Table of Content

28 June 2026, Volume 59 Issue 6

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Intelligence, Green, Resilience: Technology and Market Integration for the New Electricity System Toward 2035

The connotation, characteristics and construction ideas of the new rural energy system

WANG Peng, SHI Zebang, LIU Hongtao

2026, 59(6):  1-12.  DOI: 10.11930/j.issn.1004-9649.202508059

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The new rural energy system is the intersection of the two major strategies of green and low-carbon energy transition and rural revitalization. Clarifying its connotation and construction ideas has the theoretical value for achieving high-quality development. Based on an analysis of the development history and current situation of rural energy, combined with the evolution process of the energy system and the demands of agricultural and rural modernization, this paper proposes connotative characteristics of the new rural energy system, including green and low carbon, simplicity and timeliness, multi-energy integration, intelligence and appropriateness, rule-abiding harmony, and common prosperity. Aiming at the existing problems in the current rural energy system in terms of consumption structure, supply-demand coordination, utilization forms, technical system, governance system, and achievement sharing, the ideas, measures, and key technologies for constructing the new rural energy system are put forward.

Analysis on the spatiotemporal evolution mechanism of electricity consumption inequality in China

LIU Tian, ZHANG Shining, WENG Yuwei, ZHANG Chaoyi, LIU Tongming

2026, 59(6):  13-23.  DOI: 10.11930/j.issn.1004-9649.202508055

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Energy transition needs to balance equity, which is a core demand of the United Nations Sustainable Development Goals. From the dual perspectives of population and regional area, this study calculates the Gini coefficient of electricity consumption to quantitatively assess the spatiotemporal evolution of electricity consumption inequality, and employs a Bayesian linear regression model to identify its key driving factors. The findings indicate that significant disparities exist in the level of electricity consumption inequality between China and the global average, with China's rapid power development playing a crucial role in mitigating global electricity consumption inequality. The gravity center of electricity consumption has shown a trend of "shifting south – advancing west – stabilization" in turn, with the westward movement driving a decline in the Gini coefficient of electricity consumption. GDP and urbanization rate are positively correlated with the equalization of electricity consumption distribution, and the expansion of transmission lines of high-voltage and above has played a role in cross-regional resource allocation. Based on the analysis of key driving factors, a multidimensional development path for promoting the balanced development of electricity consumption is proposed, which takes economic activation, gap reduction, technological empowerment, and institutional reform as the key measures.

Deep reinforcement learning-driven decision-making paradigm for electricity-carbon-hydrogen collaboration

ZHANG Fuchun, CHEN Wenjun, ZENG Tianze, LIU Nian, GUO Hongzhen, LIU Dunnan, WANG Peng, XU Chuanbo

2026, 59(6):  24-36.  DOI: 10.11930/j.issn.1004-9649.202602036

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To achieve synergistic optimization of low-carbon energy systems, electricity-carbon-hydrogen synergy has become one of the critical pathways. However, its high dimensionality, nonlinearity, and strong uncertainties limit traditional optimization methods. Deep reinforcement learning (DRL), with its ability to learn from data, adapt to dynamic environments, and support multi-objective decision-making, offers a promising solution. This paper reviews the mechanisms of electricity-carbon-hydrogen synergy and the necessity of applying DRL, summarizing recent progress in electricity markets, carbon markets, electricity-carbon synergy, electricity-hydrogen synergy, and their integration. The results show that DRL holds significant potential for enhancing renewable energy integration, optimizing carbon trading, and coordinating multi-energy flows, though challenges remain in model complexity, interpretability, safety, and multi-objective trade-offs. Future research should focus on integrating DRL with large language models, improving robustness, safety, and interpretability, and enabling cross-scale coordination to facilitate practical deployment.

