Pub Date : 2026-01-22DOI: 10.1016/j.ijepes.2025.111537
Zheng Li, Juan Su
Extreme disasters, such as typhoons, can cause serious damage to lines, which poses a significant threat to the power supply of critical loads. An effective approach to enhance supply security is the flexible adjustment of the power system topology to redirect power flows and optimize load transfer. However, many studies have addressed optimal transmission switching (OTS) and distribution network reconfiguration (DNR) in isolation, with limited research on coordinated transmission and distribution topology optimization for improving resilience. This gap may result in overly conservative preventive dispatch strategies and an underestimation of resilience. To overcome these limitations, this paper incorporates coordinated topology optimization measures, including OTS, DNR, and post‑fault line repair, into a unified preventive scheduling framework to withstand disasters. An improved alternating direction method of multipliers (ADMM) algorithm, with a penalty multiplier updating strategy based on objective function deviation, is employed to solve the proposed preventive scheduling model. Finally, the proposed method is validated by two modified test systems. In the test system consisting of one IEEE-118 and five IEEE-33 bus systems, the results indicate an 8.91% reduction in load shedding and a 2.89% decline in total cost.
{"title":"Preventive dispatch method for coordinated topology optimization of transmission and distribution networks","authors":"Zheng Li, Juan Su","doi":"10.1016/j.ijepes.2025.111537","DOIUrl":"10.1016/j.ijepes.2025.111537","url":null,"abstract":"<div><div>Extreme disasters, such as typhoons, can cause serious damage to lines, which poses a significant threat to the power supply of critical loads. An effective approach to enhance supply security is the flexible adjustment of the power system topology to redirect power flows and optimize load transfer. However, many studies have addressed optimal transmission switching (OTS) and distribution network reconfiguration (DNR) in isolation, with limited research on coordinated transmission and distribution topology optimization for improving resilience. This gap may result in overly conservative preventive dispatch strategies and an underestimation of resilience. To overcome these limitations, this paper incorporates coordinated topology optimization measures, including OTS, DNR, and post‑fault line repair, into a unified preventive scheduling framework to withstand disasters. An improved alternating direction method of multipliers (ADMM) algorithm, with a penalty multiplier updating strategy based on objective function deviation, is employed to solve the proposed preventive scheduling model. Finally, the proposed method is validated by two modified test systems. In the test system consisting of one IEEE-118 and five IEEE-33 bus systems, the results indicate an 8.91% reduction in load shedding and a 2.89% decline in total cost.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111537"},"PeriodicalIF":5.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025371","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}
As the intraday market plays an important role in absorbing uncertainty from renewable energy sources, deriving trading decisions that optimizing economic benefits across sequential day-ahead and real-time markets become increasingly complex. This paper develops a multi-time scale charging strategy for electric vehicles (EVs) to participate in different electricity market segments. The EV charging optimization is done in two stages: first in a day-ahead scheduling of energy and regulation capacity, and then refined during the day when close to real-time delivery. The EV charging energy and the capacity reserved for frequency regulation are optimized in the day-ahead market. The trading decisions for EV charging energy and regulation capacity in the real-time market are determined considering the uncertainties of EV charging behaviors and the energy deviation from actual delivery of frequency regulation. A dynamic EV control model for frequency regulation is used to quantify the regulation capacity during unforeseen contingencies, which is added to the EV charging optimization as a security constraint. Real data of market prices and regulation signals from PJM (ISO in the United States) is used to analyze the flexibility of EV charging and market revenue potentials by considering all market segments as a whole. Numerical results reveal that providing frequency regulation achieves a cost saving up to 59% for EV charging. Around 27% of the cost saving is obtained by energy transactions in the real-time market. Furthermore, the negative impacts of uncertainties from EV availability and the deployment of frequency regulation are also effectively mitigated by integrating real-time market bidding process into the proposed multi-scale optimization.
