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}
In the islanded DC Microgrid (MG) with the significant presence of renewable energy sources (RES), the integration of energy storage units (ESU) becomes crucial in mitigating the stochastic and intermittent nature of these RES. This research article introduces an intelligent distributed collaborative control scheme for managing multiple hybrid energy storage systems (HESS) within the islanded DC MG. The hierarchical control assembly is built to ensure coordinated and secure operation among the HESS units, and accurate power sharing and voltage regulation. The primary control layer utilizes a virtual-resistance droop control approach, employing a low-pass filter (LPF) to distribute the power between a battery and a supercapacitor. The state of charge (SoC) based Control schemes are presented to achieve safe and coordinated operation among the HESSs. Operating on a sparse communication network, the secondary control layer focuses on regulating the average voltage and proportional current of each hybrid energy storage system. This approach addresses issues arising from significant bus voltage deviations and inaccurate power-sharing due to virtual and line resistances. To enhance the performance of the islanded DC microgrid, an intelligent control scheme is implemented, utilizing the attributes of an Artificial Neural Network (ANN) controller alongside a traditional PI controller. To validate the proposed control method’s effectiveness and robustness in an islanded DC microgrid, extensive simulations and analyses are conducted using MATLAB/Simulink software. The results are compared with those obtained using a PI-based distributed collaborative control strategy. The Performance of ANN demonstrates that the presented controller has the capability to maintain the voltage stability of the islanded DC MG and achieve accurate power-sharing.
{"title":"An Intelligent Control Strategy for Microgrid Energy Storage Systems Using Distributed Collaborative Approach","authors":"Fawad Nawaz;Ehsan Pashajavid;Yuanyuan Fan;Munira Batool","doi":"10.1109/OAJPE.2025.3619584","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3619584","url":null,"abstract":"In the islanded DC Microgrid (MG) with the significant presence of renewable energy sources (RES), the integration of energy storage units (ESU) becomes crucial in mitigating the stochastic and intermittent nature of these RES. This research article introduces an intelligent distributed collaborative control scheme for managing multiple hybrid energy storage systems (HESS) within the islanded DC MG. The hierarchical control assembly is built to ensure coordinated and secure operation among the HESS units, and accurate power sharing and voltage regulation. The primary control layer utilizes a virtual-resistance droop control approach, employing a low-pass filter (LPF) to distribute the power between a battery and a supercapacitor. The state of charge (SoC) based Control schemes are presented to achieve safe and coordinated operation among the HESSs. Operating on a sparse communication network, the secondary control layer focuses on regulating the average voltage and proportional current of each hybrid energy storage system. This approach addresses issues arising from significant bus voltage deviations and inaccurate power-sharing due to virtual and line resistances. To enhance the performance of the islanded DC microgrid, an intelligent control scheme is implemented, utilizing the attributes of an Artificial Neural Network (ANN) controller alongside a traditional PI controller. To validate the proposed control method’s effectiveness and robustness in an islanded DC microgrid, extensive simulations and analyses are conducted using MATLAB/Simulink software. The results are compared with those obtained using a PI-based distributed collaborative control strategy. The Performance of ANN demonstrates that the presented controller has the capability to maintain the voltage stability of the islanded DC MG and achieve accurate power-sharing.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"727-738"},"PeriodicalIF":3.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11197558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352127","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}
The increasing integration of renewable energy sources (RESs), particularly distributed PV systems, poses significant challenges to voltage stability in modern distribution systems. Existing studies use reactive power control to address voltage deviations but incur high losses, with no systematic solution achieving both voltage regulation and loss minimization. This paper proposes a novel voltage control strategy based on multi-agent deep reinforcement learning (MADRL), leveraging decentralized agent coordination to maintain voltage levels while minimizing PV inverter and system losses. Also, a new framework is formulated based on a Markov game, wherein each PV inverter operates as an autonomous agent that adjusts its reactive power output via a centralized training process. The agents, defined as PV inverters, employ the multi-agent twin-delayed deep deterministic policy gradient algorithm to collaboratively minimize voltage deviations. Through the use of local observations and shared global information during training, agents learn robust control policies that generalize to varying conditions and enable decentralized execution without ongoing coordination. Performance of the proposed control strategy is validated on a modified IEEE 33-node distribution system under high variability in PV generation and load demand. Results show that the proposed control strategy significantly improves voltage regulation and reduces power losses compared to state-of-the-art MADRL techniques.
