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.

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.

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.

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.

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.

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.

The iron core in the circuit breaker of oil dashpot is mainly affected by the electromagnetic force, spring force and oil damping force, which can play the function of anti-time protection when overload current is excited. At low temperature, the viscosity of the methyl silicone oil damping fluid will change, and the parameters of spring and iron core will also change with temperature. The change of this parameters will lead to the change of the resultant force on the iron core, which will affect the operation time. To analyze this problem, a correlation model is established. Through the measurement of parameters at low temperature, numerical calculation, electromagnetic analysis, simulation analysis and compared with the experimental results. The study found that the viscosity of the damping fluid increases, the iron core is deformed, and the spring stiffness increases at low temperature. The influence of a single factor on the operation characteristics of the tripper is not enough to reflect its overall characteristics, and the comprehensive effect of each factor needs to be considered. After comprehensively considering all factors, the results are closer to the experimental results. The results provide a theoretical basis for the optimization design of the circuit breaker.

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

As electric vehicles(EVs) begin to participate in the peak regulating auxiliary service market, it has become a major problem that how can price aggregators maximize the peak shaving capacity provided by EVs and maximize their own interests. This paper proposes a bargaining game pricing method based on the psychology cost of EVs and the risk assessment of aggregators. First of all, according to the impact of users’ participation in peak shaving on the battery life of EVs, the impact of participation in peak shaving on users’ original travel plans and time, and the impact of aggregator pricing on users’ psychology, the comprehensive psychology cost of EV users is obtained. Then, based on the user’s psychology cost and the law of gravitation, the evaluation scheme for the peak shaving capacity of EVs is obtained. On the basis of conditional value at risk(CVaR), the mixed CVaR is obtained by considering the behavior of users who may chase risks. Based on the mixed CVaR, the risk assessment of aggregators’ participation in the peak regulating auxiliary service market is carried out. According to the above information, the aggregators and the EV teams are engaged in a bargaining game based on the peak shaving pricing problem, which is divided into complete information game and incomplete information game. Finally, the feasibility of the proposed method is verified by an example analysis.

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.

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.

A novel parameter estimation method is proposed for the permanent magnet synchronous generator (PMSG), which is implemented by an enhanced self-learning particle swarm optimization algorithm with Levy flight (SLPSO), and the problem of lower parameter estimation precision of standard PSO is obviated. This method injects currents of different intensities into the d-axis in a time-sharing manner to solve the problem of equation under-ranking, and the mathematical model for full-rank parameter estimation is developed. The speed term of PSO is simplified to expedite the convergence of PSO, and a strategy with Chaotic decline for the inertia weight of PSO is adopted to strengthen its ability to jump out of the local optimum. Moreover, the self-learning dense fleeing strategy (SLDF) is proposed where particles perform diffusion learning based on population density information and Levy flight, the evolutionary unitary problem and human intervention in the evolutionary process is averted. Furthermore, the memory tempering annealing algorithm (MTA) and greedy algorithm (GA) is integrated into the algorithm, MTA can facilitate the exploration of potentially better regions, and GA for local optimization enhances the convergence speed and accuracy in late stage of the algorithm. Comparing the proposed method with several existing PSO algorithms through simulation and experiments, the experimental data show that the proposed method can effectively track variable parameters under different working conditions and has better robustness.

Most of the first eight pilot regions in China have entered the long-period continuous operation stage, and the second batch of six power spot pilots have started their simulation trial runs; however, the construction of power markets in the hydropower-rich regions of China (i.e., Sichuan and Yunnan) still faces multiple problems, including poor reliability of medium- and long-term power supplies, inefficient hydropower resource utilization and consumption scales, and unstable market prices. In this study, we establish a multi-scale joint clearing model for different market models (considering both long- and short-term operation strategies) and perform simulations of different market models at different time scales, to compare and analyze their optimization results and electricity price fluctuations. The model can stabilize the market price and reasonably reflect the true value of hydropower; it can also promote the consumption of clean energy and ensure the reliability of power supplies in the medium and long term. Finally, suggestions are made regarding the selection of electricity market models, the security of energy supplies, and the design of sustainable development mechanisms for hydropower in hydropower-rich regions, with a view to providing useful references for the development of China’s electricity market.

Multi-terminal high-voltage direct current technology based on voltage-source converter stations (VSC-MTDC) is expected to be one of the most important contributors to the future of electric power systems. In fact, among other features, it has already been shown how this technology can contribute to improve transient stability in power systems by the use of supplementary controllers. Along this line, this paper will investigate in detail how these supplementary controllers affect electromechanical oscillations, by means of small-signal stability analysis. The paper analyses two control strategies based on the modulation of active-power injections (P-WAF) and reactive-power injections (Q-WAF) in the VSC stations which were presened in previous work. Both control strategies use global signals of the frequencies of the VSC-MTDC system and they presented significant improvements on transient stability. The paper will provide guidelines for the design of these type of controllers to improve both, large- and small-disturbance angle stability. Small-signal stability analysis (in Matlab) has been compared with non-linear time domain simulation (in PSS/E) to confirm the results using CIGRE Nordic32A benchmark test system with a VSC-MTDC system. The paper analyses the impact of the controller gains and communication latency on electromechanical-oscillation damping. The main conclusion of the paper is that transient-stability-tailored supplementary controllers in VSC-MTDC systems can be tuned to damp inter-area oscillations too, maintaining their effectiveness.

In this paper, the impact of residential photovoltaic (PV) battery systems in a real test system with the goal of system peak load shaving is investigated. In order to encourage residential investors, a Levelized feed-in tariff (LFiT) scheme is introduced. Accordingly, two proposed cases and relevant suggestions are presented to reach a better performance. The profitability of the project is apprised via several economic criteria such as net present value (NPV), payback period years (PBY), internal rate of return (IRR), benefit to cost ratio (BCR), net cash flow (NCF), and Levelized cost of energy (LCOE). Moreover, different levels of peak shaving subject to customers’ participation and the size of the PV-battery system are also obtained. An actual test system regarding one-year recorded data is employed to elevate the precise of results. Furthermore, results are economically compared with the current situation in the test system to assess the effectiveness of the suggested system. Obtained results determine the best size of PV and battery to attain the preferable profitability. Meanwhile, the LFiT with respect to the economic criteria is specified as well. Finally, a sensitivity analysis on systems’ parameters is performed to identify the impact of these parameters on economic indices.

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.