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The rapid and accurate identification of single‐phase earth fault is of prime concern for many scholars from industry and academia, because its fault characteristics are extremely weak while the related potential hazard is extremely severe. In this context, this paper borrows from Deep Learning and in turn innovatively puts forward a multilayer perceptron (MLP)‐based single‐phase earth fault detection model augmented with kernel principal component analysis (KPCA). First, KPCA is applied to building fault feature extraction engineering via the transformation of fault features from low‐dimensional linear indivisible space to high‐dimensional one. Second, a MLP‐based model is used for basic model reference where many optimization strategies together with dropout technique are introduced and custom‐designed in tuning this model to achieve the best detection accuracy and convergence characteristic. Numerical studies demonstrate the superiority of the proposed KPCA‐MLP‐based single‐phase earth fault detection model. [ABSTRACT FROM AUTHOR]

Balanced truncation and singular perturbation approximation for linear dynamical systems yield reduced order models that satisfy a well-known error bound involving the Hankel singular values. We show that this bound holds with equality for single-input, single-output systems, if the sign parameters corresponding to the truncated Hankel singular values are all equal. These signs are determined by a generalized state-space symmetry property of the corresponding linear model. For a special class of systems having arrowhead realizations, the signs can be determined directly from the off-diagonal entries of the corresponding arrowhead matrix. We describe how such arrowhead systems arise naturally in certain applications of network modeling and illustrate these results with a power system model that motivated this study. [ABSTRACT FROM AUTHOR]

With the progress of society, the supply of electricity has become an inevitable choice for the development of all walks of life. The stability, safety, and economic dispatch of the electric energy system directly impact the entire country's economic development, from national defense, military, and even people's daily life, all closely associated with the smooth running of the network. In the aspect of power grid planning and operation, the economic security dispatching problem of power system is a very typical optimization problem. The problem is how to maximize the operating cost of the system under the premise of satisfying the system's load limit and safety and stability. Due to the rapid development of power technology, the research on power system security and economic planning is essential. As an important means to promote China's energy structure adjustment and promote the supply side structural reform, the regional spot electricity market is playing an increasingly important role in building a resource optimization allocation mechanism, improving China's energy resource allocation efficiency, and promoting social and economic development. However, the development of China's regional spot power market is still in its infancy, and the transaction price of the regional spot power market is quite different from that of other countries. It was given these problems, this article discusses the use of particle swarm algorithms for secure and economic scheduling of power systems in the context of securely reading blockchains and distributed data. The research results showed that: The introduction of environmental pollution penalty programs improved the priority dispatch of wind and photovoltaic power when considering environmental penalty programs and backup penalties for wind and photovoltaic power. The setting of the coefficient of the environmental penalty term depended on the designer's emphasis on the priority scheduling of wind power and photovoltaic (PV) power generation (PG), and the standby capacity penalty clause achieved reasonable dispatch and utilization of wind power and PV PG, which reduced the dramatic fluctuations and intermittent volume of the system, and facilitated safe and consistent operation of the network. The research of this paper shows the positive relationship between the blockchain and distributed data security reading algorithm and the clearing method of regional spot power market, and points out a new method for its development. [ABSTRACT FROM AUTHOR]

A frequency/phase/amplitude estimator is arguably one of the most crucial components in the control and synchronization of grid-connected equipment. Such an estimator may also be useful for monitoring and protection purposes in power systems. In this paper, an open-loop (and therefore unconditionally stable) estimator for the accurate and rapid extraction of the grid voltage phase, frequency, and amplitude is presented. In designing the proposed technique, special focus is given to applications operating at a low sampling rate. Examples of such applications include high-power converters, where both switching and sampling frequencies are very low. The study concludes with a comprehensive evaluation of the proposed estimator, demonstrating its effectiveness in accurately and swiftly estimating the fundamental parameters of grid voltage under low sampling rates. It highlights the estimator's enhanced performance in scenarios of distorted grid conditions and its superiority in filtering capabilities compared to traditional methods. These findings underline the estimator's potential for broad applicability in power system monitoring, protection, and control. [ABSTRACT FROM AUTHOR]

