Pub Date : 2026-03-01Epub Date: 2026-03-02DOI: 10.1016/j.ijepes.2026.111723
Shilong Cai , Junjie Lin , Xiaoqian Li , Bo Chang , Chao Lu
Flexible DC traction power supply systems (FTPSSs) have been increasingly deployed in urban rail transit. The high converter-level flexibility enables them to supply subway power while simultaneously contributing to volt/var control (VVC) in urban power grids. However, their practical application is hindered by fluctuating reactive power capacity and the complexity of coordinating with other devices. To overcome these issues, a reactive power optimization method exploiting the overload capability of flexible traction substations (FTSSs) is developed to ensure sustained long-term reactive support, and a hierarchical distributed VVC framework is proposed to manage these VVC devices across two timescales. At the upper layer, a parallel alternating direction method of multipliers (ADMM) algorithm is designed to achieve a threefold acceleration in hourly optimization, whereas at the lower layer, second-level real-time control with adaptive power sharing enables communication-free coordination among FTSSs. Case studies on the Beijing Changping District power grid and Beijing Subway system demonstrate that the proposed strategy reduces network losses by 6.6%, suppresses 67.6% of cable-induced reverse reactive power flow, and preserves voltage stability at second-level timescales. The fully distributed architecture further ensures low communication overhead and high computational efficiency, highlighting its practical applicability for modern urban power grids.
{"title":"Hierarchical distributed volt/var control of urban power grids with integrated flexible DC traction power supply systems","authors":"Shilong Cai , Junjie Lin , Xiaoqian Li , Bo Chang , Chao Lu","doi":"10.1016/j.ijepes.2026.111723","DOIUrl":"10.1016/j.ijepes.2026.111723","url":null,"abstract":"<div><div>Flexible DC traction power supply systems (FTPSSs) have been increasingly deployed in urban rail transit. The high converter-level flexibility enables them to supply subway power while simultaneously contributing to volt/var control (VVC) in urban power grids. However, their practical application is hindered by fluctuating reactive power capacity and the complexity of coordinating with other devices. To overcome these issues, a reactive power optimization method exploiting the overload capability of flexible traction substations (FTSSs) is developed to ensure sustained long-term reactive support, and a hierarchical distributed VVC framework is proposed to manage these VVC devices across two timescales. At the upper layer, a parallel alternating direction method of multipliers (ADMM) algorithm is designed to achieve a threefold acceleration in hourly optimization, whereas at the lower layer, second-level real-time control with adaptive power sharing enables communication-free coordination among FTSSs. Case studies on the Beijing Changping District power grid and Beijing Subway system demonstrate that the proposed strategy reduces network losses by 6.6%, suppresses 67.6% of cable-induced reverse reactive power flow, and preserves voltage stability at second-level timescales. The fully distributed architecture further ensures low communication overhead and high computational efficiency, highlighting its practical applicability for modern urban power grids.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111723"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405440","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}
Pub Date : 2026-03-01Epub Date: 2026-02-16DOI: 10.1016/j.ijepes.2026.111654
Mohamed M. Elgamal , Bishoy E. Sedhom , Abdelfattah A. Eladl , V. Oboskalov , Akram Elmitwally , Juan C. Vasquez , Amir Abdel Menaem
Modern distribution networks face significant protection challenges, as conventional relays and existing multi-agent system-based relaying schemes struggle with reliable directional discrimination, high-impedance faults, busbar faults, and cyber vulnerabilities—particularly when voltage measurements are unavailable or costly. This paper proposes a fully distributed, cyber-resilient multi-agent protection scheme for power distribution systems using current-only directional overcurrent relays (DOCRs). Fault direction is determined solely by local current measurements, eliminating voltage transformers and enabling cost-effective and low-complexity implementation. Relay agents exchange only 4-digit binary messages with neighboring agents during fault suspicion, resulting in lightweight communication suitable for legacy infrastructure. The proposed scheme accurately detects both close-in line faults and busbar faults, reliably classifies fault types, and maintains robust performance across a wide range of fault resistances, including high-impedance scenarios. To ensure cyber resilience, each relay is equipped with an anomaly detection unit (ADU) that combines Principal Component Analysis Algorithm (PCAA) and Local Outlier Factor Algorithm (LOFA), enabling precise real-time detection and mitigation of false tripping cyberattacks. Under ideal measurement conditions (noise-free), the ADU achieves 100% classification accuracy, while requiring only 15% of the Multilayer Perceptron (MLP) model training time and 80% of the Isolation Forest Algorithm (IFA) model’s training time, with significantly faster real-time classification. Under degraded or noisy measurement conditions (35 dB SNR), the ADU maintains 98.5% accuracy, outperforming the MLP (96.8%) and IFA (95%) models. Extensive co-simulation integrating power system dynamics and multi-agent system logic validates fast, selective fault clearing across diverse fault types, resistances, topologies, and cyberattacks. The proposed scheme offers a practical, cost-effective, and inherently secure solution ready for real-world deployment in modern distribution networks and microgrids.
