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最新刊期
卷 49 ， 期 18 ， 2025
- 摘要：As the world’s second-largest economy, China’s annual carbon emissions rank first in the world, of which the power industry accounts for 40% of China’s total carbon emissions. To achieve the goal of “carbon emission peak and carbon neutrality”, the power system urgently needs green and low-carbon transition, and accurate and credible carbon emission accounting methods provide the scientific basis for effectively supporting the construction of a new power system and the in-depth promotion of the goal of “carbon emission peak and carbon neutrality”. At present, China needs to develop real-time, accurate, and comprehensive carbon emission accounting methods to provide technical support for the carbon emission reduction work. This paper first analyzes the development process and application scenarios of carbon emission accounting methods in power systems and expounds on the necessity and urgency of carbon emission accounting in the power industry. Secondly, the basic elements of carbon emission accounting in power systems are analyzed. Then, the carbon emission accounting methods of each link are summarized respectively from the power supply side, the power grid side, and the load side. To improve the accuracy, authority, and integrity of the accounting results, this paper focuses on the carbon emission accounting methods for the whole links of the power system, which provides a theoretical basis for the subsequent innovation of carbon emission accounting methods. Finally, the development trend of carbon emission accounting methods in power systems is prospected.关键词：power system;carbon emission;carbon emission peak;carbon neutrality;accounting method29|4|0更新时间：2025-09-25
- 摘要：The marginal carbon emission factor (MCEF) is an important index to reflect the influence of user load adjustment on carbon emission level of power system. The system operators can estimate and publish future MCEFs to guide consumers to use electricity in a low-carbon manner and reduce the total carbon emission. Aiming at the need of low-carbon guidance, the MCEF theory and its uncertainty considering the coupling of multiple time periods is studied. Firstly, from the economic dispatch model, the calculation method of MCEF considering the coupling of multiple time periods is derived theoretically, and the MCEF is decomposed into equilibrium component and blocking component by analogy with marginal electricity price. Then, based on multi-parametric linear programming theory, an adaptive fast calculation method for MCEF uncertainty analysis is proposed. Finally, case analysis based on IEEE 30-bus system is carried out to verify the effectiveness of the proposed method, and the key factors affecting the calculation results and uncertainties of MCEF are analyzed.关键词：new power system;marginal carbon emission factor;carbon emission;uncertainty;multi-parametric linear programming;marginal electricity price;low-carbon power system;demand response11|2|0更新时间：2025-09-25
- 摘要：The regional power carbon emission factor serves as the foundation for accounting power carbon emissions. The traditional calculation method requires the aggregation of data on power generation and inter-provincial exchanges in each province and the establishment of equations to solve the calculation. For the grid companies managing power grids in various provinces, the above data contain sensitive information, which leads to a lack of willingness of the companies to share and aggregate the data. To solve this problem, this paper utilizes the principle of matrix block iteration and adopts a distributed architecture for the collaborative calculation of power carbon emission factors across power grids. The proposed algorithm can quickly converge to an accurate solution, and the grid companies do not need to share sensitive data, but can realize the accurate calculation of the power carbon emission factor in each province and region only by exchanging the results of the factor calculation.关键词：carbon emission peak;carbon neutrality;carbon emission factor;matrix block iteration;sensitive data8|2|0更新时间：2025-09-25
- 摘要：To fully explore the potential value of various components in the economy and low-carbon operation of the power system and reduce the safety risk of system operation, a bi-level planning model for the power system is proposed, which takes into account the N-1 safety criteria and the low-carbon economic contribution (LCEC) of components. At the planning level, the contribution of each unit and line to be built to the low-carbon and economy of the system is characterized through LCEC, and the contribution results are combined with N-1 safety criteria in the form of membership function to realize the optimal matching of low-carbon and economy in space. At the operation level, aiming at analyzing the synergy ability between low-carbon operation and economic operation, the LCEC of each unit and line to be built in the current planning scheme is calculated, and then the contribution weight of each unit and line to the low-carbon and economy synergy operation of the power grid is obtained. On this basis, the gradual expansion method is used to calculate the effectiveness indicators of each line one by one, and it is used as one of the influencing factors for evaluating the construction sequence of the line, reducing the potential safety risks in the low-carbon and economy synergy operation. The practicality of the proposed model is validated through modified PJM-5 and IEEE RTS-24-bus systems.关键词：optimal planning;low-carbon and economy synergy;low-carbon economic contribution; safety criteria;membership function7|0|0更新时间：2025-09-25
Power System Analysis and Optimization from Carbon Perspective
