencies, load demand variations and then quantify the impacts with Monte Carlo simulation. The viability of the approach is investigated by simulating a set of scenarios, representing realistic physical power system operating conditions with the cyber network interactions. Findings justify the presence of cyber-attacks in a cyber-physical power system components operation could lead to severe insecurities. However, the impacts on physical power system security does not always correlate with the severity of cyber-attacks.

Moving towards low-carbon electricity systems through the massive deployment of renewable energy sources (RES) presents a unique opportunity to combat climate change, but it also poses enormous technical challenges, especially from a frequency viewpoint. To ensure a secure RES integration in terms of frequency stability, system operators worldwide have adopted new grid codes requiring RES to provide fast frequency response (FFR). However, if not properly justified, stringent requirements may pose an unnecessary barrier to further RES development and slow their network integration. In this context, this paper presents a methodological framework for systematically defining FFR requirements for RES to ensure system frequency stability. The proposal comprises: i) a model for simulating the dynamic response of system frequency following a contingency with reduced computational effort, ii) a model for reallocating contingency reserves with economic criteria to avoid loss of load following a contingency, and iii) novel indices for characterizing the dynamic performance of system frequency in terms of key operational characteristics, which are then used for defining frequency related grid codes. The benefits and practicability of our proposal are demonstrated in a case study on the Northern Interconnected System in Chile. We show how our proposal can be used to i) identify system operating conditions in which the contribution of RES with FFR is necessary to avoid loss of load and ii) to propose a technically and economically justified grid code that allows both to foster further RES integration while ensuring power system security.

This letter develops a mathematical model to distribute the under-frequency load shedding amount across the power system load points. The objective is to preserve the system operating point at post-contingency and after the load shedding as close as possible to that of pre-contingency. To do so, the total absolute change in line flows is sought to be minimized. Hence, the system security constraints satisfied before contingency occurrence are less vulnerable to violation. It is proven that when the number of credible contingencies exceeds load shedding candidates, an even distribution across the load points is optimal. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of Security-Constrained Unit Commitment (SCUC) which is a long-standing challenge in power system engineering faced by system operators and utility companies on a daily basis. We consider a detailed variant of this problem that suffers from complexities posed by the presence of binary variables, the uncertainty of renewable sources and security constraints. A convex relaxation is formulated which is capable of finding feasible solutions within a provable distance from global optimality. We demonstrate the performance of this approach on detailed and challenging instances of SCUC with IEEE and PEGASE benchmark cases from Matpower. The proposed approach is able to handle over 12,000 binary variables and 2 million continuous variables with significant improvement in solution quality over commonly-used off-the-shelf solvers and other methods of convex relaxation. [ABSTRACT FROM AUTHOR]

With the advent of the advanced metering infrastructure, electricity usage data is being continuously generated at large volumes by smart meters vastly deployed across the modern power grid. Electric power utility companies and third party entities such as smart home management solution providers gain significant insights into these datasets via machine learning (ML) models. These are then utilized to perform active/passive power demand management that fosters economical and sustainable electricity usage. Although ML models are powerful, these remain vulnerable to adversarial attacks. A novel stealthy black-box attack construction model is proposed that targets deep learning models utilized to perform non-intrusive load monitoring based on smart meter data. These attacks are practical as there is no assumption of the knowledge of training data, internal parameters, and architecture of the targeted ML model. The profound impact of the proposed stealthy attack constructions on energy analytics and decision-making processes is shown through comprehensive theoretical, practical, and comparative analysis. This work sheds light on vulnerabilities of ML models in the smart grid context and provides valuable insights for securely accommodating increasing prevalence of artificial intelligence in the modern power grid. [ABSTRACT FROM AUTHOR]