We propose a piezoelectric–piezoresistive coupling electric field sensor capable of performing large dynamic range AC electric field measurements. The electric field sensor utilizes direct coupling between piezoelectric (PE) materials and piezoresistive (PR) strain gauges, in conjunction with an external signal conditioning circuit, to measure AC electric fields effectively. We verified the feasibility of the scheme using a finite element simulation, fabricated a prototype of the electric field sensor, and characterized the properties of the prototype. The testing results indicate that the sensor exhibits an ac resolution of 50 V/m and a linear measurable electric field range of 0 to over 200 kV/m, which keeps the linearity at less than 0.94% from 1 Hz to over 5 kHz. Furthermore, the sensor also has advantages, such as a small size and low power consumption. The sensor can enhance the comprehensive observability and measurability of digital power grids. [ABSTRACT FROM AUTHOR]

The work is based on a collaboration between Hiroshima University and Kure KOSEN College. This paper presents the design concept, hardware, and applications of a single-phase synchronous inverter (SSI), a specially designed grid-forming inverter (GFM) for single-phase micro-grid (SMGs). The SSI is designed for the conventional 100/200 V distribution network and is based on the concept of "Non-Interference Core (NIC) dynamic model". Novel contributions of this paper are: (1) A root mean square (RMS) model of NIC-SSI was developed, combined with the conventional power system model, and verified through the comparison with the hardware-in-the-loop (HIL) simulation and SSI hardware experiments; (2) using the developed RMS simulation tool, the stabilization effect of the SSIs was investigated in condition under which the SSIs are massively installed in a distribution system; (3) off-grid SMG operations using SSIs under various ill-conditioned loads were demonstrated. The results show that the SSI has the considerable ability of grid stability enhancements for frequency, transient, and small-signal stabilities. The proposed SMG using SSIs is promising. [ABSTRACT FROM AUTHOR]

The increasing uncertainty of wind power brings greater challenges to the control for mitigation of cascading failures. In order to minimize the risk of cascading failures in large-scale wind power systems at a lower economic cost, a multi-stage blocking control model is proposed based on sensitivity analysis. Firstly, the propagation mechanism of cascading failures in power systems with wind power integration is analyzed, and the propagation path of such failures is predicted. Subsequently, sensitive lines that are prone to failure are identified using the power sensitivity matrix, taking into account the effects of blocking control on the propagation path. By constraining the power flow of these sensitive lines, a multi-stage blocking control model for the predicted cascading failure path is proposed with the objective of minimizing the control cost and cascading failure probability. Based on probabilistic optimal power flow calculations, the constraints related to wind power uncertainty are transformed into opportunity constraints. To validate the effectiveness of the proposed model, the IEEE 39-node system is used as an example, and the results show that the obtained control method is able to balance economy and safety. In addition, the control costs for the same initial failure are higher as the wind power penetration rates and confidence levels increase. [ABSTRACT FROM AUTHOR]

In recent decades, flash floods have become more common because of climate change and are considered a substantial risk for many cities worldwide. This catastrophic natural hazard presents a significant threat to critical infrastructure in urban areas, particularly the power distribution system. As modern societies are much more dependent on electrical energy these days, it is essential and imperative to make existing distribution systems resilient against flash flooding. Although researchers in this area have proposed various algorithms to impart resilience to a distribution system, however, the focus in these works is on wind‐related events such as hurricanes, cyclones, and windstorms. Therefore, here, the spatiotemporal effects of a flash flood on the distribution system are modelled using a grid‐based hydrodynamic model. The evolving line faults are then included in the proposed resilience‐oriented time horizon‐based service restoration model that also considers dynamic load demand, heavy uncertainties related to renewable generation, and interdependence among critical loads. Finally, the resilience of the distribution system's response is assessed using an operational resilience metric. The efficacy of the proposed framework is evaluated on IEEE 33‐bus and 69‐bus systems and the results show that the model provides an efficient restoration solution despite increased complexity caused by varying conditions. [ABSTRACT FROM AUTHOR]