Intelligent substation model conversion method based on multi-dimensional similarity optimization and XSLT

SHI Hengchu, YANG Qiaowei, YOU Hao, XU Shoudong, CHEN Xiaofan, HU Xiao

2026, 59(6):  37-47.  DOI: 10.11930/j.issn.1004-9649.202510087

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In order to meet the higher requirements of information interconnection put forward by the in-depth construction of intelligent substations, it has become a key bottleneck in the development of intelligent substations to solve the heterogeneous problem of IEC 61850 substation configuration description (SCD) models caused by the differentiation of technical routes and topological configurations of equipment manufacturers. In order to solve the problem of semantic conflict and inefficient transformation, a model transformation method based on multi-dimensional similarity optimization and extensible style sheet language transformations (XSLT) is proposed. Firstly, the ontology parsing of the common information model (CIM) model is realized through the co-mapping of unified modeling language (UML) and web ontology language (OWL). Furthermore, a node similarity calculation model combining syntactic, semantic and structural three-dimensional features is proposed, and the F-value optimization is introduced to determine the optimal weight and matching threshold, so as to realize the accurate mapping from CIM to IEC 61850 model. Finally, the XSLT script is automatically generated based on the mapping relationship, and the automatic conversion and semantic adaptation of the SCD file are completed by combining the Dom4j tool library. The simulation results show that the proposed method takes only 3.2 seconds to convert under the premise of completely retaining the source model equipment and topology information, and the efficiency is increased by more than 96% compared with the manual method, and provide reliable technical support for the multi-level integration of intelligent substations and the coordinated operation of the power grid.

A power regulation method for improving operation and maintenance efficiency of large-scale dynamic reconfigurable battery energy storage power stations

HAN Chenhui, ZHANG Chengjie, WANG Jinyu, WANG Songtao, CI Song, ZHOU Yanglin

2026, 59(6):  48-59.  DOI: 10.11930/j.issn.1004-9649.202511032

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Dynamically reconfigurable batteries can achieve intrinsically safe operation through flexible topological reconfiguration by an internal power electronics network. However, the real-time and independent reconfiguration of the battery network inside each energy storage unit leads to disparate operating states and inconsistent constraint boundaries among the massive energy storage units in the corresponding large-scale energy storage power stations, which significantly increases the operation and maintenance costs of the power stations. To address this issue, this paper proposes a dynamic power optimization control method that fully accounts for the operating states and multidimensional dynamic boundary constraints of the massive energy storage units in large-scale dynamically reconfigurable battery energy storage stations. The method fully takes into account the constraint boundaries of energy storage units after each dynamic reconfiguration, and establishes a system operation and maintenance optimization model incorporating key factors such as the degradation cost, temperature regulation cost, operation and maintenance cost of dynamically reconfigurable batteries, and the state of charge balance. In addition, a multi-variate fixed weight method is applied to determine the optimization weights of each factor. Finally, case study simulations demonstrate that the proposed method can significantly improve the operation and maintenance economy of the power station while ensuring the independent operation constraints of the massive energy storage units in the dynamically reconfigurable battery energy storage power station, and simultaneously enhance the consistency of their state parameters.

Innovation and Key Technologies of Coupled Operating Mechanisms for a Unified National Electricity Market

A dual-attention TCN-BiGRU short-term electricity-carbon price coupling prediction method incorporating time-series fluctuation information mining

LIU Siyu, ZHANG Cheng, JIANG Tao, XIAO Ya, YI Yawen, ZHANG Yuxin, CHEN Xinyu

2026, 59(6):  60-75.  DOI: 10.11930/j.issn.1004-9649.202601038

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Accurate forecasting of short-term spot electricity and carbon prices is crucial for decision-making in electricity market trading, carbon market operation, and their coordinated management. However, electricity and carbon price series are influenced by multiple complex factors, including energy structure, policy regulation, and renewable energy fluctuations, exhibiting high volatility and nonlinearity, which poses significant challenges to forecasting accuracy. Therefore, this paper proposes a dual-attention temporal convolutional network, bidirectional gated recurrent network (DA-TCN-BiGRU) short-term electricity-carbon price coupling forecasting method considering time-series fluctuation information mining. First, the central collision optimization-based variational mode decomposition algorithm is used to decompose the electricity and carbon price series into multi-frequency subsequences, so as to fully extract their fluctuation modes at different time scales. Second, the correlation strength between each feature in the high-dimensional feature set and electricity-carbon prices is evaluated based on the maximal information coefficient, and key features are selected. On this basis, a dual-attention TCN-BiGRU deep learning model is constructed to forecast carbon prices, and the predicted carbon price values are further input into the same framework as key exogenous variables to predict electricity prices, achieving progressive coupling forecasting of electricity-carbon prices. Finally, the case study based on actual data from the Hubei Province electricity-carbon market shows that the proposed method has higher accuracy and stronger stability in electricity-carbon price prediction, verifying the effectiveness and superiority of the model.