Grounding electrodes with good current discharging performance can ensure the reliability of power system security. Discharging current is not only the reason for accelerating the corrosion, but also the key indicator of the grounding electrodes' grounding performance, which directly affects the grounding resistance, step voltage, ground potential distribution, and other macro grounding parameters. This paper focuses on the discharging current distribution of horizontal and vertical grounding electrodes. Based on the FEM of space tetrahedron and space coordinate transformation method, the calculation model of the horizontal grounding with ground lead and the vertical grounding is proposed, and distribution of discharging current on grounding electrodes with different length is analyzed. Results reveal that discharging current distribution on different location of the aforementioned grounding electrodes. Components of discharging current density on grounding electrodes surface and ground lead exhibit sinusoidal distribution along the circumference of the section on grounding electrodes, and discharging current inside the section are more concentrated on the surface of the section and less on the center. Most discharging current near the junction between horizontal grounding electrodes and ground lead is perpendicular to plane defined by grounding electrodes and ground lead.

The voltage stability is an essential security concern when a system is operating in peak load hours or subjected to an N-1 contingency. Among various voltage stability indices, Local Thevenin Index (LTI) has been a popular one, but mostly applied to the measurement-based framework. The reason is that it requires significant computing effort if applied to model-based approach, even though the model-based LTI calculation can provide more accurate results. In this paper, a new model-based LTI calculation is proposed using graph-computing-based power flow calculation, which is fast enough to fully utilize the advantage of model-based LTI. Then, when the voltage stability index of one or multiple buses exceeds the security limit, a demand response scheme is activated to increase the voltage security margin at the lowest cost. The simulation study of a thousand-level-bus system verifies the computation accuracy and efficiency of the proposed model-based LTI. It also demonstrates that the demand response action can enhance the system security effectively. Thus, the proposed work has advantages if applied to a large-scale Energy Management System (EMS).

Additive manufacturing (AM), a.k.a. 3D printing is increasingly used to manufacture functional parts of safety-critical systems. The AM's dependence on computerization raises the concern that the AM process can be tampered with, and a part's mechanical properties sabotaged. To address this threat, we propose a novel approach for detecting sabotage attacks based on trusted monitoring of the current delivered to each printer motor. The proposed approach offers numerous advantages: 1) it is non-invasive in a time-critical process, 2) it can be retrofitted in legacy systems, and 3) it can be air-gapped from the computerized components of the AM process, making simultaneous compromise more difficult. We evaluated the approach on five categories of toolpath command-level manipulations that impact the geometry of the 3D printed object. Our evaluation showed that all but one tested category of attacks can be reliably detected, even if a single toolpath command is modified.

Sudden loss of bulk power generation will result in significant power shortage and power flow redistribution, which may lead to insecurity and/or stability problems. Under this condition, load shedding (LS) and corrective line switching (CLS) can be used to guarantee a reliable electricity supply. However, at present, the above two procedures are implemented separately, which may deteriorate each other and cause unnecessary load loss. To address this issue, a coordination optimization method is proposed to coordinate event-driven LS and CLS for enhancing power system security and stability as well as reducing LS amount simultaneously. A two-loop integrated algorithm is designed to solve the constrained optimization problem, which takes the transient frequency/voltage deviation and overload capacity of the transmission lines as constraints. In the inner loop, iterative optimization of the LS for frequency/voltage security is achieved based on linearized sensitivity analysis, and then step-by-step summation is employed to get the LS amount for alleviating overload. Neighbor search is used in the outer loop to coordinate event-driven LS and CLS so as to optimize the total LS amount. The effectiveness of the proposed method is validated in a modified IEEE 39 bus test system and an industrial power system. The results show that the proposed scheme can decrease the LS amount without improve the transient security while maintaining the transient security and alleviating overload.

