The penetration of Renewable Energy Resources (RERs) presents a significant challenge to the functionality and reliability of electrical power grid. Issues such as largescale RERs integration and load variability create substantial operational difficulties, hampering system inertia and affecting grid frequency control. Therefore, adaptive inertia management in the considerable renewable integrated grid is the main concern of this investigation. In order to enhance the frequency control in the test grid, a unified model free adaptive fractional state observer driven dynamic control methodology is developed for unpredictable dynamic conditions. The technique improves the system’s ability to reduce the oscillations and disruptions frequently experienced by RERs by permitting adaptive inertia. By enabling adaptive inertia, the strategy enhances the system’s ability to mitigate the oscillations and disruptions which are generally associated with RERs. A comparative evaluation with the robust existing control algorithms demonstrates the supremacy of the proposed control strategy. The findings are further validated through real-time experimental testing using OPAL-RT Hardware-In-the-Loop framework.

Inverter-interfaced distributed generators (IIDGs) embedded in microgrids inject limited fault currents, thus imposing a challenge on the protection of the islanded microgrid. This article proposes a novel protection scheme that injects a negative phase sequence (NPS) current component from the IIDG interface control during fault conditions to facilitate fault detection and isolation. This injection is accomplished by augmenting the traditional droop controller of IIDG with a novel positive phase sequence (PPS) voltage versus NPS voltage ($V_{\text{PPS}}-V_{\text{NPS}}$) droop, designed to inject negative sequence current during fault conditions only. Differential NPS current relays are distributed at the line ends to provide fault detection and isolation. The effectiveness of the suggested protection algorithm has been tested and validated using PSCAD/EMTDC simulation software, which considers various fault conditions, such as different fault types, locations, and resistance. The suggested droop-based protection algorithm eradicates the need for a dedicated fault detection scheme and can accurately distinguish faulty and nonfaulty conditions.

With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset.

The electrification of the transportation system is double-edged for the smart grid. Although it is green and eco-friendly, uncontrolled charging of electric vehicles (EVs) could cause not only distribution network congestion but also long queues at the charging stations. Hence, it is necessary to ensure existing charging resources are efficiently utilized. The main objective of this article is to develop an algorithm that assigns EVs to charge stations such that distribution network congestion and time spent by the user from requesting a charging service to accessing it is minimized. The Lyapunov function is utilized for developing an EV assignment algorithm to manage a dynamic population of EVs ensuring queuing stability. Moreover, as the EV assignment algorithm relies on the communication network, an intrusion cyber-attack can occur resulting in an unstable queuing system. An intrusion detection technique is, hence, proposed which utilizes existing transportation network sensor data with the EV charging stations operator information to detect such attacks. A case study using the IEEE 69 bus system is then developed to test the proposed framework.

Islanded operation of microgrids (MGs) with parallel-operated inverters imposes many control challenges in terms of stability and dynamic behavior, especially at contingency events. Hence, improving the dynamic performance and the stability margin is essential for robust MG operation. Therefore, the fractional-order derivative (FOD) droop controller is proposed to achieve these goals. A detailed small signal model is developed for the entire MG with the proposed controller and then used to assess the stability of the MG. The FOD and the integer-order derivative (IOD) droop controllers are applied to a benchmark MG and tuned via an optimization procedure under multiple loading conditions. The results show that the extra degrees of freedom introduced by the FOD droop facilitate pushing the dominant modes toward the required stability region. The proposed FOD droop is compared to the IOD droop, conventional droop, VOC, and virtual synchronous generator controllers under several contingency events and a reconfiguration scenario using MATLAB/SIMULINK, where the proposed controller shows superior performance. The experimental validations demonstrate the improved power-sharing performance of the proposed FOD droop controller.

The utilization of hydrogen-powered vehicles as an alternative to fossil-fueled ones has been identified as a promising solution to reduce greenhouse gas emissions. To promote the deployment of hydrogen-powered vehicles, however, it is imperative that hydrogen refueling stations with on-site electricity-produced hydrogen (eHRSs) are economically utilized for concurrent services. Therefore, this article proposes a new estimation-based method for the optimal scheduling of eHRS for the provision of congestion management services to the distribution system. The method allows for real-time adaptive management of the congestion level in the feeder as decided by the distribution system operator. A new correlation-based method is proposed for online estimation of the amount of congestion and determination of the eHRSs setpoints to relieve congestion on the distribution feeders. This article also develops a congestion management pricing mechanism for the compensation of eHRSs that serve the distribution grid. Numerical results indicate how distribution feeder congestion can be adaptively managed, while the financial profile of the eHRSs is improved via joint application and provision of concurrent services to the transportation sector and grid operator.