{"title":"Leveraging the charging flexibility of electric vehicles in sequential electricity markets with frequency regulation limit","authors":"Shuang Gao , Shengyu Huang , Xiaolong Jin , Yuming Zhao , Wenjun Tang","doi":"10.1016/j.ijepes.2026.111592","DOIUrl":"10.1016/j.ijepes.2026.111592","url":null,"abstract":"<div><div>As the intraday market plays an important role in absorbing uncertainty from renewable energy sources, deriving trading decisions that optimizing economic benefits across sequential day-ahead and real-time markets become increasingly complex. This paper develops a multi-time scale charging strategy for electric vehicles (EVs) to participate in different electricity market segments. The EV charging optimization is done in two stages: first in a day-ahead scheduling of energy and regulation capacity, and then refined during the day when close to real-time delivery. The EV charging energy and the capacity reserved for frequency regulation are optimized in the day-ahead market. The trading decisions for EV charging energy and regulation capacity in the real-time market are determined considering the uncertainties of EV charging behaviors and the energy deviation from actual delivery of frequency regulation. A dynamic EV control model for frequency regulation is used to quantify the regulation capacity during unforeseen contingencies, which is added to the EV charging optimization as a security constraint. Real data of market prices and regulation signals from PJM (ISO in the United States) is used to analyze the flexibility of EV charging and market revenue potentials by considering all market segments as a whole. Numerical results reveal that providing frequency regulation achieves a cost saving up to 59% for EV charging. Around 27% of the cost saving is obtained by energy transactions in the real-time market. Furthermore, the negative impacts of uncertainties from EV availability and the deployment of frequency regulation are also effectively mitigated by integrating real-time market bidding process into the proposed multi-scale optimization.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111592"},"PeriodicalIF":5.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024860","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 : 2026-01-22DOI: 10.1016/j.ijepes.2026.111583
Shaohan Lu , Hong Liu , Qizhe Li , Peng Zhang , Bo Peng , Bin Xu
With the large-scale integration of distributed renewable energy, traditional distribution network topologies lack the flexibility to fully exploit wide-area source-load complementarity and flexible resources, limiting the efficient and secure accommodation of distributed generation (DG). To address this, we propose a mesh-shaped distribution network topology with a three-terminal soft open point (SOP) as the core device, balancing existing grid features with retrofit complexity. Given the high-dimensional, complex MILP resulting from planning under strong stochasticity, we introduce a novel geographical layered-clustered planning method. This method, utilizing an improved fast-unfolding algorithm, considers spatio-temporal source-load distribution, customer reliability, and line-construction needs during clustering, effectively decomposing the planning problem into parallelizable subproblems to improve efficiency. A real-world case study validates the proposed method, identifying the most suitable application scenarios for the mesh-shaped topology. Compared to conventional feeder-partition-based planning, our method accommodates current target structures’ construction requirements while saving over 80% of computation time with only a 1.71% loss in optimality.
{"title":"Flexible mesh-shaped distribution network topology with adaptive geographical layered-clustered probabilistic planning","authors":"Shaohan Lu , Hong Liu , Qizhe Li , Peng Zhang , Bo Peng , Bin Xu","doi":"10.1016/j.ijepes.2026.111583","DOIUrl":"10.1016/j.ijepes.2026.111583","url":null,"abstract":"<div><div>With the large-scale integration of distributed renewable energy, traditional distribution network topologies lack the flexibility to fully exploit wide-area source-load complementarity and flexible resources, limiting the efficient and secure accommodation of distributed generation (DG). To address this, we propose a mesh-shaped distribution network topology with a three-terminal soft open point (SOP) as the core device, balancing existing grid features with retrofit complexity. Given the high-dimensional, complex MILP resulting from planning under strong stochasticity, we introduce a novel geographical layered-clustered planning method. This method, utilizing an improved fast-unfolding algorithm, considers spatio-temporal source-load distribution, customer reliability, and line-construction needs during clustering, effectively decomposing the planning problem into parallelizable subproblems to improve efficiency. A real-world case study validates the proposed method, identifying the most suitable application scenarios for the mesh-shaped topology. Compared to conventional feeder-partition-based planning, our method accommodates current target structures’ construction requirements while saving over 80% of computation time with only a 1.71% loss in optimality.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111583"},"PeriodicalIF":5.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024916","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 : 2026-01-21DOI: 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).
