最新刊期
卷 50 ， 期 15 ， 2026
- 摘要：The strategic value of large-scale electric vehicles (EVs) in new power systems has been widely recognized. Serving as a critical connector for deep integration between transportation and energy systems, vehicle-to-grid (V2G) has been launched in multiple pilot projects nationwide. However, its large-scale deployment is hindered by systematic bottlenecks. From the integrated transportation-energy perspective, this paper first focuses on core features of V2G for facilitating the development of new power systems in diverse scenarios and systematically summarizes practical benefits from typical pilot practices. Then, core limitations to large-scale V2G deployment are clarified from six dimensions covering planning, equipment, platforms, mechanisms, users and standards. Finally, a systematic innovation framework is proposed, and cutting-edge research directions such as user behavior profiling driven by large models, transportation-energy system synergy, and digital twin platforms are explored. The long-term vision of V2G is envisioned, and phased technical paths and policy recommendations are proposed, aiming to provide theoretical support and practical guidance for the high-quality and large-scale development of V2G.关键词：electric vehicle (EV);vehicle-to-grid (V2G);new power system;pilot;large-scale application;transportation-energy system synergy;digital twin;technical pathway6|0|0更新时间：2026-08-21
- 摘要：Aiming at the contradiction between the theoretical potential and practical application of vehicle-to-grid (V2G), this paper proposes that its large-scale development is a process evolving from “grid’s demand” to “vehicle’s self-awareness”. The matching characteristics of V2G in capability and scale with the demands of new power systems are systematically elucidated. V2G operators, as new business entities, while undertaking multiple roles of “certainty creator, value realizer, and technology innovator”, will further develop into new entities bearing balancing responsibility and reliability responsibility. The key technologies and mechanisms required to achieve this goal are discussed. Furthermore, it is pointed out that the role evolution of electric vehicles is a key factor in promoting large-scale V2G application. A four-stage roadmap for the evolution of V2G toward deep integration of transportation and energy is constructed, which is expected to provide theoretical reference for industrial collaborative development.关键词：new power system;vehicle-to-grid (V2G);value matching;large-scale application;flexibility resource;new business entity6|0|0更新时间：2026-08-21
- 摘要：Addressing the insufficient flexible regulation capability under high renewable energy penetration and the large-scale development of electric vehicles (EVs), this paper systematically analyzes the market mechanism for EVs participating in the electricity ancillary service market against the backdrop of a preliminarily established national unified electricity market. A three-dimensional analytical framework of “technology-economy-policy” is constructed. In the technology dimension, the closed-loop process of “modeling-scheduling-aggregation” is sorted out, and the adjustable resource pool formed by large-scale EVs as well as its feasible region are characterized. In the economic dimension, the Hurwicz economic mechanism design theory is introduced, and the incentive compatibility among users, load aggregators and the market is discussed hierarchically. In the policy dimension, combined with new structural economics and the principle of technology neutrality, rules in regions with continuous spot market operation are compared. Based on existing research and practice, three key characteristics of large-scale EVs participating in the electricity ancillary service market are analyzed: regulation complexity, transaction coordination, and institutional endogenization. Prospects are provided from three dimensions: a closed-loop control technology system of physical-information-social coupling; a multi-market synergy and quantitative trading mechanism for electric energy, ancillary services and capacity; and institutional evolution and endogenous incentives based on the economic mechanism design theory.关键词：electricity market;ancillary service;load aggregator;electric vehicle (EV);feasible region;adjustable resource3|0|0更新时间：2026-08-21
- 摘要：In vehicle-to-grid (V2G), electric vehicles (EVs) can help regulate charging and discharging power and time slots, thereby assisting the power grid in peak shaving, accommodating renewable energy, and alleviating local congestion. The development of these technologies facilitates ecological synergy between EVs and the energy supply system. From the perspective of vehicle side in V2G, this paper analyzes key technologies including on-board hardware topology and dynamic response control, DC and AC interaction control, battery life degradation estimation, and user incentives. This paper also explores the driving forces and directions of these technologies and forecasts future technological trends. To address the challenges in current practice,including the lack of standardized AC charging pile hardware compatibility and protocols, the need for compatibility retrofits between new and legacy DC charging piles, insufficient development and application of user incentive technologies, unclear profit distribution rules, high vehicle costs, and the need to improve the operability of battery life assessment,this paper analyzes their current status and underlying causes. To promote the deployment of V2G, this paper provides recommendations in several areas, including the development of efficient and low-cost on-board hardware, the adaptation and compatibility of vehicle-charging pile hardware and communication systems, awareness guidance and service system design for battery life degradation, and the integration of artificial intelligence into user incentives. These recommendations are intended to serve as a reference for industry development.