This review article provides a concise view of the transformative role played by silicon carbide (SiC) semiconductors in the electric power industry, along with a description of their emerging applications in electric vehicles (EVs), renewable energy systems, aeronautics, and space. It highlights SiC chips' ability to operate at high temperatures and frequencies, surpassing the limitations of traditional silicon chips and resulting in enhanced efficiency. Additionally, it also presents the capabilities of SiC devices to manage elevated power levels within reduced size and weight parameters, emphasizing their impact on augmenting power density in EVs and enhancing reliability and efficiency in renewable energy, aeronautics, and space applications. Finally, the review concludes by providing an overview of the prevailing trends, existing challenges, and the future trajectory of SiC semiconductors within these important sectors. [ABSTRACT FROM AUTHOR]

The primary objective of implementing secure smart power networks is to reduce the risk of data privacy breaches, including adversarial data poisoning and inference attacks. This study presents a novel approach, namely the Blockchain-based Power System Security Model (BC-PSSM), to augment security measures within power systems. The proposed method utilizes blockchain technology to enhance the security of data storage and verification and the mechanisms for data signing and authentication. It employs decentralized detection to improve the effectiveness and precision of intrusion detection with a Support Vector Machine (SVM). The BC-PSSM method exhibits promising outcomes in multiple facets of power system security detection, as evidenced by thorough simulations and evaluations. The simulation results demonstrate a significant decrease in computational burden and energy consumption by maintaining a high energy output level. The BCPSSM algorithm performs remarkably in detection and false positive rates, resulting in heightened security and enhanced efficiency. The results also illustrate the positive impact of BC-PSSM in enhancing the security of power systems, offering robust protection against cybersecurity threats. BC-PSSM aims to enhance the security of power systems, thereby promoting a more robust and dependable energy infrastructure. This initiative plays a crucial role in protecting essential assets and mitigating the potential threats posed by cyber-attacks. The BC-PSSM algorithm enhanced efficiency to 92.15% and boosted overall security to 96.34%. It achieved this by achieving an amazing identification rate of 96.75% and an extraordinarily low false positive rate of 1.82%. [ABSTRACT FROM AUTHOR]

A key component for the performance, availability, and reliability of power grids is the power transformer. Although power transformers are very reliable assets, the early detection of incipient degradation mechanisms is very important to preventing failures that may shorten their residual life. In this work, a comparative analysis of standard machine learning (ML) algorithms (such as single and ensemble classification algorithms) and automatic machine learning (autoML) classifiers is presented for the fault diagnosis of power transformers. The goal of this research is to determine whether fully automated ML approaches are better or worse than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from dissolved gas analysis results. The methodology uses a transformer fault database (TDB) gathered from specialized databases and technical literature. Fault data were processed using the Duval pentagon diagnosis approach and user–expert knowledge. Parameters from both single and ensemble classifiers were optimized through standard machine learning procedures. The results showed that the best-suited algorithm to tackle the problem is a robust, automatic machine learning classifier model, followed by standard algorithms, such as neural networks and stacking ensembles. These results highlight the ability of a robust, automatic machine learning model to handle unbalanced power transformer fault datasets with high accuracy, requiring minimum tuning effort by electrical experts. We also emphasize that identifying the most probable transformer fault condition will reduce the time required to find and solve a fault. [ABSTRACT FROM AUTHOR]

This paper describes a new underwater pumped storage hydropower concept (U.PSH) that can store electric energy by using the high water pressure on the seabed or in deep lakes to accomplish the energy transition from fossil to renewable sources. Conventional PSH basically consists of two storage reservoirs (upper and lower lake) at different topographical heights. It needs special topographic conditions, which are only limitedly available in mountain regions. Furthermore, due to the lack of acceptance and the environmental impact, new conventional PSH projects are very unlikely to be built in larger numbers in Europe in the near future. The presented solution solves these issues by placing the storage system on the seabed, thus having other geographical requirements. It operates as follows: in contrast to well-known conventional PSH plants, which use two separated water reservoirs of different heights, the U.PSH concept uses the static pressure of the water column in deep waters by installing a hollow concrete sphere in deep water. Storage of electricity is achieved by using a reversible pump in the hollow sphere. Upon opening a valve, water flows into the sphere, driving a turbine/generator, thereby discharging the storage device. In order to re-charge, the water is pumped out of the sphere against the pressure of the surrounding water. The power and energy, respectively, are proportional to the surrounding water pressure at the seabed. The amount of energy stored depends on the water depth and the volume of the spheres. The spheres need a cable connection to the shore or to a close-by floating transformer station (e.g., an offshore wind plant). No other connections such as pipes are needed. The functional principle of this energy storage technology, its state of the art, its storage capacity and the shape and size of the required spheres are discussed in this paper. [ABSTRACT FROM AUTHOR]