{"title":"A cyber-resilient multi-agent protection scheme for power distribution systems","authors":"Mohamed M. Elgamal , Bishoy E. Sedhom , Abdelfattah A. Eladl , V. Oboskalov , Akram Elmitwally , Juan C. Vasquez , Amir Abdel Menaem","doi":"10.1016/j.ijepes.2026.111654","DOIUrl":"10.1016/j.ijepes.2026.111654","url":null,"abstract":"<div><div>Modern distribution networks face significant protection challenges, as conventional relays and existing multi-agent system-based relaying schemes struggle with reliable directional discrimination, high-impedance faults, busbar faults, and cyber vulnerabilities—particularly when voltage measurements are unavailable or costly. This paper proposes a fully distributed, cyber-resilient multi-agent protection scheme for power distribution systems using current-only directional overcurrent relays (DOCRs). Fault direction is determined solely by local current measurements, eliminating voltage transformers and enabling cost-effective and low-complexity implementation. Relay agents exchange only 4-digit binary messages with neighboring agents during fault suspicion, resulting in lightweight communication suitable for legacy infrastructure. The proposed scheme accurately detects both close-in line faults and busbar faults, reliably classifies fault types, and maintains robust performance across a wide range of fault resistances, including high-impedance scenarios. To ensure cyber resilience, each relay is equipped with an anomaly detection unit (ADU) that combines Principal Component Analysis Algorithm (PCAA) and Local Outlier Factor Algorithm (LOFA), enabling precise real-time detection and mitigation of false tripping cyberattacks. Under ideal measurement conditions (noise-free), the ADU achieves 100% classification accuracy, while requiring only 15% of the Multilayer Perceptron (MLP) model training time and 80% of the Isolation Forest Algorithm (IFA) model’s training time, with significantly faster real-time classification. Under degraded or noisy measurement conditions (35 dB SNR), the ADU maintains 98.5% accuracy, outperforming the MLP (96.8%) and IFA (95%) models. Extensive co-simulation integrating power system dynamics and multi-agent system logic validates fast, selective fault clearing across diverse fault types, resistances, topologies, and cyberattacks. The proposed scheme offers a practical, cost-effective, and inherently secure solution ready for real-world deployment in modern distribution networks and microgrids.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111654"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405510","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}
Pub Date : 2026-03-01Epub Date: 2026-03-05DOI: 10.1016/j.ijepes.2026.111720
Luis González Pincheira , Hector Chavez , Artjoms Obushevs , Juan Quiroz , Roberto Pérez
The increasing integration of renewable energy sources into power systems has reduced system inertia, posing significant challenges to frequency stability. Consequently, there is a critical need for low computational cost models that accurately represent dynamic frequency behavior for near real-time applications. However, existing literature lacks unified criteria to compare parameter identification techniques for simplified frequency response models, often relying on simulated data without real-world validation. This paper addresses this gap by proposing a modular web-based application designed to benchmark parameter identification algorithms under identical computational conditions. The developed platform integrates real-time data acquisition, event detection, and model fitting capabilities utilizing the synchrophasor standard. By facilitating the comparison of different identification techniques under both controlled and online conditions using realistic measurement data, the platform provides a standardized evaluation framework. The effectiveness of the proposed tool is validated through practical experiments, demonstrating its capability to assess frequency dynamics accurately. The main results highlight that while different identification techniques can achieve similar curve fitting accuracy, the estimated physical parameters vary significantly due to the practical unidentifiability of the aggregated model. This leads to the conclusion that relying solely on mathematical fitting is insufficient, emphasizing the critical need for comprehensive, standardized validation tools in modern control centers to ensure the reliability of reduced-order models.
