Abstract:

Abstract:To address the challenge of dynamic pricing mechanisms in the efficient integration of electric vehicles (EVs) in virtual power plants (VPPs), a pricing method for EV charging stations based on carbon flow tracking is proposed. The method is designed to utilize node carbon potential to drive the formulation of differentiated electricity prices, thereby guiding the charging plans of EV users and achieving the economic and low-carbon synergistic optimization of grid operations. Specifically, the process is initiated with the quantification of EV traffic and queueing time costs, followed by the optimization of road network planning through an improved Dijkstra algorithm. Then, based on the optimal power flow results and carbon flow tracking theory, the node carbon potential at the charging station access points is accurately calculated. Subsequently, an innovative electricity-carbon coupled dynamic pricing mechanism is constructed based on the carbon potential calculation results, and a hierarchical iterative algorithm is designed to enable a closed-loop feedback optimization of electricity price signals, carbon potential distribution, and user response. Simulation results demonstrate that the proposed method can reduce carbon emissions by 16.7% at the same level of revenue, and increase system revenue by 30.4% at the same level of carbon emissions, thereby its effectiveness in guiding low-carbon behavior and enhancing the economic-environmental synergy of the grid is confirmed. A technically efficient and operable path for the VPP integration of EV resources and the realization of low-carbon economic operation in the grid is provided by this method.

Abstract:Electric vehicle (EV) is the most prevalent distributed flexibility resource that are currently available, and EV clusters show advantages in providing ancillary services. However, significant heterogeneity is observed among EV consumers regarding charging demands and acceptance of controlled charging. To address this challenge, a color-coded pricing mechanism is proposed for EV aggregator participation in energy-reserve markets, designed to balance diverse consumer requirements with flexibility utilization potential. Through the provision of multi-tier charging probability options and corresponding price packages, EV consumers are guided to participate voluntarily, enabling differentiated charging behavior management. A framework is established for EV aggregator participation in energy-reserve markets, where a bi-level optimization model is formulated to simultaneously maximize aggregator profits and minimize consumer charging costs. The solution strategy is developed based on the momentum gradient descent method. Case studies demonstrate that the proposed mechanism not only enhances economic benefits for both the aggregator and consumers, but also effectively facilitates aggregator participation in reserve markets while improving responsiveness to system regulation requirements.

Abstract:Aiming at the limited revenue sources of fast-charging electrical vehicle aggregation (FEVA), a flexible operation strategy, based on cloud-edge collaboration for fast charging stations (FCS), is proposed to participate in frequency regulation ancillary service. The proposed strategy, which takes the cloud platform and edge terminal as the core, guides the electric vehicle (EV) owners participating in frequency regulation and improves aggregator revenue while ensuring the charging experience for EV owners. Alternative charging schemes are solved for EV owners to choose from, which take the relationship between the maximum charging power of EV and the state of charge as the constraint. The cloud platform decomposes the frequency regulation signals to FCS and collaborates with them to participate in frequency regulation. The scene state machine is utilized to describe the FCS scenes and their transformation relationship. The refined mathematical models around each scene are established. While, edge terminal manages FCS locally by distributing EV power in the FCS and encouraging EV owners to end charging in advance. A deep learning model is employed to predict the next day's frequency regulation capacity for declaration. The numerical case verifies that the proposed strategy can accurately predict the frequency regulation capacity, satisfy the diversified charging demand of EV owners, and significantly improve aggregator revenue. Moreover, the cloud-edge collaborative architecture is more suitable for the frequency regulation auxiliary service.