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The Electrical Secondary Distribution Networks (ESDN) are very complex with high user density, making detection of defects and faults very challenging. In many developing countries, faults in ESDN have been reported mainly by customers and visual inspection by utility personnel. This process is time-consuming, costly, and among the causes of inefficient power supply to the end-users. The existing systems using Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Units (PMU) in the transmission and primary distribution networks are not efficient for fault detection in ESDN. The PMUs and SCADA systems relied mainly on centralized processing that is inefficient and relatively expensive for the secondary distribution network. This study proposes the architecture for fault detection and classification in the ESDN using Internet of Things (IoT) based architecture on distributed processing. The deployed IoT based sensor nodes were designed using the raspberry-pi and micro-controllers. The algorithms for fault detection and classification were designed and deployed in the prototype. The results show that the deployed sensor node obtained 98% accuracy and 18 ms faults detection time. The results implies that the deployed architecture using the IoT based sensor nodes, which is based on distributed processing, can be used for fault detection and classification in the ESDN. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem is one of the most popular and important issues that researchers need to solve in power systems. In this study, a modified weighted teaching-learning-based optimization (WTLBO) algorithm was designed and implemented for the OPF problem, regarding the teaching-learning-based optimization (TLBO) algorithm, which is one of the meta-heuristic algorithms. To demonstrate the effectiveness of TLBO and developed WTLBO algorithms in OPF solutions, six different single/multi-objective functions consisting of targets, such as fuel cost, total active power losses, and voltage deviation were tested on standard IEEE 30 and 57 bus systems. Multi-objective functions were transformed into a single objective function using the weighted sum method. Analysis results were compared with different optimization algorithms used in the literature. When the developed WTLBO algorithm is compared according to the single/multi-objective functions in OPF solutions, it has been proven that it performs better than the original TLBO algorithm and other algorithms in the literature. As a result of the analysis, according to the TLBO algorithm, in the IEEE 30 bus power system, in the proposed WTLBO algorithm, a decrease of 0.05% in the total fuel cost, 2.20% in active power losses, and 13.37% in voltage deviation are observed. [ABSTRACT FROM AUTHOR]

The pursuit of environmental sustainability, energy conservation, and emissions reduction has become a global focal point. Electricity is the primary source of energy in our daily lives. Through the analysis of smart power systems, we can efficiently and sustainably harness electrical energy. However, electric power system data inherently contain a wealth of sensitive user information. Therefore, our primary concern is protecting these sensitive user data while performing precise and effective analysis. To address this issue, we have innovatively proposed three granular information models based on differential privacy. In consideration of the characteristics of enterprise electricity consumption data and the imperative need for privacy protection, we implement a reasonable modeling process through data processing, information granulation expression, and the optimization analysis of information granularity. Our datasets encompass enterprise electricity consumption data and related attributes from Hitachi Building Technology (Guangzhou) Co., Ltd's cloud computing center. Simultaneously, we have conducted experiments using publicly available datasets to underscore the model's versatility. Our experimental results affirm that granular computation can improve the utility of differential privacy in safeguarding data privacy. [ABSTRACT FROM AUTHOR]

This paper introduces a new dynamic wide‐area cooperative protection based on cooperative control of distributed multi‐agent systems and graph theoretical methods. For power system cooperative protection, a new voting method is designed that manages each agent's cooperation according to each protection area. Cooperative protection areas work together and make a wide area of cooperative protection for the specific protection scheme. As the power system configuration varies, the voting management and the wide‐area cooperative protection dynamically change. The proposed protection method decisions are collective, while a centralized master does not exist, and local information is used. A fault detection code and a wide‐area fault detection code are presented for fault detection. The suggested dynamic wide‐area cooperative protection scheme is tested on an IEEE 39‐bus test system. The proposed protection performance is studied as backup protection for distance relays during normal, stable power swings, unstable power swings, and load encroachment conditions. [ABSTRACT FROM AUTHOR]