{"title":"A web-based platform for benchmarking parameter identification algorithms in reduced-order frequency models","authors":"Luis González Pincheira , Hector Chavez , Artjoms Obushevs , Juan Quiroz , Roberto Pérez","doi":"10.1016/j.ijepes.2026.111720","DOIUrl":"10.1016/j.ijepes.2026.111720","url":null,"abstract":"<div><div>The increasing integration of renewable energy sources into power systems has reduced system inertia, posing significant challenges to frequency stability. Consequently, there is a critical need for low computational cost models that accurately represent dynamic frequency behavior for near real-time applications. However, existing literature lacks unified criteria to compare parameter identification techniques for simplified frequency response models, often relying on simulated data without real-world validation. This paper addresses this gap by proposing a modular web-based application designed to benchmark parameter identification algorithms under identical computational conditions. The developed platform integrates real-time data acquisition, event detection, and model fitting capabilities utilizing the synchrophasor standard. By facilitating the comparison of different identification techniques under both controlled and online conditions using realistic measurement data, the platform provides a standardized evaluation framework. The effectiveness of the proposed tool is validated through practical experiments, demonstrating its capability to assess frequency dynamics accurately. The main results highlight that while different identification techniques can achieve similar curve fitting accuracy, the estimated physical parameters vary significantly due to the practical unidentifiability of the aggregated model. This leads to the conclusion that relying solely on mathematical fitting is insufficient, emphasizing the critical need for comprehensive, standardized validation tools in modern control centers to ensure the reliability of reduced-order models.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111720"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405691","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}
Pub Date : 2026-03-01Epub Date: 2026-02-25DOI: 10.1016/j.ijepes.2026.111724
Dan Zhang , Minglong Zhang , Yixuan Liu , Xiaorong Xie
Flywheel energy storage (FES) is characterized by fast energy storage and release, which is suitable for use in DC power systems. In particular, it has unique advantages in the applications to DC rail transit. This paper explores the integration of modular multilevel converters (MMC) with high-speed FES on the DC traction network side of urban rail transit. To solve the problem of voltage balance control of MMC’s sub modules under high-frequency fundamental waves, a novel bubble sorting carrier-phase shift modulation method is proposed to ensure high-precision voltage balance control of MMC under low modulation ratio and high modulation ratio conditions. The algorithm achieves enhanced operational efficiency by optimising the sorting process, which involves minimising unnecessary steps and implementing separate sorting for charging and discharging submodules. In addition, the use of MMC enables stable control of the high-speed flywheel, thereby maintaining the voltage stability of the DC traction network and limiting voltage fluctuations to a reasonable range. Finally, a high-speed simulation platform was built to verify the feasibility of the proposed control strategy under subway braking and starting conditions. Further, to verify the effectiveness of the proposed bubble sort-carrier phase shifted modulation method, an experimental platform was built for validation.
{"title":"Novel hybrid control of high-speed flywheel energy storage using modular multilevel converter in a DC power system","authors":"Dan Zhang , Minglong Zhang , Yixuan Liu , Xiaorong Xie","doi":"10.1016/j.ijepes.2026.111724","DOIUrl":"10.1016/j.ijepes.2026.111724","url":null,"abstract":"<div><div>Flywheel energy storage (FES) is characterized by fast energy storage and release, which is suitable for use in DC power systems. In particular, it has unique advantages in the applications to DC rail transit. This paper explores the integration of modular multilevel converters (MMC) with high-speed FES on the DC traction network side of urban rail transit. To solve the problem of voltage balance control of MMC’s sub modules under high-frequency fundamental waves, a novel bubble sorting carrier-phase shift modulation method is proposed to ensure high-precision voltage balance control of MMC under low modulation ratio and high modulation ratio conditions. The algorithm achieves enhanced operational efficiency by optimising the sorting process, which involves minimising unnecessary steps and implementing separate sorting for charging and discharging submodules. In addition, the use of MMC enables stable control of the high-speed flywheel, thereby maintaining the voltage stability of the DC traction network and limiting voltage fluctuations to a reasonable range. Finally, a high-speed simulation platform was built to verify the feasibility of the proposed control strategy under subway braking and starting conditions. Further, to verify the effectiveness of the proposed bubble sort-carrier phase shifted modulation method, an experimental platform was built for validation.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111724"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405814","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}
Pub Date : 2026-03-01Epub Date: 2026-03-10DOI: 10.1016/j.ijepes.2026.111752
Zhihui Dai, Junyi Liu, Zhiheng Ning, Qiyue Yang, Xinya Lu
With the massive access of inverter-interfaced distributed generator (IIDG), the new active distribution network is facing problems such as the inability to effectively utilize new energy and the lack of adaptability of protection schemes. To address these issues, dynamic line rating (DLR) technology is introduced to regulate IIDG output based on real-time environmental data collected by the intelligent distribution network system. Then, considering the problems of protection misoperation and rejection caused by the adjustment of IIDG output, the amplitude and phase compensation according to the measured current on both sides of the line is proposed. Combined with the dynamically adjusted braking coefficient, an improved ratio braking current differential protection scheme for active distribution network with DLR technology is formed to ensure the correct operation of the protection when the external environmental conditions change and adaptively reshape the operating regions of the differential protection. Finally, the simulation model is built on PSCAD / EMTDC, and the results confirm the effectiveness of the proposed scheme for active distribution networks with DLR integration.
