Pub Date : 2026-01-16DOI: 10.1109/OAJPE.2026.3651206
Fangxing Fran Li
{"title":"2025 Best Papers, Outstanding Associate Editors, and Outstanding Reviewers","authors":"Fangxing Fran Li","doi":"10.1109/OAJPE.2026.3651206","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3651206","url":null,"abstract":"","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"1-1"},"PeriodicalIF":3.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11355900","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1109/OAJPE.2025.3640636
Xiaofei Wang;Jiazi Zhang;Leonardo Rese;Mingjian Tuo;Hongfei Sun
With the worldwide growth in deploying high-voltage direct current (HVDC) transmission systems, their ability to facilitate black-start (BS) restoration has been a research topic of interest. In this context, voltage source converter (VSC)-HVDC is regarded as a BS resource, and this paper proposes a VSC-HVDC-assisted parallel BS restoration strategy in bulk power systems. The proposed strategy consists of two stages: 1) determination of the VSC and generator startup sequence and 2) load restoration simulation. In the first stage, the entire blackout system is sectionalized into multiple subsystems. Each subsystem includes a VSC-HVDC station or traditional BS unit, it independently determines its generator startup timeline and the energization timelines for buses and lines. The second stage involves load restoration, conceptualized as a modified unit commitment problem, with the timelines established in the first stage work as critical inputs. The proposed BS restoration strategy is tested on the San Diego power system to simulate the 2011 Southwest blackout. The simulation results validate the effectiveness of using VSC-HVDC links as a BS resource which not only speeds up the restoration process but also reduces both energy and economic losses.
{"title":"A VSC-HVDC-Assisted Black-Start Strategy in Bulk Power Systems a Case Study in San Diego","authors":"Xiaofei Wang;Jiazi Zhang;Leonardo Rese;Mingjian Tuo;Hongfei Sun","doi":"10.1109/OAJPE.2025.3640636","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3640636","url":null,"abstract":"With the worldwide growth in deploying high-voltage direct current (HVDC) transmission systems, their ability to facilitate black-start (BS) restoration has been a research topic of interest. In this context, voltage source converter (VSC)-HVDC is regarded as a BS resource, and this paper proposes a VSC-HVDC-assisted parallel BS restoration strategy in bulk power systems. The proposed strategy consists of two stages: 1) determination of the VSC and generator startup sequence and 2) load restoration simulation. In the first stage, the entire blackout system is sectionalized into multiple subsystems. Each subsystem includes a VSC-HVDC station or traditional BS unit, it independently determines its generator startup timeline and the energization timelines for buses and lines. The second stage involves load restoration, conceptualized as a modified unit commitment problem, with the timelines established in the first stage work as critical inputs. The proposed BS restoration strategy is tested on the San Diego power system to simulate the 2011 Southwest blackout. The simulation results validate the effectiveness of using VSC-HVDC links as a BS resource which not only speeds up the restoration process but also reduces both energy and economic losses.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"870-881"},"PeriodicalIF":3.2,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278889","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1109/OAJPE.2025.3640666
Reynaldo S. Gonzalez;Ahmed Almoola;Krishna S. Ayyagari;Venkatanaga A. Aryasomyajula;Nikolaos Gatsis;Miltiadis Alamaniotis;Sara Ahmed
Optimal protection coordination (OPC) is a well-established problem with numerous solution methods, including mathematical optimization and genetic algorithms. Traditional OPC formulations for overcurrent relays typically optimize two parameters: the time dial setting (TDS) and the pickup current. However, modern relays offer additional curve characteristics, yet standard formulations do not fully utilize these additional settings. This paper introduces a novel OPC formulation for dual-setting relays that integrates inverse-time and definite-time curve characteristics. The optimization variables include TDS, pickup current, short-time delay (STD), and short-time pickup (STP) To ensure proper coordination, new constraints are developed for the interplay of these four settings per relay. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) task, solved using both a general-purpose MINLP solver and a Genetic Algorithm (GA). The approach is validated on the IEEE 123-bus network integrating inverter-based resources with limited fault current contributions under two switch configurations, which are selected to alter current flows and reassign backup roles among relays. Results demonstrate that incorporating dual-curve settings significantly reduces total relay operation time and improves discrimination times between primary and backup relays, compared to the standard OPC formulation.