关键词：vehicle-to-grid (V2G);electric vehicle (EV);on-board hardware;dynamic response;interaction control;battery life degradation;user incentive4|0|0更新时间：2026-08-21
- 摘要：The vehicle-to-grid (V2G) technology is a critical pathway for enhancing grid flexibility and promoting renewable energy accommodation. Although China has made significant progress in technological verification of V2G, it still faces bottlenecks in commercial promotion. Drawing on North America’s nearly three-decade exploration as a mirror, this paper identifies the “pilot trap” formed by the interplay of triple constraints—institutional fragmentation, market mechanism misalignment, and capability gaps. These constraints are empirically examined through case studies of California, Texas, and New York, revealing their failure mechanisms under different institutional contexts. The study indicates that technological maturity does not automatically translate into market success. For the Chinese context, this paper proposes transforming institutional strengths into technological architecture advantages by building a unified standard based “cloud-pipe-edge-device” hierarchical coordinated control architecture for V2G to address cross-regional coordination and data silo issues. This paper further proposes a differentiated implementation path of “commercial use first, official demonstration, and civilian adoption later”, along with a technical development strategy emphasizing the synergy between battery swapping and bidirectional charging. The findings caution against the limitations of policy-driven approaches and offer a systematic theoretical reference and implementation framework for V2G in China from pilot projects to large-scale application关键词：renewable energy accommodation;vehicle-to-grid (V2G);pilot;battery swapping;charging;North America3|0|0更新时间：2026-08-21
- 摘要：The vehicle-to-grid (V2G) technology has been proposed for many years, but its large-scale application has yet to achieve the expected breakthrough. This paper analyzes the current status and challenges of the V2G business, summarizing the key engineering and technical issues that need to be resolved for its implementation. Focusing on three typical scenarios of charging and battery swapping facility operation in China, this paper proposes industry-level solution ideas to accelerate the large-scale engineering application of V2G. Combined with the actual situation, this paper also presents a business system architecture required for the practical application of V2G. Substantive suggestions for promoting the engineering of V2G business are proposed from the perspectives of the governments, power grid companies, vehicle owners, electric vehicle and charging and battery swapping facility manufacturers, as well as various charging and battery swapping facility operators. Furthermore, the connotation of V2G is comprehensively discussed. The types of V2G are categorized into two patterns: autonomous interaction and profit-driven interaction. Finally, an outlook on the prospects of V2G business is provided.关键词：electric vehicle (EV);vehicle-to-grid (V2G);charging and battery swapping facility (CBSF);virtual power plant (VPP);aggregator;station area smart terminal4|0|0更新时间：2026-08-21
- 摘要：High-precision and fine-grained multi-timescale prediction of electric vehicle (EV) schedulable capacity is of great significance for large-scale EV participating in power grid ancillary services. However, existing EV schedulable capacity prediction algorithms suffer from a significant reduction in prediction accuracy and efficiency when the prediction sequences are long. To address the requirements of multiple dispatching scenarios in the power system, this paper proposes an improved Informer algorithm to construct a multi-timescale, aggregated, schedulable capacity prediction model for electric vehicle aggregators (EVAs) in terms of day-ahead, ultra-short-term, and real-time scales. First, the historical schedulable capacity data of EVAs obtained from processing large-scale individual EV charging records is used as the data set of the improved Informer prediction algorithm. Then, the convolutional sparse attention mechanism is used to replace the attention mechanism in the traditional Informer algorithm, and an improved Informer algorithm is constructed to enhance its performance in capturing trend information of historical time sequences, thereby improving the prediction accuracy of long sequences. Finally, more than half a million actual historical charging records of EVs in a city are used as samples to validate the proposed model at multiple timescales and spatial dimensions. The results show that the proposed method significantly improves the prediction accuracy on different timescales as well as reduces the prediction time of long sequences.关键词：electric vehicle (EV);vehicle-to-grid (V2G);schedulable capacity;deep learning;multi-timescale;Informer;prediction;Attention mechanism3|0|0更新时间：2026-08-21