- 摘要：When dealing with the topology class mismatch problem of uneven quantity between labeled topologies and unlabeled topologies, as well as among various labeled topologies, the existing methods for identification of the switch state in topology based on supervised learning and unsupervised learning are unable to accurately identify the topology of the distribution network. Therefore, a robust identification of switch states in dynamic topology of distribution network based on semi-supervised adversarial learning is proposed. Firstly, a method for topology change feature extraction of distribution network based on dynamic node embedding (TCFEDN-DNE) is proposed. This method represents the node voltage measurement values during the dynamic topology change process as potential low-dimensional vectors to reflect the changes in the degree of node correlation during the topology reconstruction process and reduce the computational complexity of the model. Then, for the problem of topology class mismatch in the identification of the topology switch state of the distribution network, a robust identification model of switch states in dynamic topology of distribution network based on semi-supervised generative adversarial network (SGAN) is proposed. Through the embedded semi-supervised learning mechanism and adversarial learning mechanism, the problem of topology class mismatch is alleviated, and the topology of the distribution network can be accurately identified in real time. Finally, a case analysis using the IEEE 33-bus test system is conducted. The simulation results show that the proposed method can quickly identify the dynamic changes in the topology switch state of the distribution network, and can still accurately identify the topology even in the case of topology class mismatch.关键词：distribution network;topology identification;topology change feaure extraction;dynamic node embedding;generative adversarial network;semi-supervised learning16|0|0更新时间：2025-09-25
- 摘要：With the increasing proportion of distributed photovoltaic (PV) integrated into power systems, the capability of conventional load models to describe dynamic load characteristics in distribution networks has degraded. A gray-box model of generalized load for distribution networks that incorporates both conventional load models and PV grid-connected system models is proposed considering the high/low voltage ride-through capability of distributed PV. A detailed electromagnetic transient model of the distribution network is constructed in PSCAD for sensitivity analysis to determine the unknown parameters of the generalized load model, and a parameter identification method for the generalized load is proposed. Comparisons with field measurement data show that the operation results of the detailed electromagnetic transient model of the distribution network system with distributed PV integration exhibit errors within 2%, validating the accuracy of the detailed model. Based on the detailed model, parameter identification and model validation are performed for the generalized load model. The results demonstrate that during voltage sag and swell faults on the distribution network side, the proposed gray-box model of generalized load considering the fault ride-through capability of distributed PV can effectively capture the dynamic characteristics of the distribution network and meet the analysis requirements for both steady-state and fault scenarios of power systems.关键词：distribution network;distributed photovoltaic;high voltage ride-through;low voltage ride-through;load model;parameter identification9|1|0更新时间：2025-09-25
- 摘要：To address the problem of the voltage violation in distribution networks caused by high-penetration distributed photovoltaic integration, this paper proposes a voltage regulation strategy based on an improved deep deterministic policy gradient (IDDPG) algorithm. Firstly, this paper proposes an improved variance-based adaptive noisy reward function to dynamically adjust the learning capability of agents in regions with various variances of environment, effectively mitigating policy bias. Secondly, the evaluation function is converted from a deterministic value representation to a probabilistic distribution form, thereby enhancing the adaptability of agents to uncertain environmental conditions. Finally, an N-step return method is introduced, which enhances the capability of agents to assess long-term benefits by adding N-step reward values to the Bellman equation. The simulation results of the IEEE 123-bus distribution system show that the proposed strategy has significant advantages in reducing voltage violation rates, decreasing network losses, and enhancing policy generalization capabilities in complex operational environments.关键词：active distribution network;distributed photovoltaic;deep reinforcement learning;agent;voltage regulation;data-driven;Markov decision process6|0|0更新时间：2025-09-25
- 摘要：The deep participation of photovoltaic power supply in the optimal dispatch and control of distribution networks can promote the safe and stable operation of distribution networks with renewable energy. However, it reduces the life expenditure of photovoltaic power supply and increases the complexity of solving the optimal model. Consequently, based on the data-knowledge-driven architecture, this paper proposes a data-knowledge-driven optimal dispatch and control strategy for the distribution network considering photovoltaic levelized cost of energy (LCOE). Firstly, the influence of reactive power support provided by photovoltaic power supply on the photovoltaic LCOE is analyzed, and the optimal decision-making knowledge model of distribution network optimization considering the photovoltaic LCOE is established. The optimal decision-making knowledge of the optimal dispatch and control for distribution networks is obtained. Then, the optimal decision-making knowledge of the optimal dispatch and control for distribution networks is embedded in the data-driven eXtreme gradient boosting (XGBoost) optimal dispatch and control model for distribution networks. The nonlinear mapping relationship between the distribution network state information and photovoltaic optimal decision-making is mined through the data-knowledge-driven method, and the adaptive optimization of the hyperparameters of the XGBoost model is completed by the Bayesian algorithm. Finally, the effectiveness of the proposed strategy is verified by the typical PG&E 69-bus distribution network.关键词：distribution network;optimal dispatch and control;data-knowledge-driven;photovoltaic;levelized cost of energy (LCOE);eXtreme gradient boosting (XGBoost) model5|0|0更新时间：2025-09-25
