Electric Power

Research on Optimal Dispatch with Source-Load Coordination for Micro-energy Grid Based on Energy-Carbon Coupling Model

XU Shijie, HU Bangjie, ZHAO Liang, WANG Pei

2025, 58(4): 1-12. DOI: 10.11930/j.issn.1004-9649.202408095

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At present, the low-carbon dispatch of the integrated energy system focuses on the means of carbon reduction at the source side, while ignoring the low-carbon potential of the load side and the ability of carbon reduction for source-load coordination. Taking the micro energy grid coupled with power, heat and gas as the research object, an optimal dispatch method with source-load coordination of heterogeneous energy flow system based on energy-carbon coupling model is proposed, and a two-stage optimal dispatch framework of day-ahead and intra-day based on the process of source-load coordination is built. In the source side, the combined heat and power (CHP) units with adjustable heat-to-power ratio coupling with power-to-heat equipment are used for energy supply, and the dynamic carbon emission characteristic of each unit in the energy station is considered; In the grid side, the energy-carbon coupling model of power and heat energy is established by using the carbon emission flow theory, and the obtained distribution of carbon intensity is transferred to the load side; Based on this carbon information and considering the impact of time-sharing energy price, the load side guides the load to adjust behaviors of energy consumption in real time to respond to low-carbon demand, and feeds back the updated load to the source side to re-optimize the output of each unit, so as to realize the process of source-load coordination. The effectiveness of the proposed method is verified by analyzing the micro energy grid composed of the improved IEEE 33-node power grid and the Barry Island 32-node heat grid.

Weak Connection Regulation Technology for Source-Storage-Load Collaborative Microgrids Based on Direct Power Control

REN Peng, ZHAO Zhigang, GAO Hongchao, WEN Wu, JIANG Yingyi

2025, 58(4): 13-20. DOI: 10.11930/j.issn.1004-9649.202412093

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The development of distributed energy has transformed buildings into new energy complexes integrating generation, storage, and consumption. However, the fluctuation and randomness of photovoltaic power generation and equipment power consumption leads to the grid and regional supply and demand imbalance. So it is necessary to optimize the configuration of the regional source-storage-grid-load to enhance the efficiency of the regional power consumption and tap into the adjustable potential, achieving fast response and regulation. This paper built a user-side source-storage-grid-load DC power supply system, analyzed the characteristics of each device of the system, proposed a power direct-control flexible regulation algorithm, and conducted verification in the PV future houses of Zhuhai Gree Electric Appliances. The paper also studied the regional DC power supply and flexible regulation technology, analyzed the flexible characteristics of air conditioners, energy storage, photovoltaic, etc., and verified the power direct-control flexible regulation algorithm and evaluation indexes. From the effect of testing and verification, the whole system enables real-time response and rapid adjustment, thus improving the energy utilization efficiency, reducing the grid-side quota and realizing the local consumption of PV.

Distributed Optimization for VPP and Distribution Network Operation Considering Uncertainty and Green Certificate Market

WANG Jinfeng, LI Jinpeng, XU Yinliang, LIU Haitao, HE Jinxiong, XU Jianyuan

2025, 58(4): 21-30, 192. DOI: 10.11930/j.issn.1004-9649.202409069

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In order to cope with the uncertainties brought by the growing scale of renewable energy, this paper proposed a distributed optimization for virtual power plant (VPP) considering uncertainties. On the VPP side, a scenario-based stochastic optimization model was used to depict the stochastic characteristics of photovoltaic and wind power output. On the distribution network side, considering the uncertainty of electricity prices when purchasing power from the grid, a two-stage robust optimization model and an opportunity model based on information gap decision theory were established to enhance the model's robustness against electricity price fluctuations or to capture potential benefits amidst volatile electricity prices. The problem was solved distributedly between the VPP and the distribution network through the alternating direction multiplier method algorithm to protect their privacy. The results show that the proposed model is more flexible than the robust model and can better balance the risks and benefits under uncertain conditions. The distributed algorithm has a good convergence performance and can better utilize computing resources compared to the centralized algorithm.

