Pub Date : 2025-01-06DOI: 10.1109/OAJPE.2024.3524268
Shahriar Rahman Fahim;Rachad Atat;Cihat Kececi;Abdulrahman Takiddin;Muhammad Ismail;Katherine R. Davis;Erchin Serpedin
The integration of information and communication technologies into modern power systems has contributed to enhanced efficiency, controllability, and voltage regulation. Concurrently, these technologies expose power systems to cyberattacks, which could lead to voltage instability and significant damage. Traditional false data injection attacks (FDIAs) detectors are inadequate in addressing cyberattacks on voltage regulation since a) they overlook such attacks within power grids and b) primarily rely on static thresholds and simple anomaly detection techniques, which cannot capture the complex interplay between voltage stability, cyberattacks, and defensive actions. To address the aforementioned challenges, this paper develops an FDIA detection approach that considers data falsification attacks on voltage regulation and enhances the voltage stability index. A graph autoencoder-based detector that is able to identify cyberattacks targeting voltage regulation is proposed. A bi-level optimization approach is put forward to concurrently optimize the objectives of both attackers and defenders in the context of voltage regulation. The proposed detector underwent rigorous training and testing across different kinds of attacks, demonstrating enhanced generalization performance in all situations. Simulations were performed on the Iberian power system topology, featuring 486 buses. The proposed model achieves 98.11% average detection rate, which represents a significant enhancement of 10-25% compared to the cutting-edge detectors. This provides strong evidence for the effectiveness of proposed strategy in tackling cyberattacks on voltage regulation.
{"title":"Graph Neural Network-Based Approach for Detecting False Data Injection Attacks on Voltage Stability","authors":"Shahriar Rahman Fahim;Rachad Atat;Cihat Kececi;Abdulrahman Takiddin;Muhammad Ismail;Katherine R. Davis;Erchin Serpedin","doi":"10.1109/OAJPE.2024.3524268","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3524268","url":null,"abstract":"The integration of information and communication technologies into modern power systems has contributed to enhanced efficiency, controllability, and voltage regulation. Concurrently, these technologies expose power systems to cyberattacks, which could lead to voltage instability and significant damage. Traditional false data injection attacks (FDIAs) detectors are inadequate in addressing cyberattacks on voltage regulation since a) they overlook such attacks within power grids and b) primarily rely on static thresholds and simple anomaly detection techniques, which cannot capture the complex interplay between voltage stability, cyberattacks, and defensive actions. To address the aforementioned challenges, this paper develops an FDIA detection approach that considers data falsification attacks on voltage regulation and enhances the voltage stability index. A graph autoencoder-based detector that is able to identify cyberattacks targeting voltage regulation is proposed. A bi-level optimization approach is put forward to concurrently optimize the objectives of both attackers and defenders in the context of voltage regulation. The proposed detector underwent rigorous training and testing across different kinds of attacks, demonstrating enhanced generalization performance in all situations. Simulations were performed on the Iberian power system topology, featuring 486 buses. The proposed model achieves 98.11% average detection rate, which represents a significant enhancement of 10-25% compared to the cutting-edge detectors. This provides strong evidence for the effectiveness of proposed strategy in tackling cyberattacks on voltage regulation.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"12-23"},"PeriodicalIF":3.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106688","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 : 2024-12-23DOI: 10.1109/OAJPE.2024.3521030
Antonio Bracale;Pierluigi Caramia;Giovanni Mercurio Casolino;Pasquale de Falco;Iqrar Hussain;Pietro Varilone;Paola Verde
The analysis of power quality disturbances in distribution systems has gained significance with the diffusion of electric vehicles (EVs). Waveform distortions are interesting since EV currents introduce distortions with spectral components in both low and high-frequency bands. This paper develops specific indices to assess cumulative emissions from single-phase EV on-board chargers, extending the aggregation and diversity factors to the supra-harmonic range. The methodology accounts for variables such as EV charging powers, upstream network impedance, and number of EVs. A simplified time-domain model of a low-power unidirectional converter, commonly used for EV battery charging, is employed to balance circuit complexity and computational effort. This model allows for sensitivity analyses of key parameters influencing charger emissions. Numerical applications are carried out for both individuals and groups of EV chargers at a charging station. Results highlight the need for careful quantification of aggregated EV emissions, showing that supra-harmonic emissions are highly sensitive to variations in the power absorbed by EV chargers. Notably, their cumulative impact is much lower when chargers operate at different power levels than when all chargers operate at the same power level. These findings underscore the importance of accurately assessing the impact of EV charging on power quality.
