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Substantial R &D efforts are currently directed towards the development of combined heat and power (CHP) systems that automatically and seamlessly connect to the power grid. In this paper we develop a real options model to assess the impact that the operational flexibility characterizing such systems will have on the optimal timing and capacity associated with investments in CHP plants. We take the viewpoint of a manufacturer operating in an energy-intensive industry who contemplates investing in CHP. We discuss and compare investments in two types of CHP systems: a standard one that is operationally rigid and a technologically advanced one that is operationally flexible. The interaction between temporal and operational flexibility under uncertainty and irreversibility is central to our analysis. We show that operational flexibility guarantees earlier investment but has an ambiguous effect in terms of capacity. In particular, when operational flexibility is very valuable the potential investor is opting for investing in a plant with larger productive capacity. The potential investor chooses a smaller CHP unit if otherwise. A numerical exercise calibrated using data from the Italian pulp and paper and electricity industries complements our theoretical analysis. [ABSTRACT FROM AUTHOR]

As an important high-voltage electrical equipment in the substation, the performance stability of high-voltage current transformers is directly related to the safe operation of the power grid. The insulating medium of the traditional oil-immersed current transformer is an oil-immersed paper structure. When the oil-immersed current transformer malfunctions, it is easy to cause fire and explosion, resulting in very serious consequences. If the transformer with the epoxy resin impregnated paper insulation medium is used, there is no oil and gas inside; thus, it has high reliability, which greatly reduces the risk of fire and explosion. Based on this, considering the application research on the high-voltage current transformers epoxy resin impregnated paper technology, this paper mainly analyzes some precautions for the application of epoxy resin impregnated paper technology to the high-voltage current transformers from three aspects: the research on the insulation performance of the epoxy resin impregnated paper transformers, the research on the mechanical properties, and the research on the relevant experimental methods of the epoxy resin impregnated paper technology application to the high-voltage current transformers. [ABSTRACT FROM AUTHOR]

Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid's operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. [ABSTRACT FROM AUTHOR]

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]

The distribution grid experiences node voltage fluctuations due to the growing uncertainty of large-scale renewable energy sources A practical solution is establishing a chance-constrained optimal model to deal with the uncertainties. However, using this method needs to know the accurate probability distribution of node power injections, which has limitations in application. Therefore, this paper proposes a distributionally robust chanceconstrained optimization method for power grid operation based on the ambiguity set of probability distributions. Firstly, considering voltage security constraints, this paper establishes a chance-constrained model to minimize the cost of active power regulation. Besides, based on the Wasserstein ambiguity set, a linearized method is proposed to convexify the objective function. Moreover, the conditional risk value (CVaR) is applied to convert the uncertain model into a deterministic model. The effectiveness of the proposed method is validated through optimization results obtained for the modified PG&E69-bus distribution grid. [ABSTRACT FROM AUTHOR]

This article discusses the role of power electronic converters in smart grids and electric vehicle applications. The authors highlight the technical connection between renewable energy, smart grids, energy storage, and electric vehicles. The special issue includes twelve accepted papers that cover topics such as high gain DC-DC converters, power converters for electric vehicle and motor drive applications, and power system applications. The papers present various novel topologies and control techniques, indicating the rapid growth and future potential of power converters in these fields. The authors express their appreciation to the contributing authors and anonymous reviewers for their valuable contributions to this special issue. [Extracted from the article]

Chile has a very ambitious energy transition agenda and massive challenges ahead to accomplish it. To integrate the high solar energy potential in the north an the high wind power potential of the south of the country, considerable investments in the electrical grid and new balancing mechanism and storage capacities are necessary. This study addresses the potential of individual industries, particularly the paper industry, to provide flexibility to the electrical system on-site. To achieve this, an optimization model that minimizes energy provision costs is proposed. The model considers the demand of a company including its demand side management potential, combined heat and power generation capacities, as well as on-site renewable energy generation location potential from photovoltaics and wind power. The model is computed under multiple parametrization scenarios to estimate under which conditions it would be advantageous for the company to use renewable electricity. This allows to evaluate how much of the renewable electricity produced in high peaks can be used by the company, and in how far, demand side management and combined heat and power can compensate low generation periods of renewable electricity. The model is feeded with typical hourly electricity and heat demand profiles of a prototypic paper producing company, which were statistically derived from one and a half years of measurements. Moreover, electricity generation time series from photovoltaics and wind power are derived from physical models that rely on ERA5 reanalysis data. The latter data set allows to compute the model in locations all around Chilean territory in a 30 km x 30 km spatial grid. Results are presented in the form of maps of the shares of renewables that would be enabled at avery location by industries of the same characteristics. [ABSTRACT FROM AUTHOR]

Communication plays a huge role in the operation of modern power systems. It permits a real-time monitoring coordination and control of the transmission, generation and distribution of electrical energy. As the modern grid grows towards an increased reliance on communication systems for the protection, metering and monitoring for as well as data acquisition for planning; there is a need to understand the challenge in the powers' system communication and their impact on the uninterrupted supply of electrical energy. Communication delays are one of the challenges that might affect the performance of the power system and lead to power losses and equipment damage, it is important to investigate the causes and the mitigation options available. Thus, this paper the state of arts on the cause, the effect and mitigation of communication delays in the power system. Furthermore, in this paper an analysis of different causes of the delays for different network configurations and communication systems used; a comparative analysis of different latency mitigation methods and system performance simulations of a given compensation algorithm is tested against the existing methods. The pros and cons of these control strategies are illustrated in this paper. The summary and assessment of those methods of control in this review offer scholars and utilities valuable direction-finding to design superior communication energy control systems in the future. [ABSTRACT FROM AUTHOR]

