Table of Content

28 September 2025, Volume 58 Issue 9

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Key Technologies for Enhancing the Grid Connection Safety Capability of New Energy and New Grid-Connected Entities

Wind Farm Active Power Scheduling Method Based on Improved Distributed Congestion Control

XU Jinyu, XU Hui

2025, 58(9):  1-9.  DOI: 10.11930/j.issn.1004-9649.202502026

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To address the problem of significant computational and communication burden, poor robustness and high fault risk when the centralized active power distribution method is applied to large-scale wind farms, a wind farm active power scheduling method based on distributed congestion control is developed. Firstly, considering the mechanical fatigue caused by pitch angle adjustments and the control mode switching due to excessively fast rotor speed reduction, a cost function quantifying the sensitivity of the wind turbine's power increment to pitch angle and rotor speed is introduced. Secondly, a congestion index is designed to optimize power distribution and tracking performance. Finally, the distributed consensus algorithm is used to simplify the calculation of wind turbine power reference, significantly reducing the computational and communication burden on the control center and endowing the proposed method with scalability. A comparative analysis with centralized control methods demonstrates the superiority of the proposed method in power tracking performance, power distribution, and robustness.

Disaster Resistance Enhancement Strategies for Distribution Networks Considering Configuration and Scheduling of Mobile Energy Storage

LIU Yongmei, WU Ming, LI Ying, YUAN Yuqi, XI Yanna, ZHAO Fengzhan

2025, 58(9):  10-22.  DOI: 10.11930/j.issn.1004-9649.202502064

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To mitigate the impact of high-penetration photovoltaic (PV) integration on power quality and enhance the reliability of power supply under extreme disasters, mobile energy storage (MES) with its greater flexibility and cost-effectiveness has been identified as one of the effective measures to improve the disaster resistance of distribution networks. This paper proposes a multi-objective optimization model for determining the configuration of MES under high-penetration PV integration scenarios, considering three key aspects including economic efficiency, vulnerability and MES capacity. Additionally, a two-stage resilience-enhancing optimization and scheduling model is developed for extreme disaster scenarios, which incorporates the initial deployment of separable MES and their emergency response dispatch. By dynamically scheduling the access locations and charge-discharge power of the separable MES, the proposed method ensures emergency power restoration under extreme disasters while maximizing the reuse value of MES during both normal and disaster conditions. Finally, case studies validate the effectiveness of the proposed method.

Leakage Fault Identification of PV-Integrated Distribution Networks Based on CEEMDAN and NRBO-XGBoost

LIU Han, LIU Jindong, LI He, LI Yanli, YU Qiyuan, ZHAO Yuan, GENG Yanan

2025, 58(9):  23-32.  DOI: 10.11930/j.issn.1004-9649.202501003

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To address the problem that existing residual current protection devices are difficult to accurately identify leakage faults in the PV-integrated distribution networks, a leakage fault identification model for photovoltaic-integrated distribution networks based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Newton-Raphson-based optimizer-eXtreme Gradient Boosting (NRBO-XGBoost) is presented. Firstly, the CEEMDAN is used to decompose different leakage signals of the PV-integrated distribution networks. Then, the energy entropy of each decomposed modal component is extracted to construct the leakage fault feature set. Finally, the energy entropy features are input into the NRBO-XGBoost model to achieve the recognition of different leakage states of PV-integrated distribution networks. The effectiveness of the proposed method is verified by the simulation data. The results show that compared with other models, the proposed method has the highest recognition accuracy.

Virtual Inertia Flexible Control of Photovoltaic Storage VSG Based on AHFS and RBF

XIAO Xiangqi, DENG Hanjun, ZOU Sheng, XIAO Jianhong, LI Kai, MA Bin

2025, 58(9):  33-43.  DOI: 10.11930/j.issn.1004-9649.202410050

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To address the issue of active power overshoot and the challenge of system dynamic oscillations in grid integration of traditional photovoltaic and energy storage virtual synchronous generators (VSGs), This article proposes a virtual inertia flexible control strategy for photovoltaic storage VSG based on active high-frequency feedback suppression (AHFS) and radial basis function (RBF). Firstly, a first-order filtering link is introduced to enhance the system's high-frequency suppression capability, and an active power differential term is added to the power feedback loop to modify the dynamic performance of the system. Secondly, an objective function considering both active power overshoot and rise time is constructed, and the optimal active power differential coefficient is determined via particle swarm optimization (PSO) algorithm. Finally, an adaptive control strategy for virtual inertia is designed using RBF neural network, which can flexibly adjust the virtual inertia in real-time based on the system's angular velocity and its rate of change. The coordinated control method effectively mitigates the issues of power overshoot and frequency overshooting in traditional VSGs. Simulation results show that the proposed control strategy can effectively suppress frequency deviation and active power overshoot, thereby enhancing the system's transient stability.