{"title":"Control of multiple parallel HVDC systems for frequency response sharing: A study based on synchronous frequency operation of the asynchronously interconnected systems in China","authors":"Yan Guo , Chunguang Zhou , Shengmin Qiu , Ke Wang , Yiping Chen , Zhixuan Li","doi":"10.1016/j.ijepes.2026.111586","DOIUrl":"10.1016/j.ijepes.2026.111586","url":null,"abstract":"<div><div>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).</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111586"},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025372","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 : 2026-01-21DOI: 10.1016/j.ijepes.2026.111585
Lei Hu , Longda Yao , Xiaoli Tang , Shuo Zhang , Yuandong Xu
Ensuring the reliability of pitch bearings is critical for the safe and efficient operation of wind turbines within modern power systems. However, fault diagnosis under low-speed reciprocating conditions remains extremely challenging due to weak fault-induced impulses and non-stationary dynamics. This paper proposes a Feature-Guided Adaptive Frequency Band Optimization (FAFBO) for fault diagnosis of low-speed pitch bearings. In the proposed diagnostic strategy, a signal reconstruction method is developed to eliminate the effects of reciprocating dynamics and extract relatively stationary signals for further analysis using the encoder zero-position alignment. The reconstructed signals are then analyzed by the Refined Fault Harmonics Index (RFHI) to suppress reversal impacts and stitching artifacts and extract effective fault features through two-stage coarse-to-fine grid search optimization algorithm. Experimental studies demonstrate that the FAFBO is effective and robust for pitch bearing fault diagnosis at low signal-to-noise ratios. The proposed framework provides a reliable and computationally efficient tool for condition monitoring and preventive maintenance of wind turbine pitch systems, contributing to improved reliability of wind energy generation.
{"title":"A feature-guided adaptive frequency band optimization framework for fault diagnosis of wind turbine pitch bearings","authors":"Lei Hu , Longda Yao , Xiaoli Tang , Shuo Zhang , Yuandong Xu","doi":"10.1016/j.ijepes.2026.111585","DOIUrl":"10.1016/j.ijepes.2026.111585","url":null,"abstract":"<div><div>Ensuring the reliability of pitch bearings is critical for the safe and efficient operation of wind turbines within modern power systems. However, fault diagnosis under low-speed reciprocating conditions remains extremely challenging due to weak fault-induced impulses and non-stationary dynamics. This paper proposes a Feature-Guided Adaptive Frequency Band Optimization (FAFBO) for fault diagnosis of low-speed pitch bearings. In the proposed diagnostic strategy, a signal reconstruction method is developed to eliminate the effects of reciprocating dynamics and extract relatively stationary signals for further analysis using the encoder zero-position alignment. The reconstructed signals are then analyzed by the Refined Fault Harmonics Index (RFHI) to suppress reversal impacts and stitching artifacts and extract effective fault features through two-stage coarse-to-fine grid search optimization algorithm. Experimental studies demonstrate that the FAFBO is effective and robust for pitch bearing fault diagnosis at low signal-to-noise ratios. The proposed framework provides a reliable and computationally efficient tool for condition monitoring and preventive maintenance of wind turbine pitch systems, contributing to improved reliability of wind energy generation.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111585"},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024849","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 : 2026-01-21DOI: 10.1016/j.ijepes.2026.111584
Tao Zhou , Yalun Wang , Siqi Bu , Dejian Yang
The rapid integration of wind power into modern power systems, while essential for decarbonization, significantly reduces grid inertia, leading to critical frequency stability challenges following disturbances. Traditional stepwise inertial control (SIC) strategies for wind turbines (WT) often fail to prevent secondary frequency drops (SFD) due to their fixed parameters and lack of synchronization with synchronous generator (SG) recovery dynamics. To address these limitations, this paper proposes a novel optimal SIC framework that leverages fuzzy logic control and a stacked denoising autoencoder-deep neural network (SDAE-DNN) to dynamically adapt wind turbine power output in response to real-time grid conditions. The fuzzy controller intelligently balances grid frequency support with turbine safety by prioritizing rotor speed recovery when approaching critical limits, effectively eliminating SFD. The SDAE-DNN enhances adaptability by learning optimal control parameters across diverse operating scenarios, enabling real-time, computationally efficient implementation. Extensive simulations on the IEEE 39-bus system demonstrate that the proposed strategy can effectively improve the frequency nadir compared to conventional methods and completely avoid SFD. The framework is successfully scaled to wind farm level, ensuring coordinated, secure, and optimal frequency support under high renewable penetration, offering a practical solution for enhancing grid resilience.
