International Journal of Electrical Power & Energy Systems最新文献

{"title":"Meteorological information calculation and power forecasting for regional distributed photovoltaic power based on information fusion and deep learning","authors":"Honglu Zhu ,&nbsp;Yuhang Wang ,&nbsp;Shumin Sun ,&nbsp;Duqing Zhang ,&nbsp;Siyu Hu ,&nbsp;Weidong Chen","doi":"10.1016/j.ijepes.2025.111189","DOIUrl":"10.1016/j.ijepes.2025.111189","url":null,"abstract":"<div><div>Distributed photovoltaic (DPV) generation is characterized by multiple sites, wide geographic distribution, and relatively low capacity, which pose significant challenges for accurate power forecasting. These challenges include meteorological information gaps, the excessive number of models required for individual site modeling, and difficulties in self-calibration of forecasting models. To address these issues, this study proposes a novel approach that utilizes regional DPV grid centers as virtual representative DPV sites. And by integrating information fusion and deep learning techniques, this method aims to achieve regional DPV meteorological information fusion and power forecasting. The approach capitalizes on the superior parallel computing capabilities and computational precision of the eXtreme Gradient Boosting(XGBOOST) model. It employs direct irradiance, diffuse irradiance, wind speed, wind direction, humidity, and historical power data as inputs of information fusion model for DPV sites’ solar radiation and ambient temperature. Subsequently, the DPV power forecasting model is constructed based on the Temporal Convolutional Network-Bidirectional Gated Recurrent Unit-Attention (TCN-BiGRU-Attention) architecture. Finally, SHapley Additive exPlanations (SHAP) is utilized to optimize feature variables. Validation using real-world data demonstrates that the optimized TCN-BiGRU-Attention model achieves a forecasting accuracy of 99.3 %, representing a 0.90 % improvement over traditional methods. The introduction of virtual representative PV sites enables the efficient construction of self-iterative models for DPV meteorological information and power forecasting, providing an effective solution for regional DPV power forecasting.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111189"},"PeriodicalIF":5.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Location method for PD ultrasonic signal in long high-voltage cables based on optical fiber sensing technology","authors":"Zhiheng Liu,&nbsp;Qian Chen,&nbsp;Sen Yang,&nbsp;Zhibin Liu,&nbsp;Ze Li","doi":"10.1016/j.ijepes.2025.111167","DOIUrl":"10.1016/j.ijepes.2025.111167","url":null,"abstract":"<div><div>It proposes to solve the problems of polarization attenuation and phase drift in distributed optical fiber sensing system, and improve the positioning accuracy of partial discharge signal in high-voltage cables. An optical fiber sensing detection system model based on an improved polarization control algorithm was established by analyzing the ultrasonic characteristics of partial discharge in cables. Polarization controller driving voltage control module is designed to reduce polarization attenuation. The algorithm’s attenuation rate, noise amplitude, control accuracy and other parameters were simulated based on the improved Adamax algorithm, which shown the average iteration time reduces by 35.91 %, and the control convergence accuracy is 10^-4 level. Then, an optical fiber detection system of partial discharge in high voltage cable is established, and the experimental results show that the locating accuracy based on the improved polarization control algorithm is less than ± 12 m in a 15 km high voltage cable. The reliability and locating accuracy of the polarization controller and the improved algorithm in the distributed optical fiber sensing system are verified. There, detection method above can be extended to test state perception of high-voltage transmission equipment.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111167"},"PeriodicalIF":5.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power system transient stability assessment based on hierarchical graph pooling method considering missing data","authors":"Chenhao Zhao,&nbsp;Zaibin Jiao,&nbsp;Penghui Zhang,&nbsp;Linbo Zhang","doi":"10.1016/j.ijepes.2025.111194","DOIUrl":"10.1016/j.ijepes.2025.111194","url":null,"abstract":"<div><div>The transient stability assessment (TSA) model based on graph deep learning relies on complete system topology and its characteristics. However, electrical operation data may be missing during the measurement and transmission process, which can lead to a decline in model evaluation performance. To address this issue, this paper proposes a hierarchical graph pooling TSA method for power systems that accounts for missing data. First, the power system is modeled as a graph with topological connections, and the missing data is filled using the K-order neighborhood mean (KNM). Next, a masked graph autoencoder with jumping knowledge is developed to reconstruct the missing features. Finally, considering both the topological attributes of nodes and the temporal characteristics of electrical quantities, a well-designed graph pooling method is introduced. During hierarchical graph pooling, subgraph features of the power grid at different scales are extracted and fused to achieve accurate and reliable TSA. Test results on the IEEE 39-bus system and a provincial power system in China demonstrate that the proposed method can maintain high evaluation performance under various types of missing data, exhibiting strong robustness and practicality.