{"title":"An Intelligent Voltage Control With Power Loss Model Integration in Active Distribution Network","authors":"Watcharakorn Pinthurat;Anurak Deanseekeaw;Tossaporn Surinkaew;Terapong Boonraksa;Promphak Boonraksa;Boonruang Marungsri","doi":"10.1109/OAJPE.2025.3619672","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3619672","url":null,"abstract":"The increasing integration of renewable energy sources (RESs), particularly distributed PV systems, poses significant challenges to voltage stability in modern distribution systems. Existing studies use reactive power control to address voltage deviations but incur high losses, with no systematic solution achieving both voltage regulation and loss minimization. This paper proposes a novel voltage control strategy based on multi-agent deep reinforcement learning (MADRL), leveraging decentralized agent coordination to maintain voltage levels while minimizing PV inverter and system losses. Also, a new framework is formulated based on a Markov game, wherein each PV inverter operates as an autonomous agent that adjusts its reactive power output via a centralized training process. The agents, defined as PV inverters, employ the multi-agent twin-delayed deep deterministic policy gradient algorithm to collaboratively minimize voltage deviations. Through the use of local observations and shared global information during training, agents learn robust control policies that generalize to varying conditions and enable decentralized execution without ongoing coordination. Performance of the proposed control strategy is validated on a modified IEEE 33-node distribution system under high variability in PV generation and load demand. Results show that the proposed control strategy significantly improves voltage regulation and reduces power losses compared to state-of-the-art MADRL techniques.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"678-690"},"PeriodicalIF":3.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11197541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352267","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}
The U.S. is expected to double its rate of renewable capacity from 2024 to 2030. However, the stochastic nature of renewable energy poses challenges to the operation and reliability of our power grid. The combined generation from renewable energy sources, with dispatchable sources (such as hydropower) operating as a hybrid energy plant, could mitigate this variability. In this paper, the complementarity analysis of selected U.S. reservoirs with existing hydropower assets (EHAs) and potential floating photovoltaics (FPVs) is conducted for the continuous U.S. (CONUS). The optimal FPV capacity for each site is determined by minimizing the variability of the combined output, while adhering to the FPV potential. Our results indicate that over 50% of the analyzed reservoirs achieve a stability coefficient exceeding 0.5, leading to a less-variable output after optimization. Finally, we analyze the complementary hydro-FPV hybrid reservoirs by considering both the Pearson correlation coefficient and the stability coefficient on daily, monthly, and yearly scales. Summaries are included of locations of theoretical FPVs co-located with hydropower plants that exhibit high complementarity based on the selected metrics.
