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Demand response (DR) is one of the most cost-effective and logical smart grid terms for power systems that can be used during peak load hours, whereas load shedding (LS) is the last and most expensive solution in emergency grid situations. The aim of them is to satisfy equilibrium constraints between consumption and generation and restore the power system frequency to normal bounds at the least possible time. The rapid increase in power grid costs and limitations on electricity generation resources have resulted in the increasing need for industrial customers' participation as an alternative solution to peak load spikes. Therefore, this paper considers the estimation of imbalance active power in a smart strategy to introduce a critical DR (CDR) in heavy industries (pulp and paper, cement, and medium density fibreboard (MDF)) based on direct load control (DLC) technique in order to reduce the need for LS in frequency restore. To evaluate the proposed model, a real case study including the typical pulp and paper, cement, and MDF industries processes with actual power consumption data is considered. The numerical and graphical results confirm that the proposed CDR can be used as an inexpensive solution to replace the costly spinning reserves and avoid load shedding. [ABSTRACT FROM AUTHOR]

In recent years, in order to reduce the damage from the blackout accident, the demand of the power system for its basic resistance ability has gradually increased. Therefore, this paper proposes a double‐layer optimization method for the prevention of cascading failure of the power system that takes into account the transient stability constraint for cascading trips that directly lead to the accident. In terms of models, the paper reconstructs an outer model for optimizing the safety margin based on the inner model that solves the safety margin of cascading failure prevention. In terms of algorithms, the paper uses the Extended Particle Swarm Optimization (EPSO) algorithm with higher precision and accuracy to solve the inner model and innovatively introduced the Practical Dynamic Security Region (PDSR), which uses PSASP software for offline calculation and online application, as a dynamic analysis method to quickly realize transient stability constraint to solve the outer model. Three evaluation indexes of the preventive optimization effect can be obtained by this method; dispatchers can plan more flexible and reliable optimal dispatching schemes through these indexes, to comprehensively improve the safety margin of cascading failure prevention in the current operating state and enhance the risk resistance ability of the power grid. This paper has used MATLAB and PSASP to perform simulation analysis using the IEEE39 node system as a test example to verify the effectiveness and superiority of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

The complexity of the interconnected grid and the continuous increase of new energy sources have led to an acute problem with low‐frequency oscillation (LFO) in power system. Identification and monitoring of LFO in power grid are prerequisites for effective control of low‐frequency oscillation phenomena. To address matter that the traditional S‐transform time‐frequency window function has a fixed scale and cannot be applied to the specific local characteristics of different signals, an adaptive generalized S‐transform algorithm based on a bat algorithm is proposed in this paper. It uses adjustment parameters to control the generalized Gaussian window function. The parameters are automatically adjusted by a bat algorithm adaptive optimization to find the best time‐frequency characterization. Secondly, the PMU data waveform with implicit low‐frequency oscillation information is converted into a two‐dimensional time‐frequency figure including the onset moment, frequency, and amplitude. The system enables identification and visual monitoring of low‐frequency oscillations. After that, simulation experiments of New England system are conducted. The superiority of the proposed method is verified, which can greatly improve the time‐frequency resolution of PMU active power data signal and has effective noise immunity. [ABSTRACT FROM AUTHOR]

With the deep coupling between the cyber side and the physical side of power systems, the failure of any link of both sides may lead to power outages, so it is necessary to analyze their vulnerability and vulnerable links for targeted improvement of systems. By dynamically attacking the coupled network nodes, this paper proposes a multilevel model and node-to-edge cyber-physical power system and the corresponding indexes system to analyze the vulnerability of the coupled power grid and its key components. The results showed that in the order of the indexes proposed in this paper, attacking surviving power nodes and cyber nodes results in a network crash rate of 25.0% and 66.7% faster than that in the order of "betweenness" and that attacking surviving cyber nodes results in a network crash rate of 89.4% faster than that in the order of "degree." In terms of attacking power nodes, the index proposed in this paper has the same rate as "degree." Therefore, the proposed model can better describe the vulnerability of the power grid to withstand attacks. [ABSTRACT FROM AUTHOR]

This paper is oriented to the credit investigation scenario of power grid supply chain enterprises and proposes a blockchain user credit assessment method based on improved Softmax regression in Power IoT. This method first designs a credit-rating mechanism that meets industry characteristics based on business needs. Second, it proposes a user credit evaluation model based on the blockchain architecture. Finally, the improved Softmax regression algorithm is used to train the proposed credit evaluation model, which effectively solves the credit rating. The multiclassification problem has achieved the goal of categorizing the credit rating of the enterprise. The simulation results show that the credit evaluation mechanism proposed in this paper can accurately evaluate the multisource credit data that lacks trust foundation and effectively realize the credit rating of power grid material supply chain enterprises. The credit evaluation mechanism proposed for Power IoT in this paper could have high potential for entity identity authentication and rating for securing mobile video communications. [ABSTRACT FROM AUTHOR]