Electricity Carbon Coupled Market Modeling Method and Market Optimization Mechanism Based on Dynamic Carbon Emission Intensity

ZHAO Tong, LI Xuesong, ZHOU Hao, DING Yu, YANG Bin, WANG Wentao, WANG Peng

2025, 58(4): 31-43. DOI: 10.11930/j.issn.1004-9649.202409061

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Due to the timing differences between carbon market (CM) and electricity market (EM), accurately modeling their coupling is challenging. Analysis of operating data shows that the carbon emission intensity (CEI) of thermal units fluctuates with load rates, providing a foundation for real-time coupling and accurate modeling. This study develops a bi-level equilibrium simulation model with differentiated dynamic CEI in a multi-generator game framework. The upper level models generator decisions, while the lower level models EM and CM transactions. A low-carbon optimization mechanism (LCOM) involving interactions with other industries is proposed to enhance EM synergy. A Markov decision iterative optimal coordination algorithm (MDIOCA) is proposed to solve the model. Case studies based on the IEEE 39-bus system demonstrate that the dynamic CEI model enables real-time coupling of the EM and CM, achieving an additional 5.42% reduction in carbon emissions and facilitating an economic inflow of 47,900 CNY from other industries, thereby improving the economic and environmental performance of the EM.

Multi-entity Behaviors in Electricity-Carbon-Green Certificate Coupled Markets Based on Multi-agent Reinforcement Learning

ZHOU Feihang, WANG Hao, WANG Haili, WANG Meng, JIN Yaojie, LI Zhongchun, ZHANG Zhongde, WANG Peng

2025, 58(4): 44-55. DOI: 10.11930/j.issn.1004-9649.202410025

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Establishing a national carbon emission trading market and green certificate market is one of the key strategies for China to achieve its "dual carbon" goals. However, existing research predominantly analyzes the market coupling relationships from an economic perspective, overlooking the impact of power network physical constraints and the uncertainty of renewable energy output on the coordinated optimization of markets. Additionally, the scenario of power consumers participating in the carbon market has not been considered. To address this limitation, a bi-level optimization model for the coupled electricity-carbon-green certificate market based on physical network nodes is proposed to analyzes the behaviors of market entities and the changes in coupling mechanisms under the context of carbon market expansion. In the model, a decision-making mechanism is introduced for power consumers participating in the carbon market based on the physical topology of the power grid, and by incorporating the offset rules between green certificates and carbon allowances, the impact of transmission line congestion on the decision-making of market entities is explored. The actual output data of new energy units in the Mongolia region is used to verify the rationality and effectiveness of the proposed model. The results show that the participation of electricity users in the carbon market can significantly increase the overall returns of the coupled market, and line congestion has a significant impact on the behaviors of market entities and market revenues; in the context of abundant carbon quotas, introducing the carbon credit offset mechanism can further optimize the coupled market efficiency.

Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics

JI Zhanyang, HU Yang, KONG Lingxing, SONG Ziqiu, DENG Dan, LIU Jizhen

2025, 58(4): 56-67. DOI: 10.11930/j.issn.1004-9649.202411044

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During the rapid frequency regulation process of wind turbine units, the transient active power release can induce load fluctuations in aerodynamic, transmission, and tower components. In order to reasonably characterize the fluctuation characteristics and serve the optimization of frequency regulation control, this paper presents a fast dynamic modeling method for wind turbine units that takes into account the coupling characteristics of blades, main shaft, generator, and control systems. Firstly, a wind farm-turbine coordinated primary frequency regulation control strategy is set up, and a rapid frequency regulation controller at the unit level is developed for both below and above the rated wind speed based on a refined 5MW wind turbine model. And then, the Spilman correlation analysis algorithm is used to select input and output variables with consideration of input and output delay orders, and the operational domain partitioning is completed, enabling adaptive identification and switching between operation regions both above and below the rated wind speed. Thirdly, based on balanced sampling of simulation operating data under discrete operating conditions, and guided by physical prior information, subspace identification and deep neural network algorithms are employed to conduct multi-input-multi-output modeling and simulation verification of the unit's primary frequency modulation dynamics across the full range of operating conditions. The results show that the state space model obtained has good interpretability, but the model structure inherently limits its approximation accuracy to a finite degree; in comparison, the temporal neural network model demonstrates a better ability to capture dynamic characteristics, providing a robust model foundation for subsequent optimization control of the unit's primary frequency modulation.