Modular Multilevel Converter (MMC) is widely used in applications such as High Voltage Direct Current (HVDC) transmission, AC/DC power conversion centers, and large-scale power quality management in electrical grids due to its highly modular structure, strong redundancy and low harmonic content in AC output. The application of the traditional deadbeat predictive current control to MMC enhances the fast tracking ability of the output current, but it still has the problems of low output current accuracy and high dependence on bridge arm inductor. Based on this, this paper proposes an improved two-beat deadbeat synchronous predictive current control based on Newton interpolation method. By improving the two-beat deadbeat synchronous predictive current control strategy, the accuracy of the output current can be further improved and its fast tracking ability can be enhanced. Newton interpolation is introduced to improve the accuracy of the output current while reducing the dependence on the bridge arm inductor. The results show that the control strategy proposed in this paper reduces the output current THD by 2.88% compared with the two-beat deadbeat predictive current control, thus improving the accuracy; the bridge arm inductor value is reduced by 1.28%, thus reducing the dependence; and under the transient environment, the output current can be tracked to its predicted reference value 843 µs in advance, which enhances the fast tracking capability. [ABSTRACT FROM AUTHOR]

In last period many distribution system operators (DSO) invest significant amount of money in smart metering system. Those investments are in part due to regulatory obligations and in part due to needs of DSO (utilities) for knowledge about electric energy consumption. Term - electric energy consumption, refers not only on real consumption of electric energy but also on data about peak power, unbalance, voltage profiles, power losses etc. Data which DSO can obtain from smart meters depend on type of smart metering system. Further, smart meters as source of data can be implemented in transformer stations (TS) MV/LV and in LV grid at consumer level. Generally, smart meters can be placed in any node of distribution grid. As amount of smart meters is greater, the possibility of data analysis is greater. In this paper a smart metering system of J.P Elektroprivreda HZ HB d.d, Mostar, Bosnia and Herzegovina will be presented. One statistical approach for analyzing of advanced metering data of TS MV/LV will be presented. Statistical approach presented here is powerful tool for analyzing great amount of data from distribution grid in simple way. For example, transformer in TS MV/LV can be overloaded for 30%, but overload period can be for example single one-hour period in whole year. With smart meter data, false conclusions can be avoided. In concerned case, grid operation engineer will not make decision of transformer replacing. With knowledge of only max. power of transformer (without time series data), false conclusion can be adopted about transformer replacing. This can increase costs of grid operation for DSO. Smart metering can help in transformer monitoring. Voltage drop in LV buses of TS MV/LV can be 10% for example. It means that quick intervention in grid must be performed. But, period of increased voltage drop can be only single one-hour period in whole year (for example). On other side, TS MV/LV can have voltage drop of 7% for example in halfyear period. These data, obtained by smart meters, can help in issuing of priority grid investments. Main contribution of this paper is in performing of the statistical analysis of smart meter data in distribution grid and using the obtained results in maintenance/investment planning process. [ABSTRACT FROM AUTHOR]

With the rapid development of social economy, the demand for electric power engineering is gradually increasing. The power supply system is constantly developing in the direction of large space and automation, and various high and new technologies are also constantly improving. The power monitoring data access control system is used to monitor and control the power production and supply process and improve the power supply efficiency. The further development of the region also has a higher demand for power and energy supply. For the problem that the natural environment of transmission and distribution lines in various power grids is uncertain, which makes the line operation unsafe. This paper proposed a power monitoring data access control system based on BP (back propagation, abbreviated as BP) neural network. This paper described the related concepts of BP neural network and power monitoring system, and described the functions and construction methods of power monitoring data access control system. On this basis, relevant experiments were carried out to verify the performance of the proposed system. The experimental results showed that the fault detection accuracy of the traditional algorithm was about 93 %, while the fault detection accuracy of the algorithm in this paper was more than 98 %. The highest accuracy rate was 99.88 %, and the accuracy rate of fault detection was greatly improved. [ABSTRACT FROM AUTHOR]

After the integration of large-scale Distributed Generation (DG) into the distribution network, the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network, exacerbating the phenomenon of wind and solar power wastage. As a novel power system model, the fundamental concept of Regional Autonomous Power Grids (RAPGs) is to achieve localized management and energy autonomy, thereby facilitating the effective consumption of DGs. Therefore, this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for autonomy. First, a regional Energy Storage (ES) configuration strategy is proposed. This strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load and DG output to maximize the elimination of source load deviations in the region as the upper limit constraint of ES capacity. Then, a control strategy for regional ES is proposed, the charging and discharging reference line of ES is set, and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio, the proportion of power supply matching hours, new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy capabilities. The final simulation results show that in the real regional grid example, the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9% and 4.8% on average, and the power imbalance of the tie line decreased by 7.8% on average. The proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluctuation of power exchange between the regional grid and the higher-level grid. This presents a rational and effective planning solution for the regional grid, facilitating the coordinated development between the region and the distribution network. Graphic Abstract [ABSTRACT FROM AUTHOR]

