最新刊期
卷 50 ， 期 3 ， 2026
- 摘要：The grid emission factor, as a crucial link connecting electricity consumption with carbon emissions, serves as an important essential parameter for accounting indirect carbon emissions, and plays a significant role in achieving the goals of “carbon emission peak and carbon neutrality”. This paper reviews the calculation methods and research status of grid emission factors at home and abroad. From the four aspects of accuracy degree, regional refinement, timeliness, and applicability, it points out the existing issues such as low spatio-temporal resolution, exclusion of inter-grid electricity trade, and improper representation of the environmental attributes of green electricity. On this basis, a regional comparison of grid emission factors over the years in China and a horizontal comparison with other countries are conducted, and the existing characteristics of grid emission factors in China are discussed. By constructing a carbon emission flow model that is isomorphic to the physical power flow of the grid, the accuracy of real-time grid emission factor calculations can be effectively enhanced. Some recommendations are proposed on the calculation methods, data standards, and usage guidelines for grid emission factors in China.关键词：new power system;carbon emission peak;carbon neutrality;carbon emission;power flow;grid emission factor;green electricity0|0|0更新时间：2026-02-28
- 摘要：Energy activities are the primary sources of carbon dioxide emissions. Accurate and near real-time monitoring of carbon emissions from energy activities is fundamental for governments and enterprises to grasp the total carbon emissions and formulate carbon management strategies. However, existing accounting methods are insufficient for high-frequency carbon emission calculations, whereas direct measurement methods necessitate additional equipment to be installed. Consequently, low-cost and high-frequency carbon emission estimation techniques remain unavailable. To address this issue, by using the correlation between electricity consumption and energy activities, this paper proposes an estimation technology for carbon emissions from energy activities based on power big data (abbreviated as “electricity-based carbon emission estimation”). Firstly, the methodological framework for “electricity-based carbon emission estimation” technology is proposed. From a data-centric perspective, a detailed investigation and analysis on related foundational data are conducted, and the potential application scope of existing data is explored. Secondly, a time-series based “electricity-based carbon emission estimation” model applicable at regional, industrial, and enterprise levels is constructed, followed by an evaluation of its effectiveness using actual data from China. The analysis results demonstrate that the proposed method can significantly improve the timeliness of carbon emission estimation. Finally, the future research and potential application directions for the “electricity-based carbon emission estimation” technology are prospected.关键词：carbon emission;accounting;power big data;electricity consumption;electricity-based carbon emission estimation;energy activity0|0|0更新时间：2026-02-28
- 摘要：The additional carbon emission generated during the operation of the power system due to damage or functional failure of power plants and power grids caused by natural disasters is defined as the carbon emission increase of power system operation due to natural disaster (CEI-OD). Neglecting the cumulative effects of CEI-OD from frequent natural disasters will overestimate the carbon reduction effectiveness of new power systems and affect global decarbonization process. Therefore, calculating CEI-OD and determining its obligation allocation has become an important issue. Firstly, the carbon emission equivalent factor (CEEF) for power system operation modes is proposed, and CEI-OD is calculated based on CEEF when the power system operation mode changes. Secondly, a disaster prevention and recovery measure effect model is constructed using two indicators: the intensity of designed disaster prevention and the corresponding maximum allowable repair time. Simultaneously, the criteria for component disaster prevention negligence and repair negligence are proposed, and based on these, a theory and calculation method for decomposing CEI-OD into accountability allocation and no-fault sharing are introduced. Then, for the collective sharing of the no-fault sharing, an improved Aumann-Shapley method is proposed, which effectively addresses the unfair allocation of carbon emission increase resulting from high-carbon power generation forcibly replacing disaster-affected low-carbon power generation. Finally, the feasibility of the proposed method is validated through relevant cases.关键词：new power system;natural disaster;carbon emission;carbon emission equivalent factor (CEEF);disaster prevention and recovery;obligation allocation;improved Aumann-Shapely method0|0|0更新时间：2026-02-28