Modeling and Analysis of Transient Overvoltage of Direct Drive Wind Turbine Under Symmetrical Faults

LUO Hongbo, QIN Shiyao, GUO Zixuan, LI Guanghui

2025, 58(4): 68-77, 97. DOI: 10.11930/j.issn.1004-9649.202411040

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Large-scale wind power bases typically adopt UHVAC/UHVDC transmission systems for power export, with weak grid strength in the collection area. After short-circuit faults, transient overvoltage issues are prone to occur, making it urgent to study the analytical model of transient overvoltage during short-circuit faults in wind turbine generators. Firstly, a transient voltage model after a symmetrical short-circuit fault at the remote end of the transmission line in a weak grid is established based on the typical control and voltage ride-through strategies of permanent magnet synchronous generator wind turbines. By studying the transient voltage components at the grid connection point corresponding to the voltage at the fault point, it is revealed that transient overvoltage may occur at the grid connection point after the instantaneous recovery of the fault point voltage. Secondly, a second-order simplified model suitable for transient overvoltage analysis is proposed, and clarify that the damping ratio of the simplified model is a key factor determining the peak overvoltage value. The expression of peak overvoltage is solved through time-domain analysis, and the influencing factors of transient overvoltage are quantitatively analyzed. Finally, relying on the control hardware-in-the-loop real-time simulation experimental platform, the applicability of the proposed simplified transient overvoltage model and the dominant influencing factors of transient overvoltage are verified.

Power Optimization of Wind Farms Based on Improved Jensen Model and Deep Reinforcement Learning

WANG Guanchao, HUO Yuchong, LI Qun, LI Qiang

2025, 58(4): 78-89. DOI: 10.11930/j.issn.1004-9649.202410051

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The power capture capability of wind farms is constrained by various factors. To maximize the power output of wind farms and address the impacts of wake effects and turbulent wind speeds, this paper proposes a wind farm control scheme based on deep reinforcement learning. This scheme combines both model-based and model-free control methods and integrates them into a deep reinforcement learning deep deterministic policy gradient network with an Actor-Critic architecture. In terms of control accuracy, Jensen wake model consider time delay is adopted to enhance the precision of wake effects and effectively captures the long-term impact on the wind farm's power output. Simulation results show that, compared to traditional model-based or model-free methods, this scheme significantly increases the maximum power output of the wind farm while maintaining control accuracy, and significantly reduces training time and computational resource consumption, thereby improving the overall performance of the control strategy.

Fatigue Load Prediction of Wind Turbine Drive Train Based on CNN-BiLSTM

WANG Xiaodong, LI Qing, FU Deyi, LIU Yingming, WANG Ruojin

2025, 58(4): 90-97. DOI: 10.11930/j.issn.1004-9649.202409072

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The fatigue loads of operational wind turbine drivetrain systems are typically quantified using the rainflow counting method based on stress measurements at critical components, a process that is time-consuming and costly. This paper addresses the significant deviations observed in traditional fatigue load quantification models employed for control strategies and parameter optimization in operational wind turbines. We propose a fatigue load prediction model for the drivetrain system based on a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) architecture, utilizing state data from wind turbines. First, we construct a fatigue load feature database using simulation data from OpenFAST under rated wind speed conditions and above, which is subsequently used for training and testing the model. We then compare the model's predicted data with actual data, employing relevant evaluation metrics to assess the predictive performance of the model, thereby validating its effectiveness. Finally, by comparing the prediction results with those from long short-term memory and deep neural network models, we demonstrate that the CNN-BiLSTM load prediction model significantly enhances the accuracy of load predictions for wind turbine drivetrain systems.

