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The high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network‐based comprehensive reactive power optimization method for large‐scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind‐power‐load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind‐power‐load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision‐making time of reactive power optimization are reduced. [ABSTRACT FROM AUTHOR]

This paper sets out to develop an efficient probabilistic optimal power flow (POPF) algorithm to assess the influence of wind power on power grid. Given a set of wind data at multiple sites, their marginal distributions are fitted by a newly developed generalized Johnson system, whose parameters are specified by a percentile matching method. The correlation of wind speeds is characterized by a flexible Liouville copula, which allows to model the asymmetric dependence structure. In order to improve the efficiency for solving POPF problem, a lattice sampling method is developed to generate wind samples at multiple sites, and a logistic mixture model is proposed to fit distributions of POPF outputs. Finally, case studies are performed, the generalized Johnson system is compared with Weibull distribution and the original Johnson system for fitting wind samples, Liouville copula is compared against Archimedean copula for modelling correlated wind samples, and lattice sampling method is compared with Sobol sequence and Latin hypercube sampling for solving POPF problem on IEEE 118‐bus system, the results indicate the higher accuracy of the proposed methods for recovering the joint cumulative distribution function of correlated wind samples, as well as the higher efficiency for calculating statistical information of POPF outputs. [ABSTRACT FROM AUTHOR]

This paper presents a state‐space average model of a three‐level photovoltaic (PV) inverter to understand short‐circuit currents transient characteristics. Analytical solution of the model is also derived. The proposed model is validated with PV system controller hardware in‐loop (HIL) test method. The errors between simulation and HIL tests results under various conditions are evaluated, testifying the validity of the proposed model. With the model and analytical solution, the decaying time constants of sub‐transient and transient processes are derived, which shows that the time constants of a three‐level PV inverter are not fixed and they vary with voltage dips. The analytical expression of short‐circuit currents is also formulated. The findings from this study are essential to short‐circuit current calculations of a power grid with large‐scale PV plants, coordination of power system protections, and grid planning. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the fault current contributions of small‐scale single‐phase photovoltaic inverters under grid‐connected operation and their potential impact on the protection of distribution systems. To conduct this analysis, an autotransformer‐based voltage dip generator is proposed as a means to test the photovoltaic inverters' contribution to short‐circuit currents. Laboratory tests are then performed to obtain the short‐circuit current contribution of eight single‐phase photovoltaic inverters. Using the short‐circuit current data obtained, a behaviour model is developed and simulated on Matlab‐Simulink. The model is then applied to a real distribution system and case studies are conducted to simulate the potential impacts on the protection system. Results indicate that while the massive penetration of small‐scale single‐phase photovoltaic inverters can alter the protection system's operating time, the impacts are not significant. Only in very specific scenarios, such as events related to high impedance faults, some impact can be observed. In these situations, the presence of photovoltaic inverters further complicates the already difficult task of identifying high impedance faults through conventional overcurrent protections. This study provides valuable insights into the integration of photovoltaic inverters into distribution systems, and can aid in the development of effective protection measures for future grid designs. [ABSTRACT FROM AUTHOR]

The integration of the renewable energy sources (RESs) into the power grid, drives a significant transformation in the conventional power generation landscape. This transition from traditional synchronous generators to inverter based RESs introduces unique challenges in maintaining the grid frequency stability due to the reduced system inertia. The inherent stochastic nature of the RES power generation, load demand, and grid inertia includes further complexity in the assessment of frequency stability. Existing studies have limitations, including neglecting the stochastic nature of RES generation and load demand fluctuations, relying on limited metrics, and lacking a comprehensive day‐to‐day assessment. To address these shortcomings of the existing approaches, this paper introduces a novel methodology for assessing frequency stability in power grids with high RES penetration. It proposes three indices for evaluating grid frequency sensitivity, resiliency, and permissibility amidst varying RES integration. Utilizing a stochastic approach, the study incorporates uncertainties in RES generation and load demand, offering a comprehensive framework for day‐to‐day frequency stability analysis. Additionally, it presents a systematic method to ascertain the necessary inertial support for maintaining desired frequency reliability in RES‐dominated grids. The effectiveness of these methodologies is validated through a case study on a modified IEEE 39‐bus test system, demonstrating their applicability in ensuring reliable grid operation under high RES scenarios. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study. [ABSTRACT FROM AUTHOR]

This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid‐supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent‐based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single‐area and multi‐area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation. [ABSTRACT FROM AUTHOR]

