In order to eliminate medium voltage (MV) voltage violations and low voltage (LV) three-phase voltage unbalance in PV-rich dual-level distribution networks simultaneously, a novel federated reinforcement learning (FRL)-based voltage regulation method is proposed. First, voltage regulation is formulated as a Markov Game, and each LV station is constructed as an agent. The rewards of MV-LV control goals are decomposed to hierarchically train agents, enabling simultaneous mitigation of MV voltage violations and LV three-phase voltage unbalance. The federated learning framework is employed on agent training for learning MV-LV voltage regulation policies by interacting with partial real data and policy rewards to achieve better privacy preservation and scalability. Moreover, to enhance robustness against imperfect communication environments, we implement weighted data filling for imputing missing data. Simulation results on MV-LV distribution systems demonstrate the effectiveness and advantages of the proposed method.
{"title":"Federated reinforcement learning based dual-level voltage regulation for PV-rich distribution grids","authors":"Xiao Liu, Youbo Liu, Yongdong Chen, Zhiyuan Tang, Hongjun Gao, Zhengbo Li","doi":"10.1016/j.ijepes.2025.111492","DOIUrl":"10.1016/j.ijepes.2025.111492","url":null,"abstract":"<div><div>In order to eliminate medium voltage (MV) voltage violations and low voltage (LV) three-phase voltage unbalance in PV-rich dual-level distribution networks simultaneously, a novel federated reinforcement learning (FRL)-based voltage regulation method is proposed. First, voltage regulation is formulated as a Markov Game, and each LV station is constructed as an agent. The rewards of MV-LV control goals are decomposed to hierarchically train agents, enabling simultaneous mitigation of MV voltage violations and LV three-phase voltage unbalance. The federated learning framework is employed on agent training for learning MV-LV voltage regulation policies by interacting with partial real data and policy rewards to achieve better privacy preservation and scalability. Moreover, to enhance robustness against imperfect communication environments, we implement weighted data filling for imputing missing data. Simulation results on MV-LV distribution systems demonstrate the effectiveness and advantages of the proposed method.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111492"},"PeriodicalIF":5.0,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981891","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 : 2026-01-17DOI: 10.1016/j.ijepes.2026.111591
Dan Liu , Xia Chen , Jiawen Li
Traditional centralized load frequency control (LFC) is vulnerable to power fluctuations in tie-line power due to the conflicting objectives of multiple area controllers and distributors in an isolated multi-area microgrid. To address these problems, a cuttlefish-like cooperative load frequency control (CC-LFC) method is proposed. This AI method imitates the distributed neural network structure of cuttlefish in that it equates the controllers and power distributors of each area as agents in a multi-area microgrid. In online applications, a joint global optimization decision can be obtained from the grid areas without engaging in extensive intercommunication. In addition, this paper proposes a large-scale counterfactual multiagent deep meta-policy gradient (LSCMA-DMPG), which combines centralized training with decentralized execution in a large-scale learning framework. It employs meta-reinforcement learning to realize multitask collaborative learning, which improves the robustness and quality of the obtained CC-LFC policies. The real-time experiments and simulations for a four-area LFC model of Sansha Island in the China Southern Grid (CSG) demonstrate the superior qualities of the proposed method.
