Pub Date : 2026-02-01Epub Date: 2026-01-22DOI: 10.1016/j.ijepes.2025.111446
Junjie Lin , Minghao Yang , Xiaofeng Jiang , Yachao Zhang , Shilong Cai , Chao Lu
To overcome the inefficiencies in regulating air conditioning (AC) systems in urban rail stations, which limit the effective absorption of megawatt-level regenerative braking energy (RBE) from the traction power supply system (TPSS), this paper introduces a robust and feasible hierarchical optimization control framework. The framework incorporates minimum start-stop time constraints while minimizing communication costs. At the cluster layer, response mechanisms for each AC are developed to maximize RBE utilization. At the aggregation layer, tailored regulation plans are designed to mitigate peak-to-valley variations in the energy supplied by the power grid to the AC loads. Finally, at the device layer, AC units strictly adhere to their individualized control plans, reducing switching frequency, optimizing RBE utilization, and minimizing energy fluctuations from the power grid. Case studies demonstrate that the proposed approach enhances RBE utilization by 17 % and reduces the standard deviation of grid-supplied energy to the AC systems by 33 %, thereby significantly contributing to energy savings in urban rail systems.
{"title":"Hierarchical control optimization of AC systems in urban rail stations for regenerative energy use with start-stop constraints","authors":"Junjie Lin , Minghao Yang , Xiaofeng Jiang , Yachao Zhang , Shilong Cai , Chao Lu","doi":"10.1016/j.ijepes.2025.111446","DOIUrl":"10.1016/j.ijepes.2025.111446","url":null,"abstract":"<div><div>To overcome the inefficiencies in regulating air conditioning (AC) systems in urban rail stations, which limit the effective absorption of megawatt-level regenerative braking energy (RBE) from the traction power supply system (TPSS), this paper introduces a robust and feasible hierarchical optimization control framework. The framework incorporates minimum start-stop time constraints while minimizing communication costs. At the cluster layer, response mechanisms for each AC are developed to maximize RBE utilization. At the aggregation layer, tailored regulation plans are designed to mitigate peak-to-valley variations in the energy supplied by the power grid to the AC loads. Finally, at the device layer, AC units strictly adhere to their individualized control plans, reducing switching frequency, optimizing RBE utilization, and minimizing energy fluctuations from the power grid. Case studies demonstrate that the proposed approach enhances RBE utilization by 17 % and reduces the standard deviation of grid-supplied energy to the AC systems by 33 %, thereby significantly contributing to energy savings in urban rail systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111446"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024859","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 : 2026-02-01Epub Date: 2026-01-24DOI: 10.1016/j.ijepes.2026.111621
Ao Li , Zengping Wang , Yufeng Zhao , Bo Wang , Tong Wang
After the single-phase high impedance fault (HIF) occurs in distribution networks, it is difficult to select the faulted feeder and locate the fault section due to the weak fault current and susceptibility to noise interference. To address this, a novel fault location method based on transient directional characteristics is proposed. Firstly, the phase-frequency characteristics of the zero-sequence equivalent impedance and nodal zero-sequence voltages are analytically derived using boundary conditions at the line terminal. Secondly, an adaptive time–frequency window for transient signals is selected through cross wavelet transform. By exploiting the correlation between local transient zero-sequence differential voltage and transient zero-sequence current, the transient direction discrimination method is proposed. Furthermore, considering the sensitivity degradation in section localization caused by weak transient zero-sequence current at line terminals, the transient energy criterion is constructed. The integration of transient directional and energy information enables precise faulted section localization. Finally, numerical simulations verify the sensitivity and reliability of the proposed method under both arc discharge and strong noise conditions. Crucially, the method requires lower sampling rates than traveling-wave protection, demonstrating substantial practical value.