Abstract:To address the issues of slow processing speed and low accuracy when a large number of electric vehicles (EVs) are integrated into the power grid under vehicle-to-grid (V2G) scenarios, a dynamic EV classification and multi-step Markov chain aggregation method based on a density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed. In the classification phase, the DBSCAN algorithm is improved using the k-distance curve and its differential form, and the concept of incremental clustering is introduced to dynamically classify EV data, resulting in EV clusters characterized by multi-dimensional features such as state of charge (SOC), remaining connection time, and controllable capacity. In the aggregation phase, a multi-step state transition Markov chain theory is developed to construct online aggregation models for each EV cluster. This approach addresses the limitations of traditional Markov chains in handling multi-feature EV aggregation and improves the accuracy of the aggregated power output. Simulation results demonstrate that the proposed classification method can quickly and accurately partition large-scale EVs integrated into the grid into different clusters, and that the aggregation model significantly improves the accuracy of aggregate power estimation, effectively addressing the challenges associated with large-scale EV integration.

Abstract:In recent years, the rapid development of electric vehicles (EVs) has likewise led to the construction of EV charging infrastructure, so the research on charging reliability and safety of EV charging facilities has become a focus of attention. However, most of the data used in existing research are complete and sufficient simulation data, when faced with actual data, the prediction accuracy is often affected by insufficient or incomplete data. To solve these problems, a data-driven approach is used to achieve early warning of faults during the charging process of charging equipment. Firstly, feature selection is performed to select appropriate data features. Secondly, the order data is filtered, the dataset is constructed and normalized. Secondly, the dataset is divided into a training group and a test group, the training group is used for model training, and the test group is used to judge the advantages and disadvantages of model training. Then, the divided training group is augmented with generative adversarial networks (GAN) to expand the data size and form a sufficient amount of new data. Subsequently, the data are inputted into bi-directional long-short term memory (Bi-LSTM) and the initial parameters are optimized using particle swarm optimization (PSO). A number of trials are conducted to observe the results of the modeling tests. Finally, in comparison with other prediction models, it is verified that the GAN-PSO-Bi-LSTM model has higher prediction performance, which improves the fault prediction accuracy of charging piles.

Abstract:To enhance the adaptability of electric vehicle charging stations in low-temperature environments and alleviate pressure on distribution and road networks, a charging station planning model for low-temperature regions is proposed. A model for battery capacity and energy consumption under low temperatures is established based on the degradation mechanism of power batteries, allowing for more accurate simulation of driving conditions. By considering the driving, charging, and user decision characteristics of electric vehicles in low-temperature, a Monte Carlo sampling method is used to simulate the spatio-temporal distribution of charging demand for different vehicle types in low-temperature. A hybrid queuing theory is adopted to account for the impact of low-temperatures on charging efficiency, and a weighting function is introduced to minimize social costs, leading to optimal charging station location and capacity configurations. Simulation results demonstrate that the proposed model effectively balances the interests of road networks, distribution networks, and users based on the characteristics of low-temperature regions. It enables reasonable planning of electric vehicle charging stations in low-temperature regions, improving charging service satisfaction, reducing overall costs in cold environments, and enhancing the model's feasibility for practical application.

Abstract:Temperature declines are induced by cold waves, leading to reduced electric vehicle (EV) range and triggering failures in charging infrastructure. As a result, charging demand cannot be met, and the resilience of the EV charging networks (EVCN) is compromised. To address this issue, a resilience enhancement scheme based on intelligent navigation is proposed. The impacts of cold waves on the EVCN are comprehensively analyzed. The failure mechanisms and cascading characteristics of charging stations under cold wave conditions are investigated, and historical data are processed to establish a cascading failure model. To enhance supply-side resilience, mobile emergency generators are navigated to faulty stations for power compensation using a navigation model trained via graph reinforcement learning. In parallel, the same model is utilized to recommend suitable charging stations and optimize routing for EVs in need of charging, thereby improving resilience from the demand side. Through case studies, cascading load increases are identified as the primary cause of failures during cold waves. The proposed collaborative navigation approach ensures stable power delivery and rapid recovery under fault conditions, while reducing waiting times and fulfilling users' charging demand.