Industrial advancements in information and communications technology facilitated the widespread use of line current differential relays (LCDRs) for protecting critical transmission lines due to their fast, sensitive, selective, and secure performance. Despite their advantages, LCDRs' reliance on vulnerable communication networks to swap current measurements makes them vulnerable to cyberattacks. In this article, a scheme is proposed to protect LCDRs from direct-false-tripping (DFT), fault-masking (FM), and sympathetic-tripping (ST) cyberattacks, which have not been studied together before for transmission-level LCDRs. The proposed scheme utilizes a deep neural network (DNN), trained offline on features extracted from only the measurements available for LCDRs. The trained DNN model can then be implemented within LCDRs. Unlike the previous solutions, which only differentiate between faults and DFT cyberattacks, the proposed scheme actively differentiates between authentic and manipulated LCDR measurements to detect and mitigate possible cyberattacks. The performance of the proposed scheme is evaluated using the IEEE 39-bus benchmark system. Our results show that the proposed scheme can accurately detect different forms of DFT, ST, and FM cyberattacks while maintaining the LCDR's protective characteristics. The proposed scheme is tested for real-time capability using an OPAL-RT simulator.

Ensuring the cost-effective operation of an unbalanced islanded microgrid (UBIMG) hinges on achieving a proportional power sharing relative to the capacity of the connected distributed energy resource units (DERs). However, inherent characteristics of UBIMG, such as heterogeneous line impedance and unbalanced loads, inevitably result in mismatching the reactive power-sharing (RPS) among the droop-controlled DERs. As a solution, this article introduces an advanced control scheme that combines unsymmetrical per-phase droop control with unsymmetrical per-phase virtual impedance, referred to as unsymmetrical per-phase droop-virtual impedance control (USPDVIC), to enhance the RPS among DERs within the UBIMG. To determine the settings of the proposed control scheme, this study formulates a multiobjective optimization approach to minimize the average generation costs and mismatching in the per-phase RPS within the UBIMG across a set of operating states simultaneously. The performance of the proposed USPDVIC is comprehensively evaluated within a parallel architecture UBIMG and a radial UBIMG-based IEEE 13-bus, IEEE 34-bus, and IEEE 123-bus benchmark systems under various states of operation. These states include changes in loading conditions, plug-and-play of DERs, and system reconfiguration and partitioning. The results, along with comparisons to existing literature, provide solid evidence for the effectiveness of the proposed control scheme in improving the per-phase RPS among the parallel-connected and dispersed DERs within UBIMGs.

Line current differential relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-masking attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this article, we propose a two-module framework to detect FMAs. The first module is a mismatch index (MI) developed from the protected transmission line’s equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR’s local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT’s real-time simulator confirm the proposed solution’s real-time performance capability.

In islanded microgrids (MGs), power-sharing dynamics constitute the low-frequency dominant modes that directly affect the overall MG stability. The conventional droop is known to have a tradeoff between stability margin and fast/accurate power sharing. In this paper, the fractional-order derivative (FOD) droop controller is proposed to improve the power-sharing dynamics without affecting the steady-state power-sharing accuracy. FOD terms are added to the frequency and voltage loops of the conventional droop controller to add extra degrees of freedom. The suggested controller is tested and compared with the well-known integer-order derivative (IOD) droop on a stability benchmark three DG system. The proposed controller's fractional orders are enough to achieve better dynamic performance than the IOD droop at the same controller gains. Another case study is investigated where the proposed controller is used to reduce the transient behavior of circulating current for converters connected to the same AC bus, which again shows the superiority of the proposed controller over the IOD droop.

The incremental cost (IC) has been equalized through means of distributed control in the literature to achieve optimal generation. Yet, the IC can be linear or nonlinear depending on the cost characteristics of the distributed generators (DG). Unstable microgrids can occur due to communication delays when distributed control is utilized. Prior research has examined the impact of communication delays and has determined the specific delay at which the system experiences instability. This paper analyzes the effect of nonlinear IC on the delay margin. A small-signal model has been created that contains IC equalizing and frequency restoration secondary controllers. Communication delays in the links have been taken into account within the constructed model. It has been concluded the delay margin differs from one loading condition to another, and the reliability is, accordingly, impacted.