{"title":"Bionic active power control for multi-area microgrids: A large-scale multiagent deep meta reinforcement learning approach","authors":"Dan Liu , Xia Chen , Jiawen Li","doi":"10.1016/j.ijepes.2026.111591","DOIUrl":"10.1016/j.ijepes.2026.111591","url":null,"abstract":"<div><div>Traditional centralized load frequency control (LFC) is vulnerable to power fluctuations in tie-line power due to the conflicting objectives of multiple area controllers and distributors in an isolated multi-area microgrid. To address these problems, a cuttlefish-like cooperative load frequency control (CC-LFC) method is proposed. This AI method imitates the distributed neural network structure of cuttlefish in that it equates the controllers and power distributors of each area as agents in a multi-area microgrid. In online applications, a joint global optimization decision can be obtained from the grid areas without engaging in extensive intercommunication. In addition, this paper proposes a large-scale counterfactual multiagent deep meta-policy gradient (LSCMA-DMPG), which combines centralized training with decentralized execution in a large-scale learning framework. It employs meta-reinforcement learning to realize multitask collaborative learning, which improves the robustness and quality of the obtained CC-LFC policies. The real-time experiments and simulations for a four-area LFC model of Sansha Island in the China Southern Grid (CSG) demonstrate the superior qualities of the proposed method.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111591"},"PeriodicalIF":5.0,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981892","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 : 2026-01-17DOI: 10.1016/j.ijepes.2026.111593
Zepeng Li , Qiuwei Wu , Hui Li , Litao Zheng , Shengyu Tao , Xuan Zhang , Jianfeng Wen
The increasing penetration of renewable energy threatens frequency security in future power systems, while electric vehicle charging stations (EVCSs) can provide flexible and fast frequency response (FR). This paper develops a frequency-constrained dispatch scheme for the integrated power–transportation system (IPTS) considering FR from EVCSs to co-optimize unit commitment, generation dispatch, FR, and traffic routing. The IPTS dispatch model incorporates the constraints of the power system, transportation system, and EVCSs, and assesses the impacts of EVCS-provided FR on both systems. To address the model’s nonconvexities, tailored model reformulation methods are designed to transform the original model into a mixed-integer second-order cone programming (MISOCP) form. Case studies on two test systems show that the proposed scheme maintains frequency security while reducing operating cost and carbon emissions.
{"title":"Frequency-constrained dispatch for integrated power and transportation system considering frequency response from EV charging stations","authors":"Zepeng Li , Qiuwei Wu , Hui Li , Litao Zheng , Shengyu Tao , Xuan Zhang , Jianfeng Wen","doi":"10.1016/j.ijepes.2026.111593","DOIUrl":"10.1016/j.ijepes.2026.111593","url":null,"abstract":"<div><div>The increasing penetration of renewable energy threatens frequency security in future power systems, while electric vehicle charging stations (EVCSs) can provide flexible and fast frequency response (FR). This paper develops a frequency-constrained dispatch scheme for the integrated power–transportation system (IPTS) considering FR from EVCSs to co-optimize unit commitment, generation dispatch, FR, and traffic routing. The IPTS dispatch model incorporates the constraints of the power system, transportation system, and EVCSs, and assesses the impacts of EVCS-provided FR on both systems. To address the model’s nonconvexities, tailored model reformulation methods are designed to transform the original model into a mixed-integer second-order cone programming (MISOCP) form. Case studies on two test systems show that the proposed scheme maintains frequency security while reducing operating cost and carbon emissions.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111593"},"PeriodicalIF":5.0,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024865","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 : 2026-01-16DOI: 10.1016/j.ijepes.2026.111577
Zhe Chen , Da Lin , Xiaohui Ge , Chouwei Ni , Yuhan Ma , Jiakai Qin
With the accelerating integration of distributed energy resources (DERs) into grids, enhanced bidirectional interactions between distribution and transmission systems have significantly complicated grid coordination. The optimization challenges in coordinated transmission-distribution-microgrid systems are structurally more complex than those in standalone distribution-microgrid or transmission-distribution systems. Numerous non-convex alternating current optimal power flow (ACOPF) constraints make the design of distributed algorithms more challenging. To address this issue, this paper introduces a hierarchical scheduling method for transmission-distribution-microgrids based on chordal sparsity for non-convex OPF optimization problems. First, a coordinated scheduling model for transmission-distribution-microgrids based on ACOPF is established. Subsequently, the chordal sparsity semidefinite relaxation method technique accelerates the solution process, while the proposed polynomial semidefinite programming cuts and chordal sparsity relaxation (PCCSR) model effectively alleviates the difficulties associated with large-scale problems typical of traditional semidefinite program relaxation, enabling rapid solutions. Finally, a nested alternating direction method of multipliers algorithm is applied to achieve a distributed solution for transmission-distribution-microgrids. Case studies demonstrate the PCCSR method not only simplifies the problem-solving process but also typically results in a minimal relaxation gap. Furthermore, it strikes an effective balance between computational time and efficiency, making it particularly beneficial for large-scale grids optimization.