{"title":"Harmonic and Supra-Harmonic Emissions of Electric Vehicle Chargers: Modeling and Cumulative Impact Indices","authors":"Antonio Bracale;Pierluigi Caramia;Giovanni Mercurio Casolino;Pasquale de Falco;Iqrar Hussain;Pietro Varilone;Paola Verde","doi":"10.1109/OAJPE.2024.3521030","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3521030","url":null,"abstract":"The analysis of power quality disturbances in distribution systems has gained significance with the diffusion of electric vehicles (EVs). Waveform distortions are interesting since EV currents introduce distortions with spectral components in both low and high-frequency bands. This paper develops specific indices to assess cumulative emissions from single-phase EV on-board chargers, extending the aggregation and diversity factors to the supra-harmonic range. The methodology accounts for variables such as EV charging powers, upstream network impedance, and number of EVs. A simplified time-domain model of a low-power unidirectional converter, commonly used for EV battery charging, is employed to balance circuit complexity and computational effort. This model allows for sensitivity analyses of key parameters influencing charger emissions. Numerical applications are carried out for both individuals and groups of EV chargers at a charging station. Results highlight the need for careful quantification of aggregated EV emissions, showing that supra-harmonic emissions are highly sensitive to variations in the power absorbed by EV chargers. Notably, their cumulative impact is much lower when chargers operate at different power levels than when all chargers operate at the same power level. These findings underscore the importance of accurately assessing the impact of EV charging on power quality.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"690-702"},"PeriodicalIF":3.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10811949","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912416","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 : 2024-12-19DOI: 10.1109/OAJPE.2024.3520418
Michael Eichelbeck;Matthias Althoff
Energy communities (ECs) are an increasingly studied path toward improving prosumer coordination. A central challenge of ECs is to allocate cost savings fairly to members. While many allocation mechanisms have been developed, existing literature does not account for the implications of inaccurate forecasts on the fairness of the allocation. We introduce a set of fairness conditions for imperfect knowledge allocation and show that these conditions constitute a Pareto front. We demonstrate how a well-established allocation scheme, the Shapley value mechanism (SVM), has unfavorable consequences for flexibility-providing community members and generally does not yield solutions on this Pareto front. In contrast, we interpret dispatch cost under imperfect knowledge as being composed of two components. The first represents the cost under perfect knowledge, and the second represents the cost arising from inaccurate forecasts. Our proposed mechanism extends an SVM-based allocation of the perfect knowledge cost by allocating the remaining cost in a way that guarantees finding solutions on the Pareto front. To this end, we formulate a convex multi-objective optimization problem that can efficiently be solved as a linear or quadratic program.