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111194"},"PeriodicalIF":5.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remaining useful life prediction based on hybrid CNN-BiLSTM model with dual attention mechanism","authors":"Bing Yu ,&nbsp;Haonan Guo ,&nbsp;Jianqiang Shi","doi":"10.1016/j.ijepes.2025.111152","DOIUrl":"10.1016/j.ijepes.2025.111152","url":null,"abstract":"<div><div>The precise prediction of the remaining useful life (RUL) of aircraft engines holds significant importance for airlines in formulating optimal maintenance strategies and efficiently curbing maintenance expenses. CNN is used to extract spatial sequence features and LSTM is used to capture temporal sequence characteristics in the prediction approach for aviation engine RUL. However, in the mainstream approach, both CNN and LSTM are connected in a serial manner, resulting in significant information loss and redundant computation. We present a new parallel model in this research that includes a dual attention mechanism, leveraging both CNN and BiLSTM networks, to accurately forecast the RUL of aircraft engines. Firstly, The health index (HI) is created by fusing the preprocessed sensor signals, which serves as the input sequence along with the joint sensor signals. Subsequently, a parallel network structure comprising CNN and BiLSTM is formulated, integrating the channel attention (ECA) module and multi-head attention optimization techniques to extract spatial and temporal sequence features correspondingly. The obtained features are aggregated and used to predict RUL. According to the experimental findings, the suggested model performs better on subsets FD001, FD002, and FD003 than the state-of-the-art (SOTA) methods. The RMSE evaluation metric shows a reduction of 0.95%, 2.03%, and 1.36%, respectively, while the Scores evaluation metric shows a reduction of 2.53%, 54.89%, and 20.59%. These improvements effectively mitigate the risk of delayed prediction.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111152"},"PeriodicalIF":5.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fractional order CES control for frequency regulation in deregulated realistic power systems with integrating RESs","authors":"Javad Morsali","doi":"10.1016/j.ijepes.2025.111049","DOIUrl":"10.1016/j.ijepes.2025.111049","url":null,"abstract":"&lt;div&gt;&lt;div&gt;One efficient solution to aid automatic generation control (AGC) in frequency regulation (FR) of competitive power systems integrated with renewables is to offer supplementary controllers for fast-response storage, such as capacitive energy storage (CES). Former CES models have not been presented exclusively for the CES dynamics or have fixed parameters, leading to a failure to contribute actively to the FR issue. Accordingly, the loss of an effective control approach established on the exclusive CES model represents a significant gap that warrants further investigation. To effectively contribute to the FR issue in a hybrid energy system, a maiden control approach based upon non-integer order controllers (NOCs) is offered for application in the exclusive dynamics of CES. A two-area liberal hybrid energy system incorporating photovoltaic, wind, gas, reheat thermal, and hydro plants with intrinsic nonlinearities, including generation rate constraints, communication time delays (CTDs), governor dead bands, and boiler dynamics, is considered to attain a realistic vision and reliable outputs. A CES-AGC concurrent design approach is regarded as performance-reinforcing. Accordingly, the integral of time-weighted squared error (ITSE) index is minimized using metaheuristic algorithms. The numerical analyses and time-domain simulations under an utterly liberal scenario demonstrate that the offered fractional order proportional integral derivative (FOPID)-based CES-AGC strategy is the most effective control strategy for suppressing deviations in area frequency and tie-line error power responses. Employing the proposed FOPID-based CES-AGC achieves a significant improvement in the damping criteria through a substantial reduction of nearly 17 to 13.8 times in peak magnitude (&lt;em&gt;M&lt;sub&gt;P&lt;/sub&gt;&lt;/em&gt;) of &lt;em&gt;ΔF&lt;/em&gt;&lt;sub&gt;1&lt;/sub&gt;, 2.2 to 2.4 times in &lt;em&gt;M&lt;sub&gt;P&lt;/sub&gt;&lt;/em&gt; of &lt;em&gt;ΔF&lt;/em&gt;&lt;sub&gt;2&lt;/sub&gt;, 22 to 1.7 times in ITSE, 9.5 to 1.8 times in ISE, and an increase of nearly 8 to 3.7 times in the minimum damping ratio (&lt;em&gt;ζ&lt;sub&gt;min&lt;/sub&gt;&lt;/em&gt;) compared to the lead-lag-based CES-AGC and PID-based CES-AGC, in order. Meanwhile, the FOPID-based CES-AGC outperforms the tilt integral derivative (TID)-based CES-AGC in the damping metrics associated with the FR issue. Moreover, by employing the proposed TID-based CES-AGC, a significant improvement in stability measures is obtained via a considerable reduction of closely 4.3 to 3.5 times in &lt;em&gt;M&lt;sub&gt;P&lt;/sub&gt;&lt;/em&gt; of &lt;em&gt;ΔF&lt;/em&gt;&lt;sub&gt;1&lt;/sub&gt;, 1.3 to 1.5 times in &lt;em&gt;M&lt;sub&gt;P&lt;/sub&gt;&lt;/em&gt; of &lt;em&gt;ΔF&lt;/em&gt;&lt;sub&gt;2&lt;/sub&gt;, 17 to 1.3 times in ITSE, 7.9 to 1.5 times in ISE, and an increase of approximately 5.6 to 2.6 times in the &lt;em&gt;ζ&lt;sub&gt;min&lt;/sub&gt;&lt;/em&gt; when compared with the lead-lag-based CES-AGC and PID-based CES-AGC counterparts, respectively. Additional numerical analyses and time-domain simulations, under various contract violation scenarios, indicate that employing the offered control strategy based on the NOCs has subs","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111049"},"PeriodicalIF":5.