- 摘要：As a new type of flexible energy storage device, electric vehicles (EVs) can provide valuable flexible regulation capabilities for the power system by participating in the regulation of electric vehicle aggregators. With the continuous increase in the penetration rate of renewable energy, the regulation demands of the power system are becoming increasingly complex. At present, adjustable capacity prediction of EVs faces challenges in achieving higher accuracy and resilience against risks. To this end, this paper proposes a prediction method for feasible region boundary interval of large-scale EVs based on multi-timescale integration. Firstly, considering the influence of user credibility on the evaluation of the adjustable capability of EVs, access rules for participating in regulation and the feasible region correction methods are designed for different types of EVs, and then aggregated to obtain the feasible region boundary of large-scale EVs. Secondly, a trend-preserving data amplification method for time series is proposed to achieve the amplification of a single feasible region boundary dataset to multiple datasets with similar time series information but different specific values. Again, based on multiple datasets obtained through amplification, a multi-timescale integration prediction and probabilistic interval modeling method for the feasible region boundary of EVs combining Autoformer and Gaussian mixture models is proposed. Finally, based on the large-scale historical charging records of Shenzhen,China, the effectiveness of the proposed method is verified. The results show that the proposed prediction method of feasible region boundary interval can provide more accurate and reliable information support for aggregators participating in the electricity market and power system regulation on the future adjustable capacity of EV resources.关键词：electric vehicle;aggregator;feasible region;interval prediction;adjustable capacity;multi-timescale;probabilistic interval modeling3|0|0更新时间：2026-08-21
- 摘要：Massive electric vehicle (EV) aggregation control technology is the key to achieving efficient participation of large-scale EVs in vehicle-to-grid (V2G). However, existing aggregation methods face two major challenges: first, the mutually exclusive characteristics of EV charging and discharging lead to non-convexity in the power feasible region; second, the power constraint boundaries of EVs with different user preferences exhibit significant differences. The former will result in overestimation of the adjustable boundaries of EVs, while the latter makes the evaluation of adjustable boundaries of EVs tend to be conservative. To address the above problems, this paper first achieves convex characterization of the non-convex feasible region by reconstructing the mathematical expression of the EV power feasible region, effectively solving the modeling problem of mutually exclusive charging-discharging constraints. Second, a constraint set contraction based aggregation algorithm is designed, which significantly improves the evaluation accuracy of adjustable boundaries under boundary differences by enhancing the inner approximation degrees of freedom of traditional aggregation algorithms. Case study analysis demonstrates that the proposed method effectively improves the computational efficiency and aggregation accuracy of massive EV scheduling.关键词：electric vehicle (EV);vehicle-to-grid (V2G);feasible region;Minkowski sum;adjustable boundary;aggregation4|0|0更新时间：2026-08-21
- 摘要：To exploit the adjustable capacity of large-scale electric vehicle (EV) charging and battery swapping facilities, operators typically aggregate them for vehicle-to-grid, and then decompose the aggregate power target curve into individual charging and discharging plans. However, existing research mainly focuses on charging facilities, and the decomposition methods tend to cause fluctuations in individual power. To address the above issues, based on the priority criteria of less laxity and longer remaining processing time, an aggregate power decomposition method considering look-ahead and reassignment mechanisms is proposed, which improves computational efficiency through look-ahead cycle adaptive adjustment. The real-time operation strategies based on dynamic battery reserve are constructed for battery swapping stations, and the XGBoost algorithm is used to predict the scale of power battery reserves. Furthermore, a real-time decomposition method of aggregate power is formed for combined charging and battery swapping facilities. The simulation results based on actual data indicate that the method only takes 16.65 seconds to operate in a single step in the scenario of 100 000 EVs, which meets online control requirements and can effectively reduce the aggregate power decomposition error and individual power fluctuations.关键词：electric vehicle (EV);vehicle-to-grid (V2G);charging;battery swapping;battery reserve;real-time decomposition;aggregate power5|0|0更新时间：2026-08-21