{"title":"Fair Cost Allocation in Energy Communities Under Forecast Uncertainty","authors":"Michael Eichelbeck;Matthias Althoff","doi":"10.1109/OAJPE.2024.3520418","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3520418","url":null,"abstract":"Energy communities (ECs) are an increasingly studied path toward improving prosumer coordination. A central challenge of ECs is to allocate cost savings fairly to members. While many allocation mechanisms have been developed, existing literature does not account for the implications of inaccurate forecasts on the fairness of the allocation. We introduce a set of fairness conditions for imperfect knowledge allocation and show that these conditions constitute a Pareto front. We demonstrate how a well-established allocation scheme, the Shapley value mechanism (SVM), has unfavorable consequences for flexibility-providing community members and generally does not yield solutions on this Pareto front. In contrast, we interpret dispatch cost under imperfect knowledge as being composed of two components. The first represents the cost under perfect knowledge, and the second represents the cost arising from inaccurate forecasts. Our proposed mechanism extends an SVM-based allocation of the perfect knowledge cost by allocating the remaining cost in a way that guarantees finding solutions on the Pareto front. To this end, we formulate a convex multi-objective optimization problem that can efficiently be solved as a linear or quadratic program.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"2-11"},"PeriodicalIF":3.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106572","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 : 2024-12-11DOI: 10.1109/OAJPE.2024.3513776
Ozgur Alaca;Ali Riza Ekti;Jhi-Young Joo;Nils Stenvig
Rapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a six-stage cascaded structure consisting of: (1) data collection, (2) clipping, (3) augmentation, (4) feature extraction (FE), (5) training, and (6) testing. Real-world power grid signals sourced from the Grid Event Signature Library are used for both training and testing. To improve robustness, additive white Gaussian noise (AWGN) is introduced at various signal-to-noise ratio (SNR) levels to augment the dataset. The SCF-based FE method captures distinctive event-type characteristics by exploiting the spectral correlation of signals, allowing the CNN architecture to effectively learn and generalize event patterns. The proposed method is benchmarked against seven conventional techniques, using real-world power grid signals representing four distinct event types: blown fuse, line switching, low amplitude arcing, and transformer energization. Key performance metrics-prediction accuracy, mean absolute error (MAE), precision, recall, F1-score, and confusion matrix—are employed to evaluate the performance. Results demonstrate that the SCF-CNN method outperforms traditional approaches across all metrics and SNR levels, achieving over 99% prediction accuracy and nearly zero error for SNR values above 6 dB. This signifies its efficacy in reliable event-type identification for power grid applications.
{"title":"Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network","authors":"Ozgur Alaca;Ali Riza Ekti;Jhi-Young Joo;Nils Stenvig","doi":"10.1109/OAJPE.2024.3513776","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3513776","url":null,"abstract":"Rapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a six-stage cascaded structure consisting of: (1) data collection, (2) clipping, (3) augmentation, (4) feature extraction (FE), (5) training, and (6) testing. Real-world power grid signals sourced from the Grid Event Signature Library are used for both training and testing. To improve robustness, additive white Gaussian noise (AWGN) is introduced at various signal-to-noise ratio (SNR) levels to augment the dataset. The SCF-based FE method captures distinctive event-type characteristics by exploiting the spectral correlation of signals, allowing the CNN architecture to effectively learn and generalize event patterns. The proposed method is benchmarked against seven conventional techniques, using real-world power grid signals representing four distinct event types: blown fuse, line switching, low amplitude arcing, and transformer energization. Key performance metrics-prediction accuracy, mean absolute error (MAE), precision, recall, F1-score, and confusion matrix—are employed to evaluate the performance. Results demonstrate that the SCF-CNN method outperforms traditional approaches across all metrics and SNR levels, achieving over 99% prediction accuracy and nearly zero error for SNR values above 6 dB. This signifies its efficacy in reliable event-type identification for power grid applications.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"653-664"},"PeriodicalIF":3.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10789217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875107","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 : 2024-12-09DOI: 10.1109/OAJPE.2024.3496252
Prince Waqas Khan;Yung-Cheol Byun
Presents corrections to the paper, (Correction to “Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines”).
提出了对论文的更正，（更正“使用PCA-Kmeans和集成分类器检测风力涡轮机的异常分类”）。
{"title":"Correction to “Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines”","authors":"Prince Waqas Khan;Yung-Cheol Byun","doi":"10.1109/OAJPE.2024.3496252","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3496252","url":null,"abstract":"Presents corrections to the paper, (Correction to “Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines”).","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"610-610"},"PeriodicalIF":3.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10785525","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798054","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 : 2024-12-02DOI: 10.1109/OAJPE.2024.3509964
Kenta Koiwa;Tomonori Tashiro;Tomoya Ishii;Tadanao Zanma;Kang-Zhi Liu
Wind power plants (WPPs) have been rapidly installed worldwide as an alternative source to thermal power plants. Nevertheless, since the outputs of WPPs constantly fluctuates due to variations in wind speed, WPPs expose power systems to power quality degradation, such as frequency fluctuation. This paper develops an optimal control method of energy storage systems (ESSs) that utilizes WPP output prediction to mitigate WPP output fluctuation. In the proposed method, an output reference of ESS can be obtained as the solution of an optimization problem. Specifically, the proposed method regulates the state of charge of ESS within its appropriate range by minimizing a cost function. At the same time, the minimization of ESS output and multiple grid codes related to the mitigation of WPP output fluctuation are considered as constraints. As a result, the proposed method enables us to mitigate the output fluctuation of WPP sufficiently by an ESS with small rated power. The effectiveness of the proposed method is demonstrated through comparative analysis with conventional methods via scenario simulations.