For a variety of cyber-attacks occurring in a power IoT-Cloud environment, conventional security intrusion incident detection and response technologies typically use pattern- and behavior-based statistical methods. However, they cannot provide fundamental solutions for a security intrusion or attacks, which are becoming more intelligent and diverse as time passes. Therefore, an effective response method that can respond to security intrusions intelligently while using an access control technique based on ontology reasoning is required. This can be achieved by adopting a variety of intelligent reasoning technologies for security intrusion incidents of power systems, such as various reasoning technologies based on the ontology and semantic-web technologies being actively studied in the field of intelligent systems, and malicious code detection technologies based on an intelligent access control model, text mining, and natural language processing technologies. Accordingly, a security context ontology was modeled by analyzing the security vulnerabilities of a power system in a power IoT-Cloud environment, and security context inference rules were defined. Furthermore, this paper presents an appropriate power IoT-Cloud security service framework that can be used in a power IoT-Cloud environment. In addition, a security mechanism that can be efficiently operated in such an environment is implemented. In experiments conducted for this application, attack context scenarios that commonly occur were created using a smart meter as an example, which is an essential power system device. Inference rules were then composed for each attack stage to check the paths of attacks those that exploit the vulnerability of a smart meter system. As a result, it was confirmed that a high level attack detection results can be obtained based on the inference rules.

The problems of recovering the state of power systems and detecting the instances of bad data have been widely studied in literature. Nevertheless, these two operations have been designed and optimized for the most part in isolation. Specifically, state estimators are optimized based on the minimum mean-square error criteria, which is only optimal when the source of distortions in the data is Gaussian random noise. Hence, the state estimators fail to perform optimality when the data is further contaminated by bad data, which cannot necessarily be modeled by additive Gaussian terms. The problem of power state estimation has been studied extensively. But the fundamental performance limits and the attendant decision rules are unknown when the data is potentially compromised by random bad data (due to sensor failures) or structured bad data (due to cyber attacks, which are also referred to false data injection attacks). This paper provides a general framework that formalizes the underlying connection between state estimation and bad data detection routines. We aim to carry out the combined tasks of detecting the presence of random and structured bad data, and form accurate estimations for the state of power grid. This paper characterizes the optimal detectors and estimators. Furthermore, the gains with respect to the existing state estimators and bad data detectors are established through numerical evaluations.

Frequency security is an essential issue in power system operation and planning, especially with the increasing penetration level of intermittent and non-synchronous energy sources, which causes insufficiency of inertial and primary frequency responses. Therefore, various studies must observe the frequency security in the problem formulation as objective function and/or constraints. This letter proposes a Frequency Security Index (FSI), which determines the frequency security based on all aspects of the frequency profile. This index presents a quantitative percentage for the frequency security in order to specify the relative distance from the insecure (0%), secure (100%), and absolute secure (200%) conditions. The FSI considers transient and steady-state aspects of frequency security based on the boundaries related to each power system requirements and standards. The applicability, correctness, and sufficiency of the proposed index are demonstrated based on the numerical results of a real large-scale power system. [ABSTRACT FROM AUTHOR]

Increasing the penetration of variable generation has a substantial effect on the operational reliability of power systems. The higher level of uncertainty that stems from this variability makes it more difficult to determine whether a given operating condition will be secure or insecure. Data-driven techniques provide a promising way to identify security rules that can be embedded in economic dispatch model to keep power system operating states secure. This paper proposes using a sparse weighted oblique decision tree to learn accurate, understandable, and embeddable security rules that are linear and can be extracted as sparse matrices using a recursive algorithm. These matrix rules can then be easily embedded as security constraints in power system economic dispatch calculations using the Big-M method. Tests on several large datasets with high renewable energy penetration demonstrate the effectiveness of the proposed method. In particular, the sparse weighted oblique decision tree outperforms the state-of-art weighted oblique decision tree while keeping the security rules simple. When embedded in the economic dispatch, these rules significantly increase the percentage of secure states and reduce the average solution time. [ABSTRACT FROM AUTHOR]

Incorporation of flexible AC transmission systems (FACTS) in the transmission network for improving its adaptability and resilience has a high impact on the behavior of power swing (PS) impedance characteristics and performance of protection schemes. This paper introduces a qualitative study on the effects of thyristor controlled series capacitor (TCSC) on the distance protection strategies for PS and also suggest modifications in the conventional distance protection logic. Power Swing Blocking (PSB) is a complimentary feature integrated with the conventional distance relay to detect PS. However, the addition of TCSC along with its fast-acting control loop results in the PSB scheme to experience a reduction in the recorded time by the timer thus leading to failure in detecting the encroachment of the apparent impedance to be a PS and thus causes maloperation of the distance relay. An Empirical Wavelet Based technique has been proposed to rectify these maloperations in TCSC incorporated transmission lines to contribute to improve stability by preventing undesirable tripping. The case studies have been implemented on the WSCC 9 bus system with the detailed dynamic model of the TCSC. The results demonstrate the efficacy of the suggested technique in overcoming the shortcomings of the conventional distance relay strategy. [ABSTRACT FROM AUTHOR]

