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.

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 intermittency of renewable energy sources (RESs) leads to the incorporation of energy storage systems into microgrids (MGs). In this article, a novel strategy based on model predictive control is proposed for the management of a wind–solar MG composed of RESs and a hydrogen energy storage system. The system is involved in the daily and regulation service markets, characterized by different timescales. The long-term operations related to the daily market are managed by a high-layer control, which schedules the hydrogen production and consumption to meet the load demand, maximizes the revenue by participating in the electricity market, and minimizes the operational costs. The short-term operations related to the real-time market are managed by a low-layer control (LLC), which corrects the deviations between the actual and forecasted conditions, by optimizing the power production according to the participation in the market and the short-term dynamics and constraints of the equipment. In addition, the LLC is in charge of smoothing the power provided to the grid. Numerical simulations demonstrate that the strategy effectively operates the MG by satisfying constraints and energy demands while minimizing device costs. Moreover, when compared to other strategies, the controller yields fewer state switches in the hydrogen devices, thus extending their lifespan. The efficacy of the control strategy is further validated through a lab-scale MG setup.

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.

Reducing the battery charging time of an electric vehicle (EV) is one of the key factors to boost the widespread adoption of EVs. The commercial, off-board high power, dc fast charging station need high initial investment and maintenance cost. On the other hand, the standard on-board type-1 and type-2 ac chargers with$3.3~kW$to$19~kW$need long time to charge. This paper proposes a combinatory ac and dc charging approach to increase the charging rate of EV batteries. The proposed combinatory charging approach provides a technique to charge EV battery from the on-board type-2 ac charger and drivetrain integrated dc charger. For drivetrain integrated dc charging, a dc input port$(N (+),O(-))$is formed using the neutral of the EV motor winding$(N)$and negative rail of the drivetrain inverter$(O)$. Through this dc input port, power from the renewable energy source-based dc microgrids, solar rooftops and other EV battery can be accepted for charging. The EV drivetrain inverter is controlled as an integrated interleaved dc-dc converter (IDC) to receive power from dc sources with EV motor windings reutilized as filter inductors. The control scheme for regulating the voltage across common dc-link accepting power from type-2 ac charger and integrated interleaved dc charger is presented. The performance analysis of EV motor and drivetrain integrated DC charger is validated through Finiet Element methods (FEM) co-simulation using Ansys Maxwell and Simplorer. A scaled experimental prototype is developed to validate the proposed combined ac and dc charging approach.

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.

The proliferation of electric vehicles (EVs), owing to their advantages over internal combustion engine vehicles, has introduced many challenges, due to the lack of charging infrastructure that can handle such a large surge of EVs. Therefore, alternative feasible charging solutions, such as EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence thanks to the bidirectional charger. However, there are several challenges hindering the adoption of V2V energy sharing solutions. The existing frameworks that detail the main aspects in energy management protocols emphasize solely on the EV integration with the grid, with the assumption of the grid availability and capability of supporting V2V energy sharing. In this article, a novel holistic energy management framework for an efficient V2V energy sharing is proposed. The proposed framework offers a complete overview of the different stages in the V2V charging problem and introduces possible solutions for each stage and its integration with other stages to form a comprehensive V2V solution, that is not only cost-effective but also maximizes user satisfaction and social welfare, while simultaneously fulfills the highest number of energy demands.

A multistage approach to passive islanding detection is proposed that utilizes a decision tree (DT) like classification algorithm. The novelty of the proposed method is centered on the way in which features are passed to subsequent stages of the DT. Feature sets extracted using different sized time windows are passed to successive stages of the tree. This provides two important advantages: 1) cases that can be easily determined as either islanding or nonislanding events are flagged as soon as possible without waiting for the full feature set to become available; 2) because the algorithm allows for the use of different sized time windows, features are analyzed in time-scales that fit their natural patterns of temporal evolution. In this article, the proposed classifier is trained and tested using a database of feature vectors, obtained using PSCAD, which were designed to reflect a variety of commonly encountered events on an IEEE 34-bus distribution system. One of the key requirements for the proposed algorithm was that easy cases should be flagged as soon as possible; this property was confirmed by the observation that most events ($\approx$79%) were detected within 10–20 ms, while at the same time retaining a very high detection rate overall cases ($>\!99$%).

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.

Electric Vehicles (EVs) are being widely adopted to completely replace vehicles with internal combustion engines. However, the development of charging infrastructure to support the growing number of EVs has been lacking primarily due to the high cost of installation. Additionally, the immature and non-uniform deployment of charging stations has led to the absence of charging infrastructure in areas such as highways and rural areas. As a result, emerging concepts such as, EV-to-Home (V2H), EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence. In this paper, the V2V energy sharing concept is exploited, where an intelligent and comprehensive framework to manage and allocate energy between EVs is proposed. This work presents a realistic modeling of the V2V energy sharing problem and proposes a two-layer matching approach that can efficiently match the EVs. The proposed approach not only optimizes the cost, but also the time, system energy efficiency, user satisfaction, and social welfare; hence making the approach more realistic and inclusive. Gale-Shapley is utilized to produce stable matchings, in the first layer, whereas a user-satisfaction model is devised to ensure realistic matchings, in the second layer. Additionally, a real-life dataset is developed from commercially available EVs and the performance of the system is evaluated using this dataset, in addition to realistic parameters derived from real-life data. The proposed approach is compared with a benchmark, where the results show that efficient, realistic, and effective V2V matches are achievable, paving the way for the adoption of such framework.