Operation optimization of central heating system coupled with green power peak-shaving based on bounded rationality

ZHOU Yong, SHA Xueli, LIU Yanfeng, LI Xiang

2026, 59(6):  76-88.  DOI: 10.11930/j.issn.1004-9649.202601048

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To address the pronounced problems of long regulation time lag and severe indoor temperature unevenness in central heating, the central-heating peak-shaving systems achieves regulation optimization through a combined heating mode of basic central-heating guarantee and fluctuating supplement by the peak shaving system, and has become one of the prevailing heating configurations in northern China. Meanwhile, the central-heating peak-shaving system coupled with distributed green power can facilitate building–grid interaction, enhance renewable power accommodation, and satisfy residents' personalized thermal comfort demands. Therefore, this paper proposes a central-heating peak-shaving system coupled with distributed green power and oriented toward personalized heat demands, and incorporates the bounded rationality of residents participating in central-heating demand response into the modeling. A cost–comfort model is employed to characterize the demand-response behaviors of heterogeneous users, and a bi-level pricing and scheduling optimization framework for heating enterprises and users is established to achieve coordinated optimization of green power accommodation and heating benefits. Taking a residential community in Xi'an for case study, the results show that the daily average accommodated green power reaches 10~18 MW, the operating cost of the heating enterprises is reduced by 36.5%, and the user temperature compliance rate is increased from 2.9% to 90.3%. The proposed approach can alleviate the scheduling deviation caused by the idealized assumption of residents' demand response, improve system economy and terminal comfort guarantee level, and provide a reference for the participation of green power in heating peak shaving and the design of differentiated pricing mechanisms.

Provincial optimal decision-making technology based on multi-time-scale combined agent power purchase review

LI Bin, LI Ruosong, MENG Zixuan, ZHANG Yumeng

2026, 59(6):  89-100.  DOI: 10.11930/j.issn.1004-9649.202506029

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With the advancement of electricity market reform, industrial and commercial users will gradually enter the market, while the users not yet entering the market will purchase electricity through agents from grid companies. As key entities in agent-based electricity purchase, grid companies play a significant role in coordinating electricity purchases across multi-time-scale markets to reduce purchase costs, which is crucial for their operational efficiency. This study outlines the workflow of agent power purchase for power grid companies and establishes an annual power purchase decision review and optimization model that considers annual, monthly, and spot markets. The model is solved using the genetic algorithm and convolutional neural network (CNN) - long short-term memory network (LSTM) - attention mechanism (Attention), and the model's effectiveness is verified through case studies. The review results show that adjusting the power purchase ratio between the medium and long-term market and the spot market can effectively achieve cost reduction and efficiency improvement of power purchase strategies.

A fast assessment method for multiple values of power systems based on VCG mechanism and state-space compression

FAN Menghua, ZHANG Shengnan, DAI Fengzhe, ZHENG HaiFeng, ZHANG Xiaoxuan, LIU Zhaoxi, JING Zhaoxia

