Electric Vehicles (EV) have the potential to completely transform the transportation industry, slash carbon emissions significantly, and pave the road for significant climate progress. According to surveys, worries regarding range and charging accessibility are a significant barrier to EV customer adoption. The electricity grid will be significantly impacted by EV charging. A coordinated charging approach that facilitates EV charging while avoiding the power grid from overloading is required to manage EV charging. In this work, the EVs are classified based on their charging capacity. This classification includes Car, truck and bus. With this a chargingdischarging scheme has been established using Real Time Pricing (RTP) for each hour for a 20 min block. Augmenting this, in the present work the power loss is analyzed when Distributed Generation (DG) units are installed after the EVs are located in the system for charging and discharging. The DG units are installed considering the Voltage Stability Index (VSI) and the power losses incurred due to the presence of EVs. The Monetary benefit of such an investment is analyzed for different topologies of IEEE-33 and 69 bus systems and compared with the existing methods where DGs are installed before the EVs are located in the system for charging and discharging.

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, we provide insights into the transformative potential of ML in shaping the future of power system asset management.

This paper proposes an intelligent approach based on the empirical Fourier decomposition (EFD) to identify harmonic sources at the point of common coupling (PCC) when different inverter-based distributed generations (DGs) like microturbine (MT), Battery energy storage system (BESS), photovoltaic (PV), superconducting magnetic energy storage (SMES), wind turbine with a permanent magnet synchronous generator (PMSG), and doubly-fed induction generator (DFIG) wind turbine are presented. In order to decrease memory storage and computational burden, strife feature selection is used. Applying just voltage signals consumes less processing time and decreases measurement devices. Moreover, the whale optimization algorithm (WOA) as the optimizer of the parameters of the support vector machine (SVM) classifier is used. Consequently, the results from the proposed method can be helpful for both engineers and researchers to plan and develop a better strategy to mitigate harmonic distortion.

In the era of cyber-physical-social systems, research lacks a comprehensive framework to optimize dynamic coordination strategies for electric vehicles (EVs) to enhance modern power distribution networks (MPDNs) resilience. Public studies have not comprehensively addressed the impact of intelligent transportation systems (ITS) and smart charging systems (SCS) on MPDN resilience strategies after rare events. To bridge this gap, a two-stage EVs coordination framework is proposed to consider MPDN smart restoration. The first stage involves a novel proactive EV prepositioning model to optimize EV prepositioning plans before a rare event in order to enhance MPDN survivability during the immediate aftermath of the event. The second stage involves an advanced spatial-temporal EV dispatching model to maximize the number of available EVs for discharge, thereby improving MPDN recovery after a rare event. The proposed framework includes an information system facilitating dynamic data exchange between EVs and ITS/SCS automated systems and introduces a novel geographic graph to optimize EV routes between charging points. Effectiveness of the framework is assessed on a modified IEEE 123 node test feeder incorporating real-world transportation and charging infrastructure. The results suggest that significant enhancement in MPDN resilience is possible with smart restoration strategies. Sensitivity analysis specifically demonstrates that integrating recently invented technologies of the smart charging systems and automated systems potentially provides significant benefits if dynamic coordination strategies are employed in MPDNs.

Generally, wind speed alters continuously during a day and lead to fluctuating output power of a wind farm. Connecting such a wind farm to the transmission line of grid causes a malfunction in the performance of the backup zone of the distance relays due to the random nature of generation and the number of connected wind turbines (wind farm penetration). In this study, a new adaptive setting groups-based method has been proposed to set the second zone of the distance relay in the grid with a wind farm equipped with a doubly fed induction generator (DFIG). In this method, we also taken into account the uncertainty of wind fluctuations and the availability of wind turbines. A Markov model is developed to implement these features. To determine the impedance setting of each of the set groups of the second zone in the distance relays of the remote bus, the obtained impedances are clustered in four clusters by using the combination of K-means and particle swarm optimization (PSO) algorithms. Finally, details of the analysis results of this issue have been compared with other methods available in the literature. The validity and accuracy of the proposed approach also have been assayed and confirmed.

The multi-scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system.This paper first considered the interaction mechanism of multi-type storage peak regulation time sequences based on the Euclidian distance, Dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source-grid-load-storage (SGLS) was constructed. To attain a low-carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time-series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K-means clustering and dual-structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy.