{"title":"Single-Phase high impedance ground fault location method based on transient directional characteristics","authors":"Ao Li , Zengping Wang , Yufeng Zhao , Bo Wang , Tong Wang","doi":"10.1016/j.ijepes.2026.111621","DOIUrl":"10.1016/j.ijepes.2026.111621","url":null,"abstract":"<div><div>After the single-phase high impedance fault (HIF) occurs in distribution networks, it is difficult to select the faulted feeder and locate the fault section due to the weak fault current and susceptibility to noise interference. To address this, a novel fault location method based on transient directional characteristics is proposed. Firstly, the phase-frequency characteristics of the zero-sequence equivalent impedance and nodal zero-sequence voltages are analytically derived using boundary conditions at the line terminal. Secondly, an adaptive time–frequency window for transient signals is selected through cross wavelet transform. By exploiting the correlation between local transient zero-sequence differential voltage and transient zero-sequence current, the transient direction discrimination method is proposed. Furthermore, considering the sensitivity degradation in section localization caused by weak transient zero-sequence current at line terminals, the transient energy criterion is constructed. The integration of transient directional and energy information enables precise faulted section localization. Finally, numerical simulations verify the sensitivity and reliability of the proposed method under both arc discharge and strong noise conditions. Crucially, the method requires lower sampling rates than traveling-wave protection, demonstrating substantial practical value.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111621"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024863","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 : 2026-02-01Epub Date: 2026-01-19DOI: 10.1016/j.ijepes.2026.111575
Weijie Xia , Gao Peng , Chenguang Wang , Peter Palensky , Eric Pauwels , Pedro P. Vergara
Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of low-carbon technologies such as solar panels and electric vehicles. Traditional ECP modeling methods typically assume the availability of sufficient ECP data. However, in practice, the accessibility of ECP data is limited due to privacy issues or the absence of metering devices. Few-shot learning (FSL) has emerged as a promising solution for ECP modeling in data-scarce scenarios. Nevertheless, standard FSL methods, such as those used for images, are unsuitable for ECP modeling because (1) these methods usually assume several source domains with sufficient data and several target domains. However, in the context of ECP modeling, there may be thousands of source domains, e.g., households with a moderate amount of data, and thousands of target domains, e.g., households that ECP are required to be modeled. (2) Standard FSL methods usually involve cumbersome knowledge transfer mechanisms, such as pre-training and fine-tuning. To address these limitations, this paper proposes a novel FSL framework that integrates Transformers with Gaussian Mixture Models (GMMs) for ECP modeling. The proposed approach is fine-tuning-free, computationally efficient, and robust even with extremely limited data. Results show that our method can accurately restore the complex ECP distribution with a minimal amount of ECP data (e.g., only 1.6% of the complete domain dataset) and outperforms state-of-the-art time series modeling methods in the context of ECP modeling.
{"title":"Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains","authors":"Weijie Xia , Gao Peng , Chenguang Wang , Peter Palensky , Eric Pauwels , Pedro P. Vergara","doi":"10.1016/j.ijepes.2026.111575","DOIUrl":"10.1016/j.ijepes.2026.111575","url":null,"abstract":"<div><div>Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of low-carbon technologies such as solar panels and electric vehicles. Traditional ECP modeling methods typically assume the availability of sufficient ECP data. However, in practice, the accessibility of ECP data is limited due to privacy issues or the absence of metering devices. Few-shot learning (FSL) has emerged as a promising solution for ECP modeling in data-scarce scenarios. Nevertheless, standard FSL methods, such as those used for images, are unsuitable for ECP modeling because (1) these methods usually assume several source domains with sufficient data and several target domains. However, in the context of ECP modeling, there may be thousands of source domains, e.g., households with a moderate amount of data, and thousands of target domains, e.g., households that ECP are required to be modeled. (2) Standard FSL methods usually involve cumbersome knowledge transfer mechanisms, such as pre-training and fine-tuning. To address these limitations, this paper proposes a novel FSL framework that integrates Transformers with Gaussian Mixture Models (GMMs) for ECP modeling. The proposed approach is fine-tuning-free, computationally efficient, and robust even with extremely limited data. Results show that our method can accurately restore the complex ECP distribution with a minimal amount of ECP data (e.g., only 1.6% of the complete domain dataset) and outperforms state-of-the-art time series modeling methods in the context of ECP modeling.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111575"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024867","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 : 2026-02-01Epub Date: 2026-02-03DOI: 10.1016/j.ijepes.2026.111651
Huanan Yu, Zhi Zhang, Shiqiang Li, He Wang, Jing Bian, Guoqing Li
The frequency of sub-synchronous oscillation (SSO) incidents has increased due to the ongoing expansion of wind power integration into power systems, which presents substantial challenges to the secure and reliable operation of power grids. Conventional methods often lack sufficient resistance to noise and fail to achieve high recognition precision. This study proposes a dynamic SSO monitoring technique that employs a three-tier collaborative architecture for wind power grid-connected systems. The architecture comprises a Low-Computational Monitoring tier, an Adaptive Decomposition tier, and a High-Resolution Identification tier, designed to address these limitations. The Low-Computational Monitoring tier utilizes the periodic oscillation characteristics of three-phase instantaneous power for dynamic oscillation detection. If an SSO is detected, it triggers the subsequent tiers; otherwise, monitoring continues. The Adaptive Decomposition tier employs an enhanced variational mode decomposition (VMD) algorithm to dynamically determine the optimal decomposition parameters, reduce noise, and perform adaptive signal decomposition. The High-Resolution Identification tier applies the Hilbert transform and nonlinear least squares method to the intrinsic mode functions (IMFs) derived from the previous tier to extract instantaneous parameters, thereby enabling precise SSO parameter identification within this collaborative framework. Simulation results demonstrate that the proposed method effectively mitigates mode mixing, exhibits substantial noise immunity, and achieves excellent identification accuracy and robustness.