{"title":"Coordinated scheduling for transmission-distribution-microgrids via chordal-based semidefinite programming","authors":"Zhe Chen , Da Lin , Xiaohui Ge , Chouwei Ni , Yuhan Ma , Jiakai Qin","doi":"10.1016/j.ijepes.2026.111577","DOIUrl":"10.1016/j.ijepes.2026.111577","url":null,"abstract":"<div><div>With the accelerating integration of distributed energy resources (DERs) into grids, enhanced bidirectional interactions between distribution and transmission systems have significantly complicated grid coordination. The optimization challenges in coordinated transmission-distribution-microgrid systems are structurally more complex than those in standalone distribution-microgrid or transmission-distribution systems. Numerous non-convex alternating current optimal power flow (ACOPF) constraints make the design of distributed algorithms more challenging. To address this issue, this paper introduces a hierarchical scheduling method for transmission-distribution-microgrids based on chordal sparsity for non-convex OPF optimization problems. First, a coordinated scheduling model for transmission-distribution-microgrids based on ACOPF is established. Subsequently, the chordal sparsity semidefinite relaxation method technique accelerates the solution process, while the proposed polynomial semidefinite programming cuts and chordal sparsity relaxation (PCCSR) model effectively alleviates the difficulties associated with large-scale problems typical of traditional semidefinite program relaxation, enabling rapid solutions. Finally, a nested alternating direction method of multipliers algorithm is applied to achieve a distributed solution for transmission-distribution-microgrids. Case studies demonstrate the PCCSR method not only simplifies the problem-solving process but also typically results in a minimal relaxation gap. Furthermore, it strikes an effective balance between computational time and efficiency, making it particularly beneficial for large-scale grids optimization.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111577"},"PeriodicalIF":5.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981890","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 : 2026-01-16DOI: 10.1016/j.ijepes.2025.111514
Ximu Liu , Yujian Ye , Hongru Wang , Cun Zhang , Hengyu Liu , Zhi Zhang , Xi Zhang , Dezhi Xu , Goran Strbac
Integrated PV-ESS-EV Stations (IPEES) are emerging as significant market players that integrate variable photovoltaic (PV) generation with time-coupled energy storage system (ESS) and the flexible demand of electric vehicles (EVs), participating in day-ahead and intra-day energy and ancillary services markets. However, current studies on charging station operations and bidding often employ short-term or two-stage models, which overlook the temporal correlations in intra-day bidding. In addition, the intricate interactions among various physical energy systems and the diverse energy market bidding options are not comprehensively captured. To fill the above research gaps, this paper proposes a multi-stage joint energy–ancillary market bidding and on-site scheduling model for IPEES, accounting for correlated heterogeneous uncertainties. Firstly, a non-homogeneous Markov process is proposed, combined with a Density-Based Spatial clustering discretization strategy, to uncover the fluctuation patterns of multiple stochastic series, such as PV generation, prices, and aggregated charging flexibilities. Then, a market bidding and operation scheduling optimization model is developed, integrating day-ahead and intra-day markets, as well as energy and ancillary services, while considering all dynamic security constraints of ESS and EV operations. To address the challenging multi-stage mixed-integer stochastic optimization problem, a Markov chain stochastic dual dynamic integer programming algorithm (MC-SDDiP) is employed, which constructs cuts from Monte Carlo resampled Markov trajectories to ensure scalability and applicability. Case studies on a PJM-calibrated IPEES demonstrate high accuracy (0.102% gap) and significant economic benefits from joint participation: downward reserve activation reduces procurement costs — often through opportunistic ESS charging — shifting outcomes from near break-even to positive returns, while extensive upward activation increases intra-day purchases and diminishes profits. The findings provide a practical approach for IPEES to convert PV/ESS/EV flexibility into multi-market revenue under realistic settlement frictions and correlated, time-evolving uncertainties.