{"title":"Optimal Protection Coordination of Dual-Setting Relays With Inverse-Time and Definite-Time Characteristics","authors":"Reynaldo S. Gonzalez;Ahmed Almoola;Krishna S. Ayyagari;Venkatanaga A. Aryasomyajula;Nikolaos Gatsis;Miltiadis Alamaniotis;Sara Ahmed","doi":"10.1109/OAJPE.2025.3640666","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3640666","url":null,"abstract":"Optimal protection coordination (OPC) is a well-established problem with numerous solution methods, including mathematical optimization and genetic algorithms. Traditional OPC formulations for overcurrent relays typically optimize two parameters: the time dial setting (TDS) and the pickup current. However, modern relays offer additional curve characteristics, yet standard formulations do not fully utilize these additional settings. This paper introduces a novel OPC formulation for dual-setting relays that integrates inverse-time and definite-time curve characteristics. The optimization variables include TDS, pickup current, short-time delay (STD), and short-time pickup (STP) To ensure proper coordination, new constraints are developed for the interplay of these four settings per relay. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) task, solved using both a general-purpose MINLP solver and a Genetic Algorithm (GA). The approach is validated on the IEEE 123-bus network integrating inverter-based resources with limited fault current contributions under two switch configurations, which are selected to alter current flows and reassign backup roles among relays. Results demonstrate that incorporating dual-curve settings significantly reduces total relay operation time and improves discrimination times between primary and backup relays, compared to the standard OPC formulation.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"882-894"},"PeriodicalIF":3.2,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
False Data Injection Attacks (FDIAs) pose a substantial risk to the reliability and stability of Cyber-Physical Power Systems (CPPS). While federated learning (FL) has emerged as a promising approach to detect such attacks without exposing sensitive data, security concerns remain in FL, including untrusted central aggregators and potential malicious client updates. This research integrate a private Ethereum blockchain layer and homomorphic encryption into a secure FL framework for FDIA detection to verify model updates and authenticate participating nodes. We design smart contracts to immutably log model update hashes and enforce client authentication, enhancing traceability and tamper-resistance. A prototype implementation uses Ethereum smart contracts for model update verification and client identity management. We simulate the blockchain-integrated FL on a cyber-physical power system dataset using three detection models – XGBoost, LSTM, and a Transformer – and analyze the blockchain-induced latency and communication overhead under a specific network configuration. Results show that the blockchain layer has negligible impact on detection accuracy (global AUC $sim 0.94 text {-}0.96$ across models) while introducing a moderate training time overhead ($sim 13- -40%$ increase in training duration due to block confirmation delays). The proposed research demonstrates a viable approach to blockchain-aided federated learning for critical infrastructure security, combining data privacy, model integrity, and participant trust in a unified framework.
{"title":"Blockchain-Integrated Federated Learning Framework for Detecting False Data Injection Attacks in Power Systems With Homomorphic Encryption","authors":"Firdous Kausar;Sajid Hussain;Karl Walker;Ayesha Imam","doi":"10.1109/OAJPE.2025.3631069","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3631069","url":null,"abstract":"False Data Injection Attacks (FDIAs) pose a substantial risk to the reliability and stability of Cyber-Physical Power Systems (CPPS). While federated learning (FL) has emerged as a promising approach to detect such attacks without exposing sensitive data, security concerns remain in FL, including untrusted central aggregators and potential malicious client updates. This research integrate a private Ethereum blockchain layer and homomorphic encryption into a secure FL framework for FDIA detection to verify model updates and authenticate participating nodes. We design smart contracts to immutably log model update hashes and enforce client authentication, enhancing traceability and tamper-resistance. A prototype implementation uses Ethereum smart contracts for model update verification and client identity management. We simulate the blockchain-integrated FL on a cyber-physical power system dataset using three detection models – XGBoost, LSTM, and a Transformer – and analyze the blockchain-induced latency and communication overhead under a specific network configuration. Results show that the blockchain layer has negligible impact on detection accuracy (global AUC <inline-formula> <tex-math>$sim 0.94 text {-}0.96$ </tex-math></inline-formula> across models) while introducing a moderate training time overhead (<inline-formula> <tex-math>$sim 13- -40%$ </tex-math></inline-formula> increase in training duration due to block confirmation delays). The proposed research demonstrates a viable approach to blockchain-aided federated learning for critical infrastructure security, combining data privacy, model integrity, and participant trust in a unified framework.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"819-832"},"PeriodicalIF":3.2,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11237138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1109/OAJPE.2025.3630180
Nupur;Yaosuo Xue;Fred Wang
The nodal admittance matrix (NAM)-based approach is well-suited for small-signal stability analysis of large-scale power electronics-based power systems (PEPSs), as it preserves the system structure through its admittance matrix. Previous studies have explored partitioning such systems into subareas and interconnections to reduce computational burden; however, they lacked a formal algorithmic procedure for determining feasible partitions. While several grid partitioning methods, such as those based on graph theory or machine learning, exist in the literature, they cannot be directly applied to NAM-based analysis due to differing objectives and constraints. This paper addresses this gap by presenting a systematic, step-by-step procedure for applying a spectral partitioning algorithm that yields a division of the system into subareas suitable for NAM-based analysis. The computational complexity of the proposed method is also derived to demonstrate its efficiency and justify the practicality of the resulting subarea decomposition. The performance of the partitioning method is evaluated by applying the spectral clustering-derived subareas and interconnections to the NAM-based partitioning approach on a 140-bus system. Computational times for the full-system and partitioned NAM analyses are compared using MATLAB. Additionally, PSCAD simulations of the complete system and partitioned subareas are carried out to verify the effectiveness of the proposed method.