{"title":"Dynamic monitoring and identification method for sub-synchronous oscillation in wind power Grid-Integrated systems under a Three-Level collaborative architecture","authors":"Huanan Yu, Zhi Zhang, Shiqiang Li, He Wang, Jing Bian, Guoqing Li","doi":"10.1016/j.ijepes.2026.111651","DOIUrl":"10.1016/j.ijepes.2026.111651","url":null,"abstract":"<div><div>The frequency of sub-synchronous oscillation (SSO) incidents has increased due to the ongoing expansion of wind power integration into power systems, which presents substantial challenges to the secure and reliable operation of power grids. Conventional methods often lack sufficient resistance to noise and fail to achieve high recognition precision. This study proposes a dynamic SSO monitoring technique that employs a three-tier collaborative architecture for wind power grid-connected systems. The architecture comprises a Low-Computational Monitoring tier, an Adaptive Decomposition tier, and a High-Resolution Identification tier, designed to address these limitations. The Low-Computational Monitoring tier utilizes the periodic oscillation characteristics of three-phase instantaneous power for dynamic oscillation detection. If an SSO is detected, it triggers the subsequent tiers; otherwise, monitoring continues. The Adaptive Decomposition tier employs an enhanced variational mode decomposition (VMD) algorithm to dynamically determine the optimal decomposition parameters, reduce noise, and perform adaptive signal decomposition. The High-Resolution Identification tier applies the Hilbert transform and nonlinear least squares method to the intrinsic mode functions (IMFs) derived from the previous tier to extract instantaneous parameters, thereby enabling precise SSO parameter identification within this collaborative framework. Simulation results demonstrate that the proposed method effectively mitigates mode mixing, exhibits substantial noise immunity, and achieves excellent identification accuracy and robustness.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111651"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173808","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 : 2026-02-01Epub Date: 2026-02-10DOI: 10.1016/j.ijepes.2026.111643
Zhou Li , Zihao Wang , Yujian Ye , Xiao-ping Zhang
This paper conducts a detailed simulation analysis on the feasibility, effectiveness and necessity of Embedded HVDC participating in power flow optimization regulation through PSCAD/EMTDC, and establishes an optimization model framework considering the coordinated dispatching of AC and DC power flows and the global optimization calculation of transmission corridors. Then optimization objective functions corresponding to different operating conditions are proposed to release the potential transfer capability of the transmission corridors. Moreover, the effectiveness of this optimization strategy is verified by simulations on an IEEE 39-bus system and a practical large-scale power grid.