{"title":"Multi-stage day-ahead and intra-day resource scheduling and market bidding strategy for integrated PV-ESS-EV station under multiple uncertainties","authors":"Ximu Liu , Yujian Ye , Hongru Wang , Cun Zhang , Hengyu Liu , Zhi Zhang , Xi Zhang , Dezhi Xu , Goran Strbac","doi":"10.1016/j.ijepes.2025.111514","DOIUrl":"10.1016/j.ijepes.2025.111514","url":null,"abstract":"<div><div>Integrated PV-ESS-EV Stations (IPEES) are emerging as significant market players that integrate variable photovoltaic (PV) generation with time-coupled energy storage system (ESS) and the flexible demand of electric vehicles (EVs), participating in day-ahead and intra-day energy and ancillary services markets. However, current studies on charging station operations and bidding often employ short-term or two-stage models, which overlook the temporal correlations in intra-day bidding. In addition, the intricate interactions among various physical energy systems and the diverse energy market bidding options are not comprehensively captured. To fill the above research gaps, this paper proposes a multi-stage joint energy–ancillary market bidding and on-site scheduling model for IPEES, accounting for correlated heterogeneous uncertainties. Firstly, a non-homogeneous Markov process is proposed, combined with a Density-Based Spatial clustering discretization strategy, to uncover the fluctuation patterns of multiple stochastic series, such as PV generation, prices, and aggregated charging flexibilities. Then, a market bidding and operation scheduling optimization model is developed, integrating day-ahead and intra-day markets, as well as energy and ancillary services, while considering all dynamic security constraints of ESS and EV operations. To address the challenging multi-stage mixed-integer stochastic optimization problem, a Markov chain stochastic dual dynamic integer programming algorithm (MC-SDDiP) is employed, which constructs cuts from Monte Carlo resampled Markov trajectories to ensure scalability and applicability. Case studies on a PJM-calibrated IPEES demonstrate high accuracy (0.102% gap) and significant economic benefits from joint participation: downward reserve activation reduces procurement costs — often through opportunistic ESS charging — shifting outcomes from near break-even to positive returns, while extensive upward activation increases intra-day purchases and diminishes profits. The findings provide a practical approach for IPEES to convert PV/ESS/EV flexibility into multi-market revenue under realistic settlement frictions and correlated, time-evolving uncertainties.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111514"},"PeriodicalIF":5.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981894","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 : 2026-01-15DOI: 10.1016/j.ijepes.2026.111568
Karim El Mezdi , Abdelmounime El Magri , Ilyass El Myasse , Fouad Giri , Pankaj Kumar
This paper proposes an advanced nonlinear control strategy coupled with energy flow optimization () for a hybrid -microgrid integrating a photovoltaic () generator, a lithium-ion battery energy storage system (), a proton exchange membrane () electrolyzer for hydrogen production, and a fuel cell for backup power. All subsystems are interconnected, via power electronic converters, to a common -bus supplying diverse loads. The proposed control strategy ensures five key objectives: tight -bus voltage regulation, optimal power extraction from (/), intelligent battery operation in constant current/voltage () modes, and specified hydrogen production tracking, and secure fuel cell activation under power deficit. An integrated energy-management algorithm dynamically manages power sharing among sources and storage based on renewable availability and battery state-of-charge (). Nonlinear backstepping controllers are designed for all converters (-side DC/DC boost, bidirectional / buck-boost, electrolyzer / buck, and fuel-cell / buck) to guarantee stability and fast dynamics. Simulation results across multiple operating scenarios show smooth mode transitions, reduced battery charge/discharge cycling, accurate hydrogen-production tracking, tight -bus regulation, and reliable continuity of supply, confirming the effectiveness and robustness of the proposed control and framework.