Power System Analysis and Optimization from Carbon Perspective
- 摘要：With the continuous advancement of transportation electrification, the coupling degree between the urban transportation network (UTN) and the distribution network (DN) is gradually increasing, which makes the calculation of traffic flow and power flow more complex. In order to comprehensively describe the impact of electric vehicles on UTN and DN, this paper proposes a dispatching strategy of hierarchical coordinated optimization for UTN-fast charging station (FCS)-DN based on dynamic traffic flow and power flow to realize the economical and flexible operation of UTN, FCS and DN. First, the power flow optimization is carried out in the DN optimization layer with the goal of minimizing the operation cost, the traffic flow assignment is carried out based on the mixed user balance in the UTN optimization layer, and the electric vehicles are dispatched with the goal of maximizing the operator’s revenue in the FCS optimization layer. Then, in order to simplify the model, the traffic flow allocation problem is transformed into the constraints of the FCS revenue model and linearized, and then the alternating direction method of multipliers is used to solve the above problem to protect the information privacy of various subjects. Finally, the effectiveness of the proposed model is verified by the system coupled with the IEEE 33-bus distribution network and the Nguyen-Dupuis transportation network.关键词：electric vehicle;urban transportation network (UTN);distribution network (DN);power flow;traffic flow;fast charging station (FCS)0|0|0更新时间：2026-02-28
- 摘要：Collaborative optimal operation of thermostatically-controlled load (TCL) clusters and distribution networks can improve both economy and flexibility. However, existing collaborative dispatch methods struggle to simultaneously ensure privacy protection and solution efficiency. To address this problem, this paper proposes a collaborative dispatch method for a distribution network with multiple TCL clusters based on aggregation dimensionality-reduction. Firstly, a non-iterative dispatch framework for the distribution network incorporating multiple TCL clusters is constructed. Secondly, based on inner approximation via polytopic affine transformation, a aggregation dimensionality-reduction modeling method for TCL clusters is proposed to characterize the aggregate feasible region and the aggregate cost function of the TCL clusters. Then, a collaborative optimal dispatch model for the distribution network is established by considering various dynamic reconfigurations of switches and multiple TCL clusters, which enables efficient solution of the proposed dispatch problem of the distribution network while guaranteeing cluster information privacy. Finally, the effectiveness of the proposed method is verified on a modified 75-node distribution network.关键词：distribution network;thermostatically-controlled load;cluster;feasible region;aggregation dimensionality-reduction;collaborative dispatch0|0|0更新时间：2026-02-28
- 摘要：With the massive integration of distributed energy resources, microgrids have become a critical platform for supporting the coordinated operation of source-grid-load-storage systems. The regulation flexibility provided by microgrids at the point of common coupling (PCC) serves as an important adjustable resource for power systems. However, due to constraints such as internal voltage stability and branch capacity limitations, the flexibility of distributed resources cannot be fully manifested at the PCC, and the available flexibility varies significantly under various network reconfiguration strategies. To address this challenge, this paper proposes a network reconfiguration based method for grid-connected flexibility enhancement. First, a characterization model for grid-connection flexibility based on microgrid aggregation is established, and a two-stage robust optimization framework is developed, in which the first stage maximizes the aggregated flexibility at the PCC through topology optimization, while the second stage verifies the feasibility of disaggregation. The strong duality theory and the column and constraint generation algorithm are employed to efficiently solve the model. Case studies demonstrate that the proposed method can fully tap into the flexibility of distributed energy resources through network reconfiguration, significantly improving the grid-connected flexibility of microgrids.关键词：microgrid;distributed energy resource;flexibility;aggregation network reconfiguration;point of common coupling (PCC);robust optimization0|0|0更新时间：2026-02-28