{"title":"Intelligent optimal stepwise inertial control for wind power frequency regulation: a fuzzy logic and SDAE-DNN framework","authors":"Tao Zhou , Yalun Wang , Siqi Bu , Dejian Yang","doi":"10.1016/j.ijepes.2026.111584","DOIUrl":"10.1016/j.ijepes.2026.111584","url":null,"abstract":"<div><div>The rapid integration of wind power into modern power systems, while essential for decarbonization, significantly reduces grid inertia, leading to critical frequency stability challenges following disturbances. Traditional stepwise inertial control (SIC) strategies for wind turbines (WT) often fail to prevent secondary frequency drops (SFD) due to their fixed parameters and lack of synchronization with synchronous generator (SG) recovery dynamics. To address these limitations, this paper proposes a novel optimal SIC framework that leverages fuzzy logic control and a stacked denoising autoencoder-deep neural network (SDAE-DNN) to dynamically adapt wind turbine power output in response to real-time grid conditions. The fuzzy controller intelligently balances grid frequency support with turbine safety by prioritizing rotor speed recovery when approaching critical limits, effectively eliminating SFD. The SDAE-DNN enhances adaptability by learning optimal control parameters across diverse operating scenarios, enabling real-time, computationally efficient implementation. Extensive simulations on the IEEE 39-bus system demonstrate that the proposed strategy can effectively improve the frequency nadir compared to conventional methods and completely avoid SFD. The framework is successfully scaled to wind farm level, ensuring coordinated, secure, and optimal frequency support under high renewable penetration, offering a practical solution for enhancing grid resilience.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111584"},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025373","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 : 2026-01-21DOI: 10.1016/j.ijepes.2026.111559
Hangdong An, Jin Ma
The increasing integration of intermittent renewable energies amplifies uncertainty in power system operation, complicating stability assessment and limiting the effectiveness of traditional model-based approaches. To address this challenge, data-driven methods have emerged as flexible alternatives, but they often suffer from severe class imbalance, with unstable cases being relatively rare. Generative models like Generative Adversarial Models (GANs) and Variational Autoencoders (VAEs) have been explored for sample augmentation, yet frequently encounter issues such as mode collapse and insufficient diversity. More recently, Denoising diffusion probabilistic models (DDPMs) have shown promise, but their high computational cost—often requiring hundreds of denoising steps—hinders practical deployment. To overcome these limitations, this paper proposes an FDDA that directly learns the residuals between noise samples at adjacent time steps, thereby eliminating the need for larger attention mechanism modules. This design significantly reduces the number of diffusion steps by more than 60% compared to standard DDPMs—without compromising sample quality. Combined with support vector machine(SVM) and random forest(RF) classifiers, our approach is evaluated on multiple benchmark power system cases. Results demonstrate improved generalization, robustness to class imbalance, and lower computational cost compared to conventional augmentation techniques, thus providing a scalable and efficient data-driven strategy for enhancing power system stability assessment under high uncertainty.
{"title":"Fast diffusion-driven data augmentation for imbalanced power-system stability classification","authors":"Hangdong An, Jin Ma","doi":"10.1016/j.ijepes.2026.111559","DOIUrl":"10.1016/j.ijepes.2026.111559","url":null,"abstract":"<div><div>The increasing integration of intermittent renewable energies amplifies uncertainty in power system operation, complicating stability assessment and limiting the effectiveness of traditional model-based approaches. To address this challenge, data-driven methods have emerged as flexible alternatives, but they often suffer from severe class imbalance, with unstable cases being relatively rare. Generative models like Generative Adversarial Models (GANs) and Variational Autoencoders (VAEs) have been explored for sample augmentation, yet frequently encounter issues such as mode collapse and insufficient diversity. More recently, Denoising diffusion probabilistic models (DDPMs) have shown promise, but their high computational cost—often requiring hundreds of denoising steps—hinders practical deployment. To overcome these limitations, this paper proposes an FDDA that directly learns the residuals between noise samples at adjacent time steps, thereby eliminating the need for larger attention mechanism modules. This design significantly reduces the number of diffusion steps by more than 60% compared to standard DDPMs—without compromising sample quality. Combined with support vector machine(SVM) and random forest(RF) classifiers, our approach is evaluated on multiple benchmark power system cases. Results demonstrate improved generalization, robustness to class imbalance, and lower computational cost compared to conventional augmentation techniques, thus providing a scalable and efficient data-driven strategy for enhancing power system stability assessment under high uncertainty.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111559"},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025380","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 : 2026-01-21DOI: 10.1016/j.ijepes.2026.111605
Wei Xiao , Shuang Duan , Xiaohan Ren , Qiongyan Fang , Wangyan Li , Fuwen Yang
Smart grids have been widely studied for their ability to improve efficiency and reliability, with prior research focusing on demand response, secure data sharing, and distributed optimization. However, existing approaches often address privacy protection and energy efficiency separately, leaving a gap in simultaneously achieving both within a scalable and incentive-compatible framework. To address this challenge, this work proposes a blockchain-based energy conservation mechanism that integrates an adaptive weighted average consensus scheme, optimized via Non-Dominated Sorting Genetic Algorithm II, with a dynamic incentive contract supported by cryptocurrency rewards. The mechanism allows prosumers to validate transactions by exchanging only the percentage of power change, thereby preserving privacy, while the incentive design motivates active participation in energy scheduling. Simulation results show that the proposed approach reduces average daily energy consumption per prosumer by 7.5 kWh (30-node case) and 8.8 kWh (300-node case), and decreases 24-hour weighted electricity costs by up to 8.66%. These findings highlight the effectiveness of the mechanism in achieving measurable energy and cost savings.