Oscillation Suppression Strategy for Droop Control Converter Based on Active Harmonic Resistance Control

HU Xuekai, MENG Liang, LI Lei, YANG Yang, YIN Yilin, LEI Wanjun

2025, 58(9):  44-53, 67.  DOI: 10.11930/j.issn.1004-9649.202502048

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The droop control converter exhibits capacitive characteristics in the frequency band below 50 Hz, which leads to the problem of RLC oscillation when it interacts with the inductive power grid, thus causing harm to the power grid. Based on this, this paper firstly establishes a sequence impedance model of droop control converter using the harmonic linearization method, which reveals the mechanism of oscillation after the interaction between the droop control converter and the power grid. Then, a control strategy for active harmonic resistance based on droop control converter is proposed, which only requires modifying the voltage loop command of the converter to shape the impedance characteristics (excluding the fundamental frequency) into resistive behavior, thereby avoiding oscillations when interacting with an inductive grid. To address the possible influence of active harmonic resistance on the stability of the system, an adaptive control strategy is proposed to adjust the parameter value, thus ensuring consistent suppression of system oscillation. Finally, simulation and hardware-in-the-loop (HIL) experiments validate the effectiveness of the active harmonic resistance control strategy.

Multi-branch Distribution Network Fault Location Based on LSD Algorithm

JI Xingquan, ZHANG Xiangxing, ZHANG Yumin, YE Pingfeng, WANG Delong, HUANG Xinyue

2025, 58(9):  54-67.  DOI: 10.11930/j.issn.1004-9649.202411045

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In order to solve the problems of low wavefront detection accuracy and high ranging cost of traveling wave fault localization methods in multi-branch distribution networks, a multi-branch distribution network fault localization method based on line segment detector (LSD) is proposed. Firstly, after clarifying the characteristics of the steep straight-line segments of the faulty traveling wave, the fault traveling wave data can be image processed. The LSD algorithm is used to realize the sub-pixel level detection of steep-slope straight lines in the image, and to establish the conversion relationship between the pixel position of steep-slope straight lines and the position of traveling wave data, so as to realize the faulty traveling wave head calibration. Secondly, in order to solve the problem that it is difficult to recognize the subsequent wavefronts, a multi-branch distribution network fault localization technique method based on the combination of single and double-ended traveling wave method is proposed. For the faulty traveling wave, after analyzing the transmission characteristics, the traveling wave screening conditions is formulated based on the arrival time and polarity of the wavefront and the segment traveling wave containing valid information is identified. The location of branch line faults is determined by initial traveling wave, and the location of trunk line faults is determined by extrapolating based on double-ended starting traveling wavefronts. Thus, the accurate localization of faults in multi-branch distribution networks can be realized without the need to increase the measuring devices network simulation model is built in Matlab/Simulink and used to test the proposed method. The simulation results show that the proposed method can effectively calibrate the wavefront and has high fault localization accuracy.

Fault Location Method for Secondary System of Smart Substations Based on Network Flow Algorithm and Deep Neural Network

TAO Jun, ZHONG Ming, ZHANG Yi, LIU Feng, WU Yuzhu, XIA Zhenxing

2025, 58(9):  68-78.  DOI: 10.11930/j.issn.1004-9649.202502061

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The existing intelligent substation secondary system fault location method relies on specific types of fault feature quantities, lacking the ability toively process multiple fault types. It is unable to quickly modify the scheme when facing dynamic changes in the power network. To address this challenge, a fault location method based on the network algorithm and deep neural network (DNN) is proposed. A new fault type classification method is adopted, and the simple fault, pseudo-complex fault, and complex fault are re. A fault feature coding and matrix relationship model are constructed, and the network flow algorithm is introduced to solve the problem of fuzzy positioning of link fault and node fault in complex fault. The network flow algorithm is deeply integrated with the deep neural network model to achieve accurate positioning of intelligent substation secondary system faults. Through simulation example comparison, it is found that the method can not only improve the accuracy of complex fault recognition and shorten the fault location time, but also effectively cope with the dynamic changes of the power system, and improve the fault location.