{"title":"Optimal Complementarity Analysis of Potential Floating Solar Co-Located With Existing Hydropower Assets Across the Contiguous United States","authors":"Jingyi Yan;Juan Gallego-Calderon;Mucun Sun;Tyler Phillips;Carly Hansen","doi":"10.1109/OAJPE.2025.3619445","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3619445","url":null,"abstract":"The U.S. is expected to double its rate of renewable capacity from 2024 to 2030. However, the stochastic nature of renewable energy poses challenges to the operation and reliability of our power grid. The combined generation from renewable energy sources, with dispatchable sources (such as hydropower) operating as a hybrid energy plant, could mitigate this variability. In this paper, the complementarity analysis of selected U.S. reservoirs with existing hydropower assets (EHAs) and potential floating photovoltaics (FPVs) is conducted for the continuous U.S. (CONUS). The optimal FPV capacity for each site is determined by minimizing the variability of the combined output, while adhering to the FPV potential. Our results indicate that over 50% of the analyzed reservoirs achieve a stability coefficient exceeding 0.5, leading to a less-variable output after optimization. Finally, we analyze the complementary hydro-FPV hybrid reservoirs by considering both the Pearson correlation coefficient and the stability coefficient on daily, monthly, and yearly scales. Summaries are included of locations of theoretical FPVs co-located with hydropower plants that exhibit high complementarity based on the selected metrics.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"751-762"},"PeriodicalIF":3.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11196930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352276","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 : 2025-10-02DOI: 10.1109/OAJPE.2025.3615786
Jongchan Choi;Yaosuo Xue;Hong Wang
The power grid is undergoing a significant transformation with the rapid increase in inverter-based resources (IBRs), including large-scale photovoltaic (PV) plants. Ensuring reliable and resilient grid operation in this new paradigm necessitates high-granularity electromagnetic transient (EMT) modeling that accurately captures the behavior of individual inverters and their interactions within IBR plants. Central to this approach is the detailed representation of both the IBR plant’s collector system and the dynamics of individual inverters. To achieve this, a high-granularity EMT model of a large-scale PV plant has been developed using advanced simulation algorithms, including matrix splitting and the Schur complement. These proposed techniques significantly enhance simulation speed, numerical stability, and accuracy while improving the modularity and efficiency of the collector system’s representation. The effectiveness of the proposed methods is validated through simulations of a representative large-scale PV plant consisting of 125 individual PV inverters, 25 IBR unit transformers, and a 52-bus collector system.
{"title":"Electromagnetic Transient Simulation of Large-Scale Inverter-Based Resources With High-Granularity","authors":"Jongchan Choi;Yaosuo Xue;Hong Wang","doi":"10.1109/OAJPE.2025.3615786","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3615786","url":null,"abstract":"The power grid is undergoing a significant transformation with the rapid increase in inverter-based resources (IBRs), including large-scale photovoltaic (PV) plants. Ensuring reliable and resilient grid operation in this new paradigm necessitates high-granularity electromagnetic transient (EMT) modeling that accurately captures the behavior of individual inverters and their interactions within IBR plants. Central to this approach is the detailed representation of both the IBR plant’s collector system and the dynamics of individual inverters. To achieve this, a high-granularity EMT model of a large-scale PV plant has been developed using advanced simulation algorithms, including matrix splitting and the Schur complement. These proposed techniques significantly enhance simulation speed, numerical stability, and accuracy while improving the modularity and efficiency of the collector system’s representation. The effectiveness of the proposed methods is validated through simulations of a representative large-scale PV plant consisting of 125 individual PV inverters, 25 IBR unit transformers, and a 52-bus collector system.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"664-677"},"PeriodicalIF":3.2,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11189236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256013","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 : 2025-10-01DOI: 10.1109/OAJPE.2025.3616259
Ehsan Shokouhmand;Mostafa Darvishi;Mehrdad Tahmasebi;Pitshou N. Bokoro
From an environmental standpoint, sustainability focuses on conserving natural resources, reducing pollution, promoting biodiversity, and addressing the effects of climate change. In recent years, global warming and carbon emission reduction have become pressing global concerns, prompting governments to revise their policies and shift toward greater reliance on renewable energy sources (RESs). This study introduces the concept of a sustainable virtual power plant (SVPP), which is designed to manage the power output of distributed energy resources (DERs), maintain real-time balance between supply and demand using available resources, and minimize emissions. Given the increasing integration of RESs into power generation and the inherently variable nature of their input data, this study models uncertainties using a scenario-based approach. In addition, power system reliability is emphasized, as it ensures a consistent and stable supply of electricity, which is crucial for grid efficiency and resilience. The study explores the role of reliability in influencing both the operational costs and emission levels of a SVPP. Five case studies are examined, incorporating components such as demand response strategies and energy storage systems (ESSs). The findings demonstrate that optimizing the number of resources while accounting for reliability indices is a practical and efficient method for SVPP scheduling. The proposed strategy achieves a reduction in both costs and emissions by 3.73% and 47.9%, respectively when compared to traditional energy resource utilisation.