To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning. [ABSTRACT FROM AUTHOR]

Due to the EV (Electric Vehicles) charging stations are characterized by weak damping and low inertia, the EV with a high degree of uncertainty can easily have an impact on the stability of the charging station system. Therefore, this paper proposes an optimization control method to improve the system inertia effect based on the fractional order impedance model of the charging station. This paper presents a study on establishing a fractional impedance model for charging stations, using the deviation between theoretical impedance spectra and actual measurements as a criterion. The goal is to enhance system inertia and optimize the parameters of the fractional-order controller to improve the supporting capacity of the charging station system and enhance its dynamic response. Initially, considering the fractional characteristics of the EV load, a fractional impedance model of the charging station is established. The analysis demonstrates that the fractional-order capacitor provides inertia to the system, enhancing its inertia support capability. In addition, a virtual inertia control strategy based on fractional-order PID (FOPID) is designed. Finally, an improved particle swarm optimization algorithm is utilized to optimize the control parameters. Through experimental verification under different operating conditions, it has been demonstrated that the fractional-order control strategy can achieve a dynamic response time of approximately 0.025s and limit the voltage deviation within 5%. Furthermore, the rotational inertia can rapidly increase to the maximum value satisfying the objective function within 0.05s. The results indicate that this control method effectively suppresses the DC voltage and power oscillations in the distribution grid. [ABSTRACT FROM AUTHOR]

With the development of renewable energy power systems, electric vehicles, as an important carrier of green transportation, are gradually having an impact on the power grid load curve due to their charging behavior. However, the significant influx of electric vehicles (EVs) and distributed power sources has led to multiple uncertainties, increasing the difficulty in making grid scheduling decisions. Traditional robust scheduling strategies tend to be overly conservative, resulting in poor economic performance. Therefore, this paper proposes a robust and economic dispatch strategy for park power grids based on the information gap decision theory (IGDT). Firstly, based on the probabilistic characteristics of the spatial and temporal distribution of EVs charging, the Monte Carlo method is used to generate typical electricity usage scenarios for EVs. Simultaneously, an economic dispatch model for the park power grid is established with the objective of minimizing operating costs. Taking into account the uncertainty of renewable energy output, simulation analysis is conducted through the IGDT model. Finally, through the verification of the improved IEEE-33 node test system and comparison with other methods, the proposed approach in this paper can reduce decision conservatism and effectively reconcile the contradiction. Through analysis, the proposed method in this paper can reduce the total operational cost of the system by up to 3.2%, with a computational efficiency of only 8.9% of the traditional stochastic optimization time. [ABSTRACT FROM AUTHOR]

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications. [ABSTRACT FROM AUTHOR]

With the integration of large-scale nonlinear loads and distributed power sources into the grid, composite power quality disturbances (PQDs) events are becoming increasingly common, which significantly degrade the quality of power supply. Therefore, this paper focuses on studying the accurate classification of composite PQDs to mitigate the risk of power quality deterioration. However, traditional classification methods perform barely satisfactory in terms of accuracy and robustness in the classification of PQDs. To address these issues, this paper proposes a method for recognizing composite PQDs based on relative position matrix (RPM). Initially, utilizing the RPM method, the initial one- dimensional PQD time series data is transformed into two-dimensional image data while preserving its high-frequency characteristics. This process results in the creation of an informative and feature-rich image training set. Subsequently, an end-to-end framework for PQDs classification was developed. The framework utilizes convoLutionaL neural networks to automatically extract multi-scale spatial and temporal features from image data. This design aims to automate the classification of composite PQDs, eliminating the need for labor-intensive manual signal processing and feature extraction. This integration ensures a more accurate and robust classification. Finally, the proposed method is tested on a case involving 30 types of PQDs at varying noise levels and compared with existing power quality disturbance classification methods, and results show that the proposed method has better performance than the previously established methods. [ABSTRACT FROM AUTHOR]

Vanadium redox flow battery technology can support the spread of energy storage in stationary applications and allow higher penetration of renewables in the electric grid. Currently, its market competitiveness is hindered by low power density, which stems from complex interplay between kinetic and mass transport losses. The quantitative interpretation of experimental observations should rely on physics-based models, which allow a consistent comparison of different operative conditions. In this work, a fast analytical physics-based 1D model of the impedance of vanadium flow battery is presented and validated with respect to experimental data. The model, made available online at http://mrtfuelcell.polimi.it, employs a macro-homogeneous approach and considers losses due to kinetics, reactant distribution within the electrode (Sigracet® SGL 39 AA carbon paper), convection in flow channel and vanadium transport to electrode surface. Additionally, analytical expressions of contributions to impedance of single physical phenomena are derived through an asymptotical analysis. The results show that, at negative electrode, transport of ions to active surface is the limiting phenomenon at lower flow rates, while at higher flow rates depletion of reactants within electrode becomes critical together with charge transfer processes. At positive electrode, the main contribution to performance loss is the vanadium transport to electrode surface. [ABSTRACT FROM AUTHOR]