The subject of resilience in electrical grids has become more popular among researchers in recent decades due to the rise of worldwide natural disasters such as floods, severe storms, snow, and hailstorms as well as the imposition of high costs resulted from widespread outages. Various methods have been proposed to improve the resilience of electrical grids under different conditions. In each work, the authors have validated their proposed method based on a function or metric they have defined to clarify its effect on improving the resilience of electrical grid. However, to date, there is no standardized metric for assessing the resilience of an electrical grid and providing the possibility to compare the many strategies discussed in different papers. This paper tries to explain the metrics that have been presented in various researches in this regard so far, and it compares these metrics from different aspects in order to determine the most comprehensive metric. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study on the implementation of incentive-based demand response programs in power grid management, comparing the results obtained from linear and nonlinear models. The challenges faced by power grids in balancing supply and demand and managing price spikes during peak periods are addressed, and demand response programs are proposed as an effective strategy. The focus is on implementing incentive-based programs where load serving entities determine the optimal incentive price and demand reduction. A novel approach is presented for simulating consumer behavior based on the price elasticity of demand and consumers' utility function, incorporating both linear and nonlinear economic models. The calculation of demand reduction aims to maximize consumers' welfare, while the determination of the optimal incentive price maximizes the profit of the load serving entities. Real data are utilized, and the proposed models are implemented using the mixed-integer nonlinear programming (MINLP) method. The results demonstrate the effectiveness of providing incentives to consumers during peak hours compared to other approaches, with a comparison between the linear and nonlinear models. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the scale, capacity, and coverage of the modern transmission network, the role of the power system in the national economy is increasingly prominent, and the disconnection of the power system will have a huge impact on society and people's lives. Due to the long transport distance and wide coverage of transmission lines, natural conditions and human factors have caused great difficulties in line operation and maintenance. How to effectively improve the operation and maintenance of transmission lines to ensure the stability and safety of the power grid has become a common problem for the power industry and scientific researchers to discuss. In recent years, the information society has stepped into the era of big data, and big data has developed rapidly, becoming a hot area favored by academia and industry, and is widely used. Through big data analysis, potential operation rules can be discovered from a large amount of grid information, providing maintenance personnel with corresponding maintenance decision support. Using big data technology for transmission line fault analysis can effectively reduce accident processing time and avoid accident expansion. Therefore, this paper combines the underground transmission line fault of power grid with the fuzzy KNN algorithm model to apply the underground transmission line state intelligent monitoring system and conducts the study of real-time data collection and fault diagnosis analysis of the underground transmission line fault of power grid, and this paper conducts the transmission line fault analysis experiment, which fully confirms the feasibility and effectiveness of the algorithm model proposed in the paper and concludes that the data analysis model proposed in this paper. The proposed data analysis model has good innovation and practical feasibility. [ABSTRACT FROM AUTHOR]

With the development of green energy, the penetration rate of new energy in the power grid is rising rapidly. Incorporating a large amount of new energy into the power grid will affect the power supply balance of the power grid, and even in severe cases will affect the safe and stable operation of the power grid. The information park has various small and medium data centers. The adjustable load of the data center can be transferred in time and space through the optical fiber and other communication networks, which can enhance the controllability of the power grid to a certain extent and promote the consumption of new energy. This paper proposes a collaborative planning method for information parks that considers the temporal and spatial transfer characteristics of data center loads, builds a load transfer model for data centers and builds a park power grid expansion model with the goal of minimizing the total cost of construction and operation. In this paper, an example of a park is used to verify the calculation example, and it is compared with other schemes to verify the feasibility of the method in this paper. [ABSTRACT FROM AUTHOR]

The modular multilevel converter of the battery energy storage system (MMC-BESS) not only is suitable for the large-scale energy storage and dispatching of AC and DC grids, but also has a strong ability to suppress power fluctuations caused by the new energy output or grid failures. When an asymmetric voltage or a sudden change in DC load occurs in the AC grid, in order to compensate for the power difference between AC and DC sides, the energy storage submodule of the MMC-BESS will have a large unbalanced charging and discharging current, destroying the equalization state of SOC and seriously affecting energy storage capacity utilization and battery service life. In order to deal with the above problems, in this paper, the characteristics of the power difference between the MMC-BESS phase unit and the upper and lower bridge arms are analyzed. It is found that when considering the fluctuation of the submodule capacitor voltage, the phase unit power has the fundamental frequency AC circulating current component, and the power difference between the upper and lower bridge arms has the DC circulating current component. Therefore, the three-level SOC equalization correction control strategy is proposed based on interphase, upper and lower bridge arms, and submodules, and the feedforward sliding window integral method is introduced into the SOC equalization correction control layer of upper and lower bridge arms, so as to achieve the purpose of more balanced and accurate power distribution among phases and among upper and lower bridge arms of each phase. The simulation results show that the MMC-BESS has effective compensation ability when there is a large power difference in the AC and DC power grid. Under the unbalanced working conditions of the three-phase power grid, the three-phase AC current can quickly reach equalization, and the total harmonic content is 1.38%, and the unbalance degree is 2.4%. Under the same operating conditions, compared with the traditional optimal one-third average method, the SOC equalization correction control strategy proposed in this paper has smaller submodule capacitor voltage fluctuation rate, harmonic distortion rate, and three-phase system circulating current. And the SOC of each phase has a faster equalization convergence speed. [ABSTRACT FROM AUTHOR]

With the continuous progress of UAV technology and 5G technology, the safety and reliability of UAV are gradually improved. In addition, UAV has many advantages, such as light weight, small size, low cost, and fast response in view of the low economy, high labor intensity, low efficiency, and other problems of the traditional inspection of UHV intensive transmission channels. This paper proposes a multi machine collaborative autonomous inspection scheme for UHV dense transmission channels based on 5G technology. This method is to realize the intelligent detection service for UHV dense transmission channels by building a self-service inspection model under the joint cooperation of 5G technology and UAV collaborative operation technology. The research of this paper shows that the multi machine coordinated autonomous inspection scheme of UHV dense transmission channel based on 5G technology constructed in this paper is not only more efficient but also safer than the traditional inspection scheme and single UAV inspection scheme. The related technologies can not only improve the maintenance efficiency of power grid dense transmission channel but also provide theoretical and technical reference for the improvement of multi machine coordinated technology. [ABSTRACT FROM AUTHOR]

At present, due to the large-scale use of different kinds of power electronic devices in the power system, the problem of harmonic pollution in the power grid is becoming more and more serious, which will lead to a serious decline in the production, transmission, and utilization rate of electric energy, overheat electrical devices, generate vibration and interference, and then affect the aging and service life of the lines. In order to effectively reduce the harmonic problems caused by different levels of the power system, it is necessary to analyze the harmonic components. In this paper, the BP neural network learning algorithm is introduced into the harmonic problems of the power system. The mapping relationship between input and output signals is obtained by using the BP neural network algorithm, and the harmonic frequency, amplitude, and phase contained in the obtained data are analyzed. According to the type of equipment with problems in the operation of the power system and the rapid diagnosis of existing defects, the problems are quickly located and the causes are analyzed. The practical results show that the BP neural network learning algorithm proposed in this paper has higher detection accuracy and analysis speed for the difficult problems in the power system. [ABSTRACT FROM AUTHOR]