2026, 59(6):  101-111.  DOI: 10.11930/j.issn.1004-9649.202508002

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With the increasing penetration of renewable energy resources (RES), different types of electricity market entities, such as traditional thermal power, energy storage, and RES, have demonstrated distinct differentiated characteristics in terms of the core attributes of supporting the operation of power systems and society. To address the issue that the current electricity market mechanism struggles to comprehensively and accurately quantify the contributions of different market entities in dimensions such as safety and environmental friendliness, this paper proposes a multi-value evaluation method for power systems oriented towards multiple types of entities. Firstly, an evaluation index system and calculation model covering economic value, security value, and environmental value are established. The Vickrey-Clarke-Groves (VCG) auction mechanism is introduced, and based on the joint optimization clearing model of main and auxiliary services in power systems, the multiple values of various market entities are quantified through alternative benefits. Secondly, to tackle the problems of heavy computational burden in the VCG framework and high solving complexity of the security-constrained unit commitment (SCUC), a parallel solving acceleration algorithm based on time-period decoupling and state-space compression is proposed. Case studies are conducted on the modified IEEE 30-bus and IEEE 118-bus systems. The results demonstrate that the proposed method comprehensively and objectively evaluates the multiple values and externalities of various market entities, and the proposed acceleration algorithm significantly improves the solving efficiency of mixed-integer linear programming (MILP) problems while meeting the requirements of solving accuracy.

Power user classification and recognition method based on multimodal hybrid features

ZHANG Haijing, LIU Yijuan, SHAN Shuaijie, JIANG Yuan, FENG Yankun

2026, 59(6):  112-124.  DOI: 10.11930/j.issn.1004-9649.202504090

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To address the irregularity and diversity of electricity consumption behavior among proxy power purchase users, this paper proposes a classification and recognition method based on multimodal hybrid features combining one-dimensional statistical features, Gramian Angular Field (GAF), and Recurrence Plot (RP), namely, 1D-GAFs-RP, to realize accurate identification of power consumer types. Initially, in accordance with the current time-of-use electricity price policy, the annual electricity consumption data of users are divided into four typical consumption curves corresponding to spring, summer, autumn, and winter. Additionally, considering the differences in electricity consumption behaviors between weekdays and holidays, the typical consumption curves for holidays are extracted. Subsequently, based on these five typical consumption curves, the statistical features such as users' price sensitivity coefficient and electricity consumption stability are calculated. Combining these with shape distances, the k-shapes clustering method is employed to generate user type labels. Finally, by applying GAFs and RPs to visualize the annual time-of-use electricity consumption curves and integrating them with statistical features, a multimodal hybrid feature-based user classification and recognition model is constructed. Experimental results indicate that the k-shapes clustering method based on shape distances can accurately classify user types. The proposed user type recognition scheme achieves an identification accuracy rate of over 94% for each cluster label, providing effective technical support for user-side management of proxy power purchase companies.

New Energy and Energy Storage

Wind power prediction based on PCA and SOFTS fusion model

CHEN Zhongzhong, GENG Xiaofei, DONG Xiangming, LI Lianghao, HU Jiawei, WU Congwen, KANG Fuquan

2026, 59(6):  125-132.  DOI: 10.11930/j.issn.1004-9649.202510044

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In order to improve the prediction accuracy and robustness of non-stationary wind power series, a wind power prediction method based on principal component analysis (PCA) and series-core fused time series forecaster (SOFTS) is proposed in this paper. Firstly, PCA is used to calculate the principal components of wind power variables to construct model feature variables, which are input into the SOFTS multi-layer perceptron for wind power related variables sequence encoding. The core representation of each input sequence is obtained based on the spatiotemporal aggregation and redistribution module. Secondly, complex correlations and global feature information between channels are captured effectively through horizontal and vertical intersections. Finally, by training the SOFTS network parameters, a wind power prediction model based on PCA and SOFTS fusion is established. The actual operation data of a wind farm in a certain area is used for verification. The prediction accuracy of the proposed wind power prediction model is 98.38%. Compared with other models, it has higher prediction accuracy and stronger robustness to non-stationary sequences.

Multi-time scale optimal scheduling of port microgrids considering flexible loads and electric ships