This paper proposes a situational awareness‐based method of load control for safe electricity consumption by industrial users to assess the risk of regulated load equipment within industrial users and to ensure their safe use of electricity. Firstly, in the situational perception stage, collecting key parameters such as controllable equipment within industrial users, and constructing a risk indicator system for industrial users in the process of load control from four perspectives: the degree of controllability of load equipment, the correlation and mutual influence of load equipment, the uncertainty of self‐provided power and economic losses; Then, in the situational understanding and presentation stage, a risk assessment method that integrates a self‐learning weight model and a cloud model is proposed to analyze the risk of industrial users under control; Finally, in the situational guidance stage, based on the assessment results of the risk situational of industrial users, industrial users' safe electricity consumption is controlled in multiple rounds to achieve the objective of maintaining safe and reliable operation of the power grid by accepting control at a low‐risk level. The simulation of the industrial users of steel mills is used to verify the rationality and effectiveness of the method proposed here. [ABSTRACT FROM AUTHOR]

Controlled electric vehicle (EV) charging at commercial locations has been seen as the key solution to mitigate the negative effects of uncontrolled charging on the power grid. In the scientific literature, EV users' willingness to participate in charging control has been analyzed, and various control algorithms have been studied. However, there is a gap regarding the best practices to encourage users to participate in charging control and the potential influences of the EV users' decisions on charging site operator's profits. In this article, the EV users' perspective on charging control is assessed to form a user‐friendly charging control approach and compensation scheme for commercial charging locations. Then, simulations are carried out using real charging session data to analyze the potential influences of EV users' decisions on charging site operator's profits. According to the results, the profits of the charging site operator are more heavily dependent on the number of customers than the optimality of the charging control. Hence, charging site operators should carefully consider the attractiveness of the implemented control strategy to maximize profits. [ABSTRACT FROM AUTHOR]

Load demand forecasting is very important for the management, designing and analysis of an electrical grid system. Load forecasting has progressively become a crucial component of the energy management system with the growth of the smart micro grid. This study presents a new framework to long term load forecasting in the world of electricity power with the help of historical load trends. The main objective of this research work is estimating monthly electricity demand of an Indian state Chhattisgarh, in terms of per day average load demand using a machine learning model—Long Short‐Term Memory (LSTM). This framework considers average of each day load demand for every month of years 2018–2022 and forecasted per day average load demand for each month of the year 2023. Furthermore, the predicting accuracy is evaluated for training and testing phase, in terms of error metrics like Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The MAPE values under the training and testing phase are in the range of 0.010%–0.652% and 0.378%–10.54%, respectively. A comparative study of LSTM model with Artificial Neural Network (ANN) model indicates the proposed LSTM model is more accurate and can be applied for real time load demand forecasting. [ABSTRACT FROM AUTHOR]

In order to meet the current needs of power transmission, people have turned their attention to direct current (DC) transmission. When the alternating current(AC) power grid (PG) fails, it is easy to cause commutation failure in the DC system, thereby increasing the fault scope of the system. Therefore, this article adopts a flexible DC transmission system. Flexible DC converters have many control parameters, and there are mutual influences and conflicts between these parameters. By applying intelligent algorithms to multi‐objective optimization (MOP), these complex MOP problems can be effectively solved and an optimal set of control parameter combinations can be found. This method has an important contribution and novelty for optimizing the converter control parameters of flexible DC transmission systems. This article achieves fast and accurate MOP of the control parameters of flexible DC transmission converters by continuously detecting the similarities between antibodies and maintaining the diversity of the population. The research results show that the average DC side current is about 405A in the first 20 seconds, and the DC side current fluctuates regularly after 20 seconds. This article lays the foundation for establishing a real‐time simulation model of flexible DC transmission systems suitable for real‐time simulation systems. [ABSTRACT FROM AUTHOR]

Spatial power load forecasting is crucial for power grid planning, generation planning, dispatching, efficient power utilization, and sustainable development. The integration of new energy sources and electric vehicles has significantly altered grid loads, increasing the complexity of spatial load forecasting. However, existing techniques fail to fully consider the temporal and spatial correlation characteristics of data, leading to challenges in data identification and summarization. This reduces load forecasting accuracy and prolongs prediction time. To address these issues, a spatial electric load forecasting method based on improved scale limited dynamic time warping (LDTW) and graph convolutional network (GCN) are proposed. Firstly, the improved scale LDTW is used to improve the clustering effect of K‐Mediods++, refine the type of load data, and make the subsequent model training more targeted. Secondly, the interconnections and distances of substations in a real network structure is used to build a graph model to capture the power load distribution. Finally, based on the clustering results and the graph model, GCN‐LSTM is used to construct the spatio‐temporal forecasting algorithm. The proposed algorithm is tested using load data from a region in Shanghai and compared with other advanced algorithms. Results show that the algorithm achieves higher prediction accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Due to the limitations imposed by urban power grid outages for maintenance, on‐line harmonic current detection technology for distribution network cables is expected to become an effective supplement to traditional offline diagnostic methods, enhancing the real‐time diagnosis of distribution network cable insulation conditions. This study established a 10 kV distribution network cable test platform and prepared typical defective cables subjected to moisture and long‐term thermal aging. Using COMSOL finite element electromagnetic simulation, the magnetic flux evolution laws of the cable insulation under typical defects were obtained. Experimental tests provided the harmonic current characteristics and statistical features of cables with typical defects. Based on these data, a method for analysing the degradation degree of distribution network cables was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis. Furthermore, a defect‐type identification method based on cluster analysis was proposed. Results indicate that the odd harmonics and the 4th harmonic of the distribution network cable's harmonic current are closely related to the cable's degradation state. A model integrating principal component analysis (PCA) data dimensionality reduction and expectation‐maximization clustering analysis achieved a recognition accuracy of up to 75.64% in distinguishing between moisture‐affected and normal cable states. The proposed on‐line detection and evaluation methods can effectively identify high‐risk cables with latent defects. [ABSTRACT FROM AUTHOR]