Minimum Risk Quantification Method for Equivalent Error Threshold of Wind Farm Based on Bayes Criterion

ZHU Qianlong, JIN Xiaoqiang, WANG Xuli, SU Fanya, DENG Tianbai, TAO Jun

2025, 58(4): 98-106. DOI: 10.11930/j.issn.1004-9649.202406035

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The equivalent error threshold is the cornerstone to balance the mathematical complexity and simulation speed of wind farm (WF) model, and can promote the standardization process of WF equivalent model. Major wind power countries in the world have different starting points and emphases in quantifying the error threshold of wind power models, and the form and indicators of the error threshold have not been unified. Therefore, this paper puts forward a method based on Bayes criterion to quantify the minimum risk of equivalent error threshold of WFs. Firstly, taking the time distribution characteristics of equivalent errors as the starting point, the Euclidean errors of equivalent models of WFs in different periods are quantified, and then the probability density distributions of the above errors are fitted by kernel density estimation. Secondly, the real-time weighted prior probability algorithm is used to obtain the effective prior probability of the WF model, and based on the Bayes criterion, the equivalent error threshold quantization model of the WF is established for the minimum risk, with consideration of the different losses caused by the misjudgment of the model validity to the power system. Finally, the feasibility of the proposed method is verified by an actual WF example, and compared with the error threshold at home and abroad, the effectiveness of the WF equivalent model can be determined more quickly and accurately.

Modular Mobile Charging Facility Layout Optimization Method and Deployment Strategy

ZHENG Jiajun, DUAN Xiaoyu, HU Zechun, HU Xiaorui, ZHU Bin

2025, 58(4): 107-118. DOI: 10.11930/j.issn.1004-9649.202501026

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This paper investigates the layout planning and deployment strategy for modular mobile charging facilities, a novel type of charging facilities for electric vehicles. Firstly, an electric vehicle user travel mechanism model is established to predict the spatial and temporal distribution of charging demand at the node level. Secondly, a socket component planning model is developed for the upper layer with the objective of optimal operator revenue. Then, a charger components optimal deployment model is established for the lower layer based on the vehicle routing problem. Finally, an algorithm based on iteration and feedback is designed to solve it. The example simulation results show that, as an effective complement to fixed charging facilities, the modular facilities can reduce the operator's operational cost and increase the equipment utilization rate while meeting the users' charging demand.

Optimization and Scheduling Strategy for Regional Multi-dimensional Charging of Electric Vehicles Based on Compensation Mechanism

YUAN Jianhua, ZHANG Tianyu, CHEN Guangsheng, HUANG Tao

2025, 58(4): 119-130. DOI: 10.11930/j.issn.1004-9649.202404112

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To improve the power supply service provider’s economic efficiency and user’s satisfaction under the background of electric vehicle (EV) charging-disorder in varied charging settings, this paper proposes a compensation-based optimization and scheduling strategy for regional multi-dimensional charging of electric vehicles. Firstly, clusters are formed based on energy consumption patterns and temporal flexibility, and a compensation perception model is established based on charging compensation and time pressure. A user satisfaction model and a regret theory-based user charging decision model are developed with consideration of charging cost, time and quantity of users. And then, a daily rolling energy replenishment model is established for batteries at BCSS. Finally, an optimization scheduling model is created to optimize the charging compensation for maximizing the provider’s revenue and minimizing the regional consumption imbalances. The proposed strategy, verified through Matlab simulation, boosts the power supply service provider’s revenue and slashs the regional consumption deviation, while maintaining the user’s satisfaction with the charging solution.