LI Hao, LI Huangqiang, XIANG Kun, YANG Lingxi, MA Hui

2026, 59(6):  133-144.  DOI: 10.11930/j.issn.1004-9649.202507065

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To address the problems of limited wind-solar accommodation capacity and insufficient utilization of adjustable resources in port microgrids, this paper proposes a multi-time scale optimal scheduling strategy for port microgrids considering flexible loads and electric ships. Firstly, the system architecture of the port microgrid is established with consideration of adjustable resources including flexible loads and electric ships. Secondly, the response model of port flexible loads and the charging-discharging model of electric ships are constructed. On this basis, a multi-time scale optimal scheduling model is built. In the day-ahead phase, the flexible loads baseline power and electric ships scheduling plan are formulated with the goal of minimizing the system operation cost; in the intraday phase, a rolling optimization model is established, realizing the dynamic matching of source-grid-load-storage by dynamically adjusting the response strategy of flexible loads and the charging-discharging power of electric ships. Simulation results indicate that compared with the day-ahead scheduling scenarios without considering flexible loads, electric ships and energy storage, the proposed method can reduce the total operating cost of the port microgrid by 20.2% and increase the local wind power accommodation rate by 8.1 percentage points in the intraday optimization phase. Furthermore, the economic benefits and wind power accommodation performance of the proposed method is superior to the scheduling results under single-scenario conditions, highlighting the comprehensive advantages of multi-source coordination and multi-time scale optimization.

Equivalent energy storage model for district heating network and its application in non-iterative combined electricity-heat scheduling

WENG Liangtao, XIAO Tianying, ZHENG Weiye

2026, 59(6):  145-153.  DOI: 10.11930/j.issn.1004-9649.202509039

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District heating networks exhibit considerable energy storage potential due to their thermal inertia, enabling sufficient operational flexibility for power systems. However, the complex energy transfer characteristics and data privacy concerns of heating networks pose great challenges to the accurate evaluation of their storage capability. To address this issue, an approximate model considering dynamic properties of heating networks is proposed to aggregate the storage capacity of heating networks. First, the flexibility of the heating network is modeled as a physically meaningful equivalent energy storage model to quantify the thermal storage capacity of pipelines. Building upon this, the basic polyhedron translation and scaling method is employed to determine the parameters of the heating network equivalent energy storage model. Furthermore, the proposed flexibility aggregation model is applied to the coordinated optimization of integrated electricity-thermal energy systems, and numerical simulations are conducted for validation. Simulation results demonstrate that the proposed aggregation model can solve the combined power-heat dispatch problem non-iteratively while avoiding the detailed heating network modeling required by conventional centralized approaches. Meanwhile, the optimized results can maintain a high level of computational accuracy.

Equivalent modeling of large-scale photovoltaic hydrogen production stations driven by physics-data collaboration

BAI Zhijun, LI Rui, LI Jiankang, HE Chenglong, HU Wei, LIU Yongpan, YUAN Tiejiang

2026, 59(6):  154-165.  DOI: 10.11930/j.issn.1004-9649.202507070

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To address the problem of the traditional single-driven modeling methods being difficult to accurately characterize the complex dynamic characteristics of photovoltaic (PV) hydrogen production stations under different operating states, an equivalent modeling method for large-scale photovoltaic hydrogen production stations driven by physics-data collaboration is proposed. Firstly, key factors that can characterize the station's dynamic characteristics are extracted based on physical mechanism analysis. Secondly, a sparse autoencoder is used for dimensionality reduction of the key factor data; under single-phase fault, the data dimension of a single PV unit can be reduced to 3 dimensions with an information retention rate as high as 92.2%. Then, the spectral clustering algorithm is applied to cluster the dimensionality-reduced data features to achieve the equivalent modeling of the entire station. Finally, the effectiveness and accuracy of the proposed equivalent modeling method are verified through single-phase and three-phase fault simulations on the PSCAD/EMTDC platform. Quantitative comparison results indicate that under single and three-phase fault, the fitting accuracy of the power response curve of the proposed equivalent model is superior to the traditional physical-based grouping method and the pure data-driven method.

Multi-dimensional evaluation of renewable energy plant access strategy considering node centrality

ZHANG Yubao, GAO Yuhan, XIE Feng, XIONG Chao, HE Cuichao, LIU Hongtao, WANG Peng

2026, 59(6):  166-178.  DOI: 10.11930/j.issn.1004-9649.202508011

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At present, the installed capacity of renewable energy in China is growing rapidly. However, differences of renewable energy stations in access nodes, grid connection structures, and organizational modes lead to unclear grid-integration characteristics, posing a critical challenge to power system planning. This paper uses the eigenvector centrality method to classify grid-accessible nodes, and combines different organizational modes and access structures to build typical access scenarios for simulation. An evaluation index system is established covering economy, reliability, and settlement convenience. Comparative analysis results show that short connections with fewer power flow constraints have better economic performance, with construction costs reduced by 3%~12% compared with alternative schemes. Direct connection at conventional nodes offers high reliability, yet elaborate structures are needed to improve system resilience under extreme weather conditions. In addition, Metering and liability allocation become complicated and settlement convenience declines markedly when the number of access entities exceeds four. This work provides a theoretical references for refined power system planning.