{"title":"An Optimal Control of Energy Storage Systems Using Wind Power Prediction","authors":"Kenta Koiwa;Tomonori Tashiro;Tomoya Ishii;Tadanao Zanma;Kang-Zhi Liu","doi":"10.1109/OAJPE.2024.3509964","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3509964","url":null,"abstract":"Wind power plants (WPPs) have been rapidly installed worldwide as an alternative source to thermal power plants. Nevertheless, since the outputs of WPPs constantly fluctuates due to variations in wind speed, WPPs expose power systems to power quality degradation, such as frequency fluctuation. This paper develops an optimal control method of energy storage systems (ESSs) that utilizes WPP output prediction to mitigate WPP output fluctuation. In the proposed method, an output reference of ESS can be obtained as the solution of an optimization problem. Specifically, the proposed method regulates the state of charge of ESS within its appropriate range by minimizing a cost function. At the same time, the minimization of ESS output and multiple grid codes related to the mitigation of WPP output fluctuation are considered as constraints. As a result, the proposed method enables us to mitigate the output fluctuation of WPP sufficiently by an ESS with small rated power. The effectiveness of the proposed method is demonstrated through comparative analysis with conventional methods via scenario simulations.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"637-652"},"PeriodicalIF":3.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825855","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 : 2024-11-27DOI: 10.1109/OAJPE.2024.3508576
Shayan Ebrahimi;Mohammad Seyedi;S. M. Safayet Ullah;Farzad Ferdowsi
Space missions would not be possible without an available, reliable, autonomous, and resilient power system. Space-based power systems differ from Earth’s grid in generation sources, needs, structure, and controllability. This research introduces a groundbreaking approach employing digital twin (DT) technology to emulate and enhance the performance of a physical system representing a space-based system. The system encompasses three DC converters, a DC source, and a modular battery storage unit feeding a variable load. Rigorous testing across diverse operating points establishes the real-time high-fidelity DT, with root mean square error (RMSE) values consistently below 5%. The principal innovation leverages this DT to fortify system resilience against unforeseen events, surpassing the capabilities of existing controllers and autonomy levels. The approach offers an invaluable tool for scenarios where the system may not be primed for or physical access to components is limited. This research introduces a modular battery storage solution that seamlessly compensates for power shortages due to dust effects on the Lunar surface or unexpected system faults. This holistic approach validates the DT’s fidelity and underscores its potential to revolutionize system operation, safeguard against uncertainties, and expedite response strategies during unexpected contingencies. The proposed approach also paves the way for future development.