{"title":"Energy flow optimization for a hybrid DC-microgrid integrating hydrogen production via a PEM electrolyzer and fuel cell backup","authors":"Karim El Mezdi , Abdelmounime El Magri , Ilyass El Myasse , Fouad Giri , Pankaj Kumar","doi":"10.1016/j.ijepes.2026.111568","DOIUrl":"10.1016/j.ijepes.2026.111568","url":null,"abstract":"<div><div>This paper proposes an advanced nonlinear control strategy coupled with energy flow optimization (<span><math><mrow><mi>E</mi><mi>F</mi><mi>O</mi></mrow></math></span>) for a hybrid <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>-microgrid integrating a photovoltaic (<span><math><mrow><mi>P</mi><mi>V</mi></mrow></math></span>) generator, a lithium-ion battery energy storage system (<span><math><mrow><mi>B</mi><mi>E</mi><mi>S</mi><mi>S</mi></mrow></math></span>), a proton exchange membrane (<span><math><mrow><mi>P</mi><mi>E</mi><mi>M</mi></mrow></math></span>) electrolyzer for hydrogen production, and a <span><math><mrow><mi>P</mi><mi>E</mi><mi>M</mi></mrow></math></span> fuel cell for backup power. All subsystems are interconnected, via power electronic converters, to a common <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>-bus supplying diverse loads. The proposed control strategy ensures five key objectives: tight <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>-bus voltage regulation, optimal power extraction from <span><math><mrow><mi>P</mi><mi>V</mi></mrow></math></span> (<span><math><mrow><mi>M</mi><mi>P</mi><mi>P</mi><mi>T</mi></mrow></math></span>/<span><math><mrow><mi>A</mi><mi>P</mi><mi>P</mi><mi>T</mi></mrow></math></span>), intelligent battery operation in constant current/voltage (<span><math><mrow><mi>C</mi><mi>C</mi><mo>/</mo><mi>C</mi><mi>V</mi></mrow></math></span>) modes, and specified hydrogen production tracking, and secure fuel cell activation under power deficit. An integrated energy-management algorithm dynamically manages power sharing among sources and storage based on renewable availability and battery state-of-charge (<span><math><mrow><mi>S</mi><mi>o</mi><mi>C</mi></mrow></math></span>). Nonlinear backstepping controllers are designed for all converters (<span><math><mrow><mi>P</mi><mi>V</mi></mrow></math></span>-side DC/DC boost, bidirectional <span><math><mrow><mi>B</mi><mi>E</mi><mi>S</mi><mi>S</mi></mrow></math></span> <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>/<span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span> buck-boost, electrolyzer <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>/<span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span> buck, and fuel-cell <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>/<span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span> buck) to guarantee stability and fast dynamics. Simulation results across multiple operating scenarios show smooth mode transitions, reduced battery charge/discharge cycling, accurate hydrogen-production tracking, tight <span><math><mrow><mi>D</mi><mi>C</mi></mrow></math></span>-bus regulation, and reliable continuity of supply, confirming the effectiveness and robustness of the proposed control and <span><math><mrow><mi>E</mi><mi>F</mi><mi>O</mi></mrow></math></span> framework.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111568"},"PeriodicalIF":5.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981819","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 : 2026-01-15DOI: 10.1016/j.ijepes.2026.111562
Huibin Jia , Longyue Su , Shuaikang Wang , Shaoyan Li , Jiahe Li
As power systems evolve into deeply integrated cyber–physical power systems (CPPS), isolated system failures are increasingly transforming into becoming coupled faults across both the cyber and physical domains. During the restoration process, focusing solely on a single system, thus neglecting the CPPS’s various interdependencies, can lead to longer recovery times, secondary outages, or frequency violations. In this paper, we proposed a rapid recovery method for concurrent cyber–physical faults based on software-defined networking (SDN). First, the coupling mechanism between the physical and information systems for the grid in question was analyzed to investigate how communication failures constrain the restoration times of the generators and loads. A temporal coordination model for CPPS recovery was then established under the SDN architecture. A staged and iterative recovery strategy comprising “SDN re-routing, manual repair, and dynamic updates was proposed to coordinate the concurrent restoration of both the communication and power systems, thereby reducing any additional outage times during manual operations. Finally, to minimize outage losses, a coordinated optimization model was developed for the power and communication networks, in order to determine the best sequence in which to restore power delivery to the various loads. A commercially available software application was then used to find the most efficient solution. Virtual simulation results of the approach, based on the IEEE 30-bus system, demonstrated that our proposed method reduced outage losses by 9.23% and shortened the average recovery time by 8.6% vs traditional approaches, validating its efficacy for this application.