In the modern age, the Internet of Things forms the basis of all the infrastructures for improving efficiency, reliability, and comfort. As applied to the power system, increasing the penetration of information and communication technologies at the device and process level is enabling devices to communicate with each other, thereby, facilitating wide-area situational awareness of the power grid. Dependence on the communication infrastructure has also made the power grid vulnerable to cyber-intrusions. Cybersecurity of the power system is a huge concern, which must be addressed with topmost priority. Securing all the entry points for cyber-attacks may not be economically feasible; hence, a critical set of sensors must be identified and secured to protect the grid against malicious actors. In this article, we propose a defense strategy to select the most critical measurements for securing the power system operation against false data injection attacks. The proposed approach is compared with the existing methodologies in the literature and validated using standard IEEE test cases. [ABSTRACT FROM AUTHOR]

For ac electricity grids, the aim has always been at low-cost and disruption-proof solutions. The power grid is inherently vulnerable to environmental disturbances. However, the introduction of power electronics (PEs) and changes in load and generator characteristics have introduced a game changer at an unprecedented pace over the last decade. During this change, load specifications, load characteristics, and control and monitoring requirements for power system security have also dramatically evolved. Efficiency and reliability of system components have progressed in parallel with this evolution. However, high penetration of renewable energy sources has reduced the percentage contribution of conventional electricity based on synchronous generation, which affects grid stability and hence reliability. Furthermore, numerous camouflaged dc generators and dc loads are being embedded in ac and dc microgrids at ever increasing rates. While embedded generation rapidly increases its share in the hybrid power grid (where ac and dc coexist), the current developments in PE switches—based on wide bandgap (WBG) technology—indicate that within the next two decades Max Planck’s well-known sentiment that science advances one funeral at a time will repeat itself on the ac grid. This article aims to summarize the technical developments and problems facing the utilization of ac during widescale electrification of the world using numerous distributed energy resources (DERs). Then, it will map out evolutionary changes toward a future dc grid. [ABSTRACT FROM AUTHOR]

This paper considers the problem of releasing optimal power flow (OPF) test cases that preserve the privacy of customers (loads) using the notion of Differential Privacy. It is motivated by the observation that traditional differential privacy algorithms are not suitable for releasing privacy preserving OPF test cases: The added noise fundamentally changes the nature of the underlying optimization and often leads to test cases with no solutions. To remedy this limitation, the paper introduces the OPF Load Indistinguishability (OLI) problem, which guarantees load privacy while satisfying the OPF constraints and remaining close to the optimal dispatch cost. The paper introduces an exact mechanism, based on bilevel optimization, as well as three mechanisms that approximate the OLI problem accurately. These mechanisms enjoy desirable theoretical properties, and the computational experiments show that they produce orders of magnitude improvements over standard approaches on an extensive collection of test cases. [ABSTRACT FROM AUTHOR]

The availability of high-fidelity energy networks brings significant value to academic and commercial research. However, such releases also raise fundamental concerns related to privacy and security as they can reveal sensitive commercial information and expose system vulnerabilities. This paper investigates how to release the data for power networks where the parameters of transmission lines and transformers are obfuscated. It does so by using the framework of Differential Privacy (DP), that provides strong privacy guarantees and has attracted significant attention in recent years. Unfortunately, simple DP mechanisms often result in AC-infeasible networks. To address these concerns, this paper presents a novel differentially private mechanism that guarantees AC-feasibility and largely preserves the fidelity of the obfuscated power network. Experimental results also show that the obfuscation significantly reduces the potential damage of an attack carried by exploiting the released dataset. [ABSTRACT FROM AUTHOR]