Communication Link Fault Localization Method Adapted to New Smart Substations Under Partition Collaboration

YE Yuanbo, WANG Jiwen, SUN Zhenxing, WANG Zhiqiang, ZHAO Yongyi

2025, 58(9):  79-87.  DOI: 10.11930/j.issn.1004-9649.202411017

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The new generation of smart substation integrates some physical device, and the physical link carries more information. The link fault is prone to massive alarm information, which increases the difficulty of link localization. To address this problem, communication link fault localization method adapted to new smart substations under partition collaboration was proposed. A graph-theoretic characterization of communication links is first established. The defined node attributes, node relative positions and link connectivity relationships are characterized by the connectivity matrix. Relying on the alarm information deduction to represent the node fault state, the switch node state reconstruction method is constructed to make up for the lack of alarm monitoring in the switch. Based on the idea of partition collaboration, three types of connection units are constructed and the link localization objective function is established. A multi-strategy improved coati optimization algorithm (MSCOA) algorithm is used to solve and to achieve the link localization objective. The localization results based on a 220kV smart substation show that the method is simple to implement, which converges quickly and covers the network comprehensively. The method can achieve accurate localization for different locations and types of link faults, which will provide critical guidance for on-site troubleshooting.

AC Filter Switching Combination Optimization Strategy for "AC-to-DC" System

FENG Xuan, KONG Xiangping, DU Xiaozhou, FEI Juntao, ZHANG Xianmeng, JIANG Qiuyu, LI Zhenxing

2025, 58(9):  88-96.  DOI: 10.11930/j.issn.1004-9649.202504051

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The "AC-to-DC " high voltage direct current transmission project can effectively improve power transmission efficiency and address challenges such as limited space for new transmission lines. However, during actual operation, the switching of AC filters can cause harmonic distortion, affecting power quality. To address this issue, this paper analyzes field-recorded waveform data, extracts key characteristics during the AC filter switching process, and proposes engineering optimization measures. Additionally, considering the limitations of practical operational methods and experimental conditions, a refined simulation model was established based on actual system parameters. Using reinforcement learning algorithms, an optimized switching strategy for AC filter combinations was proposed. Simulation results demonstrate that this strategy can effectively reduce the total harmonic distortion (THD) and significantly improve system power quality, providing crucial technical sup-port for the stable operation of "AC-to-DC Conversion" projects.

Multi-objective Optimization Control of Flexible Distribution Networks with Microgrid Clusters Based on Improved Memetic Algorithm

ZHAO Shanshan, Gao Fei, LI Yajie, LI Jianfang, ZHANG Yu, DUAN Xiangjun, DONG Weijie

2025, 58(9):  97-104.  DOI: 10.11930/j.issn.1004-9649.202411003

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To fully leverage the coordination control of the soft open point (SOP) for distributed power generation, electric vehicles, energy storage, and other diversified micro groups, this paper investigates the coordinated optimization control strategy for flexible distribution networks with microgrid groups. The functions of different types of SOP access and the operation control modes of S are analyzed. Considering the load balancing and peak-valley shifting effects of SOP access and the economy of power grid operation, a multi-objective optimization model with the minimization of load variance, load rate variance, and active power network loss is established. Considering the complexity of the flexible distribution network, an improved memetic algorithm is proposed to solve the multiobjective optimization problem. Through simulation verification, it can be known that the proposed optimization model can reduce network loss and balance load by controlling SOP.