- 摘要：With the increasing integration of a high proportion of renewable energy into power grids, the frequency regulation pressure on power systems has intensified, making flexibility resources an important supplementary means for frequency regulation. However, the characteristics of wide distribution and heterogeneous response parameters of flexibility resources make it difficult for centralized control to adapt to their dynamic disparities, leading to low coordination efficiency. To address these challenges, this paper proposes a consensus modeling method for multi-type flexibility resources and a cooperative frequency regulation strategy with dynamically allocated weights. By extracting common parameters such as power and state of charge, a consensus model (CM) based on the first-order lag element is constructed to uniformly characterize the dynamic response characteristics of resources. On this basis, a collaborative architecture with “centralized training-decentralized execution” is designed. The upper layer coordinates global frequency deviation and frequency regulation cost optimization, and the lower layer proposes a differential reward-driven state interaction weight allocation mechanism, which dynamically screens the high-value interaction information by quantifying the contribution of agent actions to the frequency control, realizing the coordinated control with “higher contribution weights leading to stronger strategy influence”. Finally, the effectiveness of the proposed method is verified through a modified IEEE 39-bus system.关键词：consensus model;flexibility resource;agent;frequency control;frequency regulation0|0|0更新时间：2026-02-28
- 摘要：In the power grids with a high proportion of renewable energy, the spatio-temporal differentiation characteristics of frequency across various regions become increasingly prominent. Traditional frequency characterization based on the center of inertia fails to capture these characteristics, leading to unclear mechanisms of regional frequency differentiation and an inability to perform analytical frequency analysis or rapid deviation estimation for low-inertia regions. To address this challenge, this paper establishes a two-area frequency response model incorporating inter-regional frequency spatio-temporal differentiation. By leveraging the aggregation of average system frequency models to reduce transfer function modes, analytical expressions for two-area frequency responses characterizing spatio-temporal differentiation mechanisms are derived. The maximum frequency deviation moment is determined through oscillatory components of these expressions, enabling an analytical estimation method for the regional maximum frequency deviations. Mechanism analysis reveals that larger inter-regional inertia gaps exacerbate the spatio-temporal differentiation of regional frequency deviations. To ensure frequency safety in all regions without exceeding the limit, a multi-area frequency coordinated optimization model is constructed with the objective of minimizing differences of the regional maximum frequency deviations between each region and the center of inertia. Simulation results demonstrate that the proposed method can accurately describe the impact of spatio-temporal characteristics on regional frequency deviations and enable the effective estimation of regional frequency deviations, while the proposed optimization model can significantly improve excessive inter-regional frequency deviation discrepancies.关键词：frequency deviation;spatio-temporal differentiation;frequency response;ineria;coordinated control0|0|0更新时间：2026-02-28
- 摘要：The islanded microgrid composed of synchronous generator (SG) and virtual synchronous generator (VSG) is prone to power oscillation under load disturbances, while existing suppression strategies are difficult to take into account the frequency characteristics of the system. Therefore, a power oscillation suppression method considering frequency stability is proposed. Firstly, based on the difference of power angle response between SG and VSG, the output power is decomposed into dynamic and steady-state components, and the influence of the dynamic component on the power oscillation is quantified by 2-norm, thereby revealing the stabilization mechanism. Secondly, the state-space model of SG-VSG heterogeneous parallel system is established to analyze the effects of physical difference and parameter mismatch of heterogeneous power sources on the power and frequency stability characteristics of the system. Then, an oscillation suppression strategy based on frequency-division power compensation is proposed, which utilizes a high-frequency power component to accelerate oscillation attenuation. Furthermore, the control structure is optimized with the rate of change of frequency (RoCoF) constraint to achieve coordinated optimization of oscillation suppression and frequency performance. Finally, the effectiveness of the theoretical analysis and the proposed method is verified by experiments.关键词：islanded microgrid;synchronous generator (SG);virtual synchronous generator (VSG);heterogeneous power source;power oscillation suppression;frequency stability;rate of change of frequency (RoCoF)0|0|0更新时间：2026-02-28