New grid equipment startup programs are widely used in various countries to regulate the commissioning of new equipment; these programs have unique differences in terms of strictness, information asymmetry, and complexity relative to other types of startup programs. With respect to rule-based generation methods, because the method of revising the rules weakens their migration ability, it is difficult to adapt these methods to the status quo of high-speed power grid construction; moreover, most of the current generation methods based on deep learning improve upon the rule-based methods but do not eliminate the rules of the constraints. Therefore, this paper presents a fusion topology for generating a new grid equipment startup scheme, which generates the scheme from end to end. The method utilizes the powerful processing capabilities of the GATv2 model and the ERNIE-GEN model for topology and text, respectively. The device type-based coding strategy and the scheme complexity-based self-attention layer selection strategy are used in the GATv2-based device identification model to address information asymmetry and complexity variability, and the device information modification strategy is applied to solve the strictness variability problem in the ERNIE-GEN-based scheme generation model. Finally, through the testing and verification of field data from four types of new equipment startup schemes in real power grids, it is verified that the method can effectively generate new equipment startup schemes for power grids, and the reasonableness and efficiency of the three strategies are verified through ablation experiments, which verify that the method can effectively generate new equipment startup schemes for power grids that meet the requirements of real power grids. [ABSTRACT FROM AUTHOR]

The accumulation of wind power prediction deviations will make it difficult to maintain the long-term stable operation of energy storage. To solve this problem, this paper proposes a hybrid energy storage system configuration method containing second-use batteries. This paper establishes a three-battery hybrid energy storage operation strategy that considers the accumulation of prediction deviation and prevents the accumulation of prediction deviation by changing the energy storage used at the end of the dispatch cycle. It also establishes an optimal allocation model for energy storage capacity, which takes into account the performance parameters and life loss of the second-use batteries and the new power battery. Finally, Gurobi is used to simulate the field data of a wind farm. The simulation results show that this method is effective in preventing the accumulation of prediction deviation while reducing wind power grid deviation and improving the level of energy storage utilization. It can play a certain reference role in the configuration of energy storage for wind farms. [ABSTRACT FROM AUTHOR]

To steadily promote the reform of sales electricity prices, China has innovatively proposed to carry out grid enterprises' agency power purchase business (GEAPPB) as a transitional scheme for industrial and commercial users to enter the power market in the future. However, because the GEAPPB covers a wide range of users and involves complex business processes, the risks throughout the process of the GEAPPB will affect the organization and operation of the business. This paper proposes a GEAPPB risk assessment technique. First, a risk evaluation index system containing 10 indicators is designed based on the characteristics and development process of GEAPPB. Second, a risk assessment model based on the fuzzy full consistency method (FUCOM-F) and gray relational analysis (GRA)-improved MARCOS is constructed. Finally, seven grid enterprises in China are taken as examples to verify the validity and scientificity of the model. The results show that improving the accuracy of power forecasting, optimizing the agency power purchase strategy, and strengthening demand response management are the key schemes to reduce the risk of GEAPPB. Compared with the other three evaluation models, the proposed model has good applicability and effectiveness for the risk evaluation of GEAPPB. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into the virtualization of substation protection IED functions using a sophisticated co-simulation environment that integrates virtual intelligent electronic devices (vIEDs) with a real-time power grid simulation. Anchored by the IEC 61850 protocol, this study constructs a virtualized IED framework, emphasizing the encapsulation of protection schemes using the example of different types of overcurrent protection within a containerized vIED. Using open-source software, this study aims to replicate the communication and functional aspects of physical IEDs. This study uses a co-simulation environment that couples virtualized network components with a real-time power grid simulation to validate the vIEDs against real substation hardware. Simulation results from induced short-circuit events confirm the operational congruence of the vIEDs with their physical counterparts, demonstrating their potential to serve as cost-effective and adaptable testbeds for substation automation. This paper concludes that virtualized IEDs represent a cost-effective, flexible alternative for substation automation testing, with future research directed towards increasing the functional complexity and real-world applicability of these virtual systems. [ABSTRACT FROM AUTHOR]

There are several complex and unpredictable aspects that affect the power grid. To make short-term power load forecasting more accurate, a short-term power load forecasting model that utilizes the VMD-Crossformer is suggested in this paper. First, the ideal number of decomposition layers was ascertained using a variational mode decomposition (VMD) parameter optimum approach based on the Pearson correlation coefficient (PCC). Second, the original data was decomposed into multiple modal components using VMD, and then the original data were reconstructed with the modal components. Finally, the reconstructed data were input into the Crossformer network, which utilizes the cross-dimensional dependence of multivariate time series (MTS) prediction; that is, the dimension-segment-wise (DSW) embedding and the two-stage attention (TSA) layer were designed to establish a hierarchical encoder–decoder (HED), and the final prediction was performed using information from different scales. The experimental results show that the method could accurately predict the electricity load with high accuracy and reliability. The MAE, MAPE, and RMSE were 61.532 MW, 1.841%, and 84.486 MW, respectively, for dataset I. The MAE, MAPE, and RMSE were 68.906 MW, 0.847%, and 89.209 MW, respectively, for dataset II. Compared with other models, the model in this paper predicted better. [ABSTRACT FROM AUTHOR]