Since the human society entered the electrification era, people's work, life, and even the production and development of various industries in society are inseparable from the supply of electricity. The purpose of this paper is to establish a cable insulation life evaluation model based on the improved fuzzy analytic hierarchy process to test the current insulation characteristics and life of various types of cables, so as to ensure the quality of power supply and safe production of power. This paper first understands the cable insulation characteristics test process and electrical power-related knowledge through a large number of literature studies and consultation with power grid professionals. Then this paper combines the theoretical research and improvement of the fuzzy analytic hierarchy process based on the actual situation of the cable insulation characteristics, thereby constructing the cable insulation life evaluation model. In this evaluation model, this paper combines the fuzzy comprehensive evaluation method and the analytic hierarchy process as well as the actual situation of the cable characteristic test to analyze and predict the insulation life of the cable. Finally, the linear test and reliability estimation of the experimental results are carried out, and the application of this evaluation model is extended to the evaluation of insulation life of other types of cables. Based on this, this paper proposes a cable insulation life evaluation model based on the improved fuzzy analytic hierarchy process. This model combines the Weibull model and the Arrhenius model commonly used in the assessment of cable insulation aging life and the improved fuzzy analytic hierarchy process. Experiments have proved that, within a certain range, temperature factors have a significant impact on the cable insulation life. For example, when the temperature is below 55°C, the insulation life of the cable is usually 30 to 50 years. However, the evaluation model of the improved fuzzy analytic hierarchy process in this paper has much higher accuracy in evaluating cable insulation life than other life evaluation methods. [ABSTRACT FROM AUTHOR]

In recent years, the application of wireless sensor networks (WSN) in power systems has received a great deal of attention. As we all know, the most important issue for the power system is security and stability, especially due to the massive outages caused by cascading trips. Therefore, in today's era, from the perspective of cascading trips, how to effectively use WSN to analyze and evaluate the security level of the power grid is an important direction for future power development. In this paper, an algorithm based on the WSN collection of online data to calculate the corresponding security level of the system is proposed for the cascading trip phenomenon, to achieve the online evaluation of the cascading trips. First, this paper proposes a hybrid layered network structure based on WSN for monitoring system and details the acquisition of power grid parameters by its acquisition layer. Secondly, combined with the manifestation of cascading trips and the action equation of current-type line backup protection, the mathematical representation of the grid cascading trips is given, and the mathematical form corresponding to the critical situation is strictly proved, and an index for evaluating the security level of the power grid is proposed and then further combined with the actual physical constraints of the power grid and the establishment of a mathematical model for calculating the security level of the grid cascading trips. For this model, this paper relies on evolution particle swarm optimization (EPSO) to give specific ideas for solving the model. Finally, a case analysis is performed by the IEEE39 node system and the results of the case show the effectiveness of the model and method. [ABSTRACT FROM AUTHOR]

Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness. [ABSTRACT FROM AUTHOR]

In recent years, electric vehicles have been widely promoted by virtue of environmental protection and pollution-free advantages. In the future, they are expected to become a mainstream means of transportation. However, as new services and business models become more and more closely related to the distribution network, the traditional AC distribution district has limited ability to respond to the large-scale access of electric vehicles. Therefore, it is becoming increasingly important to improve the ability to respond to electric vehicle access, reduce the negative impact of large-scale electric vehicle access on the grid, and predict and calculate the uncertainty caused by electric vehicle load access. Therefore, this paper proposes the concept of flexible distribution district, constructs the security operational boundary model for flexible distribution district, and uses "security margin percentage" as the measurement mechanism to provide dispatchers with an intuitive system operating status. In addition, this paper also proposes an effective uncertainty quantification method for the problem that it is difficult to measure the changes of bus voltage and line current at network nodes when large-scale electric vehicles are connected to the network. Numerical results demonstrate both the extensibility and the practicality of the proposed security operational boundary. In addition, the proposed uncertainty quantification method can reflect the impact of load changes on the characteristics of the power grid, which is helpful to study the improvement significance of adjusting electric vehicle load changes to distribution network optimization. [ABSTRACT FROM AUTHOR]

Multihop smart grid is built on the basis of an integrated and high-speed communication network. Through the application of advanced sensing and measurement technology, equipment technology, control method, and advanced decision support system technology, the goal of reliable, safe, economic, efficient, environment-friendly, and safe use of the power grid is realized. In order to solve the problem of excessive demand for power supply, new energy power generation and demand response are proposed. According to the above background, the demand side economic scheduling problem is a complex optimization problem, which is difficult to be solved by ordinary algorithms. The adaptive global search algorithm based on a genetic algorithm can better solve complex optimization problems. The genetic algorithm proposed in this paper can effectively manage a large number of controllable loads in the selected area. The algorithm minimizes the cost and peak to the average ratio by changing the load. Home users can arrange their maximum load when the price is low. The peak load of residential buildings decreased from 98.5 kw/h to 90 kw/h, and the peak load decreased by about 7.53%. Through appropriate load dispatching, users minimize the daily electricity charge, which is reduced from 1352 yuan to 1245 yuan per day, and the daily electricity charge is reduced by about 7.25%. In addition, the advanced measurement, communication, and control means under the framework of the smart grid also play a key role in promoting all aspects of demand side management (DSM). [ABSTRACT FROM AUTHOR]

As an important part of power infrastructure, a power monitoring system provides real-time data acquisition, state detection, and remote control of power equipment for the power grid and can deal with sudden anomalies in time. The operation and maintenance of the power monitoring system are very important to ensure the stable operation of power grid. The current mainstream remote operation and maintenance mode has internal threats such as misoperation of operation and maintenance personnel or malicious damage caused by attackers stealing operation and maintenance authority. Meanwhile, the existing operation and maintenance audit has the problems of high human resource cost and limited supervision of operation and maintenance personnel. To solve this problem, this paper proposes a collaborative filtering method for operation and maintenance behavior of power monitoring system called CFomb. Exploiting a keyword matching algorithm, CFomb determines the power resources accessed by operation and maintenance users from multiple operation instructions and extracts operation and maintenance behaviors. Referring to the collaborative filtering idea, the feature matrix decomposition scheme is introduced to train the access probability model based on the historical normal behavior of multiple operation and maintenance users, which provides a basis for real-time prediction of the access behavior probability of target operation and maintenance users. The OTSU binarization technique is used to determine the probability threshold of abnormal operation and maintenance behaviors, identify abnormal behaviors through threshold comparison, and send real-time alarms to operation and maintenance audit. The simulation experiment results show that the method in this paper can effectively identify the abnormal behavior of operation and maintenance users, reduce the overhead of manual audit, and help improve the power monitoring system's ability to respond to internal threats of operation and maintenance. [ABSTRACT FROM AUTHOR]