{"title":"Improved current differential protection scheme for active distribution network integrated with dynamic line rating","authors":"Zhihui Dai, Junyi Liu, Zhiheng Ning, Qiyue Yang, Xinya Lu","doi":"10.1016/j.ijepes.2026.111752","DOIUrl":"10.1016/j.ijepes.2026.111752","url":null,"abstract":"<div><div>With the massive access of inverter-interfaced distributed generator (IIDG), the new active distribution network is facing problems such as the inability to effectively utilize new energy and the lack of adaptability of protection schemes. To address these issues, dynamic line rating (DLR) technology is introduced to regulate IIDG output based on real-time environmental data collected by the intelligent distribution network system. Then, considering the problems of protection misoperation and rejection caused by the adjustment of IIDG output, the amplitude and phase compensation according to the measured current on both sides of the line is proposed. Combined with the dynamically adjusted braking coefficient, an improved ratio braking current differential protection scheme for active distribution network with DLR technology is formed to ensure the correct operation of the protection when the external environmental conditions change and adaptively reshape the operating regions of the differential protection. Finally, the simulation model is built on PSCAD / EMTDC, and the results confirm the effectiveness of the proposed scheme for active distribution networks with DLR integration.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111752"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147406053","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}
Pub Date : 2026-03-01Epub Date: 2026-03-10DOI: 10.1016/j.ijepes.2026.111754
Lanyi Wei , Xuanyue Hong , Zili Chen , Lichen Liu , Guangzeng Sun , Zhaoyuan Wu
With the rapid growth of renewable penetration, accurately quantifying multi-timescale flexibility requirements has become a central challenge for power system planning. Yet existing flexibility and storage adequacy assessment studies often evaluate climate extremes and carbon constraints separately, and rarely translate climate-driven net-load ramping into differentiated requirements for short- and long-duration storage. This study proposes a carbon-aware, ramping-driven assessment framework for multi-duration energy storage requirements under extreme climate events. We first develop a meteorology-driven net load modeling approach that integrates observations and CMIP6 projections to capture climate-induced renewable suppression and temperature-sensitive demand fluctuations. We then quantify ramping stress across multiple time scales and construct a carbon-constrained multi-timescale storage planning model with a dual-layer temporal decomposition, explicitly separating short-duration (intra-day ramping) and long-duration (inter-day/seasonal balancing) storage dynamics. Case studies for Northeast China show that extreme-event frequency and severity induce nonlinear and coupled shifts in storage demand patterns across time scales: In the extended sensitivity analysis, when extreme-event frequency increases by 15%, long-duration storage duration rises by 23.4%, while short-duration storage power demand and duration decrease by 39.5% and 16.2%, respectively. Carbon emission prices further amplify the sensitivity and volatility of storage requirements, especially under intensified climate extremes. These findings indicate that neglecting ramping-driven climate risks and carbon constraints can substantially underestimate flexibility needs, and provide quantitative support for climate-resilient, carbon-constrained storage deployment and planning.