Aiming at the "net-zero carbon" target, a higher proportion of variable renewable energies (VREs) has been integrated into power grids, and pumped storage plants (PSPs) are crucial for guaranteeing the safe and stable operation of hybrid energy systems. As secondary frequency regulation (SFR) is related to the economic operation and the quality of auxiliary services provided by PSPs, it is critical to clarify its performance and compensation. Therefore, the corresponding quantitative evaluations of the SFR of pumped storage units (PSUs) are carried out in this paper. First, the performance of SFR is quantified based on the fuzzy analytic hierarchy process (FAHP). Considering the current situation in China's electricity market, the average compensation of the PSP with each MW is determined to be 9.336 CNY per day, and a method for calculating the compensation of SFR is constructed. Then, taking a real Chinese PSP as an example, based on different wind power deviation signals, the quantitative evaluation of the compensation of SFR is conducted in accordance with the fixed-speed pumped storage units (FSPSUs) and the variable-speed pumped storage units (VSPSUs) in power priority and speed priority modes. The results show that the compensation of FSPSUs and VSPSUs (power priority mode and speed priority mode) is 10,900 CNY per day; 54,400 CNY per day; and 17,300 CNY per day. This paper could contribute to clarifying the compensation of SFR and providing technical support for the development of the auxiliary service market. [ABSTRACT FROM AUTHOR]

In this paper, an improved hybrid regularized grounded network imaging algorithm (ITR-Lp) combining Tikhonov regularization and Lp regularization is proposed; through the improvement of the filtering function, the correction of small magnitude for large singular values and increasing magnitude of correction with decreasing singular values for small singular values is implemented for the improvement of the convergence of the solution. The proposed algorithm constructs a regularization matrix to achieve selective correction of singular values and improve the convergence of the solution, while Lp regularization is used to enhance the sparsity of the solution and improve the boundary contrast. the effect of node distribution on convergence is investigated, and finally the ITR-Lp algorithm is validated by simulation and experiment. The results show that the ITR-Lp algorithm proposed in this paper achieves the lowest resistivity relative errors of 0.1695 and 0.1089 for resistive networks with 1 corrosion and 2 corrosions, respectively. The method has good convergence and boundary contrast, which effectively improves the pathology of the inverse problem of imaging the electrical impedance tomography of grounding grid. [ABSTRACT FROM AUTHOR]

When the electromagnetic transient simulation of a large-scale power grid is carried out, because the simulation is limited by the scale of simulation software, it is necessary to divide the grid into internal and external grids, and the external grid is modeled with equivalent simplification. Aiming at resolving the difficulties of the traditional grid equivalence method, such as a cumbersome calculation process and harsh calculation conditions, a static grid equivalence method based on the genetic algorithm is proposed in this paper. The method first constructs an external grid equivalence network, which includes coupling branches between boundary nodes, and each boundary node is connected to the external grid equivalence power supply through the branches. Then, the external grid equivalence model is used to establish an optimization model for solving the equivalence network parameters based on the information of the internal network, the difference of the power input from the external grid to the boundary nodes before and after equivalence is used as the objective function, and the genetic algorithm is used to realize the iterative optimization of the objective function to obtain a set of optimal post-equivalence network model parameters so that the state variables of the internal power grid before and after the equivalence are matched. The accuracy and effectiveness of the proposed method are verified through simulation with the CERPI36V7 node system. [ABSTRACT FROM AUTHOR]

Under the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy. [ABSTRACT FROM AUTHOR]

The traditional electrical grid has to be digitally improved as digitalization and effective integration of renewable energy bring better efficiency, intelligence, and safety into the grid; hence, the transition from the traditional grid to a smart grid. A smart grid is a modernized and digitalized standard electrical infrastructure that has a key component known as the advanced metering infrastructure (AMI). AMI, also known as smart metering, is a key technological enabler of the smart grid that allows automatic collection and reporting of power-consumed data via two-way communication networks. However, the collected power consumption data is confidential; hence, its privacy must be maintained. Similarly, for the benefit of the smart grid to be consistently maximized, the AMI data and network security must always be intact despite the evolving threats and attacks targeted at it. This paper provides a comprehensive review of the existing vulnerabilities/attacks, security and privacy challenges associated with the smart metering data and network system, its open issues, and future direction. The major contributions of this review paper lie in the AMI vulnerabilities, AMI state-of-the-art security schemes with their pros and cons, its communication protocols analysis, and its emerging security measures. This gave enumerated recommendations for the efficiency improvement of AMI security in terms of its latency reduction while implementing efficient security measures in its future work. [ABSTRACT FROM AUTHOR]

The scale of multi-microgrid (MMG) and hydrogen fuel cell vehicles (HFCVs) is increasing dramatically with the increase in the new energy penetration ratio, and developing an integrated energy system containing a multi-microgrid for hydrogen fuel vehicles brings great challenges to power grid operation. Focusing on the difficulties of the access of multiple microgrids for the low-carbon and economic operation of the system, this paper proposes an optimal interconnected heterogeneous multi-microgrid power–heat–carbon scheduling strategy for hydrogen-fueled vehicles. Firstly, an HFCV model is established, and then an optimal scheduling model is constructed for the cooperative trading of power–heat–carbon in a multi-microgrid, on the basis of which the low-carbon economic operation of the multi-microgrid is realized. The results of the case study show that the scheduling strategy in this paper reduces carbon emissions by about 7.12% and costs by about 3.41% compared with the independent operation of the multi-microgrid. The degrees of interaction of each multi-microgrid are also analyzed under different HFCV penetration rates. [ABSTRACT FROM AUTHOR]