{"title":"Power Flow Optimization Strategy for Regional Grid Transmission Corridors with Embedded HVDC","authors":"Zhou Li , Zihao Wang , Yujian Ye , Xiao-ping Zhang","doi":"10.1016/j.ijepes.2026.111643","DOIUrl":"10.1016/j.ijepes.2026.111643","url":null,"abstract":"<div><div>This paper conducts a detailed simulation analysis on the feasibility, effectiveness and necessity of Embedded HVDC participating in power flow optimization regulation through PSCAD/EMTDC, and establishes an optimization model framework considering the coordinated dispatching of AC and DC power flows and the global optimization calculation of transmission corridors. Then optimization objective functions corresponding to different operating conditions are proposed to release the potential transfer capability of the transmission corridors. Moreover, the effectiveness of this optimization strategy is verified by simulations on an IEEE 39-bus system and a practical large-scale power grid.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111643"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173817","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 : 2026-02-01Epub Date: 2026-02-03DOI: 10.1016/j.ijepes.2026.111574
Huan Quan , Zhaoxi Liu , Dawei Qiu , Zigang Liu , Fengzhe Dai , Wenhao Wang
Volt-VAR control to address the challenges of limited historical data and high environmental uncertainty in distribution networks (DNs), and to meet the requirement of Volt-VAR control for real-time scheduling, this paper proposes a transformer-enhanced multi-agent reinforcement learning method integrated with diffusion-driven data augmentation. This method integrates a conditional diffusion model to generate synthetic training samples to expand the replay buffer, thereby alleviating the problem of data scarcity. Meanwhile, a multi-agent twin delayed deep deterministic policy gradient (MATD3) architecture based on transformer is adopted, where the self-attention mechanism of the transformer serves as the feature encoder to capture the complex spatiotemporal coordination relationships among distributed photovoltaic (PV) inverters, and its output is sent to the actor-critic network for policy learning. The coordinated real-time Volt-VAR control of PV inverters using only local information is realized via the framework of centralized offline training and decentralized online execution. The proposed strategy exhibits strong adaptability to DNs with constrained communication resources, while achieving computationally efficient control and high operational economy. Case studies on the modified IEEE 33-bus system and 141-bus system demonstrate superior performance of the proposed method.
{"title":"Distribution system Volt-VAR control via transformer-enhanced reinforcement learning with diffusion-driven data augmentation","authors":"Huan Quan , Zhaoxi Liu , Dawei Qiu , Zigang Liu , Fengzhe Dai , Wenhao Wang","doi":"10.1016/j.ijepes.2026.111574","DOIUrl":"10.1016/j.ijepes.2026.111574","url":null,"abstract":"<div><div>Volt-VAR control to address the challenges of limited historical data and high environmental uncertainty in distribution networks (DNs), and to meet the requirement of Volt-VAR control for real-time scheduling, this paper proposes a transformer-enhanced multi-agent reinforcement learning method integrated with diffusion-driven data augmentation. This method integrates a conditional diffusion model to generate synthetic training samples to expand the replay buffer, thereby alleviating the problem of data scarcity. Meanwhile, a multi-agent twin delayed deep deterministic policy gradient (MATD3) architecture based on transformer is adopted, where the self-attention mechanism of the transformer serves as the feature encoder to capture the complex spatiotemporal coordination relationships among distributed photovoltaic (PV) inverters, and its output is sent to the actor-critic network for policy learning. The coordinated real-time Volt-VAR control of PV inverters using only local information is realized via the framework of centralized offline training and decentralized online execution. The proposed strategy exhibits strong adaptability to DNs with constrained communication resources, while achieving computationally efficient control and high operational economy. Case studies on the modified IEEE 33-bus system and 141-bus system demonstrate superior performance of the proposed method.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111574"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173788","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}
High-frequency oscillations (HFOs) have emerged as a recurrent phenomenon in large-scale renewable energy integration via VSC-HVDC transmission projects. Based on HFOs at Kangbao Station of the Zhangbei VSC-HVDC Project in China, this paper identifies how time delays in the conventional closed-loop control system of Modular Multilevel Converter (MMC) induce negative damping, thereby triggering HFOs. Then, a novel current control strategy combining predictive feedforward and deviation feedback is proposed to suppress HFOs. The strategy employs the nominal closed-loop transfer function of the conventional current inner loop as the current-tracking reference model to generate the desired valve-side current dynamic response. Meanwhile, a predictive feedforward controller generates the main modulation signal. The deviation between the desired and actual valve-side current is fed into a compensator to generate the compensation modulation signal, forming a feedback closed-loop that ensures rapid convergence of the actual response to the desired response. Furthermore, a phase-lead compensator is integrated into the output path of the current tracking model to provide supplementary damping in the high-frequency range. This proposed strategy can eliminate negative damping of the converter stations while preserving medium-to-low frequency performance, robustness, and disturbance rejection capability. Simulation results demonstrate that the proposed strategy can effectively suppress HFOs under various operating conditions and time-delay uncertainties, while ensuring the VSC-HVDC system operates stably under normal conditions and meets fault ride-through (FRT) requirements under fault conditions.