New-Type Power Grid

Cooperative Scheduling of Active Distribution Network Based on Two Layer Master Slave Game

WANG Shiqian, HAN Ding, WANG Nan, BAI Hongkun, SONG Dawei, HU Caihong

2025, 58(9):  105-114.  DOI: 10.11930/j.issn.1004-9649.202412002

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To solve the problem of insufficient power demand side resources mobilization, this paper proposes an active distribution network bilevel game collaborative dispatch model based on-subject master-slave game and comprehensive demand response. A multi-interest subject differential benefit model is designed to reflect the multi-type power demand response and carbon trading situation. order to seek the optimal goal of themselves, the lower multi-subject master-slave game decision model is proposed, the leader is microgrid operators, the followers include energy storage, distributed power generation operators and load aggregators, etc. The active distribution network bilevel game collaborative dispatch model is established, and the different goal characteristics of the system are. The model is solved by application of solver and the Sparrow Search Algorithm. The simulation example shows that the proposed method can coordinate multi-type power demand response of different levels ensure the economic benefits of the main participants, and improve the overall comprehensive benefits of the system.

Analysis and Optimization of Voltage Dynamic Control Performance of Renewable Energy Microgrid based on Grid-Forming Energy Storage

ZHANG Chenyu, YU Jianyu, SHI Mingming, LIU Ruihuang

2025, 58(9):  115-123.  DOI: 10.11930/j.issn.1004-9649.202411028

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The configuration of grid-forming energy storage devices can effectively suppress the voltage dynamic overvoltage caused by load switching and distributed new energy output fluctuation in new energy microgrid. However, the existing research is mostly based on the single-machine grid-forming energy storage sensitive line access model, ignoring the influence of the system active-active coupling and the ratio of the microgrid line resistance to inductance, and only the conclusion that the reactive control parameter is the dominant factor of the voltage dynamic control can be obtained Therefore, this paper constructs a system voltage dynamic model containing grid-forming energy storage, distributed new energy and microgrid network, and uses the mapping relationship between the minimum singular value of closed-loop response matrix of the system voltage dynamic model and the voltage dynamic control performance to analyze the influence of the configuration of the grid-forming energy storage control parameters on the dynamic control performance under different resistance-inductance ratios. A parameter design method for the grid-forming energy storage with the goal of enhancing the voltage dynamic control performance is proposed verified by a simulation system. The proposed method reveals the synergy and constraint relationship between the active and reactive control parameters of the grid-forming energy storage under different resistance-induct ratios.

Frequency Control Method for Regional Power Grids Based on Model Predictive Control

LIN Jikeng, SHI Tao

2025, 58(9):  124-137.  DOI: 10.11930/j.issn.1004-9649.202503050

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The volatility and rapid variation of renewable generation pose new challenges to frequency control in new-type power systems. To address this problem, this paper proposes a novel regional grid frequency-control method that combines model predictive control with stochastic optimization techniques. First, a prediction-driven frequency-control framework is established for the regional grid, in which the tie-line power deviations caused by neighboring-area frequency fluctuations are treated as local disturbance variables, effectively decoupling the local frequency-control process from neighboring regions. Next, an online estimation scheme for system inertia constant is developed via system-parameter identification. Together with an adaptive online kernel density estimation technique, probabilistic prediction models are constructed for unit inertia time constants, load demand, renewable power output, and neighboring-area frequency deviations, enabling accurate representation of multiple uncertainties. Building on these models, a stochastic-model-predictive-control-based frequency control optimization model and its fast solution algorithm are formulated. Finally, the proposed method is validated on a modified IEEE-39-bus system. The case study results show that, compared with the deterministic strategy and the strategy that considers only power-disturbance uncertainty, the proposed method improves the mean value of the control performance standard by 14.98 percentage points and 11.38 percentage points, respectively, and reduces the mean absolute area control error by 5.30 MW and 2.22 MW, respectively, which confirms the effectiveness and superiority of the proposed method. The proposed model and algorithm offers a valuable reference for regional grid frequency control in new-type power systems with high renewable penetration.

Key Issues in Security and Stability Analysis for the "15th Five-Year" Power Grid Planning

ZHOU Qinyong, HAO Shaoxu, DONG Wu, ZHANG Libo, ZHANG Jian, HE Hailei, ZHAO Leilei

2025, 58(9):  138-147.  DOI: 10.11930/j.issn.1004-9649.202504017

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The "15th Five-Year Plan" period represents a critical phase for China to achieve its landmark carbon peaking target. With the development of new energy power generation exceeding expectations, power grid planning faces unprecedented challenges and opportunities. This paper examines the technical characteristics of power grids during this period, including the normalization of high-proportion new energy output, the dynamic process coupling of transmission and distribution networks, and the weakening effect of system scale growth on stability improvement. To address these characteristics, the paper proposes key research directions for security and stability analysis in grid planning during the "15th Five-Year Plan" period, which include: screening typical scenarios for stability analysis, load modeling incorporating distributed generations, load capacity of power electronics-dominated generation systems, and comprehensive short-circuit current management. In technological innovation, the paper suggests such scenario-specific studies as application of grid-forming technologies, optimal selection of transmission technology solutions, etc.