{"title":"Software-defined networking-enabled rapid recovery for concurrent cyber–physical faults in cyber–physical power systems","authors":"Huibin Jia , Longyue Su , Shuaikang Wang , Shaoyan Li , Jiahe Li","doi":"10.1016/j.ijepes.2026.111562","DOIUrl":"10.1016/j.ijepes.2026.111562","url":null,"abstract":"<div><div>As power systems evolve into deeply integrated cyber–physical power systems (CPPS), isolated system failures are increasingly transforming into becoming coupled faults across both the cyber and physical domains. During the restoration process, focusing solely on a single system, thus neglecting the CPPS’s various interdependencies, can lead to longer recovery times, secondary outages, or frequency violations. In this paper, we proposed a rapid recovery method for concurrent cyber–physical faults based on software-defined networking (SDN). First, the coupling mechanism between the physical and information systems for the grid in question was analyzed to investigate how communication failures constrain the restoration times of the generators and loads. A temporal coordination model for CPPS recovery was then established under the SDN architecture. A staged and iterative recovery strategy comprising “SDN re-routing, manual repair, and dynamic updates was proposed to coordinate the concurrent restoration of both the communication and power systems, thereby reducing any additional outage times during manual operations. Finally, to minimize outage losses, a coordinated optimization model was developed for the power and communication networks, in order to determine the best sequence in which to restore power delivery to the various loads. A commercially available software application was then used to find the most efficient solution. Virtual simulation results of the approach, based on the IEEE 30-bus system, demonstrated that our proposed method reduced outage losses by 9.23% and shortened the average recovery time by 8.6% vs traditional approaches, validating its efficacy for this application.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111562"},"PeriodicalIF":5.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981816","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 : 2026-01-15DOI: 10.1016/j.ijepes.2026.111588
Valéria M. de Souza, Hugo R. de Brito, Kjetil O. Uhlen
This paper presents a novel measurement-based voltage stability indicator suited for real-time monitoring at the transmission level. Building upon a previously defined approach that uses variations in apparent power and impedance, the proposed indicator includes significant modifications; in particular, an adaptive estimation algorithm designed to improve robustness to random variations, measurement noise and short-term dynamics. A mathematical framework is established as basis for the definition of voltage stability regions of operation and their respective thresholds. Moreover, this paper introduces an emergency control scheme based on load shedding as a means to counteract imminent voltage collapse. Dynamic simulations conducted in the IEEE Nordic Test System evaluate the effectiveness of the combined actions of the proposed indicator and emergency control scheme in identifying and preventing voltage instability. Further validation of the novel indicator is carried out through analysis of synchrophasor data pertaining to a real voltage collapse event in the Nordic Grid. The overall results demonstrate that the developed approach constitutes a promising asset for system operators to ensure sufficient voltage stability margins in transmission networks.
{"title":"Real-time identification and prevention of voltage instability using synchronized phasor measurements","authors":"Valéria M. de Souza, Hugo R. de Brito, Kjetil O. Uhlen","doi":"10.1016/j.ijepes.2026.111588","DOIUrl":"10.1016/j.ijepes.2026.111588","url":null,"abstract":"<div><div>This paper presents a novel measurement-based voltage stability indicator suited for real-time monitoring at the transmission level. Building upon a previously defined approach that uses variations in apparent power and impedance, the proposed indicator includes significant modifications; in particular, an adaptive estimation algorithm designed to improve robustness to random variations, measurement noise and short-term dynamics. A mathematical framework is established as basis for the definition of voltage stability regions of operation and their respective thresholds. Moreover, this paper introduces an emergency control scheme based on load shedding as a means to counteract imminent voltage collapse. Dynamic simulations conducted in the IEEE Nordic Test System evaluate the effectiveness of the combined actions of the proposed indicator and emergency control scheme in identifying and preventing voltage instability. Further validation of the novel indicator is carried out through analysis of synchrophasor data pertaining to a real voltage collapse event in the Nordic Grid. The overall results demonstrate that the developed approach constitutes a promising asset for system operators to ensure sufficient voltage stability margins in transmission networks.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111588"},"PeriodicalIF":5.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981818","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 : 2026-01-15DOI: 10.1016/j.ijepes.2025.111538
Hao Wu , Biao Wang , Mingbo Niu
In the process of traffic energy scheduling, the proportion of renewable energy utilization is gradually increasing, and accurate photovoltaic (PV) power generation forecasting is a prerequisite for the safe and stable integration of high proportions of PV into the power grid. However, existing machine learning models for PV power forecasting generally suffer from low prediction accuracy and weak generalization capability. To address these issues, this paper proposes a Stacking ensemble forecasting model to improve PV prediction accuracy. An improved stacked ensemble algorithm is adopted, integrating three base models – Artificial Neural Network (ANN), Long Short-Term Memory network (LSTM), and Random Forest (RF) – to combine time-series features, nonlinear relationships, and robustness, with the outputs of the base models used as inputs to a Linear Regression (LR) meta-model to effectively avoid overfitting and enhance prediction stability. Data from two different locations were collected, with missing and abnormal values processed, and the base models were trained using five-fold cross-validation to ensure data diversity. The model performance was evaluated using RMSE, MAE, MAPE, R, and a performance comparison was conducted between the proposed model and individual baseline models. Furthermore, the SHapley Additive exPlanations (SHAP) method was applied to analyze the importance of nine input features, quantifying their contributions to the prediction results. Based on the forecast results of photovoltaic output and load demand, an economically-oriented energy dispatch scheme has been formulated. The improved whale optimization algorithm is employed to maximize the economic benefits of the grid system while meeting load demands, followed by an analysis of the system’s self-sufficiency rate during this period.