- 摘要：The participation of electric vehicle (EV) charging stations in demand response (DR) can effectively promote energy conservation and carbon emission reduction, while supporting the stable and economic operation of power grids. DR optimization strategies for charging stations must be explainable and capable of enabling safe and rapid adaptation to environmental uncertainty. This paper proposes a bi-level optimization method, in which the upper level uses explainable reinforcement learning (XRL) and the lower level uses mathematical programming (MP). First, a bi-level optimization framework is established, where the upper level handles temporal dimension optimization to address uncertainties, and the lower-level handles spatial dimension optimization. Then, an interpretable Kolmogorov-Arnold network (KAN) soft actor-critic (KAN-SAC) algorithm is developed for the upper level, which leverages the analytical function structure of KANs to enhance strategy interpretability while achieving excellent generalization capability and training stability. Subsequently, for the lower level, the optimization problem is reformulated as a continuous quadratic programming problem, thereby constructing a collaborative optimization paradigm tailored for spatial-temporal coupled scheduling problems. Testing results show that the proposed method ensures transparent and concise decision-making paths, delivering explainable and safe day-ahead DR decision schemes under uncertain environments and reducing the electricity consumption cost of charging piles.关键词：electric vehicle (EV);demand response (DR);uncertainty;charging station;explainable reinforcement learning (XRL);mathematical programming (MP)2|0|0更新时间：2026-08-21
- 摘要：Low-carbon scheduling of electric vehicles (EVs) is of great significance for carbon emission reduction and economic operation of power grids. Existing low-carbon scheduling for EVs suffers from problems including insufficient full-life-cycle consideration for carbon emission accounting and poor coordination between low-carbon guidance signals and grid carbon reduction targets, so the potential of EVs participating in power grid low-carbon scheduling cannot be fully exploited. To address the above issues, firstly, three types of charging behavior control models are established by considering the charging-discharging power ramp characteristics and differentiated demand of batteries. Secondly, combined with the carbon emission flow theory and the life cycle assessment method, the spatio-temporal differences in carbon emissions caused by EV charging behaviors are quantified from the life-cycle perspective, and a life-cycle carbon quota allocation mechanism as well as a synergistic low-carbon guidance system oriented by carbon-based time-of-use electricity prices are formulated. Then, a bi-level optimization model for low-carbon vehicle-to-grid (V2G) is constructed to realize the in-depth coupling of grid low-carbon economic dispatch and low-carbon demand response of EV aggregators. Finally, numerical case results verify that the proposed scheduling strategy can fully tap the carbon reduction potential of EVs, and effectively reduce the system carbon emissions and the costs of EV users.关键词：electric vehicle (EV);low-carbon scheduling;vehicle-to-grid (V2G);demand response;carbon emission flow;carbon quota;life cycle;spatio-temporal coordination2|0|0更新时间：2026-08-21
- 摘要：Electric vehicles (EVs) can serve as potential supportive resources to participate in demand-side regulation of power systems. Nevertheless, their inherent resource characteristics including high randomness, small individual rated capacity and diversified user psychological preferences lead to poor practical controllability, making stable aggregated utilization difficult. To address this issue, this paper proposes an online electricity-carbon package design method for EVs based on virtual queues and stochastic differential evolution of parameters. Firstly, an optimal package design model for charging and discharging station operators is established considering the dual electricity-carbon attributes of EVs and user rationality constraints. Secondly, aiming at the temporal coupling and stochastic demand features of EVs, the demand-matching virtual queues are constructed. And the offline package problem is decoupled and converted into an online problem via Lyapunov optimization. Then, stochastic differential equations are adopted to characterize the evolutionary law of user behavioral preference parameters, and a closed-loop feedback calculation process integrating real-time parameter identification and online package solution is developed with the particle filter algorithm. Finally, the simulations verify the effectiveness and rationality of the proposed electricity-carbon package design method.关键词：electric vehicle (EV);package;Lyapunov optimization;parameter identification;virtual queue;stochastic differential equation2|0|0更新时间：2026-08-21