In this article, we are interested in the problem of adaptive synchronization of a voltage source converter with a possibly weak grid with unknown angle and frequency. To guarantee a suitable synchronization with the angle of the three‐phase grid voltage we design an adaptive observer for such a signal requiring measurements only at the point of common coupling. Then we propose an alternative certainty‐equivalent, adaptive phase‐locked loop that ensures the angle estimation error goes to zero for almost all initial conditions. Although well‐known, for the sake of completeness, we also present a PI controller with feedforward action that ensures the converter currents converge to an arbitrary desired value. Relevance of the theoretical results and their robustness to variation of the grid parameters are thoroughly discussed and validated in the challenging scenario of a converter connected to a grid with low short‐circuit‐ratio. [ABSTRACT FROM AUTHOR]

While energy, which is an indispensable part of everyday life, maintains its importance and place in socio-economic structures of countries, importance of electricity energy, as an important component of energy, is increasing its gravity exponentially. Importance of electricity energy demand maintains its increasing trend in line with growing population, urbanization, industrialization, becoming widespread of technology and welfare. While meeting electricity energy demand, correct and effective load forecasting is needed in order to ensure operational safety without losing the stability of voltage, frequency and power flows within the limits determined under the real time conditions of electric power systems, operate and plan secure and low-cost electricity transmission systems and supply sustainable, reliable, high quality and affordable electricity to consumers. In recent years, use of the artificial intelligence models are quite common in many areas of the power system analysis and control, static and dynamic security analysis, dynamic load modelling and alarm processing and error diagnostics. Therefore, in this study, hybrid artificial neural network model optimized with genetic algorithm has been proposed to perform short-term load forecast since fairly good forecasting results are being obtained especially for non-linear complex problems. The proposed short-term load forecasting model has been tested by making 24 hours load forecasting using actual load data of Ankara Region in Turkey. The results obtained from the proposed model have been compared with classical ANN. In this study, for short-term electric load forecasting based on artificial neural networks and genetic algorithm an adaptive hybrid system has been proposed. Using artificial neural network based on Genetic Algorithm (GA) a new approach for short-term electric load forecasting has been developed. This proposed hybrid algorithm has been implemented for short-term load forecasting. In the proposed model, the actual forecast was made by ANN, Genetic Algorithm is used to select the most appropriate activation function for each node of ANN. Thus, it has been observed that network error decreased even more than classical ANN. The results show that hybrid ANN gives better result than classical ANN. So, the accuracy of the proposed model for short-term load forecasting has been increased. These results showed that the proposed model has a higher accuracy rate than the classical ANN in the short-term load forecasting. [ABSTRACT FROM AUTHOR]

Abstract 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.

As the informatization of power systems advances, the secure operation of power systems faces various potential network attacks and threats. The false data injection attack (FDIA) is a common attack mode that can lead to abnormal system operations and serious economic losses by injecting abnormal data into terminal links or devices. The current research on FDIA primarily focuses on detecting its existence, but there is relatively little research on the localization of the attacks. To address this challenge, this study proposes a novel FDIA localization method (GA-CNN-LSTM) that combines convolutional neural networks (CNNs), long short-term memory (LSTM), and a genetic algorithm (GA) and can accurately locate the attacked bus or line. This method utilizes a CNN to extract local features and combines LSTM with time series information to extract global features. It integrates a CNN and LSTM to deeply explore complex patterns and dynamic changes in the data, effectively extract FDIA features in the data, and optimize the hyperparameters of the neural network using the GA to ensure an optimal performance of the model. Simulation experiments were conducted on the IEEE 14-bus and 118-bus test systems. The results indicate that the GA-CNN-LSTM method achieved F1 scores for location identification of 99.71% and 99.10%, respectively, demonstrating superior localization performance compared to other methods.