Conventional cyber-security intrusion detection systems monitor network traffic for malicious activity and indications that an adversary has gained access to the system. The approach discussed here expands the idea of a traditional intrusion detection system within electrical power systems, specifically power distribution networks, by monitoring the physical behavior of the grid. This is achieved through the use of high-rate distribution Phasor Measurement Units (PMUs), alongside SCADA packets analysis, for the purpose of monitoring the behavior of discrete control devices. In this work we present a set of algorithms for passively learning the control logic of voltage regulators and switched capacitor banks. Upon detection of an abnormal operation, the operator is alerted and further action can be taken. The proposed learning algorithms are validated on both simulated data and on measured PMU data from a utility pilot deployment site. [ABSTRACT FROM AUTHOR]

The precision time protocol (PTP) is considered as one of the most favorable mechanisms for providing unified and precise time at the substation level in the smart grid. Nevertheless, PTP was shown to be vulnerable to cyber-attacks targeting its components and synchronization services. In this article, we capitalize on the theory and outcome of our previous work to contribute a more complete solution that addresses PTP cyber security. We propose to close the PTP loop through an extension that introduces new functionality and messages. This extension covers the PTP attack surface and enables the detection of attacks on PTP time synchronization. We formally model and verify the proposed extension using UPPAAL model checker. In addition, we validate the proposed extension using Omnet++ simulation. The evaluation demonstrates that our approach preserves PTP functionality, while successfully detecting cyber attacks against PTP components in a timely manner. [ABSTRACT FROM AUTHOR]

In modern power systems, the stochastic and interactive characteristics of mixed generations have gained increasing interest, especially when more renewable energy sources are connected to the grid. The uncertainty of renewable energy has notable effects on power system security. In this paper, a set of composite security indices, which are derived from the Hyper-box and Hyper-ellipse Space theory, are extended by a Latin hypercube sampling method to model multiple probabilistic scenarios under uncertainty. Thus, the proposed approach is suitable for power system security assessment with wind power integrated. According to the indices, a security-based active demand response (DR) strategy is proposed. This strategy is able to provide expected active DR capacity based on the forecast wind power fluctuations. Therefore, it can be applied to day-ahead power system dispatches.

In real-time operations, the forecasts and dispatches are associated with the ending time instants of dispatch intervals, while the wind power variations during the interval are unknown. When a high-wind-penetrated system encounters large short-term wind variations, the intrainterval variations (IIVs) of wind power and generation ramping can lead to unsecured operating conditions inside of an interval, referred to as the intrainterval security (IIS) issue. The goal of this study is to reveal the IIS issue under high wind penetration and analyze its impacts. To this end, this study investigates the nature of the IIS problem based on the dispatch interval mechanism, reveals the nonlinearity of wind IIVs as the root cause of the IIS issues (regulation shortage and transmission overloads), and proposes an assessment tool for assessing such issues. The tool screens the worst-case wind IIVs and assesses the issues using severity indices. The tool also considers the spatial correlation among wind farms and demonstrates that the IIS issues still exist under such correlation. Simulations on the IEEE 118-bus system verify the effectiveness of the proposed tool and highlight the IIS risks in power systems with high wind penetration. [ABSTRACT FROM AUTHOR]

