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The papers address the following key areas: ' B I Applications of IoT and digital twin in electrical power systems: A comprehensive survey i b ': This research paper reviews the applications of the Internet of Things (IoT) and digital twin technology in electrical power systems. With the growth of Internet of Things (IoT) techniques, applications have become smarter, and linked gadgets allow them to be used in many parts of power systems. ' B I A new low-cost and low-power industrial Internet of Things infrastructure for effective integration of distributed and isolated systems with smart grids i b ': This research paper provides an Internet of Things (IoT) infrastructure solution based on a newly designed low-cost microcontroller-based IoT remote terminal unit (RTU) to integrate new, old, and conventional sites of existing grids with smart grids. [Extracted from the article]

Uncertainty analysis deals with the fluctuations and unpredictability of the electrical power generated from renewable resources (RRs), such as solar PV and wind energy systems. This paper gives an insight into various techniques used for the uncertainty analysis and a probabilistic Monte Carlo Simulation is applied for modelling the uncertainties concerned with RRs and electric vehicle (EV) load in the MATLAB platform. The uncertainty associated with the price sensitivity of EV charging and the state of charge of EVs is taken as a prime factor for analysis in the present work. Despite the fluctuations and unpredictability of electricity generation and consumption, the considered system ensures that the total amount of electricity supplied by solar PV, wind and grid matches the total amount of electricity demanded by EV load. Rao‐1, Rao‐2 and Rao‐3 algorithms are applied in this work to optimize the operation cost of charging stations under uncertain conditions and without any uncertainties. The results obtained without uncertainties by Rao algorithms are compared with the existing particle swarm optimisation method. In the presence of uncertainties, Rao‐1 and Rao‐2 algorithms are compared with Rao‐3 and it is found that the Rao‐3 algorithm performed better. [ABSTRACT FROM AUTHOR]

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research. [ABSTRACT FROM AUTHOR]

Unit commitment (UC) stands out as a significant challenge in electrical power systems. With the rapid growth in power demand and the pressing issues of fossil fuel scarcity and global warming, it has become crucial to enhance the utilization of renewable energy sources. This study focuses on addressing the UC problem by incorporating a wind farm and proposes a modified version of the metaheuristic African vultures optimization algorithm (AVOA) in binary form, utilizing the sigmoid transfer function. The modified AVOA employs multiple phase‐shift tactics to overcome premature local optima. By determining the on/off status of generating units, the modified AVOA improves the algorithm's effectiveness. Additionally, the paper develops an auto‐regressive moving average model (ARMA) to forecast wind speeds, with the AVOA assisting in selecting the optimal orders (q and p) of the ARMA model. This is done using historical wind speed data to capture uncertainty in the wind speed. The wind power is then calculated using various models and integrated into the UC problem. The effectiveness of the modified AVOA is examined on the IEEE 30‐bus system. The binary AVOA (BAVOA) outperforms several algorithms presented in the case study, demonstrating its superiority. Furthermore, the results indicate that BAVOA delivers superior outcomes within the discrete search space when compared to the continuous search space. [ABSTRACT FROM AUTHOR]

Spacers in the compact power distribution network are essential components for the support, organization, and spacing of conductors. To improve the reliability of these components and have an optimized network design, it is necessary to evaluate the performance of the variation of their geometric parameters. The analysis of these components is fundamental, considering that there are several models available that are validated by the electric power utilities. Due to the various possible design shapes, it is necessary to use an optimized model to reduce the electric potential located in specific sites, improving the reliability in the component, as the higher electrical potential results in a greater chance of failure to occur. The finite element method (FEM) stands out for evaluating the distribution of electrical potential. In this paper, an FEM is used to evaluate variations in vertical and horizontal dimensions in spacers used in the 13.8 kV power grid. The models are analyzed in relation to their behavior regarding the potential distribution on their surface. From the results of these variations, the model is optimized by means of a mixed‐integer linear problem (MILP), replacing the FEM output with a ReLU network substitute model, to obtain a spacer with more efficiency to be used in semi‐insulated distribution networks. [ABSTRACT FROM AUTHOR]

Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand‐side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application‐oriented review of state‐of‐the‐art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre‐processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper‐parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained. [ABSTRACT FROM AUTHOR]