Operating experience has shown that vibration can cause pressure fluctuations and initiate bubbles at the oil gap, which seriously threatens the insulation properties of oil-immersed power equipment. The main aim of this study was to determine the characteristics and mechanism of bubble initiation at the transformer oil gap under vibration in this paper. The results show the existence of the vibration-dependent bubbling phenomenon at the oil gap. Interestingly, bubbles initiate during the process of gap expansion, and the evolution of bubble morphology was further analyzed. The threshold characteristic of the bubble initiation process was found, and the relevant influencing factors were experimentally obtained. In the range considered in this study, higher gas content, higher temperature, and lower moisture content are conducive to bubble initiation at the oil gap under vibration, while vibrational frequency does not affect the threshold characteristic of bubble initiation process. The vibration-dependent bubbling phenomenon can be attributed to the vibration-induced cavitation process caused by the decrease in pressure at the oil gap. The influence mechanism of the above factors on the threshold characteristics of bubble initiation was further discussed. This study contributes to providing a reference for risk assessment of abnormal vibration of oil-immersed power equipment.

When acting as the shared energy storage and participating in electricity market services, the schedulable capacity that shared electric vehicle (shared EV) will provide to the grid needs in future time needs to be predicted accurately. This research first proposes a method to construct a schedulable capacity dataset for shared EVs based on publicly available shared vehicle rental service data. Secondly, a schedulable capacity evaluation model based on model-agnostic meta-learning, convolutional neural network, long short-term neural network and attention mechanism (MAML-CNN-LSTM-Attention) is proposed. Through the model, the aggregated schedulable capacity of shared EVs in different functional communities for the coming 60 minutes is predicted. Model uses MAML to fine-tune the meta-prediction network through multi-task training to quickly adapt to feature changes caused by different travel habits of different functional communities; CNN-LSTM is used to learn spatial features of schedulable capacity and efficiently extract high-dimensional temporal features from historical sequences; Attention mechanism is used to further improve model prediction accuracy. Simulations show that the model proposed in this paper outperforms other existing models and can reliably predict the schedulable capacity for different date types and functional areas, providing useful decision aids for shared EV operators to participate in market services.

Power systems around the world are rapidly transitioning to much higher shares of inverter-based resources (IBRs) with few synchronous generators remaining online. IBRs and synchronous generators have fundamentally different dynamic performance characteristics resulting in a difference in the overall power system dynamic performance. IBRs are generally more flexible and controllable than synchronous generators, however at the same time exhibit significantly more complex control systems. Furthermore, new and emerging capabilities are being developed progressively and in particular the so-called grid-forming inverters. Grid-forming inverters (GFM) offer several new capabilities not previously possible with conventional grid-following inverters (GFL). However, they are not well understood currently when applied in a mega scale and moving forward when they will likely take over the role synchronous generators have been performing for several decades as the workhorse of system security support. Key questions currently in the technical community include the extent to which GFM shall be similar or different to each of the synchronous machines and conventional GFL, and how various control strategies can assist in maximising the grid support capabilities sought and minimise or ideally eliminate any adverse impacts. The objective of this special issue is to provide insights into some of these unknowns.

This paper proposes a recurrent neural network (RNN) based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi-directional long short-term memory (BI-LSTM) model with two different types of attention mechanism is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, attention layer is added to the BI-LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI-LSTM model. Finally a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The performance of proposed BI-LSTM model with attention gate and attention layer mechanism is compared with the performance of baseline models based on recurrent neural network (RNN) and convolution neural network (CNN). With this model, the PQD signal is easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time.

Distributed energy resources (DERs) provide flexible load restoration strategies, which can effectively enhance the resilience of active distribution systems (ADSs). Whereas, the widespread DERs in ADSs complicate the supply-demand relationship and make the system resilience difficult to access. Therefore, this paper proposes a simulation-based resilience assessment algorithm of ADSs considering the microgrid formation based on grid-edge DERs. Microgrid formation is used to depict the resilience gain of grid-edge DERs on ADSs. Specifically, a resilience assessment framework for ADSs is firstly proposed, where the uncertainty of component state and supply-demand is modelled based on probability statistics. Then the mixed integer linear programming is used to search for optimal load restoration strategies with microgrid formation. On this basis, a set of resilience indices are defined to quantitatively analyse the resilience of ADSs, and a resilience assessment algorithm with uncertainty scenario generation is proposed to obtain these indices. Furthermore, extensive numerical results based on a modified IEEE 123-bus feeder validate the effectiveness of our proposed method.