{"title":"Carbon-aware ramping-driven assessment of multi-duration energy storage requirements under extreme climate events","authors":"Lanyi Wei , Xuanyue Hong , Zili Chen , Lichen Liu , Guangzeng Sun , Zhaoyuan Wu","doi":"10.1016/j.ijepes.2026.111754","DOIUrl":"10.1016/j.ijepes.2026.111754","url":null,"abstract":"<div><div>With the rapid growth of renewable penetration, accurately quantifying multi-timescale flexibility requirements has become a central challenge for power system planning. Yet existing flexibility and storage adequacy assessment studies often evaluate climate extremes and carbon constraints separately, and rarely translate climate-driven net-load ramping into differentiated requirements for short- and long-duration storage. This study proposes a carbon-aware, ramping-driven assessment framework for multi-duration energy storage requirements under extreme climate events. We first develop a meteorology-driven net load modeling approach that integrates observations and CMIP6 projections to capture climate-induced renewable suppression and temperature-sensitive demand fluctuations. We then quantify ramping stress across multiple time scales and construct a carbon-constrained multi-timescale storage planning model with a dual-layer temporal decomposition, explicitly separating short-duration (intra-day ramping) and long-duration (inter-day/seasonal balancing) storage dynamics. Case studies for Northeast China show that extreme-event frequency and severity induce nonlinear and coupled shifts in storage demand patterns across time scales: In the extended sensitivity analysis, when extreme-event frequency increases by 15%, long-duration storage duration rises by 23.4%, while short-duration storage power demand and duration decrease by 39.5% and 16.2%, respectively. Carbon emission prices further amplify the sensitivity and volatility of storage requirements, especially under intensified climate extremes. These findings indicate that neglecting ramping-driven climate risks and carbon constraints can substantially underestimate flexibility needs, and provide quantitative support for climate-resilient, carbon-constrained storage deployment and planning.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111754"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147406057","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}
Pub Date : 2026-03-01Epub Date: 2026-03-11DOI: 10.1016/j.ijepes.2026.111728
Andrea Di Martino, Michela Longo, Dario Zaninelli
To reduce emissions produced, transportation sector is pushing towards the electrification of vehicle fleets. Topography or lack of proper charging infrastructure can limit this process. Ropeways systems can contribute to realize electric mobility solutions also in harsh geographic contexts, being considered in the Local Public Transportation (LPT) network. This paper provides an example of modelling applied to a ropeway system aimed to estimate the energy requested to the grid. Based on the actual service operations, two loading conditions were considered, no load and full capacity. Results pointed out the extreme convenience in operating ropeways systems at full capacity, requiring only of the energy compared to the empty-load case. This helped to estimate the amount of energy required per year based on the actual service deployed, with a further discussion on how to improve energy efficiency and its robustness against fluctuations of energy market price.
{"title":"Ropeway modelling for energy demand and deviations — Case study","authors":"Andrea Di Martino, Michela Longo, Dario Zaninelli","doi":"10.1016/j.ijepes.2026.111728","DOIUrl":"10.1016/j.ijepes.2026.111728","url":null,"abstract":"<div><div>To reduce emissions produced, transportation sector is pushing towards the electrification of vehicle fleets. Topography or lack of proper charging infrastructure can limit this process. Ropeways systems can contribute to realize electric mobility solutions also in harsh geographic contexts, being considered in the Local Public Transportation (LPT) network. This paper provides an example of modelling applied to a ropeway system aimed to estimate the energy requested to the grid. Based on the actual service operations, two loading conditions were considered, no load and full capacity. Results pointed out the extreme convenience in operating ropeways systems at full capacity, requiring only <span><math><mrow><mn>5</mn><mo>−</mo><mn>9</mn><mspace></mspace><mi>%</mi></mrow></math></span> of the energy compared to the empty-load case. This helped to estimate the amount of energy required per year based on the actual service deployed, with a further discussion on how to improve energy efficiency and its robustness against fluctuations of energy market price.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111728"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147406059","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}
Pub Date : 2026-03-01Epub Date: 2026-03-12DOI: 10.1016/j.ijepes.2026.111758
Ke Wang , Yinan Zhou , Yuanxing Xia , Jun Liang , Xiangkuan Wan
Scenario generation plays a critical role in short-term power system operations with high renewable penetration. Data-driven scenario generation typically requires extensive sample data, however, due to confidentiality constraints or limited historical records—such as those associated with extreme weather scenarios—only small datasets may be available, thereby making credible scenario generation challenging. This paper proposes a combined methodology that integrates statistical knowledge and adversarial learning for few-shot renewable scenario generation. Specifically, the framework incorporates statistical knowledge that captures historical fluctuations and power prediction errors, together with conditional generative adversarial networks (CGANs), to generate accurate and reliable day-ahead or intraday look-ahead scenarios. This approach enables exploration of more diverse regions within the data space, generates a broader range of samples, and compensates for the lack of diversity resulting from limited datasets (e.g., one month or less). Case studies are conducted on a provincial power grid in China with abundant wind power resources. Compared with the traditional CGAN, the proposed methodology, when implemented with appropriate parameter settings, improves the coverage of the generated scenarios without increasing the corresponding power interval width.