Distributed Volt-Var Control (VVC) is a widely used control mode of smart inverters. However, necessary VVC curve parameters are remotely communicated to the smart inverter, which opens doors for cyberattacks. If VVC curves of an inverter are maliciously manipulated, the attacked inverter's reactive power injection will oscillate, causing undesirable voltage oscillations to manifest in the distribution system, which, in turn, threatens the system's stability. In contrast with previous works which proposed methods to mitigate the oscillations after they are already present in the system, this paper presents an intrusion detection method to detect malicious VVC curves once they are communicated to the inverter. The proposed method utilizes a Multi-Layer Perceptron (MLP) that is trained on features extracted from only the local measurements of the inverter. After a smart inverter is equipped with the proposed method, any communicated VVC curve will be verified by the MLP once received. If the curve is found to be malicious, it will be rejected, thus preventing unwanted oscillations beforehand. Otherwise, legitimate curves will be permitted. The performance of the proposed scheme is verified using the 9-bus Canadian urban benchmark distribution system simulated in PSCAD/EMTDC environment. Our results show that the proposed solution can accurately detect malicious VVC curves.

This paper proposes a new approach for optimal operation of an Electric Vehicle (EV) battery-swapping station (BSS) based on Rolling-Horizon optimization (RHO). The BSS has several swapping bays such that each can accommodate an EV for swapping single or multiple battery units. The proposed BSS model considers serving different types of EVs using a heterogeneous battery stock. The charging of the depleted batteries (DBs) is performed using continuously controlled variable chargers which makes it more flexible for providing grid services. While previous studies focused on day-ahead modeling of BSSs, our study considers BSS dynamic scheduling. The goal is to maximize the daily profit using an RHO mechanism to provide optimal swapping and charging/discharging processes. The problem is defined as mixed-integer nonlinear programming (MINLP), then it’s linearized into a mixed-integer linear problem (MILP) to reduce the computational complexity. To predict the EV’s swapping demand, a long short-term memory (LSTM) recurrent neural network is utilized as a time series forecasting engine. The proposed model is validated through a set of case studies comparing the LSTM-based RHO mechanism versus unscheduled operation and day-ahead scheduling. Simulation results demonstrate that the proposed dynamic scheduling mechanism increases the profit between 10% and 25.7% compared to the day-ahead scheduling. Furthermore, the number of EVs served using the proposed approach increases between 11% and 14% compared to the day-ahead model.

As the penetration rate of flexible loads and distributed energy resources in the distribution networks increases, congestion management techniques that utilize demand-side management (DSM) have been developed. These are indirect methods that rely on information exchange between the distribution network operator, aggregators, and consumers’ meters to encourage customers to change their demand to relieve congestion. Cyber attacks against aggregators can compromise the operation of DSM-based congestion management methods, and hence, affect the security and reliability of electrical networks. In this article, the vulnerability of indirect congestion management methods to load-altering attacks is studied. An optimization algorithm is developed to determine the aggregators a cyber attacker would compromise, via minimum alteration of their load profiles, to cause congestion problems. The impact of such attacks on congestion and consumers’ electricity bill is then studied. A mitigation scheme is formulated to determine the most critical aggregators in the network. The security of these aggregators is then reinforced to mitigate such cyber attacks.

The U.S. Department of Homeland Security (DHS) has recently identified digital relays as targets vulnerable to cyber-attacks. The DHS has also noted that attacks to multiple relays can bring about cascading outages of transmission lines, leading to blackouts. As a result, making protective relays cyber-resilient is a prominent security issue in power networks. Line current differential relays (LCDRs) are among the potentially vulnerable digital relays that are increasingly deployed for protecting critical transmission lines. LCDRs, however, lack the required resiliency against cyber attacks, due to their high dependence on communication systems. This paper unveils that such susceptibilities can result in unwarranted trip signals through false data injection attacks (FDIAs), and so cause instability if several attacks are coordinated. It also presents a solution for detecting FDIAs and distinguishing them from real internal faults. To detect attacks, the proposed method compares the estimated and locally measured voltages at an LCDR's terminal for both the positive sequence (PS) and negative sequence (NS). To estimate the local voltage for each sequence, the proposed technique uses an unknown input observer (UIO), the state-space model of the faulty line, and remote and local measurements, all associated with that sequence. The difference between the measured and estimated local voltages for each sequence remains close to zero during real internal faults because, in this condition, the state-space model based on which the UIO operates correctly represents the line. Nevertheless, the state-space model mismatch during FDIAs leads to a large difference between measured and estimated values in both sequences. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus network.