- 摘要：With the continuous expansion of the scale and the increasing complexity of the structure of power systems, the risks of cascading failures increase accordingly. To enhance the risk prevention and control capabilities of systems, accurately identify the key links in cascading failures, and reveal the coupling relationship and interactions among various links, the definition of key links in cascading failures is extended, and an identification method of expanded key links based on structural relationship and risk quantification is proposed by introducing the concepts of key propagation relationship and key areas. Firstly, a coordination-overload fault chain model for cascading failures is established to analyze the fault situation and propagation paths. Secondly, a local expansion algorithm based on the conductance optimization is employed to discover the fuzzy overlapping dynamic community structure of the power grid, and realize the identification of both core and fuzzy areas of power grids. Thirdly, by using the fault chain set and partitioning results, a risk spatiotemporal propagation graph with temporal and spatial characteristics is constructed, and the expanded key links are identified by employing the multi-dimensional fault impact quantification metrics. Finally, case simulations are conducted on the IEEE 39-bus system and the IEEE 118-bus system. The results demonstrate that the proposed method can effectively identify the expanded key links, and reveal the coupling relationship and cascading effects among various links.关键词：key link;cascading failure;fault chain;fault situation;propagation path;dynamic community;risk quantification3|0|0更新时间：2025-09-25
- 摘要：The fast and accurate oscillation source location of “multi-locomotive network” is of great significance for the oscillation prevention and control of the hub traction power supply system. This paper proposes a low-frequency oscillation source location method of hub traction power supply systems based on inter-harmonic transient energy. First, the subsystem is divided based on the locomotive grid-connected node, and the calculation formula for the subsystem inter-harmonic transient energy is derived by using port data. It is proven that the direction of subsystem inter-harmonic transient energy flow is an important condition to judge whether the subsystem is a low-frequency oscillation source. Then, based on the Fourier analysis method, the port modal information is extracted to calculate the inter-harmonic transient energy. The oscillation source is located by monitoring the inter-harmonic transient energy flow direction of each subsystem. Finally, the low-frequency oscillation scenario of the negative damping type of the hub traction power supply system is constructed. Combined with the dynamic movement of the traction power supply system and the time-varying characteristics of the working conditions, the influence of the locomotive working conditions and position on the oscillation source location results is analyzed. Combined with the on-site measured data, the effectiveness of the proposed method is verified.关键词：traction power supply system;low-frequency oscillation;negative damping oscillation;oscillation source location;inter-harmonic transient energy;Fourier transform5|5|0更新时间：2025-09-25
- 摘要：The sub-/super-synchronous oscillation of renewable energy is generally regarded as system stability issue of a local power grid. In order to balance the analysis accuracy and simulation efficiency, it is necessary to establish a network model and renewable energy equivalent model with an appropriate scale. To this end, a frequency-domain aggregation modeling method for direct-drive wind farm clusters connected to collection station oriented to power grid equivalence is proposed. Firstly, an improved relative gain array (RGA) method is proposed to quantify the dynamic coupling strength among wind farms. Through decoupling analysis, the wind farm set strongly coupled with the stations to be studied in the power grid is constructed. Secondly, the five-point numerical difference formulas are introduced to calculate the impedance sensitivity. By analyzing the influence of control parameters on the impedance of full-power converter equipment, the dominant factors of the grid-connected oscillation characteristics of direct-drive wind farm clusters are selected as the clustering objects. Thirdly, the artificial fish swarm algorithm (AFSA) is adopted to optimize the bias parameters in the affinity propagation (AP) algorithm so as to obtain the clustering number of the AP algorithm, and then determine the wind farm clustering set and the aggregation model parameters. Finally, taking an actual power grid as a case, the effectiveness of the aggregation modeling method in the analysis of sub-/super-synchronous oscillation characteristics is verified.关键词：direct-drive wind farm cluster;sub-synchronous oscillation;super-synchronous oscillation;aggregation modeling;power grid equivalence;relative gain array;affinity propagation algorithm5|0|0更新时间：2025-09-25