Artificial intelligence (AI) as a multi-purpose technology is gaining increased attention and is now widely used across all sectors of the economy. The growing complexity of planning and operating power systems makes AI extremely valuable for the power industry. Until now, there has been a lack of clarity regarding the specific points along the power system supply chain where AI applications demonstrate significant value, as well as which AI domains are best suited for such applications. This study employs an AI taxonomy and automated web search to qualitatively and quantitatively unveil the biggest potentials of AI in the power industry. Our analysis, based on a review of 258’919 publications between 1982 and 2022, reveals where AI applications are particularly promising. We consider six AI domains (reasoning, planning, learning, communication, perception, integration & interaction) and 19 use cases from the power supply chain (i.e., generation, transmission networks, distribution networks, isolated grids/ microgrids, market operations and retail). Our findings indicate that, as of now, the focus is predominantly on AI applications in power retail (55 %), transmission (14 %) and generation (13 %). Most analyzed works describe applications built on algorithms of the AI domains “learning” (45 %) and “planning” (14 %). Results also suggest that the current definition of AI domains is ambiguous, and they highlight missing information on the actual use and successful implementation of AI in power system use cases.

This paper describes OpenIMDML, an open source Modelica-based library implemented for the representation of electrically driven loads in a multi-domain engineering system. This novel approach consists of providing a physically meaningful equation-based mechanical interface of the motor’s torque/speed coupled to the electrical model of the induction motor. Because the newly developed motor models have a phasor domain representation of the electrical domain and a mechanical flange for rotational component coupling, this allows the user to integrate different domain subsystems with power grid models in one single multi-domain model utilizing the Modelica language and the OpenIPSL library. The library has been developed entirely using the Modelica programming language and has been tested in both Dymola and OpenModelica software tools.

The critical need to mitigate climate change brings with it prospects to transform societies globally onto more sustainable pathways. The articles in this special issue of e-Prime - Advances in Electrical Engineering, Electronics and Energy provide timely research advances that support integrated approaches for effective climate mitigation based on the sustainable development of energy, water and environment systems. The articles are organized into three main themes: (1) smart energy systems and sector coupling, (2) Energy Systems Modeling and Analysis, and (3) Emission Reduction Strategies and policies.The Decarbonization and Sector Coupling theme includes articles analyzing electrification and heat pump integration in industrial processes, optimization of nuclear-renewable hybrid energy systems, electric vehicle growth projections in Brazil, and impacts on electricity demand and fossil fuel reduction. The Energy Systems Modeling and Analysis theme covers contributions on offshore wind farm design software tools, optimization of future European energy mixes considering risks, climate change impacts on wind resources in the Black Sea region, and visualizations for home electricity monitoring systems. The Resource and Emission Reduction Strategies theme comprises articles evaluating policy instruments for CO2 utilization, machine learning for projecting passenger vehicle fleet growth in Brazil, and resulting energy and emissions outcomes. Together, these articles provide timely research advances across a range of topics relevant to integrated approaches for climate change mitigation through sustainable energy, water and environment systems. The articles emphasize opportunities for decarbonization, renewable energy deployment, resource efficiency, and emission reductions across multiple sectors and systems.

Decision‐dependent (endogenous) uncertainties (DDUs), as a new type of uncertainties revealed recently, couple dispatch decisions with uncertainty parameters and thus render power system dispatch more challenging. However, most previous works handled various DDUs via stochastic programming (SP) or robust optimization (RO) in a two‐stage framework, which undoubtedly introduces the drawbacks of SP and RO, and cannot meet the nonanticipativity requirements in power scheduling. In this paper, a multistage distributionally robust optimization (DRO) method for generation dispatch with demand response (DR) is proposed considering the DDUs of deferrable loads and the decision‐independent (exogenous) uncertainties (DIUs) of wind power and regular loads. By analyzing the structure of decision‐dependency parameters, a novel data‐driven decision‐dependent ambiguity set is proposed, which provides a generic framework for formulating DDUs and DIUs simultaneously. Then a multistage DRO model with nested max‐min structure is developed to integrate the merits of DRO and nonanticipativity into generation dispatch. The proposed model is solved by tailored reformulation method and improved stochastic dual dynamic integer programming (SDDiP). Case studies illustrate the effectiveness of the proposed approach by comparing with the multistage SP, RO, and decision‐independent DRO methods. [ABSTRACT FROM AUTHOR]