{"title":"Combined methodology of statistical knowledge and adversarial learning for few-shot renewable scenario generation","authors":"Ke Wang , Yinan Zhou , Yuanxing Xia , Jun Liang , Xiangkuan Wan","doi":"10.1016/j.ijepes.2026.111758","DOIUrl":"10.1016/j.ijepes.2026.111758","url":null,"abstract":"<div><div>Scenario generation plays a critical role in short-term power system operations with high renewable penetration. Data-driven scenario generation typically requires extensive sample data, however, due to confidentiality constraints or limited historical records—such as those associated with extreme weather scenarios—only small datasets may be available, thereby making credible scenario generation challenging. This paper proposes a combined methodology that integrates statistical knowledge and adversarial learning for few-shot renewable scenario generation. Specifically, the framework incorporates statistical knowledge that captures historical fluctuations and power prediction errors, together with conditional generative adversarial networks (CGANs), to generate accurate and reliable day-ahead or intraday look-ahead scenarios. This approach enables exploration of more diverse regions within the data space, generates a broader range of samples, and compensates for the lack of diversity resulting from limited datasets (e.g., one month or less). Case studies are conducted on a provincial power grid in China with abundant wind power resources. Compared with the traditional CGAN, the proposed methodology, when implemented with appropriate parameter settings, improves the coverage of the generated scenarios without increasing the corresponding power interval width.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111758"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449464","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}
Pub Date : 2026-03-01Epub Date: 2026-03-12DOI: 10.1016/j.ijepes.2026.111722
Ma Tinghao , Du Xiong , Du Chengmao
Grid-forming converters controlled by virtual synchronous generator control (GFM-VSG) are gradually becoming integral components of modern power systems. During fault ride-through, the power angle and frequency of the GFM-VSG often exhibit significant fluctuations. Excessive power angle fluctuations can cause transient instability in the GFM-VSG, while excessive frequency fluctuations compromise the safe operation of grid-connected systems. To address this issue, this paper first analyzes the transient stability of the GFM-VSG based on the distribution pattern of its power angle equilibrium points during fault ride-through. Subsequently, a transient stability enhancement method based on angular frequency dynamic PI feedback compensation (AFDPI) is introduced into the active power control loop. Compared to conventional methods, this method features a simpler control structure. The introduced compensation coefficients km and kn require no real-time adjustment, significantly reducing the GFM-VSG’s output frequency and power angle fluctuations during grid faults. Furthermore, this paper mathematically demonstrates that the output power angle of the GFM-VSG converges to a stable equilibrium point (SEP) after implementing the AFDPI control method. Then, based on nonlinear perturbation theory, the dynamic influence mechanism of compensation coefficients on transient power angle trajectories is analyzed. A systematic design criterion for compensation coefficients is derived with the control objectives of minimizing transient power angle and frequency fluctuations. Finally, simulations demonstrate the superiority of this method over others in suppressing frequency and power angle fluctuations, and experiments validate its effectiveness in practical engineering control.