- 摘要：With the increasing prevalence of power electronic devices in power systems, the characteristics of system resonance are becoming more diverse. Existing resonance suppression strategies are usually designed for specific scenarios closely related to the impedance frequency-magnitude characteristics. It is challenging to unify the effectiveness of resonance suppression in various resonance scenarios, and there is a lack of evaluation methods for the effectiveness of suppression strategies in various scenarios. In this paper, based on the changes in node impedance before and after resonance suppression, the feasible region of local/remote resonance suppression is proposed to achieve a unified evaluation of the effects of resonance suppression strategies in the system on changing the amplitude of resonance impedance. The equivalent impedance of the resonance suppression link is decoupled from the system impedance, avoiding the complex matrix inversion required for each resonance suppression strategy in the system impedance calculation, thus simplifying the evaluation process. Meanwhile, the relative feasible region and the ideal feasible region are defined to provide guidance for selecting more appropriate resonance suppression strategies and parameters. On this basis, this paper proposes an impedance reshaping design method for the active damper based on the ideal feasible region of resonance suppression. This method involves parameter design of impedance reshaping in various resonance scenarios, thereby achieving global resonance suppression for multiple nodes in the system. Finally, simulation models and hardware-in-the-loop simulation platforms are constructed to verify the effectiveness of the proposed evaluation method and impedance reshaping.关键词：power-electronized device;distribution network;power quality;harmonic resonance;resonance suppression;node impedance;feasible region active damper;impedance reshaping0|0|0更新时间：2026-02-28
- 摘要：The inter-provincial multi-channel centralized bidding transaction is a medium- and long-term transaction mechanism in the electricity market, which adopts a multi-channel centralized optimization clearing model based on path combination to achieve optimal resource allocation. The network loss of the transmission channel in this model is calculated by multiplying each power flow through the transmission channel by the network loss coefficient and adding it up. However, with the expansion of the electricity market scale, there will be reverse power flows on the transmission channels that are opposite to the basic power flows, creating an “offsetting” effect of positive and negative power flows. This will lead to the calculation of network losses being repeatedly superimposed, making the actual network loss smaller than the calculated network loss, resulting in a loss of total social welfare. In order to make the calculation of network losses more accurate and restore the loss of social welfare, this paper uses directional coefficients to characterize the net power flow of transmission channels, and proposes a sequential correction method and for the network loss coefficient of transaction paths and optimization modeling, reflecting the reduction of network losses caused by the “offsetting” of power flows on the network loss coefficient. Finally, simulation operation data of the inter-provincial multi-channel centralized bidding transaction are used for numerical verification. Results demonstrate that the proposed method effectively avoids duplicate network loss calculation caused by “offsetting” of positive and negative power flows and improves the total social welfare after optimal clearing.关键词：electricity market;inter-provincial medium- and long-term transaction;centralized bidding;sequential correction;network loss;clearing;social welfare0|0|0更新时间：2026-02-28
- 摘要：With the rapid growth of electric vehicles, it is crucial to effectively steer user charging decision-making behaviors in a highly complex charging environment through well-designed price control of charging stations. To this end, a real-time coordinated pricing method for charging stations based on multi-agent deep reinforcement learning is proposed. Firstly, a coordinated pricing framework for charging stations under a cooperative coalition mode is constructed, and the transportation network, charging stations, and user decision-making behaviors are modeled. A decision-making model for charging station selection is established considering the comprehensive charging costs of user traveling time, waiting time, charging time, and charging fees. Secondly, the charging station pricing problem is formulated as a stochastic game Markov decision process with unknown transition probabilities, treating charging stations as agents and solving the real-time pricing problem using a multi-agent actor-attention-critic algorithm. Finally, the performance of the proposed method is analyzed through simulation cases, and the necessity of the proposed coordinated pricing method for charging stations to equalize the spatial distribution of charging loads is verified.关键词：electric vehicle;charging station;pricing;agent;deep reinforcement learning;stochastic game0|0|0更新时间：2026-02-28