- 摘要：During system faults, the doubly-fed wind turbine and the power grid form a meshed parallel circuit, jointly feeding current to the fault point. The existing methods simplify the external power grid as an ideal voltage source at strong power grid situations, and represent the system fault voltage with step signals. However, when the short-circuit capacity at the tie point is relatively low, the input current of wind power affects the fault voltage of the system, and ignoring the characteristics of the power grid leads to the inaccurate calculation of transient short-circuit current. To address the above issues, a calculation method for transient short-circuit current in the meshed parallel circuit with doubly-fed wind turbines is proposed. By analyzing the transient response of the stator and rotor flux linkage, equivalent inductances representing the steady-state, transient DC and transient AC flux components are derived. These inductances are combined with the equivalent circuit of the corresponding fault voltage source, enabling rapid calculation of current component amplitudes and time constants. Simulation results demonstrate that neglecting the external grid leads to an omission of the grid-side circuit time constant, increasing calculation errors. The proposed method achieves a peak current error of less than 3% under varying fault distances and grid impedance ratio, which is superior to existing methods.关键词：doubly-fed wind turbine;transient short-circuit current;system impedance ratio;time constant;fault response9|0|0更新时间：2025-09-25
- 摘要：To address the challenges brought by the integration of a large number of distributed generators, the synchronous measurement technology has been introduced into the distribution network to provide more-dimensional data for the safety and stability analysis of the system. To further enhance the situational awareness of the distribution network, a synchronous measurement data-driven classification method for disturbance sources in the distribution network is proposed in this paper. Firstly, a fast identification method for disturbance sources based on Granger causality analysis theory is proposed to identify if the disturbance source is from the main grid side or the distribution network side. Subsequently, a misjudged disturbance identification method based on kernel support vector machine algorithm is proposed to identify misjudged disturbances caused by errors in instantaneous estimation algorithms, thus achieving the rapid classification and extraction of real-time distribution network disturbance data. Finally, the effectiveness of the proposed method is verified based on IEEE 123-bus system simulation data and on-site synchronous measurement data.关键词：distribution network;distributed generator;situational awareness;synchronous measurement;kernel support vector machine;disturbance7|1|0更新时间：2025-09-25
- 摘要：To mitigate carbon emissions from offshore oil and gas fields, operators are actively promoting joint development of offshore wind power and offshore oil and gas fields. The existing scheduling strategies designed for stable relationship between sources and loads, have difficulties in coping with the wind power integration with uncertainty. To reduce the operation costs and improve the accommodation capability of wind power, an optimal day-ahead scheduling strategy for integrated energy system of offshore oil and gas field (OIES) is proposed, which coordinates turbine generators and offshore wind power. Firstly, a typical OIES structure that integrates multiple oil and gas platforms with offshore wind power is presented. Then, a suppressing strategy of wind power fluctuation with equivalent load rate and a promotion strategy of wind power accommodation level with dynamic management governing the minimum load rate for multiple turbine generators are proposed. Finally, an optimal robust day-ahead scheduling model is established, considering the coupling constraints of power, gas and oil systems, the operation constraints of turbine generators, and wind power uncertainty. Based on a case study of an offshore oil and gas field in Northern China, the results show that the proposed strategy can effectively reduce total operation cost and curtailment rate.关键词：offshore oil and gas field;optimal day-ahead scheduling;uncertainty;load rate;wind power accommodation;integrated energy system4|1|0更新时间：2025-09-25
- 摘要：The high proportion of renewable energy connected to the power grid has led to increasingly prominent problems in frequency regulation. As a frequency regulation resource, the electric vehicle (EV) cluster faces the problems such as the lack of effective incentive mechanisms, high uncertainty in charging and discharging, and significant risks associated with independent frequency regulation. To solve these problems, considering the dual uncertainties of the market frequency regulation environment and the EV cluster frequency regulation incentive, this paper proposes a robust optimization model for coordinated frequency regulation of the EV cluster and conventional frequency regulation units, so as to achieve the optimization of coordinated frequency regulation economy. Firstly, by using the random optimization method to cluster and analyze historical frequency regulation data, a frequency regulation signal scenario set for coordinated frequency regulation of the EV cluster and units is constructed. Secondly, the charging and discharging incentive mechanisms considering user behavior preferences are proposed. The kernel density estimation interval is used to predict the probability of EV users actively participating in charging and discharging regulation. The distributionally robust optimization method based on conditional value at risk is used to analyze the uncertainty of EV charging behavior under incentives. Thirdly, to minimize the frequency regulation cost, a distributionally robust optimization model for coordinated frequency regulation of the EV cluster and units is constructed, and the model is reconstructed as a second-order cone programming problem. Finally, the case verifies the advantages of coordinated frequency regulation in terms of expected revenue, standard deviation of revenue, and conditional loss expection. Compared with traditional unit frequency regulation, coordinated frequency regulation effectively improves economic benefits.关键词：electric vehicle (EV);frequency regulation;uncertainty;charging and discharging incentive;interval prediction;robust optimization;value at risk7|0|0更新时间：2025-09-25