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new topology and coordinated control strategy of power quality conditioner based on time-sharing multiplexing integrated converter","authors":"Xinjun Qian ,&nbsp;Qi Guo ,&nbsp;Yuchao Hou ,&nbsp;Chunming Tu ,&nbsp;Weijie Zeng ,&nbsp;Yubo Han ,&nbsp;Zhi Zhou ,&nbsp;Zhishuang Wang ,&nbsp;Chao Pang","doi":"10.1016/j.ijepes.2025.111186","DOIUrl":"10.1016/j.ijepes.2025.111186","url":null,"abstract":"<div><div>Owing to their advanced comprehensive treatment capabilities, the unified power quality conditioner (UPQC) composed of series and parallel parts has attracted much attention. However, most existing UPQCs suffer from disadvantages, such as low utilization of series parts, high converter capacity and high cost. To address these issues, a new topology and coordinated control strategy of power quality conditioner based on time-sharing multiplexing integrated converter (TMIC-PQC) are proposed in this paper. By reconstructing the connection architecture of the series part, the integrated converter can operate in the parallel-connected mode under normal grid voltage conditions to achieve reactive power compensation, and operate in the series-connected mode under fluctuating grid voltage conditions to achieve voltage compensation. In addition, a coordinated control strategy and parameter design criteria for the parallel and integrated parts of the TMIC-PQC are formulated under both normal voltage and fluctuating voltage conditions, thus ensuring low power levels of each part across different modes. Compared to the traditional UPQC, the proposed topology merely requires the addition of a decoupling branch consisting of an auxiliary capacitor, thereby reducing the converter capacity by approximately 30%. The simulation and experimental results demonstrate the validity of the proposed topology and coordinated control strategy.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111186"},"PeriodicalIF":5.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Faulty feeder identification method for active distribution network based on depth feature extraction and semi-supervision domain adaptation","authors":"Guomin Luo,&nbsp;Changyu Liu,&nbsp;Boyang Shang,&nbsp;Xiaojun Wang,&nbsp;Jinghan He","doi":"10.1016/j.ijepes.2025.111161","DOIUrl":"10.1016/j.ijepes.2025.111161","url":null,"abstract":"<div><div>Faulty feeder identification is a key technology for active distribution network, and the deep learning-based method have attracted great attention in the field of fault diagnosis. However, many challenges are still exiting, including complex working conditions, insufficient valid data samples and practical scenario verification. Therefore, a deep transfer learning-based faulty feeder identification method is proposed in this work by fusing convolutional neural network, attention mechanism and semi-supervision domain adaptation. Firstly, a depth feature extraction model integrating with temporal-spatial attention mechanism is designed to achieve both local and global fault feature enhancement of zero-sequence current. Secondly, adaptive clustering loss is proposed to realize the alignment between the simulation data and the actual data. Furthermore, the pseudo-label loss is applied to the unlabeled samples, and the pseudo-label is retained with high confidence, so as to promote the cluster classification results. Finally, the proposed method is verified by building distribution networks on the hardware-in-loop and filed test platform. Its identification ability is demonstrated through different fault scenarios, and the performance compared with other methods.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111161"},"PeriodicalIF":5.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conservative Q-learning with physics guidance for generator tripping control","authors":"Jiemai Gao ,&nbsp;Siyuan Chen ,&nbsp;Shixiong Fan ,&nbsp;Jun Zhang ,&nbsp;Kezheng Jiang ,&nbsp;Jun Hao ,&nbsp;David Wenzhong Gao","doi":"10.1016/j.ijepes.2025.111181","DOIUrl":"10.1016/j.ijepes.2025.111181","url":null,"abstract":"<div><div>Maintaining transient stability following large disturbances is a critical challenge in modern power systems with high renewable energy penetration. Generator tripping control serves as an essential emergency strategy to mitigate rotor angle instability. However, conventional reinforcement learning approaches lack physical interpretability and robustness, limiting their operational acceptance. To address these concerns, this paper proposes a novel Physics-Informed Conservative Q-learning framework for emergency generator tripping. Specifically, the proposed approach integrates physics-informed neural networks (PINN) with conservative Q-learning (CQL) in a modular framework. A two-layer long short-term memory-based PINN, independently trained to predict generator rotor angle trajectories by embedding the governing swing equations to ensure physical consistency. Meanwhile, a convolutional neural network-based CQL agent is employed to learn robust generator tripping control policies, where the PINN outputs are incorporated as auxiliary dynamic features to enhance learning stability and safety. An action masking mechanism guided by physics-informed trajectory clustering further improves policy robustness by restricting decisions to critical generators. The proposed method is validated on a modified IEEE-39 bus system under multiple fault scenarios with different levels of renewable energy integration. Furthermore, to demonstrate scalability and generalization, additional validation is performed on the larger and more complex IEEE-118 bus system. Results from both testbeds show that the proposed approach significantly improves training efficiency, enhances transient control performance, ensures stable deployment behavior, and reduces generator tripping costs.