Valley-filling Potential Evaluation of Urban Public Charging Stations Based on Price Incentive

SU Dawei, FAN Yihui, ZHAO Tianhui, PAN Hongjin, HUANG Youhui, WANG Gang, JIA Yongyong

2025, 58(4): 131-139. DOI: 10.11930/j.issn.1004-9649.202406054

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With large scale integration of renewable energy, motivating valley-filling users to participate in power grid regulation to tap the valley-filling potential of charging loads has become one of the important means to enhance the space for renewable energy consumption. Firstly, an evaluation framework for valley-filling potential of urban public charging stations is built in this paper. Probability modeling is conducted on charging orders, and a random sampling-based Monte Carlo simulation is carried out to obtain the sampling data representing the starting charging time. Then, the typical load characteristic indicators including valley-period and flat-period load rates are proposed, and the charging load valley-filling potential evaluation parameters are introduced to carry out valley-filling potential evaluation of urban public charging stations. Finally, based on the actual charging order data of the urban charging stations in a pilot city under different price incentives, a quantitative evaluation of the valley-filling potential of urban public charging stations is carried out. The results show that the simulated charging load is consistent with the actual load in tendency, and the change pattern is similar to the actual situation. When the service fee discount exceeds 30%, for every additional 10% discount, the average valley-filling response will increase by approximately 23.6 MW. Therefore, the proposed evaluation framework realizes the quantification of valley filling potential of public charging stations.

Optimal Equipment Configuration Method for Electric Vehicle Charging Stations Considering User Response Characteristics

YAN Jun, LUO Yujie, YAN An, HE Wei, HAN Tao, YANG Jun

2025, 58(4): 140-147. DOI: 10.11930/j.issn.1004-9649.202409094

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The rapid development of electric vehicles (EVs) has brought a huge amount of potential demand-side power grid regulation resources, so it is urgent to rationally plan and configure the existing charging station equipment to meet the auxiliary service needs. Considering the strong randomness of users' charging behaviors, an optimal configuration method for EV charging stations based on user response characteristics is proposed. Firstly, considering the heterogeneous psychological weight of different users on time and price, the user satisfaction is calculated using Weber Fichner law, and then a user response characteristic model is constructed. Secondly, based on the response results of different types of users, a method for calculating the adjustable load boundary of charging stations is proposed. Finally, considering the benefits of the charging station participating in the auxiliary service, an optimal configuration model of charging stations is constructed and solved to maximize the comprehensive benefits. The simulation results show that the proposed method can accurately characterize the users' actual charging behavior decision and achieve the optimal configuration of the charging stations. thus effectively improving the comprehensive operating benefits of the charging stations, including the revenue from auxiliary services.

Frequency Regulation Incentive Mechanism and Control Strategy for Electric Vehicles Under Elastic Charging Demand

LI Xiaohan, CAO Wei

2025, 58(4): 148-158. DOI: 10.11930/j.issn.1004-9649.202407004

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The high proportion of new energy penetration in the power system poses a serious challenge to frequency stability. With the development of vehicle to grid, electric vehicles as a flexible energy storage system can provide stable frequency regulation services for the power system. In order to enable electric vehicles to provide greater frequency regulation capacity for the power grid and increase users' willingness to participate in frequency regulation, the behavioral characteristics, state of charge, and power constraints of electric vehicles were analyzed. The travel chain and transportation network of electric vehicles were modeled, and a method for optimizing the expected SOC state constraint of electric vehicles was proposed, and the elastic frequency regulation capacity space, incentive mechanism, and frequency regulation control strategy of electric vehicles were constructed. Simulation has verified that compared with traditional control strategies that do not consider elastic charging demand, the proposed control strategy that considers elastic charging demand has advantages in providing frequency regulation capacity for the power grid and increasing user frequency regulation revenue. The proposed control strategy can provide a larger frequency regulation capacity for the power grid, increase users' frequency regulation revenue, and maintain the stability of the power grid frequency.