Frequency Security Constrained Optimal Operation of Low-Inertia Power Systems: Review and Prospects

FU Guobin, YANG Kaixuan, SUN Haibin, WEN Qixuan, ZHAO Huanbei, WEN Yunfeng

2025, 58(9):  148-163.  DOI: 10.11930/j.issn.1004-9649.202502018

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With the rapid increase in penetration rate of power electronic interface resources, the level of power system inertia is significantly reduced, and the frequency dynamic characteristics of the power system dominated by the traditional synchronous machine undergoes a profound change. In this paper, we systematically sort out the key issues, modeling methods and solution strategies for the frequency security constrained optimization operation of low-inertia power systems, analyze the limitations of the existing researches, and propose the future research directions. Firstly, on the basis of elucidating the difficult problems and challenges facing the optimal operation of low-inertia power systems, the research progress of the optimal operation model of low-inertia power systems is summarized from three time scales: day-ahead, intra-day, and real-time. And then, the multi-category reconstruction methods for nonlinear frequency security constraints and the approaches to handle source-load uncertainty are discussed in-depth. Finally, the cutting-edge directions such as multi-timescale coordinated optimization and interactive influences of power electronic devices are outlined, aiming to provide insights for optimizing operation and ensuring frequency security of China's new power system under low-inertia conditions.

Adaptive Load Frequency Control Strategy for Interconnected Power Systems Considering Stochastic Access/Exit Behaviors of Energy Storage Systems

MAN Linkun, WU Keming, CHI Cheng, YOU Jinshi, ZHAO Zitong, ZHANG Yajian

2025, 58(9):  164-174.  DOI: 10.11930/j.issn.1004-9649.202502011

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Utilizing energy storage systems (ESSs) to participate in load frequency control (LFC) can effectively improve the frequency stability of power systems. However, due to such factors as state of charge or sudden physical failures, the random access or exit of ESSs may cause uncertain changes in LFC structure, which is not conducive to the controller design. In this paper, an asynchronous switched adaptive LFC strategy is proposed. Firstly, a series of possible scenarios for ESSs to participate in LFC were pre-set. For each scenario, the control parameter design constraints were determined by constructing Lyapunov functionals. Secondly, to address the uncertainties of LFC structure caused by random access/exit of ESSs, update criteria for control parameters switching between any two scenarios were derived based on the average dwell time technique. Finally, to minimize the average dwell time between any two operating scenarios, an LFC parameter optimization method based on harmony search was designed to effectively improve the tolerance of the power system for stochastic energy storage access/exit behaviors. Simulation results show that compared with existing control schemes that do not consider the factors of access/exit of ESSs, the proposed method can effectively reduce the impact of ESSs' access/exit on the dynamic behaviors of LFC system.

A Quantitative Calculation Method for Carbon Emission Reduction Contribution of Power Grid

ZHANG Xing, CHAI Yufeng, HAN Xinyang, WANG Xubin, YIN Jianguang, ZHANG Xinsheng

2025, 58(9):  175-182.  DOI: 10.11930/j.issn.1004-9649.202410052

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The power grid serves as an important platform connecting electricity production and consumption, playing a crucial role in carbon emission reduction across society. However, currently, there is a lack of quantitative research on the power grid's contribution to facilitating carbon emission reductions from grid-connected entities. Based on the contribution allocation concept of the Shapley value method and considering both simplicity and rationality, this paper proposes a method for quantifying the power grid's contribution to the carbon emission reduction of grid-connected entities. The proportion of the power grid's contribution to the carbon emission reduction is determined by the marginal effect of the power grid on the emission reduction and can be calculated through a simplified formula. Through empirical data or theoretical calculation, this study demonstrates the carbon emission reduction of grid-connected entities with and without the power grid, and based on the relative magnitude of emission reduction under these two scenarios, the power grid's contribution to carbon reduction can be categorically discussed. When the presence or absence of the power grid does not affect the carbon emission reduction of grid-connected entities, the power grid's contribution to carbon emission reduction is zero; when the absence of the power grid renders grid-connected entity unable to achieve effective carbon emission reduction, the power grid's contribution is maximized. This method provides a novel approach and perspective for evaluating the power grid's contribution to social carbon emission reduction, offering a theoretical basis for quantifying the power grid's contribution to the low-carbon transition of society.