New-Type Power Grid

Collaborative optimization strategy for flexible distribution areas based on comprehensive control devices

WANG Shuzheng, DONG Weitong, WU Zhi, SUN Yuzhu

2026, 59(6):  179-191.  DOI: 10.11930/j.issn.1004-9649.202506042

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To address the problems such as increasing line losses, voltage limit violations, and three-phase unbalances caused by the integration of high-penetration distributed photovoltaic (PV) into distribution areas, this paper proposes a collaborative optimization strategy based on comprehensive control devices. By establishing a multi-area distribution network optimization model that considers the operational costs of the control devices, distribution network losses, three-phase voltage unbalance degree, and voltage deviation penalties, the strategy employs symmetric semi-definite programming (SDP) combined with convex relaxation techniques to resolve non-convex and nonlinear problems, achieving coordinated optimization of comprehensive control devices and distribution areas. Finally, simulation results verify that the proposed strategy can ensure efficient PV accommodation and optimal economic performance of the comprehensive control devices, while significantly reducing distribution network losses and improving voltage deviation and three-phase unbalance degree.

Risk identification and prevention in substation operation and maintenance using JHA-HAZOP methodology

ZHANG Shaofan, NIU Zhenyong, OU Yanmin, LI Yanhong, HONG Jimao

2026, 59(6):  192-201.  DOI: 10.11930/j.issn.1004-9649.202510088

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With the expansion of power grid scale and the intensification of equipment aging issues, the identification and prevention of potential risks in substation operation and maintenance have become significantly more challenging. Traditional safety management methods exhibit notable shortcomings in coverage scope and targeted measures. To address this issue, a risk identification and prevention strategy based on Job Hazard and Hazard Operability Analysis (JHA-HAZOP) is proposed. First, the substation operation and maintenance process is decomposed into tasks using Job Hazard Analysis (JHA) to establish a systematic analytical framework. Second, the deviation identification logic from Hazard and Operability Analysis (HAZOP) is introduced to create a quantitative mapping model for deviation recognition and risk control, enabling systematic identification and assessment of potential risks. Finally, an information technology-based safety risk control system is developed to achieve closed-loop risk management. Simulation and case validation demonstrate that the introduction of a benefit-cost ratio model shows that optimal solutions in typical scenarios achieve approximately 66.7% higher input-output efficiency compared to conventional approaches. The proposed method outperforms traditional methods in both management precision and resource allocation efficiency, effectively enhancing the safety management level of substation operations.

Reliability assessment of SLCC DC transmission system based on GO-DBN

SHEN Xiaolin, DU Shangan, LIANG Chenguang, WU Fangjie, JI Yiming, CUI Dawei

2026, 59(6):  202-210.  DOI: 10.11930/j.issn.1004-9649.202509044

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The multi-source adaptive multi source adaptive statcom and line commutation converter (SLCC) significantly reduces the probability of commutation failure. DC transmission systems based on SLCC offer higher transmission efficiency and improved stability, and have now progressed to physical engineering implementation. It is therefore essential to evaluate the reliability of SLCC-based DC transmission systems to provide theoretical support and reference for their safe operation and maintenance. In this study, reliability parameters of components such as the three-phase static reactive power compensation and static var & filter (SVF) are calculated using the frequency and duration method. A reliability evaluation model based on the (goal oriented, GO) method is proposed and integrated with dynamic bayesian network (DBN) to assess the reliability of the SLCC DC transmission system. The time-varying curve of system energy availability is derived, verifying the feasibility and effectiveness of the proposed model and method for reliability evaluation of SLCC DC transmission systems.