Abstract:Existing electric vehicle (EV) scheduling schemes fail to address the issue of balanced utilization of charging piles, which often leads to overloading and premature aging of certain charging piles while others remain underutilized. Concurrently, vehicle-to-grid (V2G) technology enables bidirectional energy flow, enhancing grid regulation capabilities while providing users with discharge revenue. In light of this, a two-stage EV scheduling optimization method based on charging pile allocation and charge-discharge scheduling is proposed. In the first stage, the allocation of charging piles is optimized with the objective of minimizing the variance in charging pile utilization. In the second stage, the charge-discharge power of EVs is optimized to achieve threefold objectives: minimizing the variance of regional load, minimizing user charging costs, and maximizing charging pile revenue, thereby achieving tripartite collaborative optimization. An adaptive genetic algorithm (AGA) is employed to solve the established bi-level EV scheduling model. The case study results demonstrate that, compared to conventional strategies that neither consider charging pile load balancing nor incorporate V2G technology, the proposed method reduces the variance of charging pile utilization by 93.6%, decreases the variance of transformer area load by 16.5%, lowers users' net charging costs by 12.0%, and increases the charging station's daily revenue by 14.4%. These outcomes fully substantiate the method's superior performance in optimizing charging infrastructure utilization, mitigating load fluctuations, and enhancing multi-stakeholder economic benefits.

Abstract:The power grid is faced with challenges arising from the integration of electric vehicle charging, and the reliability of electric vehicle charging services is also affected by grid failures. To address the difficulties in reliability assessment caused by multi-dimensional characteristics such as randomness and sequentiality, temporal evaluation of electric vehicle charging services reliability considering photovoltaic-storage-charging integration is proposed in this paper. Firstly, the fault characteristics of distribution networks under electric vehicle grid integration are analyzed, and a distribution network fault and reliability evaluation model integrating electric vehicle charging load is constructed. Secondly, a dynamic optimization strategy for energy storage based on model predictive control is proposed, by which the reliability of charging services under both grid-connected and off-grid modes, considering distribution network faults and coordinated operation of photovoltaic-storage-charging is enhanced. Finally, multi-dimensional charging service reliability indices and a calculation method based on the sequential Monte Carlo simulation are proposed. It is shown by simulation results that the proposed evaluation indices can quantify the reliability of electric vehicle charging services reliability from multi-dimensional, and the proposed coordinated optimization strategy for photovoltaic-storage-charging can significantly improve the reliability level of electric vehicle charging services under different operation modes and fault scenarios.

Abstract:In the context of the 'dual-carbon' goal, a dual demand response within each microgrid and a cooperative peer-to-peer (P2P) scheduling strategy among microgrids are proposed to build a fairer, green, and low-carbon new electricity market. Firstly, a microgrid model incorporating integrated electricity, heat and gas is constructed, with explicit modeling of flexible loads such as electric vehicles (EVs). Then, the optimization strategies for electricity sharing among multiple microgrids and within microgrids are proposed. At the multi-microgrid level, an optimal scheduling model based on Nash bargaining theory is established, aiming to achieve the optimal benefits for both individual microgrids and the entire microgrid group after their participation in P2P collaboration. Within each microgrid, a dual demand response optimal scheduling model based on dynamic tariffs and EV carbon quota is established with the objective of reducing the microgrid carbon emissions, the peak-to-valley difference of the electric loads, and the charging cost of EVs. Finally, simulation results demonstrate that the proposed optimal scheduling strategy effectively enhances energy sharing and renewable energy consumption among microgrids, and further reduces the system carbon emissions.

Abstract:A sliding mode load-frequency control (LFC) strategy based on an adaptive triggering mechanism is proposed for a multi-area interconnected power system with electric vehicles (EVs). Firstly, the integration of EV in the frequency control of the power system is considered, and the impacts of primary and secondary frequency control on system frequency variations are investigated. The effects of renewable energy fluctuations and load disturbances on system frequency are also analyzed. Then, an adaptive event-triggered mechanism is designed to improve network utilization and address transmission delays in the network. An asymmetric Lyapunov functional is established to prove the system's asymptotic stability, and a stability criterion with low conservatism is derived. Finally, the effectiveness of the proposed scheme is verified, and an optimization algorithm is used to design the optimal participation of EV in frequency regulation. The results show that the control strategy designed in this paper can effectively improve the frequency regulation performance of the system under renewable energy and load disturbance, and EV participation in both primary and secondary frequency modulation has a significant effect on improving the system performance.