The willingness to improve the security and reliability of power supply to end-users, often pushed by prescriptions of national regulatory authorities, is bringing considerable challenges for distribution system operators. Islanding a portion of the public distribution network after a fault is considered a measure to mitigate the effects of service interruptions. This procedure is usually carried out by counterfeeding the grid through a generator set (GenSet). Even if this approach is widely adopted around the world, reenergizing the grid and keeping the electric island stable is not a trivial task. In this framework, the scope of this paper is to provide a set of technical guidelines for the usage of GenSets to supply public grids in emergency conditions. The goal is to highlight the static and dynamic limits of the GenSet operations and simplify their exploitation for the grid operators. The numerical analyses, which have been carried out through the RMS simulation tool of the DigSilent PowerFactory software, also aim to evaluate the technical constraints in the case of active networks, which involve distributed generation implementing regulations according to ENTSO-E and Italian technical standards. [ABSTRACT FROM AUTHOR]

The informatization construction of the power grid is becoming increasingly popular, business application systems are constantly emerging, and power-related data is rapidly expanding. These discrete power data are scattered in various application systems, and it is not easy to directly provide advanced enterprise applications. The establishment of intelligent power statistical model is an urgent need for constructing power grid informatization. This paper proposes a model of an electric energy metering system based on intelligent sensor data and introduces the existing digital metering system. This model is the integration and promotion of business integration based on the digital metering system. It is the first time to apply new metering equipment, such as measurement and control devices with integrated metering functions, and new metering technologies, such as IEC 61850 electricity meter reading applications. It is hoped that this paper can lay a foundation for further research. [ABSTRACT FROM AUTHOR]

Typhoons can have disastrous effects on power systems. They may lead to a large number of power outages for distribution network users. Therefore, this paper establishes a model to predict the power outage quantity of distribution network users under a typhoon disaster. Firstly, twenty-six explanatory variables (called global variables) covering meteorological factors, geographical factors, and power grid factors are considered as the input variables. On this basis, the correlation between each explanatory variable and response variable is analyzed. Secondly, we established a global variable model to predict the power outage quantity of distribution network users based on Random Forest (RF) algorithm. Then the importance of each explanatory variable is mined to extract the most important variables. To reduce the complexity of the model and ease the burden of data collection, eight variables are eventually selected as important variables. Afterward, we predict the power outage quantity of distribution network users again using the eight important variables. Thirdly, we compare the prediction accuracy of a model called the No-model that has been used before, Linear Regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), RF-global variable model, and RF-important variable model. Simulation results show that the RF-important variable model proposed in this paper has a better effect. Since fewer variables can save prediction time and make the model simplified, it is recommended to use the RF-important variable model. [ABSTRACT FROM AUTHOR]

The analysis of load characteristics is the basis and premise of load actively participating in power grid regulation. This paper proposes a multi-factor load classification method considering the load of clean energy power generation, the rapidity of load classification, and various subjective and objective factors that may affect the behavior of load consumption. First, it describes the characteristic index of load consumption behavior and analyzes the subjective and objective factors that affect the power grid consumption behavior. The effect of clean energy generation on load side is considered. Based on the load characteristics, the K-means algorithm is used for main clustering. Then, the confidence level of the uncertainty of the actual load adjustable capacity is analyzed by quantifying the load adjustable potential index and the fuzzy C-means clustering method was used for secondary clustering of the adjustable capacity. Finally, DBI and SC indexes are used to evaluate the clustering results, standard values of evaluation indexes are set, and unqualified clustering results are recalculated and corrected. 31 industrial users in a province are selected as research objects, and the load data of the past 365 days are collected to verify the effectiveness and practicability of the proposed method. The classification results show that the classification accuracy is still good when the noise is 30%, and the maximum deviation between the clustering results and the actual load regulation potential is 12%. It can meet the actual engineering error standard. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is the main aspect of new energy power generation, and it is an important means to achieve the goal of carbon neutrality. When the PV system is connected to the grid, the nonlinear load of the grid will affect the power quality and consume reactive power. This paper proposes a PV power generation grid-connected system to improve power quality, with an active power filter (APF) function. Through the maximum power point tracking (MPPT) method, PV power generation can operate at the maximum power point and play the function of harmonic and reactive power compensation at the load side. To improve the dynamic performance of the grid-connected PV system and harmonic compensation simultaneously, multistep finite control set model predictive control (FCS-MPC) is adopted for the grid-connected module. The whole system does not need additional equipment, as it plays the role of two devices and effectively reduces the input cost. In this paper, the proposed structure and multistep FCS-MPC are verified in MATLAB/Simulink. The results show that the system injects the maximum power into the power grid at the same time when the load changes and compensates the harmonic generated by the nonlinear load of the power grid so that the total harmonic distortion of the power grid can meet the operation standard, and the system has good dynamic performance and steady-state performance. [ABSTRACT FROM AUTHOR]

Because most of the production and transmission components of the power system are exposed to the natural environment, they are vulnerable to the threat of natural disasters for a long time and the resulting circuit damage is also large. With the improvement of people's living standards and the improvement of electricity demand, more and more attention has been paid to the normal operation of the power system. Improving the disaster resistance ability of the power system is an important prerequisite to ensure the national economy and life. In view of the above problems, in order to solve and improve the power supply recovery ability of the distribution network in the face of various disasters, this paper focuses on the analysis of the disaster response stage of the intelligent distribution network under natural disasters and the comprehensive data and related indicators of the power grid system, so as to establish the evaluation index system of the power grid's predisaster tolerance and postdisaster resilience and, through the introduction of the fuzzy comprehensive evaluation theory, quantifying the disaster response capability of the distribution network. Finally, through the introduction of case analysis, the case results show that the intelligent distribution network disaster response ability evaluation algorithm based on fuzzy comprehensive evaluation constructed in this paper can accurately calculate the disaster response ability of the distribution network and has an important guiding role in the disaster prevention and reduction of the distribution network. [ABSTRACT FROM AUTHOR]