This paper presents a control mechanism on high voltage direct current (HVDC) transmission line for frequency/voltage regulation, fault ride through (FRT) capability, and cyber‐attack/fault detection. The network under study consists of two areas with different frequencies that are connected through one 300 km HVDC line. The proposed control system regulates the frequency in both areas by managing power through HVDC line. The converters on both sides of HVDC line are controlled to handle faults on the DC and AC sections as well as improving fault ride through capability. The control strategies are implemented and operated depending on fault/cyber‐attack type and behaviour. In this respect, the control mechanism may change the firing angle of converters, switch their operating mode from rectifier to converter and vice‐versa or even block the converters. The proposed paradigm successfully distinguishes between the cyber‐attacks and faults. The simulations in MATLAB software validate that the proposed mechanism realizes all the objectives and the cyber‐attacks are completely identified and separated from the faults. [ABSTRACT FROM AUTHOR]

Converter interfaced distributed generations in a microgrid feed the modulated current of limited magnitude during fault conditions. The protection design and its operation are thus challenging due to limited fault current which is further reduced by Petersen coil grounding in medium‐voltage (MV) level. This paper aims to address this challenge by developing a current‐only directional relay algorithm for the protection of converter dominated MV microgrid with Petersen coil grounding. The relay's operating principle is based on the sign of the change in phase angle of the fault current with respect to the prefault which indicates the direction of fault. The negative and positive changes in current's phase angle determine the fault in forward and reverse direction, respectively. The tripping decision is derived by comparing the binary output of the relay at both ends of the line segment under protection. This requires a simple, flexible and low bandwidth communication channel. Both theoretical analyses and simulation studies have been performed on a typical distribution grid intended to be operated as microgrid. The proposed protection method is suitable for microgrid having the converters with and without reactive power support. Various operating conditions are evaluated, including bidirectional power flow, high resistance fault, different fault types, loading conditions and signals with noise. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced three‐layer predictive hierarchical power management framework for secure and economic operation of islanded microgrids. The tertiary control, guaranteeing the microgrid economic operation, is built upon the semi‐definite programming‐based AC optimal power flow model, which periodically sends power references to secondary control. To mitigate uncertainties arising from renewable generations and loads, a centralized linear model predictive control (MPC) controller is proposed and implemented for secondary control. The MPC controller can effectively regulate the microgrid system frequency by closely tracking reference signals from the tertiary controller with low computational complexity. Droop‐based primary controllers are implemented to coordinate with the secondary MPC controller to balance the system in real time. Both synchronous generators (SGs) and solar photovoltaics (PVs) are simulated in the microgrid power management framework. A unified linear input‐state estimator (ULISE) is proposed for SG state variable estimation and control anomaly detection due to compromised cyber‐physical system components, etc. Simulation results demonstrated that SG states can be accurately estimated, while inconsistency in control signals can be effectively detected for an enhanced MPC. Furthermore, comparing with conventional proportional‐integral (PI) control, the proposed hierarchical power management scheme exhibits superior frequency regulation capability whilst maintaining lower system operating costs. [ABSTRACT FROM AUTHOR]

The demand for electric power has consistently been on the rise, owing to urbanisation and technological improvements. On the generation side, renewable sources have been favoured over their polluting, exhaustible, non‐renewable counterparts. These changes in the power system have necessitated a system for maintaining the supply‐demand balance, to maintain system stability. A complex power system is also more prone to blackouts and grid failure. Islanding helps in provision of supply to consumers in a microgrid, reducing the possibility of a blackout. Depending on the power demand and generation, loads need to be shed or restored to mitigate power imbalances. A wide area distribution management system (WADMS) is proposed to dynamically shed and restore loads in the islanded mode, with the aid of micro phasor measurement units (µPMUs). A priority and consumer‐based load shedding and restoration (PCLS) algorithm is proposed in the WADMS that preferentially sheds or restores loads based on their assigned load priority indices and number of consumers. The algorithm has been tested on a modified IEEE 13 bus system, incorporated with a solar photovoltaic (PV) system, diesel generators (DGs) and an energy storage system (ESS) in MATLAB Simulink. [ABSTRACT FROM AUTHOR]

Upgrading protection schemes in distribution networks can help electric utilities to improve the reliability of their supply. Fuses and reclosers are still the primary protective devices used in distribution grids, and their optimal allocation allows isolating faults with the least number of interrupted customers. However, most models used to select and locate protective devices optimally do not consider their coordination or rescue schemes for the fuses, resulting in unfeasible or impractical solutions. This paper presents a mixed‐integer linear program model for the simultaneous selection, localization, and coordination of protective devices (reclosers and fuses), considering the fuses' rescue schemes based on electrical criteria. Local reliability indices and economic penalties for violating minimum standards are integrated into the model, thereby providing new decision‐making elements. The proposed approach is tested in different distribution networks to showcase its key features. Computational experiments show that the proposed model can satisfactorily handle the trade‐off between costs and reliability. [ABSTRACT FROM AUTHOR]