{"title":"Blockchain-based energy conservation mechanism in smart grids using adaptive weighted average consensus","authors":"Wei Xiao , Shuang Duan , Xiaohan Ren , Qiongyan Fang , Wangyan Li , Fuwen Yang","doi":"10.1016/j.ijepes.2026.111605","DOIUrl":"10.1016/j.ijepes.2026.111605","url":null,"abstract":"<div><div>Smart grids have been widely studied for their ability to improve efficiency and reliability, with prior research focusing on demand response, secure data sharing, and distributed optimization. However, existing approaches often address privacy protection and energy efficiency separately, leaving a gap in simultaneously achieving both within a scalable and incentive-compatible framework. To address this challenge, this work proposes a blockchain-based energy conservation mechanism that integrates an adaptive weighted average consensus scheme, optimized via <em>Non-Dominated Sorting Genetic Algorithm II</em>, with a dynamic incentive contract supported by cryptocurrency rewards. The mechanism allows prosumers to validate transactions by exchanging only the percentage of power change, thereby preserving privacy, while the incentive design motivates active participation in energy scheduling. Simulation results show that the proposed approach reduces average daily energy consumption per prosumer by 7.5 kWh (30-node case) and 8.8 kWh (300-node case), and decreases 24-hour weighted electricity costs by up to 8.66%. These findings highlight the effectiveness of the mechanism in achieving measurable energy and cost savings.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111605"},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024848","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 : 2026-01-21DOI: 10.1016/j.ijepes.2026.111598
Tianlu Gao , Xiao Wang , Jing Zhang , Jun Zhang , Alessandra Parisio
Virtual power plants aggregate flexible resources for market participations. This plays an important role for the secure operations of power networks with high renewable penetrations. The paper proposes a two-stage robust optimization model for the VPP considering the battery swapping service for electric vehicles. The swapping service is conducted in a distributed manner through a central battery charging station and multiple battery distribution stations, which are connected through the transportation truck. The VPP is operated to participate in the energy and reserve markets. The technical requirements of reserve calling are modeled considering the secondary response capability for the short term operating reserve (STOR) as defined by the Britain grid. The nested column and constraint generation (NCCG) algorithm is used to solve the model considering the binary variables in the second stage. Simulation with two study cases demonstrate the effectiveness of the proposed approach. It indicates that the VPP provides reserve capacity fulfilling arbitrary reserve usage, and the VPP rewards is decreased with the increased level of uncertainties.