Due to the unpredictable output characteristics of distributed photovoltaics, their integration into the grid can lead to voltage fluctuations within the regional power grid. Therefore, the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios. This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic (PV) generation and proposes a Temporal Convolutional-Long Short-Term Memory predictionmodel that combines TemporalConvolutionalNetworks (TCN) and Long Short-TermMemory (LSTM). To begin with, an analysis of the spatiotemporal distribution patterns of PV generation is conducted, and outlier data is handled using the 3s rule. Subsequently, a novel approach that combines temporal convolution and LSTM networks is introduced, with TCN extracting spatial features and LSTM capturing temporal features. Finally, a real spatiotemporal dataset fromGansu, China, is established to compare the performance of the proposed network against other models. The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy, with a single-step Mean Absolute Error (MAE) of 1.782 and an average Root Mean Square Error (RMSE) of 3.72 for multi-step predictions. [ABSTRACT FROM AUTHOR]

A high-temperature oil–paper insulation system offers an opportunity to improve the overloading capability of distribution transformers facing seasonal load variation. A high-temperature electrical insulation system (EIS) was chosen due to thermal calculation based on a typical loading curve on the China Southern Power Grid. In order to evaluate candidate high-temperature insulation systems, Nomex® T910 (aramid-enhanced cellulose) immersed in FR3 (natural ester) was investigated by a dual-temperature thermal aging test compared with a conventional insulation system, Kraft paper impregnated with mineral oil. Throughout the thermal aging test, mechanical, chemical, and dielectric parameters of both paper and insulating oil were investigated in each aging cycle. The thermal aging results determined that the thermal class of the FR3-T910 insulation system meets the request of overloading transformer needs. [ABSTRACT FROM AUTHOR]

Power system operation and control are based on power flow calculations. In order to solve the uncertainty of the increasing penetration of renewable energy, the voltage fluctuation at the load point increases in the distribution network, and the inaccuracy of the power flow calculation due to the insufficient power flow data collection capability of the traditional power system. In this paper, a data‐driven power flow analysis model is proposed, a back propagation neural network combined with genetic algorithm (GA) and adaptive moment estimation (ADAM) optimization algorithm model is constructed to analyze the power flow calculation method of distribution networks under stochasticity. Firstly, the power flow initial value information, topology characteristics, and power factor index are introduced to construct a training set, and the mapping relationship between bus voltage and power is fully explored by training the regression model. Second, the GA‐ADAM algorithm is used to optimize the initial values and weight parameters of the model. Finally, it is verified based on IEEE‐33 bus distribution model, and the model is used for power flow calculation, and compared with other methods through each relevant error evaluation indicators. The results show that the model constructed in this paper has small error indicators and high accuracy, which improves the efficiency and accuracy of power flow calculation. [ABSTRACT FROM AUTHOR]

It is very important to accurately identify the critical nodes of the power grid for its safe and stable operation. In this paper, a method for identifying the critical nodes of the power grid based on the improved entropy weight method (IEWM) is proposed, and the IEWM corrects issues with the information overlap between evaluation indices and inconsistency between the entropy weight (EW) and entropy value (EV). First, considering the power grid topology and operating conditions, structural factor evaluation indices and state factor evaluation indices are established. On this basis, the IEWM is used to assign weights to nodes with different voltage levels, which strengthens the consideration of node voltage levels in the identification method of critical nodes and makes the results more accurate and effective. Simulation experiments of IEEE 30-bus and IEEE 118-bus systems verify the accuracy of the critical node identification method proposed in this paper. [ABSTRACT FROM AUTHOR]

With the continuous increase in air conditioning installations, the proportion of air conditioning electricity consumption to total electricity consumption is growing. Research on the decomposition of air conditioning electricity consumption is of great significance for assessing electricity usage, formulating electricity scheduling plans, and ensuring the stable operation of the power grid. Currently, typical day selection strategies for the decomposition of air conditioning electricity consumption often overlook the corresponding relationship between typical daily electricity consumption and temperature. Therefore, this paper proposes an air conditioning electricity consumption decomposition method based on the dependence between temperature and electricity consumption. This method filters typical days based on Copula-based dependence indicators and equiprobable ellipses, determines the baseline electricity consumption curve through model selection voting, and ultimately calculates the air conditioning electricity consumption. The effectiveness of the proposed method is validated by applying it to electricity consumption data in Fuzhou from 2019 to 2022. [ABSTRACT FROM AUTHOR]

The short-circuit ratio index (SCR) can effectively quantify the voltage support strength in traditional DC grid-connected scenarios, yet it cannot reasonably describe the voltage support strength in diverse device grid connection scenarios. This paper introduces a new calculation method of the complex short-circuit ratio index (SCR) and derives the threshold value of the complex short-circuit ratio index to enable a comprehensive quantitative assessment of grid voltage support strength across diverse device grid connection scenarios. Firstly, critical short-circuit ratio (CSCR) under different assumed conditions were derived based on the short-circuit ratio index. Secondly, the calculation method of the complex short-circuit ratio index was introduced, considering both the equivalent impedance angle of the device and the Thevenin equivalent impedance angle. This was followed by the determination of the threshold value of the complex short-circuit ratio (CSCR), enabling a precise quantitative evaluation of power grid voltage support strength in diverse device grid connection scenarios. Finally, the example analysis proves the accuracy and efficacy of the complex short-circuit ratio index in assessing the voltage support strength of diverse devices in grid-connected scenarios. [ABSTRACT FROM AUTHOR]