{"title":"Reliable, Economical, and Environmentally Conscious Scheduling of a Multi-Energy Sustainable Virtual Power Plant","authors":"Ehsan Shokouhmand;Mostafa Darvishi;Mehrdad Tahmasebi;Pitshou N. Bokoro","doi":"10.1109/OAJPE.2025.3616259","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3616259","url":null,"abstract":"From an environmental standpoint, sustainability focuses on conserving natural resources, reducing pollution, promoting biodiversity, and addressing the effects of climate change. In recent years, global warming and carbon emission reduction have become pressing global concerns, prompting governments to revise their policies and shift toward greater reliance on renewable energy sources (RESs). This study introduces the concept of a sustainable virtual power plant (SVPP), which is designed to manage the power output of distributed energy resources (DERs), maintain real-time balance between supply and demand using available resources, and minimize emissions. Given the increasing integration of RESs into power generation and the inherently variable nature of their input data, this study models uncertainties using a scenario-based approach. In addition, power system reliability is emphasized, as it ensures a consistent and stable supply of electricity, which is crucial for grid efficiency and resilience. The study explores the role of reliability in influencing both the operational costs and emission levels of a SVPP. Five case studies are examined, incorporating components such as demand response strategies and energy storage systems (ESSs). The findings demonstrate that optimizing the number of resources while accounting for reliability indices is a practical and efficient method for SVPP scheduling. The proposed strategy achieves a reduction in both costs and emissions by 3.73% and 47.9%, respectively when compared to traditional energy resource utilisation.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"702-714"},"PeriodicalIF":3.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11185194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352129","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 : 2025-09-29DOI: 10.1109/OAJPE.2025.3615513
Mert Onur Cakiroglu;Idil Bilge Altun;Shahriar Rahman Fahim;Hasan Kurban;Mehmet M. Dalkilic;Rachad Atat;Abdulrahman Takiddin;Erchin Serpedin
Long-term electricity load forecasting is crucial for energy conservation, grid planning, and reducing carbon emissions by enabling optimal resource allocation and efficient energy utilization. However, forecasting the highly fluctuating loads in a large electrical power grid presents significant challenges due to the variability and complexity of individual load patterns across buses. Traditional models primarily focus on establishing temporal dependencies, often neglecting critical relationships between feature variables. This study introduces a novel approach that integrates de Bruijn Graphs (dBGs) with state-of-the-art time-series models to enhance predictive capabilities. By leveraging the unique structural properties of dBGs, the proposed framework improves the representation of sequential dependencies in power grid data. Advanced graph encoding techniques are utilized to extract meaningful features from dBGs that are often overlooked by traditional methods. Four enhanced architectures—FiLMdBG, iTransformerdBG, TimesNetdBG, and DLineardBG—are developed and evaluated on the Texas 2,000-bus test system across multiple forecasting horizons. The results demonstrate that dBG-integrated models significantly outperform their conventional counterparts, delivering superior accuracy in both short and long-term electricity load forecasting. These findings underscore the potential of dBGs as a transformative tool for advancing power grid management and enabling more sustainable and efficient energy systems.