{"title":"Optimal scheduling of virtual power plant for short term operating reserve considering EV battery swapping","authors":"Tianlu Gao , Xiao Wang , Jing Zhang , Jun Zhang , Alessandra Parisio","doi":"10.1016/j.ijepes.2026.111598","DOIUrl":"10.1016/j.ijepes.2026.111598","url":null,"abstract":"<div><div>Virtual power plants aggregate flexible resources for market participations. This plays an important role for the secure operations of power networks with high renewable penetrations. The paper proposes a two-stage robust optimization model for the VPP considering the battery swapping service for electric vehicles. The swapping service is conducted in a distributed manner through a central battery charging station and multiple battery distribution stations, which are connected through the transportation truck. The VPP is operated to participate in the energy and reserve markets. The technical requirements of reserve calling are modeled considering the secondary response capability for the short term operating reserve (STOR) as defined by the Britain grid. The nested column and constraint generation (NCCG) algorithm is used to solve the model considering the binary variables in the second stage. Simulation with two study cases demonstrate the effectiveness of the proposed approach. It indicates that the VPP provides reserve capacity fulfilling arbitrary reserve usage, and the VPP rewards is decreased with the increased level of uncertainties.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111598"},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024847","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 : 2026-01-21DOI: 10.1016/j.ijepes.2026.111604
Yuanshuo Guo , Jun Wang , Hong Peng , Tao Wang , Hongping Hu , Antonio Ramírez-de-Arellano
The increasing frequency of extreme weather events has brought about significant mutation in the distribution characteristics of power load, while traditional models are unable to handle such sudden changes in load and adequately characterize the coupling effects across various scales. To address this problem, this study proposes a bidirectional nonlinear spiking neural P (NSNP) model with weather-aware multi-scale fusion, which represents an enhanced NSNP framework that integrates multi-scale adaptive feature extraction network (MAFEN) and multiple encoders based on bidirectional NSNP (BiNSNP) variants, termed multi-scale spatiotemporal BiNSNP attention fusion network (MSBAF-Net). Inspired by nonlinear spiking mechanisms, this architecture captures complex nonlinear load dynamics. Moreover, this multi-source data parallel fusion network effectively achieves dynamic weighting of features across both spatial and temporal dimensions, thereby capturing local patterns at critical time steps in load sequences and cross-channel feature correlations under extreme weather. Specifically, MSBAF-Net performs channel separation, isolating the abrupt components of the load into the residual channel. Based on the characteristics of different channels, MSBAF-Net incorporates a targeted bidirectional modeling strategy alongside differentiated feature extraction pathways, implemented through two lightweight NSNP-like convolutional models. Additionally, feature fusion network (FFN) maintains the interaction of multi-scale load features in time and space. Finally, comparison study using three real-world datasets and 25 baseline prediction models is performed. Experimental results demonstrate that MSBAF-Net achieves the best comprehensive performance across all extreme weather scenarios. Notably, under the low-temperature cold wave scenario, MSBAF-Net achieves average forecasting accuracies of 97.51% and 97.38% for Lines 1–10 at the power station A and Lines 1–7 at the power station B, respectively. Our codes and datasets have been released at https://github.com/hssinne/MSBAF-Net.
{"title":"A multi-scale spatiotemporal spiking neural model for power load forecasting considering extreme weather impact","authors":"Yuanshuo Guo , Jun Wang , Hong Peng , Tao Wang , Hongping Hu , Antonio Ramírez-de-Arellano","doi":"10.1016/j.ijepes.2026.111604","DOIUrl":"10.1016/j.ijepes.2026.111604","url":null,"abstract":"<div><div>The increasing frequency of extreme weather events has brought about significant mutation in the distribution characteristics of power load, while traditional models are unable to handle such sudden changes in load and adequately characterize the coupling effects across various scales. To address this problem, this study proposes a bidirectional nonlinear spiking neural P (NSNP) model with weather-aware multi-scale fusion, which represents an enhanced NSNP framework that integrates multi-scale adaptive feature extraction network (MAFEN) and multiple encoders based on bidirectional NSNP (BiNSNP) variants, termed multi-scale spatiotemporal BiNSNP attention fusion network (MSBAF-Net). Inspired by nonlinear spiking mechanisms, this architecture captures complex nonlinear load dynamics. Moreover, this multi-source data parallel fusion network effectively achieves dynamic weighting of features across both spatial and temporal dimensions, thereby capturing local patterns at critical time steps in load sequences and cross-channel feature correlations under extreme weather. Specifically, MSBAF-Net performs channel separation, isolating the abrupt components of the load into the residual channel. Based on the characteristics of different channels, MSBAF-Net incorporates a targeted bidirectional modeling strategy alongside differentiated feature extraction pathways, implemented through two lightweight NSNP-like convolutional models. Additionally, feature fusion network (FFN) maintains the interaction of multi-scale load features in time and space. Finally, comparison study using three real-world datasets and 25 baseline prediction models is performed. Experimental results demonstrate that MSBAF-Net achieves the best comprehensive performance across all extreme weather scenarios. Notably, under the low-temperature cold wave scenario, MSBAF-Net achieves average forecasting accuracies of 97.51% and 97.38% for Lines 1–10 at the power station A and Lines 1–7 at the power station B, respectively. Our codes and datasets have been released at <span><span>https://github.com/hssinne/MSBAF-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111604"},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025381","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}
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