Line current differential relays (LCDRs) detect faults accurately and promptly, by comparing all currents flowing into the line. This type of relay has been identified in the literature as a reliable protection for lines in DC microgrids (MGs). LCDRs, however, lack the required resiliency against cyber intrusions, such as false data injection attacks (FDIAs) and time synchronization attacks (TSAs), due to their high dependence on communication infrastructure and/or the Global Positioning System (GPS). This paper first introduces coordinated attacks-i.e., several almost-simultaneous FDIAs or TSAs that are carried out independently to achieve a specific objective-as potential threats for MGs. Then, through a case study, it shows how coordinated FDIAs and TSAs can initiate a sequence of events that result in instability of an entire MG. Afterwards, an approach is presented to detect FDIAs and TSAs, and to distinguish them from real faults. The proposed method is comprised of passive oscillator circuits (POCs) installed in series with each converter. During faults, the resultant RLC circuit causes the POCs to resonate and generate a damped sinusoidal component with a specific frequency, i.e., fd. However, fdis not generated during FDIAs and TSAs, since unlike faults, which are physical events that trigger the natural frequencies of a system, cyber-attacks happen in the cyber layer without provoking natural frequencies of the physical layer. Thus, an LCDR pickup without detecting fddenotes an FDIA or a TSA. Since fdis locally measured and analyzed by each LCDR, the proposed detection approach cannot be targeted by cyber-attacks. The proposed method is evaluated on a simulated ±2.5 kV DC MG. Numerical analysis confirms that the proposed method (i) is system-independent; (ii) detects FDIAs and TSAs in less than 1 ms; (iii) is sensitive to high-resistance faults; (iv) can determine fault types, and (v) reduces faults' peak currents.

The application of line current differential relays (LCDRs) to protect transmission lines has recently proliferated. However, the reliance of LCDRs on digital communication channels has raised growing cyber-security concerns. This paper investigates the impacts of false data injection attacks (FDIAs) on the performance of LCDRs. It also develops coordinated attacks that involve multiple components, including LCDRs, and can cause false line tripping. Additionally, this paper proposes a technique for detecting FDIAs against LCDRs and differentiating them from actual faults in two-terminal lines. In this method, when an LCDR detects a fault, instead of immediately tripping the line, it calculates and measures the superimposed voltage at its local terminal, using the proposed positive-sequence (PS) and negative-sequence (NS) submodules. To calculate this voltage, the LCDR models the protected line in detail and replaces the rest of the system with a Thevenin equivalent that produces accurate responses at the line terminals. Afterwards, remote current measurement is utilized by the PS and NS submodules to compute each sequence's superimposed voltage. A difference between the calculated and the measured superimposed voltages in any sequence reveals that the remote current measurements are not authentic. Thus, the LCDR's trip command is blocked. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus test system. The performance of the proposed method is also tested using an OPAL real-time simulator.

One of the major obstacles facing the large-scale integration of renewable energy sources to existing power networks is the large magnitude of fluctuations and uncertainties introduced to aggregated demand profiles. Such variations make the supply-demand matching a more challenging task, and increase the operational cost of the power system. In this paper, we provide an effective, yet simple, methodology for smoothing power variations using the demand response of a large number of residential appliances. We first present a demand aggregation model based on queueing theory that can accommodate both deferrable and thermostatically controlled loads. Second, controllable demands are scheduled using an online algorithm to smooth the net noncontrollable demand profile. The performance of our methodology is evaluated using realistic data.