This article introduces EFlux, an interactive visualization software that uses a diagram-oriented representation of the Swiss extra-high-voltage electric grid. We explain the design principles used to visualize the network. Built on the seminal work by Harry Beck (London Underground tube map), it loosely maintains geographical locations, while conveying the topology of the network in a readable manner. The software blends quantitative and qualitative data with the map information, showing the dynamics of the network. Measurements at the substation and power line levels are mapped to graphical features (colour, thickness, line style). Animation is used to show how energy flows through the network. We describe how we use interactivity to navigate the schematic map, revealing more details upon zooming and selection, as well as how history can be replayed at comparable time intervals. Finally, potential improvements are discussed as part of our future plans. [ABSTRACT FROM AUTHOR]

The increased frequency and intensity of extreme weather events from climate change necessitates understanding impacts on critical infrastructure, particularly electrical transmission grids. One of the foundational concepts of a grid's resilience is its robustness to extreme weather events, such as hurricanes. Resilience of the electric grid to high wind speeds is predicated upon the location and physical characteristics of the system components. Previous modeling assessments of electric grid failure were done at the systems level with assumptions on location and type of specific components. To facilitate more explicit adaptation metrics, accurate component‐level information is needed. In this study, we build and utilize a data set of location, physical characteristics, and age of transmission structures for nine counties in the Florida Panhandle. These component characteristics were then simulated for failure under a variety of scenarios using fragility curves. Eight hurricanes were modeled using Hazus from the Federal Emergency Management Administration and the resulting impact to the network was assessed. The network was generated using the transmission lines and towers, showing increasing impacts to network efficiency with larger storms. Although modern transmission structures are built under the more stringent extreme wind loading construction standards, the prevalence of older, wooden transmission structures throughout the region poses a substantial risk to reliable electricity transmission during tropical cyclone events from the Gulf of Mexico. Plain Language Summary: Coastal infrastructure is increasingly vulnerable to extreme weather events. A critical infrastructure, such as electric transmission grids, must be robust and resilient to increasing intensity events to continue to provide an appropriate level of service to its customers. However, there are limited data on the component‐level assets of the transmission grid. Therefore, there are significant uncertainties in models and how they can be used in disaster preparedness and response scenarios. In this study, we build a high‐resolution transmission grid data set for nine counties in the Florida Panhandle. We then simulate eight different hurricane scenarios and detail how vulnerable the transmission grid is to hurricanes. Key Points: Component‐level characteristics of the electric transmission, such as tower type and location, are important for modelingThe tower types across the region has varying levels of vulnerability to hurricanesRobustness efforts to increase structural integrity of transmission towers would reduce overall system vulnerability [ABSTRACT FROM AUTHOR]

Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper's model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R2 reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting. [ABSTRACT FROM AUTHOR]

Electrical power systems are exposed to transient disturbances that change the voltage and current signals of the network, which can interrupt power and damage equipment. In high-frequency phenomena, it is essential to study the transient recovery voltage (TRV) to ensure the electrical insulation limits of circuit breakers are not violated, thus leading to a safe and reliable operation. Adequate models are crucial to achieving satisfactory results in the studies, according to the range of frequency of the transient being evaluated. This paper presents a comprehensive literature review of methods and models for studying electromagnetic transients, focusing on TRV requests imposed on circuit breakers, in addition to fault-clearing simulations on real system modeling. The analyses are fundamental both for the evaluation of the amplitude of the voltage signal and for its rate of rise. We also compare the reviewed models and techniques to provide a handy resource for researchers. [ABSTRACT FROM AUTHOR]

