In order to alleviate the energy crisis and environmental pollution, Electric Vehicles (EVs) have obtained the large-scale development in the exceptional period in the world. On the other hand, Virtual Power Plant (VPP), an aggregator involved in various renewable energies and diversified loads, is considered as the crucial measure to coordinate the EVs to participate in the power market. Obviously, the equivalent model of EV in the VPP is difficult to satisfy the need for large-scale development of EVs in the future. Therefore, it is of great significance to establish the detailed charging models of EVs and explore the influences on the operation of the VPP. First of all, the basic structure and operation feature of VPP considering four different kinds of EVs is introduced briefly. Moreover, in order to describe the bidding processing of VPP, the three stages of power market are introduced from the trading aspects. Then, the charging characteristics of four types EVs are analyzed from charging time, charging power and access probability. In addition, the bidding strategy models of VPP at three different stages are built and the improved Artificial Bee Colony algorithm is utilized to solve the optimal bidding strategies of the VPP. Finally, the performances of the bidding model of VPP are validated by the different scenarios which considered the multi-type EVs integrations, and the influences of the different scales of EVs on the bidding strategy of VPP are also analyzed.

When a single-phase-to-ground (SPG) fault occurs in distribution networks, the existing faulty feeder detection techniques do not present reliable detection results. This paper introduces a novel faulty feeder detection method based on transient energy and cosine similarity. First, the fault characteristics of transient zero-sequence current (TZSC) are analyzed. It is found that the fault transient characteristics of transient components decrease as the grounding resistance increases. Second, to fully maintain the fault transient features, a fundamental frequency component (FFC) shifting method is introduced to remove FFCs. Next, the transient currents without containing FFCs are utilized to generate two fault indicators: transient energy within the first half power cycle and cosine similarity after the first half power cycle. After that step, to effectively merge the two fault indicators, the Laplace distribution is utilized. The fault possibility of each feeder can be quantitatively estimated by calculating the cumulative density function (CDF). Finally, the feeder with the maximum fault possibility is determined as the faulty feeder. The effectiveness and applicability of the proposed detection method are verified in the radial distribution network and the modified IEEE 34-bus distribution network. The results of the field test also indicate the feasibility of the proposed SPG detection method.

This paper reviews scenario generation techniques for modeling uncertainty in wind and photovoltaic (PV) power generation, a critical component as renewable energy integration into power systems grows. Scenario generation enables the simulation of variable power outputs under different weather conditions, serving as essential inputs for robust, stochastic, and distributionally robust optimization in system planning and operation. We categorize scenario generation methods into explicit and implicit approaches. Explicit methods rely on probabilistic assumptions and parameter estimation, which enable the interpretable yet parameterized modeling of power variability. Implicit methods, powered by deep learning models, offer data-driven scenario generation without predefined distributions, capturing complex temporal and spatial patterns in the renewable output. The review also addresses combined wind and PV power scenario generation, highlighting its importance for accurately reflecting correlated fluctuations in multi-site, interconnected systems. Finally, we address the limitations of scenario generation for wind and PV power integration planning and suggest future research directions.

When a single-phase-to-ground (SPG) fault occurs in a distribution network, the current is weak, and the fault conditions are complex; the existing fault detection technologies do not show reliable performance. Unlike the existing detection methods, we introduced a fundamental component shift and multiple-transient-feature fusion method in this paper. To remove the fundamental component (FC) that contained in the transient zero-sequence current (TZSC), Hilbert transform was used to calculate the analytic signal of TZSC. Then, the shift factor was introduced to remove the FC such that all the transient features can be kept. Second, three typical transient features indexes were calculated. Then, the multiple evidence estimation method was used to fuse the transient features indexes. Finally, selected the feeder with the maximum fault degree as the faulty feeder. To verify the accuracy and extensive applicability of the proposed method, developed the sample radial distribution network model with different fault conditions, arc fault model, and the modified IEEE-34-node test system with power electronics generator and wind energy conversion systems, the proposed method shows good performance.

High-quality datasets are of paramount importance for the operation and planning of wind farms. However, the datasets collected by the supervisory control and data acquisition (SCADA) system may contain missing data due to various factors such as sensor failure and communication congestion. In this paper, a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder (CE), which consists of an encoder, a decoder, and a discriminator. Through deep convolutional neural networks, the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly. The proposed method can not only fully use the surrounding context information by the reconstructed loss, but also make filling data look real by the adversarial loss. In addition, the correlation among multiple missing attributes is taken into account by adjusting the format of input data. The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks, large valleys, and fast ramps. Moreover, the CE shows stronger generalization ability than traditional methods such as auto-encoder, K-means, k-nearest neighbor, back propagation neural network, cubic interpolation, and conditional generative adversarial network for different missing data scales.

When a high-impedance fault (HIF) occurs in active distribution networks, the features of the arc current are weak and can be easily confused with the features of the capacitor switching current or load switching current. Meanwhile, fault currents are easily affected by the harmonics and noises of distributed generators, which may result in low detection accuracy for existing HIF detection methods. To accurately detect high-impedance arc faults, an HIF detection method based on the arc changing tendency and nonlinear least squares (NLSs) are proposed. In the proposed method, variational mode decomposition and windowed fast Fourier transform are first employed to extract current amplitudes. Then, NLSs method is introduced to quantitatively estimate the changing tendency of the current amplitudes. Finally, an HIF detection criterion is developed based on the sign of the coefficient in the nonlinear function. Both simulation results and field test results confirm the effectiveness and applicability of the proposed HIF detection method. The inspiration for the proposed method is drawn from the physical characteristics during the initial stage of HIFs. These characteristics remain unaffected by network operation conditions. Comparative analysis reveals that the proposed method offers higher detection accuracy and reliability compared to existing conventional methods.