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111181"},"PeriodicalIF":5.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated energy management strategy for plug-in hybrid electric buses based on receding horizon control and TD3 algorithm","authors":"Yi Du ,&nbsp;Tianyi Zhang ,&nbsp;Wei Cui ,&nbsp;Naxin Cui","doi":"10.1016/j.ijepes.2025.111103","DOIUrl":"10.1016/j.ijepes.2025.111103","url":null,"abstract":"<div><div>Due to its exceptional performance in terms of fuel efficiency, emissions reduction and driving convenience, the plug-in hybrid electric vehicle (PHEV) possesses a broad application market and development potential. However, the utilization of multiple power sources necessitates a rational and efficient energy management strategy (EMS) to coordinate multiple power sources to achieve efficient power output. In this study, an EMS within the receding horizon control (RHC) framework is proposed for the plug-in hybrid electric bus (PHEB), and a strategy based on twin delayed deep deterministic policy gradient (TD3) algorithm is introduced as a complementary strategy to enhance the robustness of the EMS. First, a vehicle velocity prediction model is constructed based on the gated recurrent unit (GRU) neural network with an attention mechanism to enable accurate prediction of future velocity in a finite horizon. Subsequently, a multi-objective RHC framework is established to effectively coordinate the objectives of vehicle fuel economy improvement and battery degradation mitigation. The power allocation problem is formulated as a rolling optimization issues over a finite prediction horizon, and the optimal control sequence is solved by the alternating direction method of multipliers (ADMM) algorithm. Additionally, the real-time monitoring of velocity prediction error enables the control system to timely switch to the TD3-based EMS when the error exceeds the preset range, so as to cope with unexpected situations. The simulation results demonstrate that the proposed EMS ensures reasonable battery charging and discharging under different initial state of charge (SOC) and driving distances, and mitigates battery degradation. Meanwhile, in comparison to the separate RHC strategy and TD3-based strategy, the proposed integrated EMS reduces the total cost of PHEB by 5.50% and 7.03%, respectively, thereby highlighting the superior fuel efficiency and adaptability to different driving conditions of the EMS.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111103"},"PeriodicalIF":5.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrate-Constrained Optimal Power Flow for Integrated Electricity and Natural Gas Systems","authors":"Yumeng Liu ,&nbsp;Yuchen Qin ,&nbsp;Jiasong Li ,&nbsp;Xueping Gu ,&nbsp;Jiayi Guo","doi":"10.1016/j.ijepes.2025.111178","DOIUrl":"10.1016/j.ijepes.2025.111178","url":null,"abstract":"<div><div>As a transitional scheme in the energy transition, the integrated electricity and natural gas systems can significantly enhance energy supply security and utilization efficiency. However, as an energy carrier, the physical properties of electric energy are quite different from those of natural gas. During the natural gas transmission process, gaseous flows may form solid hydrates, blocking pipelines and rendering formulated operational strategies ineffective. The fundamental problem lies in the failure to incorporate hydrate-induced pipeline blockages in gas transmission systems into the optimal operation framework of integrated electricity and natural gas systems. Therefore, this paper proposes an optimal energy flow model for integrated electricity and natural gas systems designed to prevent natural gas hydrate formation. Firstly, according to the mapping relationship between gas pressure and gas temperature in the critical state of hydrate formation, the hydrate formation constraint is constructed, and the influence of this constraint on the operation domain of components is analyzed within the integrated system. On this basis, a novel model is proposed that the hydrate-constrained optimal energy flow for integrated electricity and natural gas systems. Simultaneously, the intractable parametric exponential constraints in the model are reformulated into linear functions to reduce the computational complexity of the optimization problem. Numerical results demonstrate that the proposed methodology effectively prevents natural gas hydrate formation across diverse operational scenarios while ensuring the reliable implementation of optimal energy flow strategies in integrated systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111178"},"PeriodicalIF":5.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}