Optimized Coordinated Scheduling of Oxy-Fuel Combustion Carbon Capture Combined Heat and Power Plant Considering Hydrogen Energy Storage

DENG Buyuan, YUAN Zhi, LI Ji

2025, 58(4): 159-169. DOI: 10.11930/j.issn.1004-9649.202406029

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As renewable energy installation scales up, traditional carbon capture combined heat and power plants face challenges such as low carbon capture efficiency and inadequate regulatory capacity due to their heating supply obligations. In order to promote renewable energy integration and reduce carbon emissions while enhancing the peak regulation capabilities of these plants, this study constructs a joint operational model integrating hydrogen energy storage with oxy-fuel combustion carbon capture units, focusing on low-carbon economic dispatch. The paper first investigates the coordination mechanisms of oxy-fuel combustion carbon capture and hydrogen energy storage, establishing a system architecture. It then considers oxygen production from air separation and oxygen recovery from hydrogen storage, developing separate models for oxy-fuel combustion carbon capture units and hydrogen energy storage systems. Finally, leveraging carbon trading and peak regulation auxiliary service markets to enhance carbon reduction and peak regulation initiatives of power plants, the study aims to optimize system operating costs, establishing a low-carbon economic dispatch model for oxy-fuel combustion carbon capture plants integrated with hydrogen energy storage. Case study results demonstrate that the proposed model not only effectively improves system carbon efficiency and economic performance but also enhances the regulatory capabilities of combined heat and power plants, facilitating renewable energy integration.

Optimization Strategy for Spatiotemporal Cooperative Operation of Multiple Data Centers Considering Load Response Characteristics

XIANG Shilin, XIANG Yue, WANG Yanliang, LU Yu

2025, 58(4): 170-181. DOI: 10.11930/j.issn.1004-9649.202412049

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Unlike traditional flexible loads that have only temporal regulation capability, the loads of multiple data centers can be migrated among them both in time and spatiality, thus becoming a flexible demand response resource with temporal and spatial regulation potential. However, previous studies on load management of multiple data centers have neglected the need for multiple time windows depending on the timeliness in batch load response characteristics. In this regard, we propose a spatio-temporal cooperative operation optimization strategy for multiple data centers considering the load response characteristics. Firstly, the coupling of business and energy flows of multiple data centers is modeled to flexibly configure the number of servers powered on. Then, the temporal and spatial migration characteristics of batch loads are analyzed to establish a multi-data center demand response mechanism. And then, a multi-data center operation optimization model is established with minimum total operation cost as the objective function and user satisfaction as the constraint. Finally, a case study is carried out with multiple data centers as an example. The simulation results show that the proposed scheme can effectively stimulate the demand response potential of multiple data centers, reduce the total operation cost while ensuring user satisfaction, and provide an effective load management strategy for multiple data centers.

A Method for Calculating the Feasible Operation Region of Active and Reactive Power in Active Distribution Networks Considering Stochasticity

WANG Xuanyuan, ZHANG Wei, LI Changyu, XIE Huan, GUO Qinglai, WANG Bin, ZHANG Yuqian

2025, 58(4): 182-192. DOI: 10.11930/j.issn.1004-9649.202412008

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The large-scale integration of renewable energy poses challenges to the power systems, such as the shortage of flexibility resources. However, renewable energy and power electronics connected to the distribution networks can provide flexibilities. This paper proposes a novel method to determine the stochastic feasible operation region of distribution networks, considering flexibility-providing units, network operational constraints, and uncertainties in renewable energy generation and loads. Firstly, an improved method for calculating the deterministic flexibility operation region is introduced. And then, a scenario-based approach is used to model the uncertainties of loads and distributed generations. Finally, a case study demonstrates the accuracy and effectiveness of the proposed method. The case study results show that the proposed method can provide both the probability distribution of the feasible operation region and the operation regions at different confidence levels, and achieves higher accuracy with the same computational cost and avoids overly optimistic results.