- 摘要：To address the challenges posed by large-scale disordered electric vehicle (EV) charging on the stable operation of distribution networks, this paper proposes an EV charging scenario generation method based on conditional denoising diffusion probabilistic models. Firstly, a multidimensional user behavior characteristic matrix and a cluster-level user behavior characteristic matrix are constructed, and a hierarchical charging scenario generation framework considering user behavior is proposed. Secondly, a conditional diffusion model fused with a multi-head self-attention mechanism is designed, embedding user behavior characteristics as conditions into the diffusion process to explore the latent correlations between user behavior and charging scenarios, thereby achieving the controllable generation of charging scenarios. Finally, user-level and station-level scenario generation experiments are conducted based on real-world charging datasets. The results demonstrate that the proposed method outperforms baseline models in fidelity, diversity, and temporal correlation. Additionally, a case study using the charging station dataset from Pudong New Area, Shanghai, China, verifies the practical engineering value of the proposed method in day-ahead market bidding optimization.关键词：electric vehicle;charging;conditional denoising diffusion probabilistic model;user behavior;self-attention scenario generation;deep learning0|0|0更新时间：2026-02-28
- 摘要：Reinforcement learning (RL) plays a pivotal role in promoting the digitalization and intelligentization of integrated energy system (IES) scheduling due to its capabilities for high-dimensional state perception and rapid decision-making. However, conventional RL is constrained in multi-scenario IES scheduling because of its limited model expressiveness and poor cross-scenario generalization. To address this, an RL scheduling method based on denoising diffusion-policy learning is proposed. First, the developmental concepts and fundamental principles of diffusion generative artificial intelligence models are introduced. Next, a diffusion-policy dispatch learning framework is constructed, and a diffusion learning layer and an agent optimization layer are designed. Finally, the generalization performance is analyzed through extensive experiments considering key diffusion parameters, sample sizes, and sample distributions. Meanwhile, the post-hoc interpretability mining of the diffusion-policy is conducted via t-distributed stochastic neighbor embedding. The results show that, compared with classic RL methods such as deep deterministic policy gradient used in IES dispatch, the proposed method retains considerable decision generalization under drastic source-load fluctuations and changing constraints while incurring only a minor degradation in optimization performance.关键词：integrated energy system;optimal dispatch;artificial intelligence;agent;diffusion model;reinforcement learning;interpretability;generalization0|0|0更新时间：2026-02-28
Basic Research
- 摘要：With the rapid growth of renewable energy sources and DC loads, AC/DC hybrid distribution networks based on voltage source converters (VSCs) have been widely studied. However, with the increase of distributed renewable energy and DC loads, both the DC and AC sides of the AC/DC hybrid distribution network are facing power fluctuation problems, which pose challenges to the stable operation of the power system. To this end, a hybrid control method for VSCs is proposed, which incorporates AC power control (APC) and DC voltage control (DVC). A mathematical model of the transient characteristics of the VSCs with hybrid control is established, which considers the fast response during the transient process and the small overshoot of the power angle. Through the local linearization method, the complex problem of power angle stabilization parameter tuning is transformed into selecting the characteristic root from the root locus diagram, thereby achieving the optimization design of VSC parameters with hybrid control. Compared with existing methods, the proposed method has better stability during system power fluctuations and reduces the risk of system power angle instability. Finally, the effectiveness of the proposed method is verified through simulation and experiments.关键词：voltage source converter (VSC);AC/DC hybrid distribution network;large-signal model;power fluctuation;power angle stability;transient stability;root locus0|0|0更新时间：2026-02-28
- 摘要：The change of operation modes of new power systems is becoming increasingly frequent and complex, which demands that static security analysis applications rapidly handle various complex anticipated faults. Complex anticipated faults involve the increases or decreases in the number of electrical nodes, for which existing partial refactorization methods exhibit poor adaptability, limiting the performance of current static security analysis applications. To address this, this paper proposes an improved partial refactorization method. Firstly, the limitations of existing partial refactorization methods considering line