Abstract:The disorderly charging of large-scale electric vehicles connected to the power grid will lead to excessive load variance in the distribution network system. Fully utilizing the dual characteristics of electric vehicles can reduce the load variance of the power grid and achieve efficient utilization of green electricity, but user scheduling capacity is an important factor affecting the application of vehicle-to-grid interaction. This article applies the Monte Carlo method and the improved multi-objective particle swarm optimization algorithm with niche technology (niche-MOPSO) to study the multi-objective optimization strategy of grid-connected photovoltaics and V2G operation based on the impact of scheduling capacity. The research results indicate that as the charging participation rate of EVs gradually increases, disordered EV charging loads will lead to an increase in grid side load variance, but the impact on users' charging costs is relatively small. With the increase in EV scheduling capacity in the work area, the photovoltaic consumption rate gradually decreases, and the load variance shows a trend of first decreasing and then increasing. When the scheduling capacity is 30%, the load variance reaches its minimum, indicating that reasonable V2G calling is beneficial to the stability of power grid operation. Under the same scheduling capacity, the niche-MOPSO algorithm reduces the load variance and peak load, and also lowers user charging costs or increases user revenues. Moreover, the revenue under the V2G price incentive mechanism is much greater than that under the time-of-use electricity price mechanism. The niche-MOPSO algorithm can effectively optimize both load variance and user charging cost.

Abstract:Transient ground potential rise (TGPR) and transient electromagnetic field generated by the switching operation of gas insulated switchgear (GIS) pose a great threat to the safety of the personnel in the station as well as the stable operation of the secondary equipment. The arc model has a great influence on the simulation calculation of TGPR and transient electromagnetic field. In this paper, a three-dimensional transient electromagnetic simulation model of GIS is established, and the effects of different arc models and model parameters on the simulation results of TGPR and spatial electric field are investigated by applying the finite integration time domain method. Through the analysis, it is found that the simulated waveform amplitude is higher under the ideal switching model and constant resistance model, and the simulated waveform amplitude is lowest under the hyperbola-like model. The simulated waveforms and pulse amplitudes under the exponential and segmented resistance models are almost the same. In the segmented resistance model, the amplitudes of TGPR and spatial electric field tend to decrease when the variable parameter of steady resistance increases. The waveform amplitudes of TGPR and spatial electric field are almost unaffected when the equivalent length and equivalent radius of the arc model change.

Abstract:With the increasing depletion of traditional fossil fuels, new energy sources such as wind and solar power become the main energy sources for building new power systems. Distributed energy resources including wind and solar power, exhibit strong characteristics of randomness, volatility, and intermittency. Their large scale grid integration poses many challenges to the stable operation of the power grid. Virtual power plant (VPP) which possess a strong ability to aggregate and regulate various types of resources, can participate in grid operation and scheduling, thereby promoting the consumption of clean energy. This article first summarizes the definition of VPP and provides an overview of the current research status both domestically and internationally. It also reviews the current status and trends of energy development in China. Secondly, the control methods and architecture of VPP are summarized, and the current VPP models are classified into four categories: VPP optimization scheduling model, VPP participation in bidding and tendering model, VPP participation in demand-side response model, and VPP model considering system risk. Various algorithms for solving VPP models are reviewed. Finally, the future prospects of VPP are discussed, especially the economic benefits of VPP participation in the electricity market. This article provides a comprehensive summary and generalization of the current research on VPP, offering references for future research in this field.