Carbon Emission Accounting Methods for Key Electric Equipment and Materials in Power Transmission and Transformation Projects

LI Keyun, ZHANG Ning, ZHAO Le, ZHAO Cheng, LI Jiayu, TANG Cheng

2025, 58(4): 193-204. DOI: 10.11930/j.issn.1004-9649.202411030

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Under “dual-carbon” goals, the carbon emission accounting for power transmission and transformation projects can provide important data reference for carbon reduction of power projects. At the present stage, the field of carbon emission accounting in power transmission and transformation projects is facing the problems including the lack of standards and weak research foundation, thus it is significant to establish specific methodology and models. This paper is oriented to the application scenario of upstream carbon reduction in the supply chain of power transmission and transformation projects. Firstly, the paper defines the scope of carbon emission accounting from perspectives of carbon accounting objects and system boundary; secondly, the paper analyzes the carbon emission activities of key equipment and materials during raw material acquisition and manufacturing stages, researches the way of collecting and converting activity data with limited raw data from the pre-engineering stage, and constructs carbon emission accounting models for key equipment and materials in power transmission and transformation projects; finally, the paper conducts a case study for an ultra-high voltage direct current transmission and transformation project, and calculates the carbon emissions of seven key electric equipment and materials during raw material acquisition and manufacturing stages based on the limited data sources from the preliminary design stage. The research results show that wires, tower materials and transformers are the main components of the total carbon emissions, thus it is essential to focus on carbon emissions of the equipment, and promote the research on carbon reduction in electrical equipment and materials.

Quantitative Analysis of Carbon Emission from Power Transmission and Transformation Projects Based on GHGP Standard System

ZHANG Feng, JIANG Jishuang, LI Chao, XIA Zhixiang, DAI Li, WANG Kaige, FANG Mengxiang, LUO Zhongyang

2025, 58(4): 205-215. DOI: 10.11930/j.issn.1004-9649.202411038

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Quantitative analysis of carbon emission due to power transmission and transformation project construction is the premise and basis of green low-carbon implementation of the project. Based on quantification framework and data processing method according to the GHGP standards, a common hierarchic quantization model and a boundary optimization model for power transmission and transformation engineering construction are developed in this paper, in addition, the behavior of carbon emission from transmission and transformation project construction is analyzed and optimized through hierarchical quantification based on the sub-projects, material flow and energy flow activities by taking a certain 500 kV substation project as an example. The results show that the full-scale distribution characteristics of carbon emission is obtained through the quantification model, the carbon emission from civil and architectural engineering works accounts for 93.02% of the total carbon emission. Compared to energy flow, the carbon emissions from material flow activities accounted for 90.28% of the total emissions, of which the carbon emissions from cement and its products and steel consumable materials accounted for 81%. When the carbon emission significance threshold is set to 0.002% ~ 0.018% of the total emissions, 90% of the unit data flow of power transmission and transformation engineering construction can be excluded from the quantification boundary. When renewable concrete and recycled steel are used in the construction of power transmission and transformation projects, significant emission reduction can be generated.

Evolutionary Game Analysis for Government Supervision of Green Electricity Certificate in a Renewable Energy Power Supply Chain with Portfolio Standard

DAI Daoming, ZHAO Ying

2025, 58(4): 216-229. DOI: 10.11930/j.issn.1004-9649.202410060

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Green certificate is an important measure to solve the consumption problem of the renewable energies such as photovoltaic and wind power. However, the applicants of green certificate may engage in "cheating" behaviors. This paper explores the evolutionary game for green certificate application and government regulation of all parties in the renewable energy power supply chain by combining the renewable portfolio standard and green certificate. Firstly, from the short-term perspective, a study is made on the renewable energy consumption problem of the renewable energy power supply chain composed of power generation enterprises and power grid companies. And then, a tripartite evolutionary game model is constructed among the power generation enterprise, power grid company and the National Energy Administration from a long-term perspective, and the dynamic evolution process and the evolutionary stability strategy of the tripartite strategies are analyzed. Finally, the research conclusions are verified through simulation analysis. The results show that increasing the penalty coefficient will impel power generation enterprise and power grid company to choose "honesty" strategy, and reducing the return coefficient of green certificate income can encourage power grid company to choose "honesty" strategy; relatively high spillover benefits and relatively low strict regulatory costs are conducive to the formation of a healthy green certificate market operation mechanism.