The growth of renewables in the energy sector, e.g., in public low-voltage networks, leads to an increasing share of installed power electronic devices, e.g., inverters for photovoltaic applications. To rely on these devices, suitable analyses have to be performed. This includes studies of the device stability in the harmonic frequency range, i.e., above 50 Hz up to 2 kHz. State-of-the-art time-domain studies for harmonic stability analyses require detailed knowledge about the inverter design. Black-box studies must identify the inverter characteristics in the laboratory, which can differ depending on specific operating points, i.e., specific operating powers. This study analyzes the operating-point dependency of inverters on the critical inductance values of the network impedance, e.g., the inductances at which the inverter is expected to become unstable. Measurements are performed for three operating powers of an unknown, commercially available single-phase inverter to validate the critical inductances. Two further commercially available inverters and four simulative implementations are analyzed as well with regard to the critical inductance and the critical frequency. The results demonstrate the importance of considering a representative range of operating powers of the inverter for the small-signal stability analysis. [ABSTRACT FROM AUTHOR]

Solar storms impact electrical power grids by causing quasi‐DC neutral point currents in transformers. These currents lead to half‐cycle saturation as well as other related and unwanted effects in the grid. To reduce the effect of these currents on the grid, DC‐blocking devices can be installed or changes in the grid topology can be made. However, these counter measures often have unwanted side effects or cannot be applied to the grid due to operational restrictions. In this work, a novel mitigation approach, based on the distribution of currents on more transformers, is presented. The number and location of grounded transformer neutral points is optimized, taking grid related constraints such as the minimal number of transformer connections into account. It is shown that the algorithm can effectively reduce the stress on transformers without any additional assets and thus increase system security. [ABSTRACT FROM AUTHOR]

The integration of power electronics-interconnected generation systems to the grid has fostered a significant number of concerns on power system operations, particularly on the displacement of synchronous generators that leads to a reduction in the grid's overall inertia and frequency response. These concerns have raised a significant amount of state-of-the-art mathematical proposals on how to estimate system inertia; however, the majority of the proposals do not differentiate generator inertia from load inertia. When inertia prediction for control room applications is required in real-time, the current state-of-the-art proposals use the inertia of generators as a proxy for a minimum, overall inertia estimate, counting the number of units committed in real-time and adding up their inertia. However, as dynamic conditions are becoming challenging with the integration of power electronics-interconnected generation systems, it is important to quantify the amount of inertia from the loads, for which the state-of-the-art proposals present very limited advancement, particularly in applications with real data. This work presents a set of recorded actual events in the Chilean power system to estimate the contribution of loads to inertia and frequency response to assess whether the loads have a significant role in frequency stability. The contribution of this work is as follows: first, reporting real data of a power system from the PMU and SCADA systems that are usually classified as not public; and, second, to derive a conclusion from the data to assess the role of loads in frequency stability in a real case. [ABSTRACT FROM AUTHOR]

In this paper, the economic load dispatch (ELD) problem which is an important problem in electrical engineering is tackled using a hybrid sine cosine algorithm (SCA) in a form of memetic technique. ELD is tackled by assigning a set of generation units with a minimum fuel costs to generate predefined load demand with accordance to a set of equality and inequality constraints. SCA is a recent population based optimizer turned towards the optimal solution using a mathematical-based model based on sine and cosine trigonometric functions. As other optimization methods, SCA has main shortcoming in exploitation process when a non-linear constraints problem like ELD is tackled. Therefore, β -hill climbing optimizer, a recent local search algorithm, is hybridized as a new operator in SCA to empower its exploitation capability to tackle ELD. The proposed hybrid algorithm is abbreviated as SCA- β HC which is evaluated using two sets of real-world generation cases: (i) 3-units, two versions of 13-units, and 40-units, with neglected Ramp Rate Limits and Prohibited Operating Zones constraints. (ii) 6-units and 15-units with Ramp Rate Limits and Prohibited Operating Zones constraints. The sensitivity analysis of the control parameters for SCA- β HC is initially studied. The results show that the performance of the SCA- β HC algorithm is increased by tuning its parameters in proper value. The comparative evaluation against several state-of-the-art methods show that the proposed method is able to produce new best results for some tested cases as well as the second-best for others. In a nutshell, hybridizing β HC optimizer as a new operator for SCA is very powerful algorithm for tackling ELD problems. [ABSTRACT FROM AUTHOR]