With the continuous development of urban intelligence, as traffic, power grids, and electric vehicles are new ideas to solve energy shortages and air control problems, they have received widespread attention from the society and strong support from the government. The charging pile is a key hub for data exchange and has typical characteristics of IoT terminals. However, the guidance of the grid connection of electric vehicles is not standardized, and the security and stability of the power grid will inevitably be affected. The blockchain has the characteristics that data is difficult to tamper with and decentralized. Based on these two characteristics, the information recorded by the blockchain is more authentic and reliable. This paper is aimed at realizing the global unified identification and management of large terminal equipment based on ubiquitous power Internet of Things equipment with the help of blockchain technology and at providing safe, efficient, and stable online management services for a large number of devices in the ubiquitous power Internet of Things. In this paper, we analyze the application possibility of blockchain technology in the current electricity market and apply blockchain technology to the electricity market to solve the drawbacks of the electricity market, as well as adding blockchain analytics to the renewable energy electricity market. The experimental data analysis shows that with the increase of the length of the blockchain, the blocking time of the new area increases correspondingly, but the overall performance declines slightly, so this scheme can meet the practical needs of a large number of concurrent access terminals in the ubiquitous power Internet of Things. It can be seen that the successful application of blockchain technology based on the power Internet of Things in electric vehicle charging piles has greatly improved work efficiency. [ABSTRACT FROM AUTHOR]

Electric buses have a significant penetration rate and high charging frequency and amount. Therefore, their charging load has a momentous influence on the power grid's operation and dispatch. There are important theoretical and practical reasons to study electric bus charging load prediction; however, the intermittent and random charging behavior of buses makes it more difficult to predict charging load predictions, particularly, in real time. To accomplish this, in this paper a WNN (wavelet neural network)-based dynamic load prediction model for charging electric buses is suggested. We start by using distance and shape to group the charging load curve which, in fact, is done with spectral clustering. As a second step, we take into account a wide range of charging load-affecting variables such as temperature and time of day, in order to better train the WNN. Moreover, the charge loads for each cluster are predicted based on model parameters; subsequently, the forecast day's total charging load is then calculated by summing the prediction results for each cluster; finally, the proposed method is validated using a real city data set. In our empirical evaluation, it has been found that, under various indicators, the proposed method's ability to precisely forecast the charging load of electric vehicles has significantly improved. In fact, this allows for better guidance of charging user, planning, and expanding the power grid in consideration of electric vehicle charging loads. [ABSTRACT FROM AUTHOR]

Power transmission and transformation projects (PTTPs) under new energy grid connections are different from ordinary engineering construction projects. With large investment amounts, various supporting facilities, and high safety and quality requirements, PTTPs are vulnerable to the volatility of new energy power generation, climate, geological conditions, geopolitical environment, technological changes, and other kinds of uncertain factors. Therefore, the investment risk of PTTPs under new energy grid connection is particularly considerable in the project management. For the owner of PTTPs, adopting the engineering-procurement-construction (EPC) mode is an effective attempt to solve the project construction problems faced by the owners. Thus, this paper deeply excavates the key risk points of PTTPs in the initial investment phase under the EPC mode and constructs the novel risk evaluation index system from the perspectives of economy, management, policy, society, and environment. An assessment model is established based on triangle fuzzy number-hesitant fuzzy linguistic term sets (HFLTS-TFNs), the entropy method, and the fuzzy comprehensive evaluation method, which is used to evaluate the investment risk of PTTPs to provide reference for power grid corporations to control project risks. Finally, a case study in the Hexi Corridor region, Gansu Province, China, is illustrated to demonstrate the rationality of the decision model and find management risks are the most vital risk factors for PTTPs under EPC mode. [ABSTRACT FROM AUTHOR]

Background: When a power system blackout occurs, it affects the economy of the country and every aspect of human life. Cascading failures can easily occur and cause a major blackout in the power grid due to the breakdown or failure of important nodes or links. Recently, transmission network reconfiguration (TNR) becomes a hot topic and has made many concerns after major blackouts of power systems. Aims: TNR is the second‐stage action plan to restore power systems and plays a major role in the process of power system restoration. On the other hand, grid resilience involves a quick dynamic reconfiguration of power systems to minimize the propagation of attack influences on the grid. The motivations to include the works in this survey are based on the quality of the research performed in the transmission network reconfiguration problem for grid resilience. In this article, the state‐of‐the‐art review of recent progress in the network reconfiguration problem of the transmission system for grid resilience is discussed with practical challenges, technical issues, and power industry practices. Materials & Methods: In this paper, complex network theory‐based indices with advantages, disadvantages, and their applications have been discussed to assess the important nodes and lines for network reconfiguration problem during sudden disturbances in power systems. Furthermore, optimization models have been presented with objective functions as well as their constraints. Taken together, optimization methodologies have been discussed to solve network reconfiguration problem with merits and demerits. Results: This survey paper presents current trends in research and future research directions concerning transmission network reconfiguration for academic researchers and practicing engineers. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations. Discussion: Based on a thorough comparison of literature some future perspectives are also discussed for transmission network reconfiguration problem for grid resilience. Conclusion: This review paper provides a comprehensive review of current practices applied to transmission network reconfiguration. The core focus of this paper will remain on complex network theory‐based indices, optimization models, optimization methodologies, challenges, and technical issues, and discusses future direction for transmission network reconfiguration problem for grid resilience. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations. [ABSTRACT FROM AUTHOR]

Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods. [ABSTRACT FROM AUTHOR]

Summary The power grid constructed with electric power router (EPR) will have the ability of flexibly controlling the voltage and power flow on each line of the grid, which will be helpful to maximize the integration of the renewable distributed generation to the grid. Most of the current methods for transmitting energy depend on storage unit or large scale calculation, which are relatively difficult. To avoid these disadvantages, this paper proposed a two‐step routing strategy based on the improved shortest path method for DC power grid with EPRs. The improved shortest path method combines Shortest Path Faster Algorithm (SPFA) with depth first search (DFS) algorithm, which could handle the negative weights and the negative cycle issue in the graph of the power flow paths. The increase of the transmission line power loss and the EPR's power loss are taken as the weight for the path search method to evaluate the cost of the path. Applying the proposed method to each power demand and supply finally helps to obtain the current distribution of the whole grid. By analysis of typical scenarios, compared with the power flow distribution of traditional DC power grid, the one produced by the proposed strategy could improve the power transmission capability as well as reduce the maximum voltage drop in the network. Besides, this strategy introduces less power loss, than many other ways do, in power routing. The effectiveness of the rioting strategy is related to the configuration of the network. [ABSTRACT FROM AUTHOR]