By increasing the number of electric vehicles (EVs) to achieve a less carbon environment, not only they consume power from the grid to be charged, but also do they deliver power to the grid, and this can play a significant role in load frequency control. However, EVs take part in frequency regulation based on their state of charge (SoC) which will cause uncertainties. In order to manage these uncertainties, EV aggregator (EVA) concept has been introduced. The EV owner participates in the demand response program provided by the EVA arbitrarily considering her/his requirements. Accordingly, EVA calculates the up and down power reserve for the power system operator. Following any frequency deviation, EVA sends the proper commands to each EV to consume or to inject power to the system. There are also communication delays between different parts, uncertainties, and non‐linearities that existed in the power system. To overcome these issues, this study proposes a new robust load frequency controller based on feedback theory and artificial neural network which is designed through the non‐linear multi‐machine power system. Simulation results on IEEE 39‐bus power system show that the proposed controller regulates frequency more desirably in comparison with other methods. [ABSTRACT FROM AUTHOR]

This study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting. [ABSTRACT FROM AUTHOR]

In power systems, voltage collapse during overload can be a significant threat. Accurate forecasting of critical operational conditions within power grids is crucial for preventing such situations. Precise predictions of voltage collapse enable operators to monitor the system closely and implement necessary corrective measures promptly, avoiding potential issues. However, monitoring networks can be costly due to the numerous loads and transformers in the distribution system. A comprehensive approach known as the voltage stability index (VSI) forecast without measurement buses (VFWMB) has been introduced to address this challenge. This approach involves innovative methods, including the seeking observation zone with weight least square (SOZWLS) technique for determining the number and location of measurements in the network based on its topology. Additionally, short‐term load forecasting is performed using the long short‐term memory (LSTM) forecasting method, followed by voltage estimation for buses without measurements. Finally, the proposed method calculates the modern voltage stability index for distribution systems (MVSIDS) for upcoming hours. All indicators and techniques in the VFWMB method have been validated. The algorithm has been thoroughly tested on various networks, including small and large, balanced and unbalanced, and both real and test networks, showing high efficiency in the electricity industry. [ABSTRACT FROM AUTHOR]

Insulators are one of the key components in high‐voltage power systems that prevent transmission lines from grounding. Since they are exposed to different kinds of harsh environments and climates, periodic inspection is indispensable for the safety and high quality of power grid. Nowadays, unmanned aerial vehicle (UAV) inspection is more widely used, facilitating incorporation of convolutional neural network‐based detectors in the insulator detection task. However, these methods are generally based on the assumption that the image samples are balanced among different categories and possess completely ideal annotations. The problem of sample imbalance or incomplete annotation is rarely investigated in depth for insulator defect detection. Here, insulator defect detection with imbalanced data and incomplete annotations is focused on. The proposed framework, named Pi‐index, introduces positive unlabelled (PU) learning to solve the problem of incomplete annotation and designs a novel index the class prior, which is a key parameter in PU learning. Moreover, focal loss is integrated in our framework to alleviate the effect of sample imbalance. Experiment results demonstrate that the proposed framework achieves better performance than the baseline methods in situations of sample imbalance and missing annotation. [ABSTRACT FROM AUTHOR]

The power characteristics of offshore wind power will change the regional power flow distribution and affect the regional voltage. Here, the direct‐drive wind turbine generator controlled by unity power factor is selected as the research object, and the influence of change of wind power on voltage at the grid connection point is analyzed from the simple integration model. Based on the two critical states of the external equivalent power of the grid connection point, an analytical expression of the output of wind power in the corresponding state is obtained, and the relationship among the critical state outputs, the output of wind power and the parameters at the grid connection point is drawn, hence the related influence laws is analyzed. The influence of change of wind power on voltage and regional reactive power and the reactance distribution of transmission lines near the grid connection point is considered to analyze the influence of offshore wind power on the vicinity region of grid connection point. The rationality of the conclusion is verified by study the cases of the integration of offshore wind power into Guangdong power grid, and some suggestions are put forward for the planning of offshore wind power integration. [ABSTRACT FROM AUTHOR]

The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security. [ABSTRACT FROM AUTHOR]