Basic Research
- 摘要：With the continuous increase in the penetration rate of renewable energy, the prediction error of the output of renewable energy units leads to continuously widening the deviation between the actual operation mode and the planned operation mode of the power grid. Especially for future multiple periods, the deviation of the operation mode may intensify the risk that the power grid does not meet the
- 摘要：The large-scale integration of renewable energy has made the frequency dynamic characteristics of power systems more complex. The data-driven transient frequency safety assessment has attracted widespread attention, but there are still limitations such as insufficient feature mining and underutilization of prior knowledge. To this end, a method for transient frequency safety assessment based on independent spatial-temporal feature extraction and grouped enhancement is proposed. First, an adaptive spatial-temporal feature extraction module is introduced to characterize the correlations among generators through independent fully-connected adjacency matrices, thereby enhancing the capability of feature extraction. Then, a grouped feature enhancement module based on physical prior knowledge is designed, which guides the feature fusion by the frequency of system inertia center, promotes interactions of initial input features, and enhances representation capabilities. Finally, the multi-scale convolutional module is used to fuse all features to obtain the final system representation, and the frequency safety assessment is achieved through model training and parameter optimization. Simulation results of the standard case demonstrate that the accuracy and robustness of the proposed model in this paper are superior to those of existing data-driven models.关键词：new power system;transient frequency;safety assessment;feature enhancement;Attention mechanism;spatial-temporal feature6|1|0更新时间：2025-09-25
- 摘要：The integration of large number of three-phase unbalanced devices into the power grid leads to three-phase voltage unbalance issues, which seriously threaten the power quality of the distribution network. In addition to the specialized power quality mitigation equipment, using the residual capacity of the photovoltaic (PV) converter in the distribution network to mitigate unbalanced voltage is an effective way to suppress power quality pollution. This paper proposes an optimal control strategy for mitigating unbalanced voltage at the point of common coupling (PCC) by using three-phase distributed PV converter. First, the minimization problem of unbalanced voltage at the PCC after mitigation is modeled as an optimization problem considering circuit theorem, current limiting, power balance, and power oscillation. Secondly, the geometric properties of the physical constraints for the problem are analyzed, and the boundary of the feasible region and the optimal value are obtained under various conditions. Then, the corresponding control strategy is designed to minimize the negative-sequence voltage at the PCC. The implementation of the control strategy does not require the power grid impedance information while the maximum output power of the PV array can be guaranteed. Finally, the effectiveness of the proposed control method is verified by the hardware-in-the-loop (HIL) experiment.关键词：distribution network;power quality;voltage unbalance;photovoltaic converter;negative-sequence voltage;mitigation;point of common coupling (PCC)7|0|0更新时间：2025-09-25
- 摘要：To enable residential electrical appliances to actively adapt to electricity demand and operation environment like intelligent robots, a construction method for autonomous agents of electrical appliances is proposed utilizing a technology roadmap based on state transitions. These agents are capable of providing appropriate electrical services in scenarios of intelligent electricity consumption and demand response. Firstly, the concept of compatibility and its evaluation method are proposed from the perspective of residential electrical appliances to quantify the alignment between the power services provided by residential electrical appliances and the electricity demand and operation environment. Secondly, a series of functional states are defined and divided according to the values provided by these electricity services. These states form the foundation for autonomously optimizing the performance of the autonomous agents of electrical appliances in various electricity consumption scenarios. Then, a self-approaching optimization operation method driven by these functional states is designed, which ensures that the autonomous agents of electrical appliances perpetually strive towards an optimal state through self-recognition and self-transition methods of functional states. Finally, based on the autonomous decentralized system theory, a simulation framework for these autonomous agents of electrical appliances is designed and implemented on autonomous agents including 10 000 lights and inverter air conditioners. The performance of these autonomous agents of electrical appliances and the effects of various driving methods are analyzed to verify the superiority of the proposed method. Meanwhile, the application effects of these autonomous agents of electrical appliances in the intelligent electricity consumption and demand response scenarios is discussed. Some of the results can serve as references for engineering implementation and policy formulation.关键词：electrical appliance;functional state;autonomous agent;compatibility;demand response;self-approaching optimization;inverter air conditioner9|0|0更新时间：2025-09-25
Application Research
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