- 摘要：With the large-scale development of electric vehicles and the comprehensive advancement of the spot market, it is difficult for revenues from a single link, either charging operation or electricity trading, to meet the demands of both charging operators and electricity retailers. To maximize the overall revenues of charging operation and electricity trading, and to address the lack of collaboration among market trading, charging operation, and charging regulation, an integrated collaborative optimization framework of “trading-pricing-regulation” is proposed. In the day-ahead market stage, based on historical data, a charging demand price elasticity matrix is constructed, and a joint optimization model for transaction bidding and operation pricing is established with the objective of maximizing the overall revenue of the operators. In the real-time market stage, a rolling optimization model based on model predictive control is developed. To address the uncertainty of real-time load, the rolling optimization model constructs distributionally robust constraints based on the Wasserstein distance, and maximizes operator revenues by rolling dispatch of real-time loads according to pre-cleared real-time prices. In the case study, simulation and field tests are carried out based on charging stations of a certain charging operator. The results demonstrate that the proposed method improves the comprehensive revenues of operators in both the day-ahead and real-time markets, thereby verifying the effectiveness of the proposed method.关键词：spot market;demand price elasticity;charging operation;electricity trading;rolling optimization;distributionally robust optimization;model predictive control3|1|0更新时间：2026-08-21
- 摘要：With a high proportion of photovoltaic (PV) systems and electric vehicles (EVs) being connected to rural distribution networks, issues such as unbalanced spatio-temporal load and voltage violation have emerged in the distribution transformer areas. To address these issues, this paper proposes a bi-level game-based optimization strategy for flexibly interconnected transformer areas and charging-storage power stations based on the Weber-Fechner law. First, the spatio-temporal complementary characteristics of sources and loads in rural transformer areas are analyzed, and the flexibly interconnected system architecture incorporating soft open points (SOPs) and charging-storage stations is established. Then, a bi-level game-based optimization model involving the distribution network operator, charging-storage power stations, and EV users is constructed. In the upper-level model, the minimization of operating cost of distribution networks, voltage deviation, and load balancing degree of transformers is taken as the objective to optimize the spatio-temporal power flow distribution of SOPs. In the lower-level model, the Weber-Fechner law is introduced to characterize the nonlinear perception mechanism of users toward electricity price incentives and state of charge, thereby quantifying the coupled effects of psychological thresholds and saturation effects on charging and discharging willingness of users. On this basis, an optimal pricing and scheduling strategy model for charging-storage power stations is established, and the particle swarm optimization and genetic algorithms are used for hybrid iterative solution. Case study results show that, in a high EV penetration scenario, the proposed strategy can fully exploit the coordinated potential of the spatial transfer capability of the SOP and the temporal shifting capability of EVs. While satisfying EV charging demand, the proposed strategy can increase user benefits, reduce peak-valley difference and power variance,and achieve the coordinated optimization of power flow and voltage distribution,thus effectively suppressing the risk of voltage violations, and significantly enhancing the spatio-temporal load balancing regulation capability and operation security of rural distribution networks.关键词：distribution network;distribution transformer area;charging-storage power station;electric vehicle (EV);soft open point (SOP);Weber-Fechner law;game4|0|0更新时间：2026-08-21
Key Technologies and Practical Applications of Vehicle\-to\-Grid in New Power System
- 摘要：The high proportion of renewable energy connected through power electronic equipment significantly weakens the oscillation damping capability and overall inertia level of the power system. Although grid-forming converters can actively provide system inertia and damping support, their security and stability characteristics are influenced by nonlinear coupling of multi-machine parameters, increasing the difficulty in configuring device inertia and damping. To address this, this paper proposes a multi-converter virtual inertia and damping collaborative optimization method integrating the matrix perturbation theory and alternating optimization mechanism. First, a multi-constraint nonlinear and nonsmooth optimization model is constructed, encompassing small-signal synchronous stability, frequency security, and device physical limits. Subsequently, an alternating update mechanism is employed to decompose it into independent optimization problems for virtual inertia and damping. Then, based on the matrix perturbation theory, the sensitivity expressions of eigenvalues to parameters are derived, transforming the nonsmooth independent optimization problem into a iteratively solvable sequence of linear subproblems. Additionally, a trust-region adaptive step size strategy based on eigenvalue second-order sensitivity is proposed to enhance convergence efficiency and stability. Case studies demonstrate that the proposed method significantly improves small-signal stability under various robustness challenges, including changes in grid-forming converter connection locations, quantities, and power flow fluctuations, with both optimization effectiveness and efficiency surpassing traditional methods.关键词：grid-forming converter;virtual inertia;damping;matrix perturbation theory;small-signal stability;frequency security3|3|0更新时间：2026-08-21