Offloading some tasks from the local device to the remote cloud is one of the important methods to overcome the drawbacks of the medical mobile device, such as the limitation in the execution time and energy supply. The challenges of offloading task is how to meet multiple requirement while keeping energy-saving. We classify tasks in the medical mobile device into two kinds: the first is the task that hopes to be executed as soon as possible, those tasks always have a deadline; the second is the task that can be executed anytime and always has no deadlines. Past work always neglects the energy consumption when the medical mobile device is charged. To the best of our knowledge, this paper is the first paper that focuses on the energy efficiency of charging from a power grid to a medical device during work. By considering the energy consumption in different locations, the energy efficiency during working and energy transmission, the available energy of and the battery, we propose a scheduling method based on DQN. Simulations show that our proposed method can reduce the number of un-completed tasks, while having a minimum value in the average execution time and energy consumption. [ABSTRACT FROM AUTHOR]

The ultra-high voltage direct current (UHVDC) transmission composed of modular multilevel converters (MMC) is an important technology for large-scale centralized transmission of renewable energy. In UHVDC system, temporary faults in the AC power grid system are a high probability fault, and the fault control strategy affects the safety and reliability of system operation. This paper studies on the condition of AC power grid faults occurring at the receiving converter station. Firstly, study the system characteristics after the occurrence of AC faults, and use theoretical analysis to derive the trend of DC voltage changes of each converter valve. Then, an AC fault ride-through control strategy with high power transmission capability is proposed with hierarchical connection structure, the strategy controls the system to synchronously reduce DC voltage and AC active power after a fault, maximizing the retention of the system's transmission capacity during the occurrence of faults, thereby reduce power shock in the system. Finally, a simulation model of the dual ended system has built based on the PSCAD simulation platform. The simulation results show that when a single-phase ground fault and a three-phase ground fault occur in the high valve group at the receiving station, the system can retain about 83% and 50% of the transmission capacity during the fault period, respectively. Meanwhile, there is no serious overvoltage or overcurrent phenomenon in the system. The simulation results verified the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Direct current microgrid (DCMG) clusters are gaining popularity in power systems due to their simplicity and high efficiency. However, DCMG clusters are susceptible to minor disturbances due to low system inertia. This paper proposes a method to enhance the small‐signal stability of a DCMG cluster by optimizing the main control parameters of the system. This paper presents a small‐signal state‐space model of a DCMG cluster system at the system level, considering a multi‐bus network topology. Then, the control parameters that significantly affect the small‐signal stability of the DCMG are selected using the participation factor method. To enhance the system damping, the Pareto‐optimal frontier of the bi‐objective problem was determined using the elite non‐dominated sorting genetic algorithm (NSGA‐II). The optimal compromise is determined by using the fuzzy membership function method to extract it from the generated Pareto optimal front. The proposed method has been verified on a three‐sub DCMG test system with droop control. [ABSTRACT FROM AUTHOR]

The study of energy sources is an open subject due to constraints on the current energy global production versus the current and future energy demands. From the consumption perspective, houses pull considerable energy from the electrical grid. With that being said, this paper investigates the theoretical thermal distribution of the heat in the basement of a house and measures the theoretical temperatures throughout different points at the same height by using statistics and numerical simulation. The numerical simulation, such as Computational Fluid Dynamic Analysis by COMSOLTM combined with Statistics by MiniTabTM was utilized to determine the most economical settings for the variables in the heating system evaluation. It is understood that thermal comfort for householders is achieved when the heat is evenly distributed in the room. To have a more realistic model set-up, the air flow in the room was considered as a turbulent model. The studied variables were intake airflow, positioning of the vents (intakes), airflow temperature, and external temperature. The results showed the significance of the variables. The latter were ranked from the highest to the lowest as: external temperature, airflow velocity, inlet location, and temperature input, while the highest interaction was found between the external temperature and air inlet velocity. This study comes up with a superior understanding of the system and generates an efficient setting for the variables for energy-saving purposes. [ABSTRACT FROM AUTHOR]

With a substantial fraction of renewable energy integrated into the electrical grid, the new power system urgently requires grid planning scheme displaying adaptability to different energy types and their volatility. Considering the indeterminacy of renewable energy generation output and the different attitudes of decision-makers towards its risk, this paper proposes an adaptability assessment methodology for power grid planning schemes considering multiple decision psychology. First, an evaluation indicator framework is established based on the adaptive requirements of the grid planning for novel power system, and the weights of indicators are calculated based on an improved AHP-CRITIC combination weighting method. Second, improved cumulative prospect theory (ICPT) is adopted to improve to the calculation method of the distance between the evaluation program and the positive and negative ideal programs in the GRA and TOPSIS, which effectively characterize the different decision-making psychologies, and a combination evaluation model is constructed based on a cooperative game (CG), namely, an adaptability evaluation model of grid planning schemes for novel power systems based on GRA-TOPSIS integrating CG and ICPT. Finally, the proposed model serves to evaluate grid planning schemes of three regions in China's 14th Five-Year Plan. The evaluation results show that the adaptability of the schemes varies under different decision-making psychologies, and under the risk-aggressive and loss-sensitive decision-making psychologies, grid planning scheme of Region 1 with the greatest accommodation capacity of renewable energy is preferable. [ABSTRACT FROM AUTHOR]