- 摘要：Icing on wind turbine blades is one of the factors that impair the operational conditions of wind turbines and the stability of the power grid. Traditional icing monitoring methods are costly and may potentially damage the original mechanical structure of the blades. This paper establishes an icing monitoring model based on meteorological data and deep learning. By analyzing the Makkonen model from a thermodynamic mechanism perspective, and addressing the limitations of traditional monitoring models in characterizing core parameters such as liquid water content that directly affect icing rate, the model fully considers feature quantities in meteorological data that are closely related to the icing intensity. Meanwhile, time series analysis methods are introduced to capture temporal variation patterns of variables. To tackle the distribution shift problem across wind farm data, a deep adaptive normalization module is designed to perform a domain-invariant transformation on input features. A dual-channel transformer-temporal convolutional network (TCN) architecture is constructed to simultaneously capture global temporal dependencies and local abrupt features of meteorological parameters. Finally, simulations are conducted using actual wind turbine data from a mountainous region. The results show that the model excellently performs in diagnosing icing conditions on wind turbine blades, thereby expanding available technical means for wind turbine blade icing monitoring.关键词：wind turbine;blade;icing;meteorological data;time series analysis;temporal convolutional network (TCN);feature extraction;deep learning0|0|0更新时间：2026-02-28
Application Research
- 摘要：When diversion-type cascade hydropower stations operate and dispatch independently by strictly matching the upstream and downstream power generation water volumes according to the power generation units that transmit electricity to the same power grid, relying solely on the generation group containing the failed generator to respond to load rejection leads to large deviations between the scheduled and actual power intake to the receiving grid. This paper proposes a coordinated real-time fault response model for cascade hydropower stations under the condition of multi-grid power transmission demands. The model aims to minimize the sum of squared deviations between the actual output to each grid after a fault and the scheduled output, enabling multi-grid cooperation at the real-time dispatch level. By sharing the load rejection among multiple power grids through mutual support, the total output deviation and the power shortage in the power grid connected to the faulty unit are reduced. Based on a real case of a diversion-type cascade hydropower station, a comparative analysis is conducted on the power delivery processes under single-grid response and multi-grid collaboration, demonstrating that the proposed approach effectively mitigates the grid impact caused by load rejection due to unit faults.关键词：cascade hydropower station;dispatch;coordinated optimization;receiving end;load rejection;fault response0|0|0更新时间：2026-02-28
- 摘要：With the evolution of smart grids, the cybersecurity of power industrial control systems has gained paramount importance. As the core of power industrial control systems, power dispatching automation system is now confronted with severe challenges such as customized false data injection attacks. Furthermore, the massive scale and diversity of smart device models, coupled with the access of heterogeneous communication links in the smart grid, significantly complicates threat detection at the network edge. This paper proposes a Markov chain-based anomaly detection model for IEC 60870-5-104 protocol used remote control behaviors that is deployed at the boundary of the power communication network. Firstly, the IEC 60870-5-104 protocol is analyzed in detail to extract key features of its remote-control behaviors. Secondly, Markov chain theory is employed to build a state-transition probability model that characterizes these behaviors. By monitoring the network traffic in real time and training this model online, a historical behavior baseline is established for each business. If the currently observed state sequence is different from the baseline, it is judged as an abnormal activity. Finally, it is verified through the simulation that the model can effectively detect various behaviors anomaly such as interaction behavior anomaly and operation frequency anomaly during the power dispatching process.关键词：power cybersecurity;power industrial control system;IEC 60870-5-104 protocol;remote control;anomaly detection;Markov chain0|0|0更新时间：2026-02-28
Engineering Application
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