{"title":"An Extended Frequency-Improved Legendre Memory Model for Enhanced Long-Term Electricity Load Forecasting","authors":"Mert Onur Cakiroglu;Idil Bilge Altun;Shahriar Rahman Fahim;Hasan Kurban;Mehmet M. Dalkilic;Rachad Atat;Abdulrahman Takiddin;Erchin Serpedin","doi":"10.1109/OAJPE.2025.3615513","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3615513","url":null,"abstract":"Long-term electricity load forecasting is crucial for energy conservation, grid planning, and reducing carbon emissions by enabling optimal resource allocation and efficient energy utilization. However, forecasting the highly fluctuating loads in a large electrical power grid presents significant challenges due to the variability and complexity of individual load patterns across buses. Traditional models primarily focus on establishing temporal dependencies, often neglecting critical relationships between feature variables. This study introduces a novel approach that integrates de Bruijn Graphs (dBGs) with state-of-the-art time-series models to enhance predictive capabilities. By leveraging the unique structural properties of dBGs, the proposed framework improves the representation of sequential dependencies in power grid data. Advanced graph encoding techniques are utilized to extract meaningful features from dBGs that are often overlooked by traditional methods. Four enhanced architectures—FiLMdBG, iTransformerdBG, TimesNetdBG, and DLineardBG—are developed and evaluated on the Texas 2,000-bus test system across multiple forecasting horizons. The results demonstrate that dBG-integrated models significantly outperform their conventional counterparts, delivering superior accuracy in both short and long-term electricity load forecasting. These findings underscore the potential of dBGs as a transformative tool for advancing power grid management and enabling more sustainable and efficient energy systems.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"691-701"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184177","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352275","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 : 2025-09-26DOI: 10.1109/OAJPE.2025.3614816
István Bara;Gautham RAM Chandra Mouli;Pavol Bauer
The increasing number of electric vehicles (EVs) means both a challenge and an opportunity for the electric grid. Different charging algorithms have been proposed in the literature to tackle these specific challenges and make use of the potential services that EVs can provide. However, to properly investigate the conflicting objectives, a multi-objective approach is paramount. These algorithms provide a family of solutions instead of just one, so the decision-maker can see the connection and trade-offs between the objectives. This paper proposes a highly customisable multi-objective framework based on an expanded version of the augmented $varepsilon $ -constraint 2 method. Together with a mixed integer linear programming (MILP) formulation, it is used to solve a charging station scheduling problem. An energy management system (EMS) executes the calculated schedules to show the effect on the individual EVs. Numerical simulations based on market and EV data from the Netherlands demonstrate the adaptability and effectiveness of the proposed algorithm.
{"title":"Multi-Objective Optimization for Bidirectional Electric Vehicle Charging Stations","authors":"István Bara;Gautham RAM Chandra Mouli;Pavol Bauer","doi":"10.1109/OAJPE.2025.3614816","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3614816","url":null,"abstract":"The increasing number of electric vehicles (EVs) means both a challenge and an opportunity for the electric grid. Different charging algorithms have been proposed in the literature to tackle these specific challenges and make use of the potential services that EVs can provide. However, to properly investigate the conflicting objectives, a multi-objective approach is paramount. These algorithms provide a family of solutions instead of just one, so the decision-maker can see the connection and trade-offs between the objectives. This paper proposes a highly customisable multi-objective framework based on an expanded version of the augmented <inline-formula> <tex-math>$varepsilon $ </tex-math></inline-formula>-constraint 2 method. Together with a mixed integer linear programming (MILP) formulation, it is used to solve a charging station scheduling problem. An energy management system (EMS) executes the calculated schedules to show the effect on the individual EVs. Numerical simulations based on market and EV data from the Netherlands demonstrate the adaptability and effectiveness of the proposed algorithm.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"652-663"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11181168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256012","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 : 2025-09-22DOI: 10.1109/OAJPE.2025.3612851
Xiao Zhang;Hao Liang;Yindi Jing
In power systems, disturbances often result from faults or operational events, making it crucial to accurately identify their sources to prevent system failures and maintain grid stability. Existing research primarily classifies disturbances based on waveform characteristics, such as sags, swells, and transients, without determining their root causes, including incipient faults, constant impedance faults, load switching, and capacitor switching events. This paper proposes a hypothesis testing-based scheme for classifying power distribution disturbances by their root causes, ensuring reliable and interpretable results without extensive datasets. The scheme uses discrete-time voltage and current measurements at substations to develop disturbance models for substation voltages, incorporating disturbance parameters and load impedance. Load impedance is estimated from recent normal cycles, and disturbance parameters are then derived using substation measurements and the estimated load impedance. By substituting these estimated parameters into the corresponding disturbance models, substation voltages for each disturbance type are estimated. The disturbance type is classified by selecting the one that minimizes the normalized mean square error between the estimated and measured substation voltages. The proposed method is evaluated using the IEEE 13-bus test feeder simulated in PSCAD/EMTDC and validated on a two-day real-world power system dataset collected by the IEEE Power & Energy Society Working Group on Power Quality Data Analytics.