Power Market

A Forecast Method for Electricity Spot Market Clearing Prices Based on Fractal Theory

WU Wenzu, WANG Xuhui, LU Yuan, CHEN Wan, TIAN Shijin, CHEN Xian

2025, 58(9):  183-193.  DOI: 10.11930/j.issn.1004-9649.202502005

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To address the complex fluctuations of electricity spot market clearing prices with high proportion integration of new energy, this paper proposes a clearing price prediction method based on fractal theory. Firstly, the clearing price fluctuations are analyzed from monofractal and multifractal dimensions, and corresponding prediction models are constructed. Secondly, based on the monofractal prediction method, the long-term memory and self-similarity of clearing prices are quantified using the Hurst indices and box dimension; based on multifractal method, the local fluctuation anomalies caused by sudden changes in new energy output are effectively captured via pattern matching. Thirdly, the proposed method is verified with clearing price data from five regions, which shows that, compared to the traditional "ARIMA + neural network" approach, the proposed method significantly improves the prediction accuracy, with the maximum error decreasing from 43.95% to 8.41%. In-depth applicability scenario analysis indicates that, when the Hurst index is large, the monofractal-based prediction method is more applicable; when multifractal features dominate, the multifractal-based prediction method is preferable.

Decentralized Peer-to-peer Trading Strategy Considering Flexibility of Multiple Microgrids

HUANGFU Xiaowen, LI Ke, XU Changqing, JIANG Xiaoliang, YU Haozheng, WANG Rujing

2025, 58(9):  194-204, 218.  DOI: 10.11930/j.issn.1004-9649.202411059

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Active distribution network dominated by renewable energy need to incentivize the participation of various distributed energy resources to enhance system flexibility. To address the power fluctuation problems caused by the large-scale access of renewable energy, this paper firstly introduced a shared energy storage system (SESS) and a peer to peer (P2P) energy trading mechanism, with full consideration of the interactions between active distribution networks and microgrids (MG), and investigated the multi-MG power fluctuation mitigation strategy and the P2P flexibility trading strategy based on SESS leasing. Secondly, a multi-MG revenue allocation mechanism based on asymmetric Nash bargaining was proposed and a nonlinear energy mapping function was used for evaluating each MG's contribution in order to attain fair allocation of cooperative revenue. Finally, the proposed model was solved distributively using the alternating direction method of multipliers (ADMM), effectively protecting information security for all stakeholders. The effectiveness of the proposed method was then demonstrated through comparative case studies.

A Trusted Contract-Based Trading Model for Distributed New Energy Aggregation Trading

ZHANG Nan, GUO Qinglei, DU Zhe, WANG Dong, ZHANG Yan, ZHAO Liang, JIANG Yu

2025, 58(9):  205-218.  DOI: 10.11930/j.issn.1004-9649.202503034

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Current pooled green electricity transactions face dual challenges: inefficient supply-demand matching and the absence of reliable trust frameworks. Addressing these issues calls for a multi-technology approach to establish a flexible and trustworthy trading system. To address this, this paper innovatively integrates the subjective logic trust evaluation model, the blockchain's immutable evidence storage mechanism, and the smart contract's automatic execution framework to construct a multi-technology collaborative trusted contract-based trading model. This model builds a reputation quantification model based on the analysis of transaction behavior characteristics to dynamically depict the evolution law of the credibility of participating entities; design a distributed game matching mechanism based on the firework algorithm to achieve autonomous and optimized matching of participating entities; and further develops a full-process smart contract chain based on blockchain to realize the trusted and automated execution of the closed loop from aggregated declaration, matching to fund settlement. Simulation results show that this solution significantly improves transaction performance compared to the traditional model: the dynamic reputation mechanism enables real-time assessment of the credibility of transaction entities, effectively purifying the trading environment; the consumption rateincreases by 46.5% and the revenue increases by 38.2%; the contract execution delay is reduced by 49.7% and the throughput is increased by 55%.