{"title":"Spectral Clustering-Based Partitioning of Large-Scale Power Electronics-Based Power Systems for Small-Signal Stability Analysis","authors":"Nupur;Yaosuo Xue;Fred Wang","doi":"10.1109/OAJPE.2025.3630180","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3630180","url":null,"abstract":"The nodal admittance matrix (NAM)-based approach is well-suited for small-signal stability analysis of large-scale power electronics-based power systems (PEPSs), as it preserves the system structure through its admittance matrix. Previous studies have explored partitioning such systems into subareas and interconnections to reduce computational burden; however, they lacked a formal algorithmic procedure for determining feasible partitions. While several grid partitioning methods, such as those based on graph theory or machine learning, exist in the literature, they cannot be directly applied to NAM-based analysis due to differing objectives and constraints. This paper addresses this gap by presenting a systematic, step-by-step procedure for applying a spectral partitioning algorithm that yields a division of the system into subareas suitable for NAM-based analysis. The computational complexity of the proposed method is also derived to demonstrate its efficiency and justify the practicality of the resulting subarea decomposition. The performance of the partitioning method is evaluated by applying the spectral clustering-derived subareas and interconnections to the NAM-based partitioning approach on a 140-bus system. Computational times for the full-system and partitioned NAM analyses are compared using MATLAB. Additionally, PSCAD simulations of the complete system and partitioned subareas are carried out to verify the effectiveness of the proposed method.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"806-818"},"PeriodicalIF":3.2,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11234885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reconfiguration in low-inertia microgrids (MGs) can often result in a critical small-signal stability margin. In this condition, the ability of inverter-based resources (IBRs) to provide voltage and frequency support may be insufficient. To maintain stable operation without interruptions, this paper presents a control strategy that first evaluates the effect of MG reconfiguration on system stability and then employs controllable loads as an enhancement mechanism to improve small-signal stability in scenarios involving reconfigurable MGs, particularly during islanded operation or high-demand situations such as sudden load changes or fault recovery. Mathematical models of system reconfiguration are presented. Then, we demonstrate how reconfiguration in MGs can result in marginal small-signal stability. The proposed framework operates in two stages: (i) assessing optimal breaker/switch configurations to ensure a baseline stability margin, and (ii) using controllable loads to fine-tune and improve damping performance. It is shown that the proposed framework can shift stability from critical or unstable levels to an acceptable range, making the initial conditions of reconfigured MGs feasible. Simulation results in a reconfigurable MG with different portions of IBRs and controllable loads demonstrate the effectiveness of the proposed framework in using controllable loads to successfully enhance small-signal stability. The proposed strategy ensures that the reconfigured MGs remain stable after reconfigurations.