{"title":"High-Frequency oscillation mechanism and suppression strategy for renewable energy integration via VSC-HVDC systems","authors":"Wei Qin , Wuhui Chen , Xiaodong Wang , Jinxin Liang","doi":"10.1016/j.ijepes.2025.111460","DOIUrl":"10.1016/j.ijepes.2025.111460","url":null,"abstract":"<div><div>High-frequency oscillations (HFOs) have emerged as a recurrent phenomenon in large-scale renewable energy integration via VSC-HVDC transmission projects. Based on HFOs at Kangbao Station of the Zhangbei VSC-HVDC Project in China, this paper identifies how time delays in the conventional closed-loop control system of Modular Multilevel Converter (MMC) induce negative damping, thereby triggering HFOs. Then, a novel current control strategy combining predictive feedforward and deviation feedback is proposed to suppress HFOs. The strategy employs the nominal closed-loop transfer function of the conventional current inner loop as the current-tracking reference model to generate the desired valve-side current dynamic response. Meanwhile, a predictive feedforward controller generates the main modulation signal. The deviation between the desired and actual valve-side current is fed into a compensator to generate the compensation modulation signal, forming a feedback closed-loop that ensures rapid convergence of the actual response to the desired response. Furthermore, a phase-lead compensator is integrated into the output path of the current tracking model to provide supplementary damping in the high-frequency range. This proposed strategy can eliminate negative damping of the converter stations while preserving medium-to-low frequency performance, robustness, and disturbance rejection capability. Simulation results demonstrate that the proposed strategy can effectively suppress HFOs under various operating conditions and time-delay uncertainties, while ensuring the VSC-HVDC system operates stably under normal conditions and meets fault ride-through (FRT) requirements under fault conditions.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111460"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173925","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 : 2026-02-01Epub Date: 2026-02-12DOI: 10.1016/j.ijepes.2026.111674
Jiahe Li, Jian Chen, Wen Zhang, Tingting Zhang, Xirui Sun
Multi-energy microgrid has emerged as a crucial carrier for renewable energy utilization, supplying multi-energy to chemical industry load which can offer flexibility via adjustable production scheduling. However, it can be difficult for the intra-day scheduling of multi-energy microgrid owing to privacy concerns of chemical industry load and intra-day renewable energy uncertainties. To address this, an intra-day scheduling framework for multi-energy microgrid incorporating flexible region of chemical industry load based on Bayesian nonparametric is proposed in this paper. Firstly, a flexible region is constructed to characterize adjustable range of multi-energy inputs to the chemical industry load considering its production constraints. A calculation method based on vertex enumeration and Quickhull algorithm is proposed to formulate the flexible region. On this basis, essential flexibility-related information of chemical industry load can be directly utilized for scheduling of multi-energy microgrid, which can preserve its privacy. Secondly, an online-offline fitting method is proposed to construct a Gaussian mixture model to characterize the renewable energy uncertainties, with historical data captured via Dirichlet process mixture model (DPMM) and online data incorporated to update the model via incremental Gaussian learning. Finally, to solve the intra-day two-sided chance-constrained scheduling problem for the multi-energy microgrid, a second-order cone programming (SOCP) formulation is employed to ensure feasibility of the chance constraints. Case studies illustrate that the exploitation of chemical industry load flexibility and updating of Gaussian mixture model can effectively reduce operation costs. Besides, the proposed two-sided chance-constrained method has the advantage of low operational violation probability.