The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data-driven models currently used assume that the input data is complete, which has obvious limitations in real-world applications. This paper suggests an IVS-GAN model to assess power system stability using incomplete PMU measurement data with random loss. The proposed method combines the super-resolution perception technology based on Generative Adversarial Network (GAN) with a time-series signal classification model. The generator adopts a one-dimensional U-Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new GRU-Attention architecture proposed in this paper to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10-machine 39-bus system.

In this study, a new short-term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short-term prediction. First, an input feature selection method based on the maximum information coefficient is proposed after considering the problem of information interference caused by excessively large input features. A dynamic time planning method is used to select the optimal input step of historical power. Then, the combined forecasting model composed of TCN and the Informer is constructed in accordance with the numerical weather forecast and historical power data. Lastly, the pinball loss function is used to expand the prediction model into a quantile regression model, measure the effect of volatility, quantify the volatility range of prediction, and finally, obtain a deterministic prediction result. The actual measured data of wind farms in the Bohai Sea area are selected for analysis and calculation. Results show that the prediction model proposed in this study achieves better accuracy in deterministic prediction and interval prediction than the traditional model.

The distributed and privacy-preserving characteristics of fine-grained smart grid data hinder data sharing, making federated learning an attractive approach for collaborative training among data owners with similar load patterns. However, malicious models can interfere with training in the federated learning aggregation process, making it difficult to ensure the accuracy and safety of the central model in load forecasting. Therefore, we propose a secure aggregation federated learning method for distributed load forecasting based on similarity and distance (Fed-SAD), which effectively eliminates the interference of malicious models by securely aggregating models, thereby ensuring accurate and safe distributed scenario prediction. Experimental results demonstrate that Fed-SAD maintains high accuracy and robustness in both the presence and absence of malicious models, while maintaining data and model security.

Reactive power management (RPM) in electric power systems is usually based on a rule-based control derived from the transmission system operator’s experience. This approach faces challenges as the number of decisions and the complexity of the system operation is increasing. With the increasing generation from renewables and the evolution of electricity markets, the available resources must be optimally utilized. In this paper, a comparison is made between the optimization-based approach (OBA) and the experience-based expert approach (EBA) for RPM. The OBA is based on security-constrained optimization with minimum redispatch cost as the objective function for different contingencies. In contrast, the EBA’s actions are based on the system operator’s experience. Comparison is made in terms of the generator redispatch cost, active and reactive power redispatch volume, nodal voltages, and the number of actions to ensure secure operation. The analysis using a reduced model of the target system shows that OBA is more beneficial than EBA, with up to 22% and 42% reduction in redispatch cost and volume, respectively. Moreover, the control decisions from both approaches are seen to be similar. This study aims to show the usefulness of the OBA and motivate TSOs to move towards optimization-based reactive power management

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

The extreme power output scenarios of renewable energy sources (RES) proposed new challenges to the safe and stable operation of the power system. Transmission expansion planning (TEP) with large-scale RES grid integration needs considering the risk of extreme scenarios. In this paper, an adaptive decision-making approach for the TEP problem based on planning-risk assessment-replanning iterative process is proposed. The method obtains massive temporal and spatial correlated wind-photovoltaic (PV) power output scenarios by generalizing the historical data to describe the uncertainties. A data-driven load loss risk assessment model (RAM) based on the power system’s actual operating state is built, referring to the degree of extreme scenario risks on the balance of supply and demand, and the probability of extreme scenario occurrence. The planning decision is progressively revised according to the risk assessment result. The Garver’s 6-bus system and the IEEE RTS 24-bus system are adopted as simulation cases. The results show that the optimal expansion plans achieve a balance between the economy and robustness, which verifies the effectiveness of the proposed method.

In order to build UHV AC transmission lines at high altitudes, it is necessary to analyze the audible noise characteristics of the existing EHV transmission lines at high altitude areas to guide the design. The typical test data of audible noise for 750 kV single circuit transmission lines measured at the Guanting long-term observation station, located at an altitude of 1854 m, were collected. Two methods of sorting the test data of audible noise and extracting valid data were given. Then the levels of audible noise under different weather conditions were analyzed, which showed that audible noise was raised with the increase of instantaneous rainfall and snowfall. Typical frequency spectrums of audible noise under different weather were also obtained, the pure tone at 100 Hz was more prominent under rainy and snowy weather, and the pure tone oscillation attenuated at different positions. Audible noise at different locations presented the better attenuation characteristics with the increase of distance during rainy days. The statistical results and calculated difference of audible noise over 5.5 hours with continuous rainfall were obtained. For the quiet plateau areas, the calculated difference between audible noise during rainy days and fair weather can be taken as 25 dB.