{"title":"Research on traffic energy scheduling based on photovoltaic forecasting using stacked ensemble learning","authors":"Hao Wu , Biao Wang , Mingbo Niu","doi":"10.1016/j.ijepes.2025.111538","DOIUrl":"10.1016/j.ijepes.2025.111538","url":null,"abstract":"<div><div>In the process of traffic energy scheduling, the proportion of renewable energy utilization is gradually increasing, and accurate photovoltaic (PV) power generation forecasting is a prerequisite for the safe and stable integration of high proportions of PV into the power grid. However, existing machine learning models for PV power forecasting generally suffer from low prediction accuracy and weak generalization capability. To address these issues, this paper proposes a Stacking ensemble forecasting model to improve PV prediction accuracy. An improved stacked ensemble algorithm is adopted, integrating three base models – Artificial Neural Network (ANN), Long Short-Term Memory network (LSTM), and Random Forest (RF) – to combine time-series features, nonlinear relationships, and robustness, with the outputs of the base models used as inputs to a Linear Regression (LR) meta-model to effectively avoid overfitting and enhance prediction stability. Data from two different locations were collected, with missing and abnormal values processed, and the base models were trained using five-fold cross-validation to ensure data diversity. The model performance was evaluated using RMSE, MAE, MAPE, R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, and a performance comparison was conducted between the proposed model and individual baseline models. Furthermore, the SHapley Additive exPlanations (SHAP) method was applied to analyze the importance of nine input features, quantifying their contributions to the prediction results. Based on the forecast results of photovoltaic output and load demand, an economically-oriented energy dispatch scheme has been formulated. The improved whale optimization algorithm is employed to maximize the economic benefits of the grid system while meeting load demands, followed by an analysis of the system’s self-sufficiency rate during this period.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111538"},"PeriodicalIF":5.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981817","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 : 2026-01-15DOI: 10.1016/j.ijepes.2026.111570
Han Yan , Jianhua Wang , Chenyu Zhang , Ruihuang Liu , Xiaodong Yuan , Jianfeng Zhao
During the power restoration process in distribution networks, costs and economics are often used as guiding factors for the restoration outcomes. However, the diversity and reliability demands of different customers are also crucial considerations. This paper proposes a cost-estimation-based restoration model with considering the differentiated reliability demands of power customers. The precise estimation of interruption costs across various industrial segments of power customers, as well as the calculation of grid interruption costs and power restoration costs form the cost-estimation-based objective together. Furthermore, an improved radial topology constraint is proposed to achieve the dynamic optimization of micro-grid island numbers in the reconfiguration process. Specially, the reliability demand constraints are proposed in this paper to satisfy the differentiated reliability requirements of power customers, based on the demand analysis of loads according to the sensitivity to reliability indices. The effectiveness and superiority of the proposed model are verified by numerical results of case studies on a modified IEEE-33 node distribution system.
{"title":"Cost-estimation-based restoration of active distribution networks considering differentiated customer reliability demands","authors":"Han Yan , Jianhua Wang , Chenyu Zhang , Ruihuang Liu , Xiaodong Yuan , Jianfeng Zhao","doi":"10.1016/j.ijepes.2026.111570","DOIUrl":"10.1016/j.ijepes.2026.111570","url":null,"abstract":"<div><div>During the power restoration process in distribution networks, costs and economics are often used as guiding factors for the restoration outcomes. However, the diversity and reliability demands of different customers are also crucial considerations. This paper proposes a cost-estimation-based restoration model with considering the differentiated reliability demands of power customers. The precise estimation of interruption costs across various industrial segments of power customers, as well as the calculation of grid interruption costs and power restoration costs form the cost-estimation-based objective together. Furthermore, an improved radial topology constraint is proposed to achieve the dynamic optimization of micro-grid island numbers in the reconfiguration process. Specially, the reliability demand constraints are proposed in this paper to satisfy the differentiated reliability requirements of power customers, based on the demand analysis of loads according to the sensitivity to reliability indices. The effectiveness and superiority of the proposed model are verified by numerical results of case studies on a modified IEEE-33 node distribution system.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"175 ","pages":"Article 111570"},"PeriodicalIF":5.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145963379","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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