As a key means to elevate low-carbon energy transformation in China, multi-energy microgrid accelerates the construction of new power systems. In order to scientifically evaluate the benefits of multi-energy microgrids, we proposed a benefit evaluation index system from the dimensions of economy, reliability, low carbonization, and intelligence. Considering the relationship between the evaluation indicators, this paper innovatively proposed a multi-energy microgrid benefit evaluation model based on AHP-VWT-MEEM. In addition, this paper selected different multi-energy microgrid demonstration projects in the China Southern Power Grid Company Limited's region for example analysis. In the example analysis, through the comparison of the comprehensive benefits of the three projects, the comprehensive benefit of the project 3 is the best. On this basis, from the specific configuration plan of Project 3, it can be concluded that the configuration of larger capacity thermal storage can realize the "thermal decoupling" of cogeneration, thereby improving the flexible adjustment capability of the demonstration area, promoting the consumption of renewable energy, and obtaining more considerable comprehensive benefits. At the same time, it is pointed out that the comprehensive benefit evaluation result can be improved by appropriately reducing the investment cost of project 3. [ABSTRACT FROM AUTHOR]

Efficiency is an important indicator of risk assessment, but the failure rate changes with time, which makes risk assessment very difficult. Traditional methods often only measure the steady-state and static indicators of relay protection. The traditional method often only measures the steady-state and static indicators of the relay protection, or directly uses the RiskWeibull analysis system to perform reliability analysis on the relevant data. However, this method can only perform quantitative static analysis on the data. Once the system is disturbed, its reliability will be affected. Analysis is not possible. Since the relay protection device has the possibility of malfunction and refusal at the same time, in order to distinguish, the risk is introduced into the reliability evaluation of the relay protection device to achieve the purpose of considering the possibility and consequences of its failure at the same time. Based on the traditional method, this paper proposes a method to calculate the time-varying failure rate of the relay protection device by using the support vector machine model, which improves the influence of hidden risks on the power grid relay protection, and not only realizes the scientific hidden danger evaluation, risk rating. It is of great significance for the development of power grids to evaluate the consequences of failures and to make the transmission lines operate safely and stably. [ABSTRACT FROM AUTHOR]

Learn about effective and uncontrolled gambling on wireless networks. Firstly, according to the control purpose, considering the reliability requirement of microgrid for network communication, the characteristics of wireless sensor network communication quality of service in microgrid, the influence of wireless connection reliability, and the mathematical standard of wireless communication are determined. Then, according to the estimation of the occurrence time of wireless communication link reliability, the reliability of wireless sensor network communication link in a smart grid is improved based on a fuzzy nonlinear control algorithm. Finally, the security and reliability of the control system described in this paper are measured by simulation. The experimental results show that the reliability of the wireless communication estimation time estimation algorithm and the power control algorithm based on fuzzy control can estimate the reliability, shortsightedness, and accuracy of good connections in real time. By adjusting the output power of the node, the connection quality and reliability can be controlled stably. [ABSTRACT FROM AUTHOR]

With the substantial increase in the number of electric vehicles, the charging of electric vehicles without regulation and scale control will bring about problems, such as overloading of distribution transformers; the proportion of new energy power generation is also increasing year by year, and the access of new energy to the power grid will cause volatility. In order to solve the problems above, this paper proposed a coordinated and orderly scheduling strategy considering new energy consumption, which protects the interests of both users and the integrated power grid. First, a two-level vehicle-network interaction model considering both supply and demand sides was established. The upper-level model optimized the indicators on the distribution grid side, and a term of charge-discharge margin as well as grid-side load variance model was proposed. The lower-level optimization model was set based on the users' condition. The average discharge rate index was defined to evaluate the battery loss satisfaction in the scheduling strategy, which fully considered users' charging and discharging cost, and finally achieved a win-win situation between the power grid and the user. Secondly, the fast nondominated sorting genetic algorithm (NSGA-II) was used to figure out the effect of the strategy proposed in this paper, and a community is taken as an example for simulation. The results confirmed the economy and rationality of the above strategy, by rationally scheduling the charging and discharging behavior of electric vehicles, consuming new energy, restraining the fluctuation of the remaining new energy power generation, realizing the dynamic balance between the charging and discharging load and the output of new energy in a certain area, and finally effectively suppressing the fluctuation of the power grid load while improving the availability of clean energy. [ABSTRACT FROM AUTHOR]

Smart grid is the inevitable trend of power grid technology development. As an important terminal apparatus in the power grid, the overload protection method of bimetal used by traditional miniature circuit breakers (MCBs) is difficult to meet the requirements of smart grid. Bimetal uses the thermal effect of current for overload protection, so in this paper, the thermal effect is used to calculate the current. Also, all current carrying conductors except superconductors will generate heat, and the heat will not be saturated, which can meet the characteristics of miniature circuit breakers with small volume and large current. Compared with other current calculation methods, the proposed method has simple structure and wide measurement range, which is an effective way to solve the problem of intelligent MCBs. After derivation, the algorithm in this paper is not affected by the convection coefficient. The experimental results show that the error of the algorithm is less than 5%. [ABSTRACT FROM AUTHOR]

Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process. [ABSTRACT FROM AUTHOR]