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact. [ABSTRACT FROM AUTHOR]

DC voltage is one of the important parameters of active power filters. Since the series hybrid active power filter does not withstand the fundamental voltage, it cannot absorb energy from the power grid, making it too difficult to control its DC voltage. In order to solve the DC voltage control problem, an improved control method for the series hybrid active power filter with magnetic flux compensation is proposed in this paper. In this improved method, the fundamental magnetic flux compensation coefficient is equal to 1, meeting the condition of fundamental magnetic flux compensation. This improved method does not rely on the precise detection and phase-locking of the fundamental voltage at the port of the series transformer. A phase-fixed active current component is generated directly by the inverter, enabling the active power filter (APF) to absorb active power from the power grid. Consequently, the active power absorbed by the APF from the power grid has a linear relationship with the active current component. Both simulation and experimentation verified the correctness and effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors. [ABSTRACT FROM AUTHOR]

The increased use of renewable energy in modern power grids has led to a demand for innovative technologies that can efficiently harness intermittent energy sources. Multi-Point Absorber (MPA) systems have emerged as a promising solution for capturing wave and tidal energy sustainably. This study focuses on developing advanced control strategies for MPA systems to improve their performance and coordination in power generation and grid integration for both AC and DC networks. The research begins with a comprehensive review of MPA technologies, highlighting their challenges and opportunities. It then explores the development of control algorithms that dynamically adjust MPA parameters in real-time, optimizing energy extraction efficiency. The paper also addresses the crucial aspect of grid integration, investigating power electronics interfaces and synchronization techniques to enhance grid stability. Additionally, bidirectional power flow capabilities are discussed, enabling functions like frequency regulation and voltage control. The novelty of this work lies in the development of adaptive control algorithms for MPA systems, surpassing previous efforts by incorporating real-time data for parameter adjustments. The study demonstrates the effectiveness of these advanced control strategies through simulations and implementation, evaluating energy conversion efficiency, grid compatibility, and system reliability across various conditions compared to conventional methods. The results emphasize the significance of bidirectional power flow capabilities in achieving enhanced functionality, such as frequency regulation and voltage control. This paper provides valuable insights into optimizing MPA systems, showcasing their potential to revolutionize sustainable energy capture and integration into modern power grids. [ABSTRACT FROM AUTHOR]

The power transmission system of Afghanistan is witnessing a significant shortage in terms of capacity, reliability, flexibility, and energy security. The goal of this paper was to identify and examine the associated issues, challenges, and opportunities for domestic transmission grid and power imports in the country. On these bases, proposals and recommendations were provided that can help the Afghan energy sector strategically enhance its transmission capacity and make the country's existing massive renewable energy potential exploitation feasible. The focus was on the expansion of high-voltage direct current transmission technology. The findings of the paper revealed that the utilization of high-voltage direct current, hybrid high-voltage alternating current, and high-voltage direct current transmission technologies can facilitate bulk power transmission and trade between south and central Asia, as well as regional energy sustainability, development, and security. [ABSTRACT FROM AUTHOR]

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a variable-power-tracking-operation-based frequency regulation scheme for a wind turbine generator (WTG) that employs a frequency-excursion-based control parameter to suppress frequency excursion under various wind speeds and disturbances. To this end, an improved system frequency response model is built, and then the frequency regulation characteristics of the WTG are investigated. The frequency-excursion-based control parameter is determined. Simulations based on the Matlab/Simulink simulator verify the effectiveness of the proposed variable-power-tracking-operation-based frequency regulation strategy. The simulation results clearly indicate that the suggested scheme is beneficial for reducing the maximum frequency deviation. The proposed frequency regulation scheme could enhance the networking capacity of wind farms and improve the capacity of the power grid to absorb renewable energy. [ABSTRACT FROM AUTHOR]

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability. [ABSTRACT FROM AUTHOR]

To address the issue of high auxiliary service costs caused by the grid connection of new energy generation, a dual layer optimization scheduling method for high penetration new energy power grids is proposed, taking into account the auxiliary service costs of grid connection. The correlation between the output fluctuations and load fluctuations of new energy generation is analyzed, and a calculation model for auxiliary service costs caused by grid connection of new energy generation is established. Taking into account the timescale division requirements for grid energy trading and auxiliary service market transactions, a new high penetration energy grid two-level optimization scheduling model is established. The upper level scheduling model aims to minimize the sum of grid energy procurement costs and auxiliary service costs caused by new energy generation and completes the optimization scheduling of the grid for several hours. The lower level scheduling model aims to closely follow the upper level scheduling plan for new energy power plants/groups. Optimization and scheduling of the power grid for tens of minutes is completed, taking into account the balanced allocation of wind and solar losses among various power plants within the new energy power plant group. The simulation results show that the method proposed in this paper can reduce the energy transaction costs and auxiliary service transaction costs of the power grid during low and peak load periods. [ABSTRACT FROM AUTHOR]