{"title":"Two-Stage Small-Signal Stability-Assisted Framework Using Controllable Loads in Reconfigurable Microgrids","authors":"Tossaporn Surinkaew;Watcharakorn Pinthurat;Boonruang Marungsri;Branislav Hredzak","doi":"10.1109/OAJPE.2025.3628911","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3628911","url":null,"abstract":"Reconfiguration in low-inertia microgrids (MGs) can often result in a critical small-signal stability margin. In this condition, the ability of inverter-based resources (IBRs) to provide voltage and frequency support may be insufficient. To maintain stable operation without interruptions, this paper presents a control strategy that first evaluates the effect of MG reconfiguration on system stability and then employs controllable loads as an enhancement mechanism to improve small-signal stability in scenarios involving reconfigurable MGs, particularly during islanded operation or high-demand situations such as sudden load changes or fault recovery. Mathematical models of system reconfiguration are presented. Then, we demonstrate how reconfiguration in MGs can result in marginal small-signal stability. The proposed framework operates in two stages: (i) assessing optimal breaker/switch configurations to ensure a baseline stability margin, and (ii) using controllable loads to fine-tune and improve damping performance. It is shown that the proposed framework can shift stability from critical or unstable levels to an acceptable range, making the initial conditions of reconfigured MGs feasible. Simulation results in a reconfigurable MG with different portions of IBRs and controllable loads demonstrate the effectiveness of the proposed framework in using controllable loads to successfully enhance small-signal stability. The proposed strategy ensures that the reconfigured MGs remain stable after reconfigurations.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"772-783"},"PeriodicalIF":3.2,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11224833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1109/OAJPE.2025.3626699
Xiangtian Zheng;Alex Lee;Shun Hsien Huang;Le Xie
This paper proposes a physics-informed graph neural network (GNN) framework for scalable and efficient AC power flow-based N-2 contingency screening in large-scale power systems. Formulated as a graph classification problem, the approach is specifically designed to identify critical N-2 contingencies that are likely to result in infeasible post-contingency AC power flow solutions. The integration of physics-based domain knowledge into the neural network architecture enhances the model’s capability to capture the underlying physical behaviors governing power flow, thereby improving classification accuracy. Comprehensive numerical experiments on the real-world Texas transmission network demonstrate that the proposed method achieves a 37-fold improvement in computational efficiency over conventional simulation-based N-2 contingency analysis techniques, underscoring its potential for operational deployment in real-time or near real-time security assessment.
{"title":"A Physics-Informed Graph Neural Network Framework for N-2 Contingency Screening: A Real-World Texas Grid Study","authors":"Xiangtian Zheng;Alex Lee;Shun Hsien Huang;Le Xie","doi":"10.1109/OAJPE.2025.3626699","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3626699","url":null,"abstract":"This paper proposes a physics-informed graph neural network (GNN) framework for scalable and efficient AC power flow-based N-2 contingency screening in large-scale power systems. Formulated as a graph classification problem, the approach is specifically designed to identify critical N-2 contingencies that are likely to result in infeasible post-contingency AC power flow solutions. The integration of physics-based domain knowledge into the neural network architecture enhances the model’s capability to capture the underlying physical behaviors governing power flow, thereby improving classification accuracy. Comprehensive numerical experiments on the real-world Texas transmission network demonstrate that the proposed method achieves a 37-fold improvement in computational efficiency over conventional simulation-based N-2 contingency analysis techniques, underscoring its potential for operational deployment in real-time or near real-time security assessment.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"784-792"},"PeriodicalIF":3.2,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11222082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-15DOI: 10.1109/OAJPE.2025.3622003
Farshad Aslani;Shuhui Li
This paper proposes a three-stage hybrid islanding detection method tailored for inverter-based distributed generation systems. In the first stage, the system passively monitors voltage deviations that may signal a power imbalance. If the rate of change of voltage (ROCOV) exceeds a predefined threshold, the second stage introduces a controlled disturbance into the inverter’s control loop, specifically phase-locked loop, within a targeted observation window. The third stage evaluates the rate of change of frequency (ROCOF) to determine whether the event indicates true islanding, a transient condition, or the need of an additional disturbance injection. The effectiveness of the controlled disturbances is rigorously assessed for reliable islanding detection. By limiting disturbance injections to brief periods following identified voltage or frequency anomalies, the proposed method minimizes adverse effects while progressively distinguishing between islanding and non-islanding events. Extensive validation under a broad spectrum of conditions, aligned with UL 1741 and related standards, demonstrates the method’s reliability, selectivity, and non-intrusive performance in various distributed generation scenarios.
{"title":"A Three-Level Hybrid Islanding Detection Method for Inverter-Based DGs by Using ROCOV, ROCOF, and Voltage Phase Angle","authors":"Farshad Aslani;Shuhui Li","doi":"10.1109/OAJPE.2025.3622003","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3622003","url":null,"abstract":"This paper proposes a three-stage hybrid islanding detection method tailored for inverter-based distributed generation systems. In the first stage, the system passively monitors voltage deviations that may signal a power imbalance. If the rate of change of voltage (ROCOV) exceeds a predefined threshold, the second stage introduces a controlled disturbance into the inverter’s control loop, specifically phase-locked loop, within a targeted observation window. The third stage evaluates the rate of change of frequency (ROCOF) to determine whether the event indicates true islanding, a transient condition, or the need of an additional disturbance injection. The effectiveness of the controlled disturbances is rigorously assessed for reliable islanding detection. By limiting disturbance injections to brief periods following identified voltage or frequency anomalies, the proposed method minimizes adverse effects while progressively distinguishing between islanding and non-islanding events. Extensive validation under a broad spectrum of conditions, aligned with UL 1741 and related standards, demonstrates the method’s reliability, selectivity, and non-intrusive performance in various distributed generation scenarios.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"739-750"},"PeriodicalIF":3.2,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11204519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-14DOI: 10.1109/OAJPE.2025.3621232
Hongchong Yang;Maimaitireyimu Abulizi
To enhance the voltage fault ride-through (VFRT) capability of grid-connected photovoltaic (PV) systems under grid voltage faults, this paper proposes an innovative solution using Superconducting Magnetic Energy Storage (SMES). Unlike conventional approaches relying on crowbar circuits or supercapacitors, our method leverages SMES’s rapid response and high efficiency to stabilize DC bus voltage during abrupt grid fluctuations. The study introduces a novel SMES-based control strategy that dynamically absorbs and releases energy to mitigate voltage disturbances, supported by comprehensive comparative simulations demonstrating SMES’s superior VFRT performance over existing methods. Additionally, the research provides practical insights for scaling this solution to other renewable energy systems. The Simulink-validated results confirm that SMES significantly improves PV system resilience while maintaining operational stability, offering a viable path for future grid integration of renewables.