Abstract:The virtual capacitor control strategy is applied to the converters at each port of DC microgrid to simulate the charge-discharge characteristics of capacitors, which provides an effective solution to solve the inertia problem of the system and improve the voltage quality. Aiming at the cooperative allocation problem of virtual capacitance control in parallel operation of multiple energy storage units, a cooperative allocation strategy of multiple energy storage units based on virtual capacitance control to give full play to the inertial support capability of each energy storage unit is proposed in this paper. The total value of the adjustable virtual capacitance is obtained according to the change rate of DC voltage and the state of charge. The entropy method and scoring matrix are used to comprehensively evaluate the indicators such as the state of charge of the battery and the adjustable capacity of the energy storage converter. The allocation coefficient is obtained according to the final evaluation score, so as to distribute the total virtual capacitance cooperatively. Finally, a hardware-in-the-loop simulation test platform is built to verify the proposed strategy. The results show that the energy storage units allocate virtual capacitors reasonably according to the inertia capacity, which avoids the energy storage converter from prematurely exiting due to excessive output power, and enhances the inertia level and safe and stable operation capability of the system.

Abstract:Renewable energy connected modular multilevel converter high-voltage direct-current (MMC-HVDC) system should have the capability of fault ride-through when the AC grid fails. However, it is difficult to quickly and reliably achieve fault ride-through via cooperative control if there is no high-speed communication between renewable energy and MMC-HVDC converters. Additionally, the droop coefficient in traditional voltage drop control is set to a linear constant, which makes it difficult to fast match under different fault conditions and results in a longer step delay, leading to high voltage operation of the DC system during the fault. Aiming at the existing problems mentioned above, the dynamic characteristics of the DC system and the principles of traditional voltage drop control are analyzed, which provides a basis for fast power matching on both sides of the DC system. Then, a fault ride-through control based on fast matching power for renewable energy connected MMC-HVDC is proposed. Finally, simulations based on PSCAD/EMTDC show that fault ride-through capability is reliable by using the proposed control method. Compared with traditional voltage drop control, the proposed control method significantly reduces the step delay, limits the rise of DC voltage and achieves fast and reliable fault ride-through.

Abstract:A two-stage adaptive robust optimization model for power restoration in distribution networks with high penetration of renewable energy under extreme disasters is proposed in this paper. Uncertainty sets and adjustable robust parameters are employed to depict the uncertainty of renewable energy output and load demand. In the pre-disaster stage, unit commitment strategy and dispatch strategy of controllable generators are obtained to guarantee the reasonable distribution of power flow. In the post-disaster stage, network reconfiguration, emergency resources dispatch, adjustment strategy of controllable generators and load shedding are employed to perform fault recovery on the distribution network. The column and constraint generation algorithm (C&CG) is used to decompose the model into the main problem and subproblem. The dual theory and the big M method are employed to dualize and linearize the subproblem. The optimal recovery strategy can be obtained by alternating iterations between the main problem and the transformed subproblem. Case studies conducted on the improved PG&E 69-node system indicate that the proposed model is able to balance the robustness and economy under extreme disaster scenarios.

Abstract:The ultra-high frequency (UHF) radiation source localization method for electrical equipment is a key technology for partial discharge (PD) localization. PD signals exhibit wide frequency bands (300 MHz-3 GHz), rapid rising edges (the shortest rise time reaching sub-nanoseconds or even hundreds of picoseconds), which are always accompanied by significant background noise. These characteristics impose stringent requirements on the sampling frequency and analog bandwidth of measurement systems. Conventional measurement systems struggle to effectively capture and completely record the rapid rising edge waveforms of PD pulse signals. To solve these problems, a radiation source localization method based on interpolation calculation and multi-scale wavelet transform is proposed. This method enables effective detection of rapid rising pulse signal arrival times under lower sampling rates. Interpolation is applied to the pulse signal, followed by multi-scale wavelet transform on the interpolated signal. The maximum wavelet coefficient is extracted to replace the original pulse signal. Based on the maximum wavelet coefficient, the energy accumulation algorithm is used to calculate the signal's arrival time. Simulation analyses and experimental validations demonstrate that this method significantly improves the temporal resolution of UHF pulse signals and exhibits robust noise resistance in high-noise electromagnetic environments.