The reliable operation of the power distribution system is a matter of national security. Increasingly, urban distribution systems rely on communications between customers and the utility to implement consumer-centric programs such as demand response that enhance the grid resilience. This paper reports an unconventional and previously unexamined mode of malicious attack on the power distribution infrastructure of cities. It demonstrates that consumer behaviors in such a system could be manipulated by an attacker using false communications, which could significantly impact the system reliability. Using a novel decision-making model for consumer response, possible network impacts of such an attack are examined, which include reduction in system reserves, increase in peak demand, lower voltage profiles, and potential system blackouts. These detrimental effects are shown to worsen in the future as more consumers join such programs and adopt flexible high-power loads. Furthermore, though the system is resilient to random errors or failures, it remains highly vulnerable to strategic attacks such as those demonstrated here. These results recommend urgency in developing solutions to detect and tackle possible injection of fake information into such critical systems, which, as shown here, can have a very real impact on the energy infrastructure reliability. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) rely on networked control supported by off-the-shelf wireless communications. This makes them vulnerable to denial-of-service (DoS) attacks. In this paper, we mitigate those attacks by: 1) separating data plane from network control plane, inspired by the software defined networking paradigm and 2) performing control plane exchanges over the power bus, thus allowing for an agile reconfiguration of the data plane connections. Specifically, in the proposed architecture, all generators in the MG operate as either voltage regulators (active agents), or current sources (passive agents), with their operating mode being determined by software-defined MG control supported by the control plane communication performed over the power bus. For the purpose of control plane communication, we adopt power talk, a modem-less, low-rate, and power-line communication solution designed for direct current MGs. The results show that the proposed software-defined MG offers superior performance compared to the static MG, as well as resilience against DoS attacks. [ABSTRACT FROM AUTHOR]

The notion of secure operation of power systems, with its present semantic, dates back to the last decade. Since then, tremendous research effort has investigated secure operation of the power system. Nevertheless, operators are still faced with security issues, with even larger uncertainty, however, caused by the integration of renewable energy sources (RES). The system's ability to cope with the volatility of RES and load becomes a critical issue in power system operation. This paper surveys the literature on the concepts of power system flexibility, indices of flexibility, and implementation of the concept of flexibility in power system security. The paper proceeds to review the origin of the reserve problem, the meaning of reserve, its technical classification and related economical aspects. The paper highlights the effect of renewables on these aspects, and suggests new research directions. [ABSTRACT FROM AUTHOR]

Developing efficient strategies for defending electric power systems against attacks is a major concern for contemporary power grids, especially when uncertainties are involved. This paper addresses the allocation of the defensive resource to minimize the damage when there are uncertainties regarding the resource that the attacker has. A multiple-attack-scenario (MAS) defender–attacker–defender (DAD) model is proposed by extending the conventional trilevel DAD model. The proposed model considers the uncertainties related to the offensive resource and the interactions involving the security personnel at the top-level, the attacker at the middle-level, and the power system operator at the bottom-level. The column-and-constraint generation algorithm is implemented by decomposing the MAS DAD model into an upper-level problem for the security personnel, and a lower-level problem for the attacker involving the optimal power flow analysis-based corrective power redispatch implemented by the power system operator. Case studies are performed based on the IEEE RTS79 and 57-bus systems, and the results validate that the proposed method is able to minimize the damage when uncertainties are involved in the offensive resource. This paper can offer meaningful insights into power system protection involving uncertainties. [ABSTRACT FROM AUTHOR]

The continuing rollout of phasor measurement units enables wide area monitoring and control (WAMS/WACS), but the difficulty of sharing data in a secure, scalable, cost-effective, low-latency manner limits exploitation of this new capability by bulk electric power grid operators. GridCloud is an open-source platform for real-time data acquisition and sharing across the jurisdictions that control a bulk interconnected grid. It leverages commercial cloud tools to reduce costs, employing cryptographic methods to protect sensitive data, and software-mediated redundancy to overcome failures. The system has been tested by ISO New England and the results reported here demonstrate a level of responsiveness, availability, and security easily adequate for regional WAMS/WACS, with the capacity for nation-wide scalability in the future. [ABSTRACT FROM AUTHOR]

Given the strong cyber-physical coupling that exists in power systems today and of the future, false data injection (FDI) attacks have been shown to be feasible in tampering measurement devices by exploiting cyber vulnerabilities to mislead state estimation and related applications. For example, a corrupt generator owner, motivated by financial gain, may manipulate meter readings associated with short-term load forecasts and subsequently misguide the decisions of security constrained economic dispatch (SCED) in ex-ante real-time markets. In this paper, we analyze the feasibility of financially motivated FDI attacks in bi-level programming settings where multi-solution uncertainty of SCED is considered. To deter such attacks, a robust incentive-reduction strategy is proposed that can prevent financially motivated FDI attacks for all the possible load distributions and solutions of SCED requiring a minimal number of protected meters. Simulations for the IEEE 14-bus and IEEE 30-bus test systems demonstrate attack feasibility and performance of the proposed mitigation strategy for SCED in real-time markets. [ABSTRACT FROM AUTHOR]