Due to the increasing penetration of distributed generations (DGs) in distribution networks, the performance of protective relays is becoming more critical to maintain the stability of the DGs especially during fault conditions. Among different types of DGs, squirrel cage induction generators (SCIGs) and synchronous DGs are sensitive to faults and might become unstable even after fault clearance. Since the fault clearing time depends on the overcurrent relays (OCRs) characteristic curves, this paper proposes a double-inverse OCR (DIOCR) characteristic to provide proper coordination while maintaining DGs stability. The proposed method defines the critical clearing time curve for each DG and combines them with DIOCRs curves and coordination time interval constraints for finding optimal DIOCRs settings. The proposed method is simulated on the IEEE 33-bus distribution network equipped with SCIG- and synchronous-based DGs. The obtained results highlight the capability of DIOCRs in protecting distribution system with DGs considering stability constraints.

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

This paper proposes a novel semiautomatic system domain data analysis method. The method is based on the iterative acquisition and analysis of a large body of bibliometric data, generation of domain taxonomies, and creation of domain models. The method was applied on a smart grid case study through collection and analysis of more than 6000 documents. We have found that our method produces domain models of comparable quality to the traditional manually produced domain models in a more cost-effective way.

New grid codes impose fault ride through (FRT) requirements on large doubly fed induction generator (DFIG)-based wind parks connected to transmission systems in order to reduce the loss of huge generation power due to temporary faults. Recently, the focus is set toward the development of different wind turbine technologies to enhance the FRT capability of wind parks with no consideration for transmission system protection schemes. This paper proposes a new communication-based dual time-current-voltage (Dual-TCV) tripping characteristic for directional overcurrent relays (DOCRs) that considers the FRT capability of wind parks by taking fast fault isolation actions in transmission systems. The protection coordination problem is formulated and solved to determine the optimal relay tripping settings. The proposed approach is tested on the IEEE 24-bus transmission system with up to eight DFIG-based wind parks. The outcome of this study reveals that protection schemes based on DOCRs governed by the proposed Dual-TCV tripping characteristic ensures fast fault isolations that significantly enhances the FRT operation of wind parks in adherence to grid code requirements. The transient analysis verifies the superior performance of the proposed protection approach in enhancing the FRT operation of wind parks.

DC Microgrids (MGs) are increasingly popular in recent years to support distributed generation based power systems. This paper focuses on the improvement of the load sharing control strategy and presents a decentralized control scheme, namely improved digital average current sharing (IDACS). Compared to the conventional hierarchical droop control scheme, the IDACS shows the structural difference and demonstrates the advantages of high modularity, automatic load sharing, low current deviation, and robust voltage regulation. In the proposed IDACS scheme, each primary converter includes both current controller and voltage controller to compensate voltage uniformity, which is caused by droop control scheme. Based on the foundation of digital average current sharing (DACS), a comprehensive approach is developed to design controllers for DC-DC converters by using anti-windup strategies. The improved performance is validated by simulation and demonstrates low voltage regulation and fast recovery time.

This paper presents a computational method that employs Natural Language Processing (NLP) and text mining techniques to support requirements engineers in extracting and modeling goals from textual documents. We developed a NLP-based goal elicitation approach within the context of KAOS goal-oriented requirements engineering method. The hierarchical relationships among goals are inferred by automatically building taxonomies from extracted goals. We use smart metering system as a case study to investigate the proposed approach. Smart metering system is an important subsystem of the next generation of power systems (smart grids). Goals are extracted by semantically parsing the grammar of goal-related phrases in abstracts of research publications. The results of this case study show that the developed approach is an effective way to model goals for complex systems, and in particular, for the research-intensive complex systems.

This paper proposes an adaptive control for grid connected photovoltaic (PV) system that allows for the active power injection (API) and active power filtering (APF) functionality while allowing the maximum utilization of the PV inverter. This PV system operates in three modes: (i) API only, (ii) API and APF with the full utilization of PV inverter VA capacity, and (iii) API and APF with specific total harmonic distortion (THD) limit for the injected currents. The maximum power point tracking (MPPT) is achieved with perturb and observe method. The proposed algorithm calculates the peak fundamental, harmonic current and then as per the available PV inverter capacity, modifies this value for the full utilization of PV inverter. Adaptive linear neuron (ADALINE) based online Fourier analysis is used to estimate the fundamental and harmonics components of the load and injected currents. The operation of the single stage PV inverter system is analyzed with the proposed control modes for two different atmospheric and load conditions.