Abstract:In recent years, the growing number of transmission and distribution lines traversing forested areas have led to an increasing frequency of line tripping incidents caused by wildfires, seriously threatening the safe and stable operation of power grids. Although numerous experimental studies have been conducted on the reduction of air insulation strength in high-voltage transmission lines to ground under wildfire conditions, little attention has been paid to the potential reduction in insulation distance when flames reach the transmission tower, particularly around line insulators. To investigate the air breakdown mechanism under conditions where vegetation flames fully bridged the short gap between a conductor and a grounded plate, experiments on the breakdown characteristics of line-to-plate gaps under various vegetation combustion conditions are conducted. The phase difference between leakage current through the flame and applied breakdown voltage is analyzed, and the breakdown mechanism under vegetation flame conditions is discussed. The breakdown voltages and average breakdown field strengths of the gaps under different vegetation flame conditions are experimentally investigated. The results show that: increasing the gap distance reduces the inductive component within the flame, causing the overall line-to-plate gap to exhibit capacitive behavior; the breakdown process under vegetation flame conditions involves multiple discharge mechanisms, and the duration of the arc channel varies significantly during breakdown; the breakdown voltage increases linearly with gap distance. Due to its high ash content and loose structure, straw vegetation produces long ash particles during combustion, which strongly bridge the gap and distort the electric field. As a result, the average breakdown field strength under straw flame conditions is the lowest among the tested vegetation types.

Abstract:DC-DC converter is the core equipment for connecting distributed photovoltaic power generation to be connected to the medium voltage DC grid. A circulating current free medium frequency DC-DC converter with auxiliary transformer is proposed in this paper. This converter comprises a main power circuit and an auxiliary control circuit, consisting of one three-level bridge arm and two two-level bridge arms in total. In this converter, the main power circuit transmits approximately 90% of the power, while the auxiliary circuit transmits the remaining small portion of the power. By utilizing high frequency chopping of some switching devices in the auxiliary circuit, the current waveform is approximated to a trapezoidal wave, thereby reducing the peak current. Due to the small current flowing through the auxiliary circuit, the switching losses generated by high frequency chopping are also very small. The switches that flow large currents only operate at a medium frequency of 500 Hz and can realize zero current switching (ZCS). In addition, the loop current in the auxiliary branch is eliminated by introducing a blocking capacitor in the auxiliary circuit, so that the loss of the whole converter is small. The paper analyzes the working principle of the converter, discusses the parameter design, and builds a 160 V/4 000 V/4 200 W principle prototype for experimental verification,The experimental results indicate that the proposed converter achieves ZCS with low switching losses and a peak efficiency of 98.5%.

Abstract:The detection and capture of partial discharge (PD) signals are crucial for sensing and assessing the insulation state of power equipment, as well as for fault warning and diagnosis. An ultrasonic sensing technology based on Sagnac unbalanced interference (SUI) is proposed to effectively detect and capture PD acoustic emission signals from power equipment. The structure of the SUI optical path and the process of optical path interference are analyzed. It clarifies the mechanism by which the SUI perceives external ultrasound and establishes the sensing equations for acoustic waves. In order to meet the demand for detecting weak ultrasound signals in power equipment, the research also analyzes the influence of SUI optical path structure parameters on ultrasonic sensing characteristics and proposes optimal structural design parameters for the sensor. Experimental test results demonstrate that the proposed SUI system can detect ultrasound in the frequency range of 30 kHz to 150 kHz, exhibiting wide frequency coverage and high sensitivity. Moreover, it successfully captures the acoustic emission signals of needle plate electrode discharge. The proposed SUI shows the advantages of low cost, simple and stable structure, which paves the way for the optical detection of partial discharges in power equipment.