This paper introduces a novel data integrity attack on the well-developed consensus-based energy management algorithm. In particular, we show that by sending out elaborately falsified information during the consensus iterations, attackers could manipulate the system operating point and gain extra economic benefits. Meanwhile, the system-level and device-level constraints are still satisfied, e.g., the power generation and demand are balanced, and the operation of individual device respects physical constraints. This data integrity attack has two major features: First, attackers rely only on local information to complete the attack; neither additional information about system topology nor additional colluders are required; second, the attacking effect is accumulative, which enables attackers to choose to finish in either single or multiple iterations. By revealing such vulnerability of consensus-based applications to data integrity attack, this paper conveys the message that besides the efforts of designing novel distributed energy management algorithms to address the renewable energy integration challenges, it is equally important to protect the distributed energy management algorithms from possible malicious attacks to avoid potential economic losses. The proposed attack is illustrated in the Future Renewable Electric Energy Delivery and Management system. [ABSTRACT FROM AUTHOR]

Time distribution mechanisms favored for use in the smart grid, such as precision time protocol (PTP), were not designed with security in mind, and thus suffer several security vulnerabilities. PTP is vulnerable to fake timestamp attacks through master impersonation and sync message injection. Such an attack will synchronize clocks to a false time reference. In this letter, we consider an IEC 61850 substation and propose an approach to detect fake timestamps communicated through false PTP sync messages. This approach builds on top of existing network and system management solutions. We introduce a new simple network management protocol data objects to monitor PTP functionality and detect the existence of fake timestamps in a PTP synchronized network. The approach is implemented on a testbed. The collected results demonstrate its ability to protect against fake timestamp attacks. [ABSTRACT FROM AUTHOR]

This paper proposes a control-oriented energy function (COEF) based on passivity for transient stability control (TSC) of power systems and investigates the feasibility of an iterative process using the COEF for determining generation shedding (GS) requirements for real-time TSC, in particular, for severe cases when autoreclosing fails due to permanent faults. The COEF for TSC set up in this paper gives directly the GS requirement without calculations of stability region and Critical Clearing Time, and exempts all modeling assumptions except for nonnegative damping the generator models. The simulation with the full planning model of Western Electricity Coordinating Council system shows that the passivity-based COEF for TSC offers a concise, practical, and effective index for GS determination for transient stability control of power systems, which is particularly suitable for the Remedial Action Schemes that BC Hydro has been using. [ABSTRACT FROM AUTHOR]

Game theory has recently been applied for analyzing the security of power systems under intelligent attack threats. This letter presents a linear game framework for optimal allocation of a defense budget to confront intelligent attacks on power systems. The presented framework offers the advantage that it can be applied in large power systems with a large number of components under attack threat without too much complexity. The presented linear framework is successfully applied on test systems. [ABSTRACT FROM AUTHOR]

More and more system operators are interested in calculating transfer capability in real time using real-time power flow models generated from the Energy Management System (EMS). However, in comparing these models with offline study models, the EMS models usually include only a limited portion of the interconnected system. It is not practical to extend the EMS model in order to capture the impact of the external systems and as such using an equivalent network becomes necessary for this purpose. Equivalent circuits derived from the offline models for certain predetermined operating conditions would suffer from invalid results under various real-time operating conditions. Hence, it is necessary to derive an equivalent circuit model that is adaptive to the real-time system conditions. This paper presents a general method of creating an equivalent circuit model based on the measured boundary station parameters such as voltages and power flows. Simulation studies show that such derived equivalent circuit models provide solutions with adequate accuracy. [ABSTRACT FROM AUTHOR]