This study proposes an optimal approach for enhancing the quality of a weak electrical power distribution system by utilizing thyristor‐controlled series capacitors (TCSC). The proposed approach considers various system components, including shunt capacitors and distributed generators that rely on renewable energy sources, which are characterized by uncertainty due to changing weather conditions. The proposed system maximizes the benefits of these components by updating its parameters to improve the distribution power system's quality under all operating conditions, which change due to the varying energy generated by the distributed generators and load changes. The optimal TCSC values are calculated for all operating conditions using the Hyper‐Spherical Search with graded objective functions management strategy (HSSA‐GOFMS) algorithm. The GOFMS converts all restrictions into objective functions with limits and prioritizes the search for objectives according to a sequential system of priorities. This strategy prevents the possibility of non‐convergence. The results demonstrate the effectiveness of the proposed approach in improving the performance quality of a weak electrical distribution system. The proposed strategy achieves the target voltage level limits within nominal values, even with the interference of the system with generators distributed at variable power levels. Furthermore, the proposed approach enhances the performance quality of the system under a wide range of load changes. [ABSTRACT FROM AUTHOR]

The still growing share of intermittent renewable sources in the electric energy matrix is a factor that greatly increases the complexity of modelling electrical power systems. Although its exploitation is conditioned to the geographic location where the natural resource emerges itself, when this location coincides with the grid‐edge of the system, close to the consumer, the composite reliability assessment is exposed to the combination of two problems: (i) representation of the intermittent energy availability of generators; and (ii) failure event rarity. To address the first problem, a fast Monte Carlo tool capable of representing chronological aspects can be used. For the second problem, an efficient variance reduction technique that adapts well to the composite problem of generation and transmission must be established. Therefore, a quasi‐sequential Monte Carlo simulation tool aided by importance sampling via the cross‐entropy method is proposed as an efficient and robust procedure for evaluating the composite reliability of systems with renewable participation at grid‐edge. The method is evaluated through a modified version of the IEEE Reliability Test System ‐ 1996, which presents renewable generation in consumer load buses and rarity in the composite failure. [ABSTRACT FROM AUTHOR]

Energy management of a virtual power plant (VPP) that consists of wind farm (WF), energy storage systems and a demand response program is discussed in the present study. The introduced strategy is realized at the electrical power transmission level and takes into account the collaboration between VPPs in day‐ahead energy and reserve markets. One notable feature of the proposed strategy is attempting to make the revenue of VPPs close to the operating cost of generating units as much as possible. The objective function is subjected to the network‐constrained unit commitment model, up and down reserve requirements and the proposed VPP constraints. This method is taking into account the uncertainty in system and VPP loads, day‐ahead market energy and reserve price and WF power generation. This strategy is applied as hybrid stochastic‐robust scheduling to the VPP at the electrical power transmission level, where the scenario‐based stochastic programming models the uncertainty of day‐ahead market prices, and the bounded uncertainty‐based robust optimization has been adopted to model the uncertainties related to the load and WF power. Scheme has been tested on IEEE systems. According to the obtained results, the proposed coordination of VPPs in the mentioned markets demonstrates capability of suggested strategy. [ABSTRACT FROM AUTHOR]

To ensure the electrical power supply, inspections are frequently performed in the power grid. Nowadays, several inspections are conducted considering the use of aerial images since the grids might be in places that are difficult to access. The classification of the insulators' conditions recorded in inspections through computer vision is challenging, as object identification methods can have low performance because they are typically pre‐trained for a generalized task. Here, a hybrid method called YOLOu‐Quasi‐ProtoPNet is proposed for the detection and classification of failed insulators. This model is trained from scratch, using a personalized ultra‐large version of YOLOv5 for insulator detection and the optimized Quasi‐ProtoPNet model for classification. For the optimization of the Quasi‐ProtoPNet structure, the backbones VGG‐16, VGG‐19, ResNet‐34, ResNet‐152, DenseNet‐121, and DenseNet‐161 are evaluated. The F1‐score of 0.95165 was achieved using the proposed approach (based on DenseNet‐161) which outperforms models of the same class such as the Semi‐ProtoPNet, Ps‐ProtoPNet, Gen‐ProtoPNet, NP‐ProtoPNet, and the standard ProtoPNet for the classification task. [ABSTRACT FROM AUTHOR]