Summary: In the electrical grids, the main target of the controller design is represented in the decreasing of the settling time and the overshoot of frequency deviations. The optimization of the controller gains represents the main challenge that affects the controller performance. Many intelligence techniques are used for the tuning of the controller gains such as the genetic algorithm (GA) and the bacterial foraging optimization algorithm (BFOA). These algorithms may have some drawbacks when the complexity of the system increases. These drawbacks may be due to the design method of the gain selection of the proportional‐integral (PI) controller. The design method may characterize the PI controller to reduce the maximum overshoot of the system response but the settling time increases. On the contrary, another different design method may characterize the PI controller to reduce the settling time of the system response but the maximum overshoot increases. This paper suggests a new variable structure gain scheduling (VSGS) for frequency regulation. The VSGS can take advantage of the BFOA‐based PI controller, which is the minimization of settling time, and the advantage of the GA‐based PI controller, which is the minimization of overshoot. The performance of the proposed method is compared with the performance of the GA‐based PI controller and the BFOA‐based PI controller. [ABSTRACT FROM AUTHOR]

Accurate short-term power output forecasting results are conducive to reducing the scheduling difficulty of grid-connected operation of distributed photovoltaic (PV) systems, thus improving the safety and stability of power grid operation. In this paper, a one-day-ahead short-term power output forecasting model based on correlation analysis and combination algorithms for distributed PV system is proposed to solve the problems within the current methods. Firstly, the basic information of distributed PV system is introduced, and the main influence factors affecting the power output of distributed PV system are determined. Secondly, the influence factors with higher correlation with PV output are selected by Spearman rank-order correlation coefficient (SROCC) analysis in multiple timescales. Then, based on the multimodel univariate extreme learning machine (ELM) submodel and the single-model multivariate long short-term memory (LSTM) submodel, the ELM-LSTM model is established. The case study analysis based on the actual data indicates that the ELM-LSTM forecasting model proposed in this paper has higher forecasting accuracy than the traditional forecasting methods. [ABSTRACT FROM AUTHOR]

Transnational power grid interconnection is an important measure to promote the construction of energy Internet. It can meet the global power demand in a clean and green way, promote the UN's concept of "sustainable energy," and tackle climate changes. But, transnational power grid projects face many complex and variable risks due to their complex background and lacking experience. According to the characteristics of transnational power grid interconnection projects, a risk assessment index system including 10 indexes such as national relation, public participation, and available transmission capacity is constructed. Then, in order to overcome the shortcomings of traditional risk assessment methods, this paper proposes a risk assessment method combined with risk theory and probabilistic model, which can not only consider the uncertainties but also integrate the probability of accidents with consequences. Therefore, it can effectively assess the transnational power grid interconnection projects under diversified risks. In addition, in order to further magnify the impact of higher risks on such projects, a method combining traditional weighting method with the variable weight theory is proposed. The study in this paper provides certain guidance and decision-making support for different participants such as the government power sector, construction enterprises, and investment enterprises when they launch on sustainable development of the transnational project business, which have obvious practical significance. [ABSTRACT FROM AUTHOR]

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi'an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher. [ABSTRACT FROM AUTHOR]

Fast and accurate fault location methods will accelerate power system restoration process and then cause improvement on availability and reliability indices. Presenting a modified scheme in order to improve accuracy of impedance-based fault location method in electric power distribution systems (EPDS) equipped with distributed generation (DG) units is the main goal of this paper. To achieve this goal, an iterative algorithm is introduced to compensate load variations. Also, a method is presented based on forward/backward sweep load flow to consider loss in all laterals of EPDS. Then, fault location will be determined by using a comprehensive method that extracts a fifth-order algebraic equation for each type of fault. This method will be modified to consider the presence of DGs in EPDS. In this paper, all the specific characteristics of real distribution systems such as unbalanced nature of network and loads, different types of loads, presence of multi-phase laterals, and capacitive effects of distribution lines are taken into account. The proposed scheme uses only 1 measurement unit at the main substation and 1 measurement unit at the DG bus. In this paper, modified IEEE 34 node test system is simulated by using PSCAD/EMTDC software, and it is used as a test system to evaluate the performance of the proposed method. Different scenarios such as the effect of fault distance, fault type, different fault resistances, and measurement unit errors are evaluated. The results indicate usefulness and precision of the proposed scheme. [ABSTRACT FROM AUTHOR]

With the fast growing of new energy technologies, the proportion of distributed renewable energy sources dominated by wind and light energy in the microgrid continues to increase. However, the uncertainty and randomness of energy itself bring challenges to the stable operation of the power system. Microgrid load forecasting with high accuracy is the key means to handle the above problems. It can provide help for power grid dispatching and decision-making, optimize resource allocation, reduce operation cost, and ensure system safety. In this paper, a load-forecasting algorithm for microgrid based on improved long short-term memory neural network (LSTM) is proposed. Firstly, the criticality analysis of load influencing factors is carried out, and the clustering classification and weight calculation are completed. Then, the input data is preprocessed to ensure the quality of database. Secondly, the LSTM gets improved from three aspects: multilayer convolution channel, lookahead optimizer, and AM weight. And a complete forecasting model is designed to accomplish the load forecasting. Finally, based on the data of a local microgrid in Zhejiang Province, China, simulation experiments are conducted. The results are quantitatively compared with other forecasting algorithms to verify the accuracy and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The current power grid planning mostly realizes the calculation and analysis based on the factors of operation reliability or operation economy, but low-carbon green operation has become the main melody of power system development. Aiming to support the green and reliable operation of the power grid, this paper proposes a power grid low-carbon collaborative planning method based on improved cat swarm optimization algorithm. First, the carbon emission characteristics of the whole cycle of power grid construction are analyzed on the edge side, and a power grid planning model including environmental, economic, and reliability is constructed; on the cloud side, the cat swarm optimization algorithm is improved based on quantum mechanics and chaotic algorithm to achieve efficient solution to the power grid low-carbon planning model, which can support the stable and sustainable operation. Finally, the simulation experiment is realized based on IEEE 39 bus system. In this experiment, the construction cost and carbon emission of the proposed collaborative planning method are 23 million yuan and 2.28 t/MWh, respectively, which can reduce carbon emission while optimizing the construction cost and maintaining the low-carbon and stable operation. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent energy management system for the smart home that combines the solar energy as well as the energy from the battery storage devices has been proposed to reduce the dependency on the power grid and make the system to be more economical. The proposed system manages the energy requirement of the smart home by properly rescheduling and arranging the power flow between the energy storage devices, grid power, and the photovoltaics. The power grid can absorb the excess power from the designed system whenever the load requirement is low, and on the other hand, it can supply the power to the load in case of peak demand. Therefore, in the designed system, a user has the flexibility to sell the extra power for the purpose of revenue. A thorough simulation of the system has been carried out, and the results obtained show the effectiveness of the approach in terms of energy management between the different sources. [ABSTRACT FROM AUTHOR]