{"title":"Transient stability control method for reducing power angle/frequency fluctuations in grid-forming converters during fault ride-through","authors":"Ma Tinghao , Du Xiong , Du Chengmao","doi":"10.1016/j.ijepes.2026.111722","DOIUrl":"10.1016/j.ijepes.2026.111722","url":null,"abstract":"<div><div>Grid-forming converters controlled by virtual synchronous generator control (GFM-VSG) are gradually becoming integral components of modern power systems. During fault ride-through, the power angle and frequency of the GFM-VSG often exhibit significant fluctuations. Excessive power angle fluctuations can cause transient instability in the GFM-VSG, while excessive frequency fluctuations compromise the safe operation of grid-connected systems. To address this issue, this paper first analyzes the transient stability of the GFM-VSG based on the distribution pattern of its power angle equilibrium points during fault ride-through. Subsequently, a transient stability enhancement method based on angular frequency dynamic PI feedback compensation (AFDPI) is introduced into the active power control loop. Compared to conventional methods, this method features a simpler control structure. The introduced compensation coefficients <em>k<sub>m</sub></em> and <em>k<sub>n</sub></em> require no real-time adjustment, significantly reducing the GFM-VSG’s output frequency and power angle fluctuations during grid faults. Furthermore, this paper mathematically demonstrates that the output power angle of the GFM-VSG converges to a stable equilibrium point (SEP) after implementing the AFDPI control method. Then, based on nonlinear perturbation theory, the dynamic influence mechanism of compensation coefficients on transient power angle trajectories is analyzed. A systematic design criterion for compensation coefficients is derived with the control objectives of minimizing transient power angle and frequency fluctuations. Finally, simulations demonstrate the superiority of this method over others in suppressing frequency and power angle fluctuations, and experiments validate its effectiveness in practical engineering control.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111722"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449466","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}
Pub Date : 2026-03-01Epub Date: 2026-02-23DOI: 10.1016/j.ijepes.2026.111661
Zhuoxiang Wu , Shunfu Lin , Jin Tan , Qiuwei Wu
The optimization models based on representative days improve the feasibility and computational efficiency of integrated energy system (IES) planning. However, most existing representative day selection methods rely heavily on the statistical features of input data and often fail to account for the inherent nonlinear characteristics of the planning models. Moreover, they tend to overlook the extreme scenarios, which can significantly undermine the accuracy and reliability of the planning results. To overcome these challenges, this paper proposes a novel representative day selection method that integrates both decision-making characteristics and comprehensive operational risk of the IES. The method starts by mapping the clustering-based representative decisions from single-day planning models to identify typical representative days. Subsequently, stochastic optimization using representative days results in an initial planning decision. Based on this, a risk-critical day selection model guided by a comprehensive operational risk assessment is established to capture rare but impactful scenarios within the planning horizon. Finally, the risk-critical days are incorporated into the representative day set, with optimized weights assigned to ensure a trade-off between economic efficiency and reliability in IES planning. Case studies demonstrate that the proposed method achieves higher planning accuracy with fewer representative days. Moreover, incorporating risk-critical days significantly reduces the operational risks and enhances the reliability of the final planning decisions.
{"title":"Integrated energy system planning framework driven by decision-making characteristics and comprehensive operational risk consideration","authors":"Zhuoxiang Wu , Shunfu Lin , Jin Tan , Qiuwei Wu","doi":"10.1016/j.ijepes.2026.111661","DOIUrl":"10.1016/j.ijepes.2026.111661","url":null,"abstract":"<div><div>The optimization models based on representative days improve the feasibility and computational efficiency of integrated energy system (IES) planning. However, most existing representative day selection methods rely heavily on the statistical features of input data and often fail to account for the inherent nonlinear characteristics of the planning models. Moreover, they tend to overlook the extreme scenarios, which can significantly undermine the accuracy and reliability of the planning results. To overcome these challenges, this paper proposes a novel representative day selection method that integrates both decision-making characteristics and comprehensive operational risk of the IES. The method starts by mapping the clustering-based representative decisions from single-day planning models to identify typical representative days. Subsequently, stochastic optimization using representative days results in an initial planning decision. Based on this, a risk-critical day selection model guided by a comprehensive operational risk assessment is established to capture rare but impactful scenarios within the planning horizon. Finally, the risk-critical days are incorporated into the representative day set, with optimized weights assigned to ensure a trade-off between economic efficiency and reliability in IES planning. Case studies demonstrate that the proposed method achieves higher planning accuracy with fewer representative days. Moreover, incorporating risk-critical days significantly reduces the operational risks and enhances the reliability of the final planning decisions.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"176 ","pages":"Article 111661"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405291","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号