Modern electrical power system design will increase renewable energy sources (RES) dominance. Rotating masses, the main source of inertia in power systems, have been greatly decreased in renewable energy producing systems. Thus, load–frequency equilibrium is an important indicator of these systems' performance and safety. This work introduces a fractional‐order (FO) operator‐based cascade control structure for islanded microgrid (μG) load‐frequency control (LFC). The structure utilizes a tilt–FO derivative with filter (TFODn) in the first level to reduce noise. The second level implements the proportional integral (PI) controller's FO form (FOPI), making it a tilt–FO derivative with a filter cascaded to the FOPI controller (TOFDn‐FOPI). The optimal controller parameters for quick dynamic responses with low frequency fluctuation are determined using a unique cost function and prairie dog optimization (PDO) algorithm. The LFC control loops have frequency deviation‐based responsive loads (RL). Control signal delays, RES output changes, parameter uncertainties, and cyber vandalism are examined. Laboratory‐scale tests also assessed the controller's practicality. The proposed controller outperforms standard and FO type proportional integral derivative (PID) and PD‐PI controllers. The TFODn‐FOPI controller is suitable for complicated closed‐loop systems like islanded μGs due to its faster error clearing, reduced RL capacity, and superior time and frequency domain indicators. [ABSTRACT FROM AUTHOR]

The energy flow calculation of the integrated electric power and natural gas system (IEGS) is generally tackled by the classical Newton–Raphson (NR) method with second‐order convergence. However, this method may fail to converge or incur oscillating iterations leading to heavy computation burden if the initial point is not selected properly, especially in heterogenous integrated energy systems containing the natural gas system. To handle this problem, a variant of Newton–Raphson method with third‐order convergence is proposed, which needs one function and two derivative evaluations per iteration without increasing the number of derivative evaluations. In order to verify the effectiveness of the proposed method, simulation studies are carried out on several different cases for the energy flow calculation of IEGS. Experiment results reveal that the proposed method is superior to the classical Newton–Raphson method and its other variants in terms of computational efficiency. [ABSTRACT FROM AUTHOR]

The large‐scale blackouts of distribution systems (DS) caused by extreme natural disasters have aroused a lot of attention. To enhance the resilience of DSs, a novel service restoration and recovery model to minimize the system‐wide load loss is proposed here, where reconfiguration systems and two kinds of moveable resources, that is, mobile sources (MS) and repair crews (RC), are considered. Then, time‐space network (TSN) is applied to model the movement of MSs and RCs over the real‐world transportation networks (TN). However, the introduction of TSN makes solving the model become a time‐consuming task as plenty of variables are involved by it. To tackle this challenge which may lead to the impracticability of the model, a Floyd‐algorithm based TN simplification method is proposed to reduce the variables without sacrificing the equivalence of the TNs before and after the transformation. Finally, the applicability and effectiveness of the proposed model are verified on a 33‐bus test DS and a real‐world DS with complicated TNs. [ABSTRACT FROM AUTHOR]

When studying the harm of geomagnetic storms to the power system, the focus is often placed on the influence of transformers reactive power loss (GIC‐Q) caused by geomagnetic induced current (GIC) on voltage fluctuation. GIC‐Q, as a reactive load, can cause power flow changes, and the small disturbance stability is affected by the power flow distribution before disturbance, so GIC‐Q connects geomagnetic storms with small disturbance stability. In this study, the probability distribution function of induced geoelectric fields is established, and the sample data is obtained by sampling. Combined with the GIC benchmark network model, multiple sets of GIC‐Q in each substation are calculated and added to the system one by one to study the influence of geomagnetic storms on the oscillation characteristic parameters and establish the risk index of small disturbance instability. The result shows that the risk index value of oscillation instability in the occurrence of geomagnetic storms increases both in the AC system and the AC/DC hybrid system, but the increased range of risk value of local oscillation instability decreases with the addition of DC systems. The research results can provide a reference for evaluating the influence of geomagnetic storms on the stable operation of power systems. [ABSTRACT FROM AUTHOR]

With the large‐scale increasing demand for passenger transportation, the alternating current (AC) 25 kV traction power supply system (TPSS) of electrified railway is introduced into the construction of urban rail transit in China instead of the direct current (DC) TPSS. In order to suppress the negative sequence current and cancel the neutral section at the exit of traction substation of the AC TPSS, a novel AC integrated power supply system (IPSS) of the urban rail transit based on single‐phase static var generator (SVG) is proposed, which the capacity of SVGs can be further reduced. The topology of the novel AC IPSS is introduced. By defining the constraint factors of negative sequence current and reactive power compensated, the comprehensive compensation model of negative sequence current and reactive power of the novel system is established based on the corresponding standards, which the full compensation and satisfactory compensation can be achieved. The method of determining the compensation scheme and the control strategy are proposed. Taking the actual data as an example, the effectiveness of the compensation scheme is proved. In addition, the correctness of the control strategy proposed is proved by simulation and experiment. [ABSTRACT FROM AUTHOR]