- 摘要：When background harmonics exist in the power grid, grid-forming converters applied to weak grids will generate harmonic voltages and harmonic currents at the point of common coupling (PCC). Most existing research focuses on harmonic voltage suppression to guarantee voltage quality, while studies on harmonic current suppression are insufficient, which may lead to excessive harmonic current distortion. Therefore, the coordinated suppression of harmonic voltage and current at the PCC has become a key technical challenge at present. Essentially, mainstream harmonic voltage suppression strategies achieve suppression by reducing the impedance magnitude of grid-forming converters in the harmonic frequency band. However, little attention has been paid to the phase characteristics of converter impedance, which exert a significant impact on the coordinated suppression of harmonic voltage and current. Accordingly, this paper proposes a harmonic voltage and current suppression strategy based on impedance vector reshaping. The proposed strategy simultaneously reshapes both the magnitude and phase of the grid-forming converter’s impedance to realize the coordinated suppression of PCC harmonic voltage and current, ensuring both comply with grid-connected standards. Based on an in-depth analysis of the magnitude and phase characteristics of the impedance model, a matched wideband controller is designed for the impedance vector reshaping strategy. The controller maintains satisfactory control performance over harmonic frequency bands while achieving decoupling from fundamental-frequency control. This strategy eliminates the need for extracting individual harmonic components, simplifying control implementation. Meanwhile, its wideband characteristic improves adaptability to fluctuations in grid frequency and harmonic frequencies. Finally, experimental results verify the effectiveness and accuracy of the proposed strategy.关键词：grid-forming converter;power quality;harmonic suppression;impedance;wideband controller1|0|0更新时间：2026-08-21
- 摘要：Power flow adjustment in power systems requires dynamic correction of control variables in response to problems such as limit violations. However, result judgment and data adjustment rely heavily on manual work, and expert experience is difficult to quantify and generalize. With the rapid iteration and advancement of large language model (LLM) technology, LLM-driven agent frameworks have demonstrated strong adaptability, flexibility, and robustness in handling complex tasks and realizing automated decision-making. This paper proposes an adaptive calculation and adjustment method for power flow in power systems based on LLMs. The reasoning capability of LLM is used to enhance the adaptability of strategy adjustment, and the power flow calculation module is invoked to verify the feasibility of strategies in real time, forming a closed-loop workflow with dynamic perception, autonomous decision-making, and continuous optimization. Retrieval augmented generation is introduced during the calculation and adjustment process to provide safe operation constraints and auxiliary decision-making support, realizing the full-process automation of power flow calculation and adjustment under complex constraints. Case studies on the IEEE 30-bus and IEEE 118-bus systems verify the application potential of LLM in the automated calculation and analysis of power systems.关键词：large language model (LLM);power system;power flow;agent;retrieval augmented generation (RAG);safe operation constraint;auxiliary decision-making1|0|0更新时间：2026-08-21
- 摘要：Accurate identification of distribution line parameters is fundamental to the safe and reliable operation of distribution networks. The coexistence of dynamic topology changes, widespread integration of distributed power sources, and missing measurements significantly increases the difficulty of distribution line parameter identification, limiting the applicability of traditional methods. To this end, a multi-scale topology-aware graph neural network method for distribution line parameter identification is proposed. The actual topology is directly embedded as a physical structural constraint within the model, ensuring that information propagation follows the true connectivity and maintains interpretability. Under topological constraints, spectral graph convolution combined with depth-wise self-attention is employed to extract and fuse multi-scale topology-aware node features. The features of adjacent nodes are aggregated via addition to construct physically consistent edge representations, which are then mapped to the corresponding line admittance parameters through a shared-weight fully-connected layer. The method is validated in various measurement scenarios for the IEEE 33-bus and IEEE 123-bus distribution networks. The results demonstrate that the method achieves superior effectiveness and robustness.关键词：distribution network;distribution line;parameter identification;topology awareness;graph neural network;self-attention1|0|0更新时间：2026-08-21
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