A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning

XU Huihui, TIAN Yunfei, ZHAO Yuyang, PENG Jing, SHI Qingxin, CHENG Rui

2025, 58(4): 230-236. DOI: 10.11930/j.issn.1004-9649.202409074

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To enhance computational efficiency and accuracy in reliability assessment of distribution networks with large-scale distributed photovoltaic integration, a novel assessment method is proposed based on conditional generative adversarial network and multi-agent reinforcement learning. Firstly, the sequential state sequences of the system are generated using Sequential Monte Carlo simulation, and a conditional generative adversarial network (CGAN) is combined with multi-resolution meteorological factors to characterize the multivariate characteristics of source-load scenarios, including temporal dependency, volatility, randomness, and source-load correlation. Secondly, a multi-agent reinforcement learning (MARL) model is established, and a training algorithm integrating imitation learning and exploratory learning is proposed, enabling the agents to acquire optimal policies through interactive learning with an expert experience model. Finally, the simulationg is verified based on the IEEE RBTS BUS-2 system. Simulation results demonstrate that the proposed method outperforms traditional methods in terms of learning curve and stability, significantly improving both the accuracy and computational efficiency in distribution network reliability assessment, possessing superior practical values.

Matching Method for Power Grid Fault Handling Plan Based on Semantic Enhancement

MENG Fei, LI Jiangpeng, LI Tao, XU Jianzhong, GAO Haiyang, QIAO Yongtian

2025, 58(4): 237-244. DOI: 10.11930/j.issn.1004-9649.202408023

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In order to improve the matching efficiency and accuracy of grid fault handling plan, a semantic enhancement-based grid fault handling plan matching method is proposed. Firstly, the multi-dispatch objects entities in the fault handling plan are characterized as computable word vectors by fine-tuning the hyperparameters of the bidirectional encoder representations from transformers (BERT) model, and integrated into the conditional random field (CRF) model to identify the dispatch objects entity categories. And then, the semantic distance between the grid fault information and dispatch objects are computed based on the residual vector-embedding vector-encoded vector (RE2), and a grid fault handling plan matching model is established based on BERT-CRF-RE2. Finally, through validation of the data of a regional power grid, the proposed model effectively solves the problem of low plan matching accuracy rate.

Reinforcement Method for Transmission Corridor to Enhance Resilience Against Typhoon Disasters

SHI Shanshan, WEI Xinchi, YUAN Zijun, CHENG Haozhong, SU Yun, ZHANG Heng

2025, 58(4): 245-250. DOI: 10.11930/j.issn.1004-9649.202406082

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Identification and reinforcement of critical transmission corridors is an effective measure for power systems to defend against extreme disasters, such as typhoon. Therefore, a two-stage reinforcement method for disaster resilience enhancement is proposed. Firstly, key corridor identification indices, including weighted grid entropy and flow transfer entropy, are introduced based on complex network theory and system state analysis. Secondly, the Batts typhoon model is used to compute real-time wind speeds, followed by Monte Carlo sampling of corridor availability under different wind speeds, generating failure scenarios for transmission corridor reinforcement. On this basis, a two-stage transmission corridor reinforcement method is proposed. Stage 1 aims to minimize the sum of costs such as reinforcement and load loss, considering measures like corridor reinforcement and generator output adjustment. Stage 2 takes into account the integrity of the grid topology, further optimizing the transmission corridor reinforcement strategy with the goal of power accessibility. The study shows that the proposed method integrates both the topology and changes in system state, thus achieving the accurate identification of critical corridors and enhancing system resilience.

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