False data injection attacks (FDIAs) against the automatic generation control (AGC) system can lead to unstable or non-optimal operation of the power grid. This paper introduces a method to detect FDIAs targeting the AGC system by developing a stochastic unknown input estimator (SUIE). The SUIE estimates the states of the load-frequency control system, which contains the AGC as a control loop. An increase in the SUIE's residual function (RF) beyond a defined threshold signifies an FDIA. The SUIE can be designed such that it works independently from some or all inputs to the system's state-space model. In addition, the effect of process and measurement noise on the estimated states is minimized through an optimal gain setting technique for the SUIE. Therefore, not only does the SUIE eliminate the need for information about real-time load changes throughout the grid, it also maximizes the state estimation accuracy. The combination of these features distinguishes the proposed method from existing FDIA detection techniques for the AGC system. This paper also develops a number of attack identification SUIEs (AISUIEs) to determine which measurements are compromised by an FDIA, thus facilitating FDIA mitigation strategies. The AISUIEs model FDIAs targeting each AGC measurement by an attack input. These inputs serve as the unknown inputs of different AISUIEs, whose RFs indicate the type of attack. The designed AISUIEs also differentiate between attacks and non-attack abnormalities such as faults. Simulation analysis of a three-area power system corroborates the effectiveness of the proposed method. In addition, the performance of the proposed method is tested using an OPAL real-time simulator, and is compared with another technique from the literature.

Although the future impact of plug-in electric vehicles (PEVs) on distribution grids is disputed, all parties agree that mass operation of PEVs will greatly affect load profiles and grid assets. The large-scale penetration of domestic energy storage, such as with photovoltaics (PVs), into the edges of low-voltage grids is increasing the amount of customer-generated electricity. Distribution grids, which are inherently unbalanced, tend to become even more so with the uneven spread of PVs and PEVs. In combination, PEVs and local generation could provide voltage support for distribution networks, and support increased penetration. This paper develops an interactive energy management system for incorporating PEVs in demand response (DR). Using this system, owners can immediately choose whether they want to discharge their PEV battery back into the grid. The system not only provides owners with a flexible scheme for contributing to DR but also ensures that, through real-time collaboration of PEVs and PVs, the three-phase grid operates within acceptable voltage unbalance. An extensive performance evaluation using MATLAB/GAMS simulation of the 123-bus test system verifies the effectiveness of the proposed approach.

Usually, protection coordination problems are solved under the assumption that the network topology is fixed. Yet, in practice, any power system can encounter changes in the network topology due to transient events. These transient events can be in the form of line or generation source outage. Furthermore, in the presence of distributed generation, the network topology can change depending on whether the system is operating in the grid-connected or islanded mode. Thus, it is essential to consider all possible network topologies while designing a protection scheme for distribution systems with distributed generation (DG). In this paper, the protection coordination problem is solved to determine the optimal relay settings considering N-1 contingency, which can result from a single line, DG unit, or substation outage. In addition, the relays are designed taking into account both grid-connected and islanded operation modes. The problem has been formulated as a mixed integer nonlinear programming problem including coordination constraints corresponding to the various possible outages. The proposed approach is tested

Stability of inverter-based distributed generation units (DGs) is highly dependent on the ratio of DG impedance to grid impedance. To assess the system stability both DG and grid impedances should be known beforehand. This paper proposes a multivariable impedance model for droop-based DGs that are typically employed in microgrids. The proposed model is essential when assessing the microgrid stability and can be utilized to design DG adaptive controllers that mitigate the instability issues triggered by grid impedance variations. A frequency sweep identification method with chirp excitation is used to validate the proposed DG impedance model. The results confirm the accuracy of the proposed DG impedance model in contrast with the estimated one.

This paper demonstrates the generic models of different hybrid ac/dc microgrids components. The operational philosophy is oriented towards decentralized schemes while considering the absence of a single slack bus in the entire system. Instead, the DG units operate based on droop characteristics, thereby forming a multi-slack distribution system, and accordingly stabilize the system frequency and voltage. Unlike in grid-connected systems, the system frequency and voltage are variable, rather than fixed. These new operational characteristics are highlighted in the models proposed for the DG units, loads, and interlinking converters as well. The solution of the nonlinear equations, represent the system steady-state behavior, are contrasted with detailed time-domain simulations performed in PSCAD/EMTDC environment to evaluate the modeling accuracy.

Out-of-plane Micro-Power Generators (MPG) are investigated to identify their optimal design and operating conditions. Those MPGs employ a variable capacitor, an electret layer embedded between its electrodes, and an inertial mass carried by a movable electrode. Using a linear model, we study the impact of varying the capacitor gap, load resistance, and electret voltage on the output power. In addition, we analyze the effect of squeeze-film damping on the MPG performance. We find that the linear model breaks down as the excitation level increases. A nonlinear model is developed to capture the MPG response and estimate its output power in closed-form.