{"title":"Intra-day scheduling framework for multi-energy microgrid incorporating flexible region of chemical industry load based on Bayesian nonparametric","authors":"Jiahe Li, Jian Chen, Wen Zhang, Tingting Zhang, Xirui Sun","doi":"10.1016/j.ijepes.2026.111674","DOIUrl":"10.1016/j.ijepes.2026.111674","url":null,"abstract":"<div><div>Multi-energy microgrid has emerged as a crucial carrier for renewable energy utilization, supplying multi-energy to chemical industry load which can offer flexibility via adjustable production scheduling. However, it can be difficult for the intra-day scheduling of multi-energy microgrid owing to privacy concerns of chemical industry load and intra-day renewable energy uncertainties. To address this, an intra-day scheduling framework for multi-energy microgrid incorporating flexible region of chemical industry load based on Bayesian nonparametric is proposed in this paper. Firstly, a flexible region is constructed to characterize adjustable range of multi-energy inputs to the chemical industry load considering its production constraints. A calculation method based on vertex enumeration and Quickhull algorithm is proposed to formulate the flexible region. On this basis, essential flexibility-related information of chemical industry load can be directly utilized for scheduling of multi-energy microgrid, which can preserve its privacy. Secondly, an online-offline fitting method is proposed to construct a Gaussian mixture model to characterize the renewable energy uncertainties, with historical data captured via Dirichlet process mixture model (DPMM) and online data incorporated to update the model via incremental Gaussian learning. Finally, to solve the intra-day two-sided chance-constrained scheduling problem for the multi-energy microgrid, a second-order cone programming (SOCP) formulation is employed to ensure feasibility of the chance constraints. Case studies illustrate that the exploitation of chemical industry load flexibility and updating of Gaussian mixture model can effectively reduce operation costs. Besides, the proposed two-sided chance-constrained method has the advantage of low operational violation probability.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111674"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173888","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 : 2026-02-01Epub Date: 2026-01-20DOI: 10.1016/j.ijepes.2026.111596
Pengning Zhang , Pengyang Li , Xiaohong Li , Dengji Miao , Jian Zhang , Ying Zhan
As the key component of modern energy conversion systems, high-frequency transformer (HFT) directly affects the reliability of the system, and the design parameters of HFT, such as power density, loss, and temperature rise, are coupled with each other. Therefore, optimizing the design of HFT while considering multiple parameters has important engineering significance. In order to reduce the operating temperature rise without compromising the optimization outcomes, this article establishes a coupled design model of HFT and heat dissipation fins, and proposes a multi-objective optimization design method for HFT considering heat dissipation based on multi-objective particle swarm optimization (MOPSO). Finally, a 10 kHz/20kVA litz-wire HFT prototype is designed, and the proposed optimization design method is verified through modeling simulation and experimental testing.
{"title":"Multi-objective optimization design method for electromagnetic structure and heat dissipation of litz-wire high-frequency transformer","authors":"Pengning Zhang , Pengyang Li , Xiaohong Li , Dengji Miao , Jian Zhang , Ying Zhan","doi":"10.1016/j.ijepes.2026.111596","DOIUrl":"10.1016/j.ijepes.2026.111596","url":null,"abstract":"<div><div>As the key component of modern energy conversion systems, high-frequency transformer (HFT) directly affects the reliability of the system, and the design parameters of HFT, such as power density, loss, and temperature rise, are coupled with each other. Therefore, optimizing the design of HFT while considering multiple parameters has important engineering significance. In order to reduce the operating temperature rise without compromising the optimization outcomes, this article establishes a coupled design model of HFT and heat dissipation fins, and proposes a multi-objective optimization design method for HFT considering heat dissipation based on multi-objective particle swarm optimization (MOPSO). Finally, a 10 kHz/20kVA litz-wire HFT prototype is designed, and the proposed optimization design method is verified through modeling simulation and experimental testing.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111596"},"PeriodicalIF":5.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025378","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 : 2026-02-01Epub Date: 2026-01-21DOI: 10.1016/j.ijepes.2026.111586
Yan Guo , Chunguang Zhou , Shengmin Qiu , Ke Wang , Yiping Chen , Zhixuan Li
To enhance frequency stability, the integration of High Voltage Direct Current (HVDC) systems for frequency control has been widely employed. In 2023, a frequency control strategy termed co-frequency control was deployed in the LUXI Back-to-Back (BTB) VSC-HVDC system of the China Southern Power Gird (CSG) to mitigate frequency deviations between interconnected asynchronous grids. Nevertheless, reliance on a single HVDC system implementing co-frequency control presents three major challenges: limited frequency regulation headroom, the absence of backup control capability during HVDC maintenance, and the occurrence of low-frequency DC power oscillations (LFPO). Consequently, the control falls short of fully satisfying the operational expectations of CSG. Since incorporating additional HVDC systems into co-frequency control is regarded as an effective measure to address the first two challenges, this paper proposes a coordinated scheme for multiple parallel HVDC systems participating in co-frequency control. The proposed scheme is formulated as an optimization problem that calculates and updates the frequency control coefficients of the HVDC systems. These coefficients are obtained by solving the developed optimization problem, which accounts for the power headroom of each HVDC system, the N-1 HVDC blocking fault security criterion, and the stability requirements of the HVDC system. As a result, the scheme ensures the secure operation of multiple parallel HVDC systems in co-frequency control during both load variations and HVDC outages. The effectiveness of the proposed method is validated through Real-Time Digital Simulator (RTDS).
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