Adjusting generator output and cutting off load can effectively solve the problem of continuous overload and disconnection of power lines caused by power flow transfer. To suppress large-scale power outages caused by overload-dominant cascading failures, a blocking method for overload-dominant cascading failures in the power grid based on source and load collaborative regulation is proposed. The shortest path algorithm was used to identify loads and generators with shorter electrical distances from overloaded lines. Under the premise of meeting the static safety constraints of the power grid, considering the regulation of generator output and the removal of interruptible loads, a multiobjective cascading failure blocking model is established with the minimum overall control cost of the system and the minimum probability coefficient of line failure. Use the NSGA-II algorithm to solve the model and obtain the optimal plan for adjusting generator output and cutting off load. Through example verification, the proposed method can effectively alleviate the phenomenon of line overload, thereby blocking the continuation of overload-dominant cascading failures. [ABSTRACT FROM AUTHOR]

South Africa first implemented rolling blackouts known as load shedding to protect the integrity and maintain stability of their ailing electrical grid over a decade ago. Since then, load shedding has become a daily occurrence, which South Africa plans to alleviate through the addition of grid-connected renewable generation. Significant renewable energy (RE) additions can however negatively affect a grid, prompting South Africa to develop RE-specific grid code requirements as mitigation. Given this strategy to address the country's supply challenges using RE, and the still unknown effects of the country's aggressive RE integration planned, South Africa must perform the necessary simulations tailored to the country's unique grid conditions, and local RE-specific grid code requirements to ensure a RE-driven solution are indeed viable. Prompted by the lack of a suitably tailorable simulation platform, this paper proceeds to discuss the development of a tailorable grid code-guided renewable power plant (RPP) behavioral studies testbed. The testing methodology involves feeding grid disturbance event data to an RPP modeled after Eskom's Sere wind farm in South Africa, which is operated in line with local RPP-specific grid code voltage and frequency requirements to assess the testbed's grid code-guided RPP operating approach. Results show the testbed to effectively distinguish between grid code-specified no-fault, fault ride-through, and trip conditions, operating the simulated RPP accordingly. Consequently, as compared to comparable RE integration simulation platforms, the reviewed testbed has the potential of producing individualized results, owing to its tailorable grid, RPP, and incorporated grid code guiding specifications. [ABSTRACT FROM AUTHOR]

This paper proposes modified Karhunen–Loeve transform with total least square estimation of signal parameters using rotational in-variance technique (MKLT-TLS-ESPRIT) to approximate the low-frequency oscillatory modes. MKLT decreases the impact of highly correlated additive colored Gaussian noise (ACGN) from the signal by differentiating the correlation matrix w.r.t from the final time instance. A quantitative study of the suggested method with other estimation methods is used to evaluate the effectiveness of the proposed method. Monte Carlo simulations with 50,000 runs are conducted to test the robustness of the estimation scheme for MKLT-TLS-ESPRIT. The evaluation of the efficiency of the proposed method in real-time perspective, the two-area system, and New England sixty-eight bus test system has been considered. The analysis shows that the suggested methodology correctly measures the interarea modes and lowers their mean and standard deviation to a minimum value. [ABSTRACT FROM AUTHOR]

At present, the electric power system is getting bigger and more complex, and its loading is also increasing. As a consequence, planning, operation, and control of the power system also become more complicated. It is known that system planning and operation are mostly based on the steady-state condition of the power system, and the system steady-state condition can only be determined from the load flow study. Thus, the development of a reliable and efficient method to solve the load flow problem is necessary so that the system steady-state condition can properly be evaluated. Since the characteristics of the electric distribution system are different from those of the transmission system, special treatments are usually required in the distribution system load flow (DSLF) analysis. In this context, several interesting techniques have been proposed in the analysis. In this paper, the application and extension of the trust-region method to solve the three-phase DSLF problem are proposed and investigated. Case studies using 19-node, 25-node, and 123-node distribution systems are also given in this paper. Results of the studies show that the output values obtained by the proposed method are in excellent agreement with those obtained by previously published methods. These results confirm the validity of the proposed method. Case study results also indicate that the proposed method has better computational performances than the forward/backward sweeping (FBS) method. [ABSTRACT FROM AUTHOR]

Summary: The purpose of this paper is to present a model for optimal placement of distributed generation units and sectionalizing switches (relocation of the existing switches and installation of new ones) to increase distribution network resilience against the threat of transmission system outage. The proposed model is formulated as a multi‐objective optimization problem with the objectives of minimizing investment costs and maximizing load restoration. In this regard, distribution system operation during normal conditions as well as transmission system outages is considered. In addition, the unique characteristics of the real power grid in Khuzestan province in southwestern of Iran are addressed in the proposed model which makes the model applicable to the real networks with similar characteristics in many parts of the Middle East. This grid has two completely different load patterns (in terms of load curve shape and peak load) corresponding to two hot and temperate periods, which causes completely different concerns for the network's operator. The proposed model is solved using multi‐objective genetic algorithm (NSGA‐II), and it is implemented on the 33‐node test system and the results are analyzed. Moreover, it is applied to the 327‐node real‐world test system in Khuzestan province to demonstrate its applicability to large networks. In both cases, the results confirm the effectiveness of the proposed optimization model. [ABSTRACT FROM AUTHOR]

The practical application of wireless sensor networks (WSNs) in hot fields is summarized. It is found that compared with traditional monitoring methods, it has better adaptability to complex environments and low cost. It is suitable for monitoring power grid operation parameters. Therefore, this paper combines the above network and cascading failures, analyzes its 24-hour continuous and dynamic monitoring of the operation parameters of the power grid, and considers how to use the obtained parameters to analyze the disturbance of the remaining lines after the initial fault of the power grid. To prevent cascading failures in the power grid, a preventive control model considering safety and economy is proposed, and the model is solved by nondominated sorting genetic algorithm II and particle swarm optimization (NSGA2-PSO). Finally, the rationality of this method is verified in the IEEE39 node system. [ABSTRACT FROM AUTHOR]