{"title":"A Novel Hypothesis Testing-Based Scheme for Root Cause Classification of Disturbances in Distribution Systems","authors":"Xiao Zhang;Hao Liang;Yindi Jing","doi":"10.1109/OAJPE.2025.3612851","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3612851","url":null,"abstract":"In power systems, disturbances often result from faults or operational events, making it crucial to accurately identify their sources to prevent system failures and maintain grid stability. Existing research primarily classifies disturbances based on waveform characteristics, such as sags, swells, and transients, without determining their root causes, including incipient faults, constant impedance faults, load switching, and capacitor switching events. This paper proposes a hypothesis testing-based scheme for classifying power distribution disturbances by their root causes, ensuring reliable and interpretable results without extensive datasets. The scheme uses discrete-time voltage and current measurements at substations to develop disturbance models for substation voltages, incorporating disturbance parameters and load impedance. Load impedance is estimated from recent normal cycles, and disturbance parameters are then derived using substation measurements and the estimated load impedance. By substituting these estimated parameters into the corresponding disturbance models, substation voltages for each disturbance type are estimated. The disturbance type is classified by selecting the one that minimizes the normalized mean square error between the estimated and measured substation voltages. The proposed method is evaluated using the IEEE 13-bus test feeder simulated in PSCAD/EMTDC and validated on a two-day real-world power system dataset collected by the IEEE Power & Energy Society Working Group on Power Quality Data Analytics.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"637-651"},"PeriodicalIF":3.2,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11175217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210160","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 : 2025-09-17DOI: 10.1109/OAJPE.2025.3611293
Xinan Wang;Di Shi;Fengyu Wang
This paper presents a novel three-stage framework for real-time foreign object intrusion (FOI) detection and tracking in power transmission systems. The framework integrates: 1) a YOLOv7 segmentation model for fast and robust object localization, 2) a ConvNeXt-based feature extractor trained with triplet loss to generate discriminative embeddings, and 3) a feature-assisted IoU tracker that ensures resilient multi-object tracking under occlusion and motion. To enable scalable field deployment, the pipeline is optimized for deployment on low-cost edge hardware using mixed-precision inference. The system supports incremental updates by adding embeddings from previously unseen objects into a reference database without requiring model retraining. Extensive experiments on real-world surveillance and drone video datasets demonstrate the framework’s high accuracy and robustness across diverse FOI scenarios. In addition, hardware benchmarks on NVIDIA Jetson devices confirm the framework’s practicality and scalability for real-world edge applications.
{"title":"Real-Time Detection and Tracking of Foreign Object Intrusions in Power Systems via Feature-Based Edge Intelligence","authors":"Xinan Wang;Di Shi;Fengyu Wang","doi":"10.1109/OAJPE.2025.3611293","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3611293","url":null,"abstract":"This paper presents a novel three-stage framework for real-time foreign object intrusion (FOI) detection and tracking in power transmission systems. The framework integrates: 1) a YOLOv7 segmentation model for fast and robust object localization, 2) a ConvNeXt-based feature extractor trained with triplet loss to generate discriminative embeddings, and 3) a feature-assisted IoU tracker that ensures resilient multi-object tracking under occlusion and motion. To enable scalable field deployment, the pipeline is optimized for deployment on low-cost edge hardware using mixed-precision inference. The system supports incremental updates by adding embeddings from previously unseen objects into a reference database without requiring model retraining. Extensive experiments on real-world surveillance and drone video datasets demonstrate the framework’s high accuracy and robustness across diverse FOI scenarios. In addition, hardware benchmarks on NVIDIA Jetson devices confirm the framework’s practicality and scalability for real-world edge applications.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"625-636"},"PeriodicalIF":3.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11168407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141697","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：