{"title":"Research on Voltage Ride-Through of Photovoltaic Grid-Connected System Based on Superconducting Magnetic Energy Storage","authors":"Hongchong Yang;Maimaitireyimu Abulizi","doi":"10.1109/OAJPE.2025.3621232","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3621232","url":null,"abstract":"To enhance the voltage fault ride-through (VFRT) capability of grid-connected photovoltaic (PV) systems under grid voltage faults, this paper proposes an innovative solution using Superconducting Magnetic Energy Storage (SMES). Unlike conventional approaches relying on crowbar circuits or supercapacitors, our method leverages SMES’s rapid response and high efficiency to stabilize DC bus voltage during abrupt grid fluctuations. The study introduces a novel SMES-based control strategy that dynamically absorbs and releases energy to mitigate voltage disturbances, supported by comprehensive comparative simulations demonstrating SMES’s superior VFRT performance over existing methods. Additionally, the research provides practical insights for scaling this solution to other renewable energy systems. The Simulink-validated results confirm that SMES significantly improves PV system resilience while maintaining operational stability, offering a viable path for future grid integration of renewables.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"763-771"},"PeriodicalIF":3.2,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11202956","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Transmission system operators maintain grid stability using reserve markets; aggregators help small participants contribute by pooling their flexibility. Moreover, Reserve market prices and capacities are uncertain for the aggregator until the bidding deadline, and this underscores strategic approaches. This paper introduces a deep reinforcement learning framework tailored for aggregators that coordinate exclusively small-scale loads, participating in the Norwegian reserve markets. The proposed framework reflects a real-life bidding process, and multiple types of reinforcement learning models are used within the framework. The two datasets are hourly data from June and October, 2023, to evaluate how seasonal variations affect the models performance. First, the different models are trained on the data from the first three weeks of the given dataset and then tested on the last week of the dataset. For the testing of the models, they are tested against baseline values to give a good indication of whether the models are able to learn or not. From the test results, most models are performing better than the minimum baseline values and thus the models are able to learn, and the framework is feasible. Regarding the different type of reinforcement learning models trained and tested within this framework, the Deep Q-Network model performs most consistently on a higher level compared to the other models.
{"title":"Developing a Deep Reinforcement Learning Framework for Demand Side Response in Norway","authors":"Sander Meland;Mojtaba Yousefi;Ahmad Hemmati;Troels Arnfred Bojesen","doi":"10.1109/OAJPE.2025.3620107","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3620107","url":null,"abstract":"Transmission system operators maintain grid stability using reserve markets; aggregators help small participants contribute by pooling their flexibility. Moreover, Reserve market prices and capacities are uncertain for the aggregator until the bidding deadline, and this underscores strategic approaches. This paper introduces a deep reinforcement learning framework tailored for aggregators that coordinate exclusively small-scale loads, participating in the Norwegian reserve markets. The proposed framework reflects a real-life bidding process, and multiple types of reinforcement learning models are used within the framework. The two datasets are hourly data from June and October, 2023, to evaluate how seasonal variations affect the models performance. First, the different models are trained on the data from the first three weeks of the given dataset and then tested on the last week of the dataset. For the testing of the models, they are tested against baseline values to give a good indication of whether the models are able to learn or not. From the test results, most models are performing better than the minimum baseline values and thus the models are able to learn, and the framework is feasible. Regarding the different type of reinforcement learning models trained and tested within this framework, the Deep Q-Network model performs most consistently on a higher level compared to the other models.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"715-726"},"PeriodicalIF":3.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11199909","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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