{"title":"Resilient Space Operations With Digital Twin for Solar PV and Storage","authors":"Shayan Ebrahimi;Mohammad Seyedi;S. M. Safayet Ullah;Farzad Ferdowsi","doi":"10.1109/OAJPE.2024.3508576","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3508576","url":null,"abstract":"Space missions would not be possible without an available, reliable, autonomous, and resilient power system. Space-based power systems differ from Earth’s grid in generation sources, needs, structure, and controllability. This research introduces a groundbreaking approach employing digital twin (DT) technology to emulate and enhance the performance of a physical system representing a space-based system. The system encompasses three DC converters, a DC source, and a modular battery storage unit feeding a variable load. Rigorous testing across diverse operating points establishes the real-time high-fidelity DT, with root mean square error (RMSE) values consistently below 5%. The principal innovation leverages this DT to fortify system resilience against unforeseen events, surpassing the capabilities of existing controllers and autonomy levels. The approach offers an invaluable tool for scenarios where the system may not be primed for or physical access to components is limited. This research introduces a modular battery storage solution that seamlessly compensates for power shortages due to dust effects on the Lunar surface or unexpected system faults. This holistic approach validates the DT’s fidelity and underscores its potential to revolutionize system operation, safeguard against uncertainties, and expedite response strategies during unexpected contingencies. The proposed approach also paves the way for future development.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"624-636"},"PeriodicalIF":3.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810337","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 : 2024-11-27DOI: 10.1109/OAJPE.2024.3507537
Victor Gonzalez;V. Torres-García;Daniel Guillen;Luis M. Castro
Fault location has been crucial in minimizing fault restoration time. Various techniques and methodologies have been deployed to enhance the performance of fault location algorithms, especially in light of the increasing integration of renewable energy sources. In this context, this paper describes a graph-theory-based method for fault location in power networks with renewable energy sources. This novel technique is designed to provide accurate fault distance estimates, even in the presence of severe noise and fault resistance. It takes advantage of graph theory and equivalent impedances applying Kirchhoff’s laws systematically to ensure accurate fault location even in the presence of fault resistances. To showcase the improved accuracy of the proposed methodology, a comparison with typical impedance-based two-terminal fault location methods is carried out. The effectiveness of the proposed algorithm was proven with different electrical systems. Average errors inferior to 0.22% and 0.48% were obtained for single-phase faults and three-phase faults with resistances up to $200~Omega $ respectively, which confirms the improved performance with respect to conventional algorithms implemented in typical impedance relays.
{"title":"Graph Theory-Based Fault Location Method for Transmission Systems With Renewable Energy Sources","authors":"Victor Gonzalez;V. Torres-García;Daniel Guillen;Luis M. Castro","doi":"10.1109/OAJPE.2024.3507537","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3507537","url":null,"abstract":"Fault location has been crucial in minimizing fault restoration time. Various techniques and methodologies have been deployed to enhance the performance of fault location algorithms, especially in light of the increasing integration of renewable energy sources. In this context, this paper describes a graph-theory-based method for fault location in power networks with renewable energy sources. This novel technique is designed to provide accurate fault distance estimates, even in the presence of severe noise and fault resistance. It takes advantage of graph theory and equivalent impedances applying Kirchhoff’s laws systematically to ensure accurate fault location even in the presence of fault resistances. To showcase the improved accuracy of the proposed methodology, a comparison with typical impedance-based two-terminal fault location methods is carried out. The effectiveness of the proposed algorithm was proven with different electrical systems. Average errors inferior to 0.22% and 0.48% were obtained for single-phase faults and three-phase faults with resistances up to \u0000<inline-formula> <tex-math>$200~Omega $ </tex-math></inline-formula>\u0000 respectively, which confirms the improved performance with respect to conventional algorithms implemented in typical impedance relays.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"611-623"},"PeriodicalIF":3.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10769500","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798055","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 : 2024-11-22DOI: 10.1109/OAJPE.2024.3504835
Yuxin Deng;Xin Fang;Ningchao Gao;Jin Tan
Hybrid power plants (HPPs) present a promising solution to address the significant challenges posed by the rapid integration of variable renewable energy sources (VREs) into power systems, particularly in maintaining power balance and frequency stability. Therefore, there is a pressing need for system operators and HPP owners to effectively manage both the energy and regulation services of HPPs within the current system operational framework. Existing studies on HPP modeling often separate dynamic control from steady-state scheduling and lack coordinated integration of self-scheduling of HPPs with the system-level scheduling, leading to over/under estimation of the flexibility of HPPs. To address this challenge, this paper presents a generic modeling framework for HPPs that integrates steady-state optimization with dynamic control across multiple timescales, enabling seamless HPP participation in day-ahead and real-time markets and real-time control. Additionally, the framework facilitates comprehensive economic and frequency performance evaluations. Case studies on a modified IEEE 39-bus system demonstrate the framework’s ability to ensure frequency performance with flexible HPP operation modes, align BESS state-of-charge (SOC) with dispatch targets, and optimize reliability and economic outcomes under various scenarios.
{"title":"Multi-Timescale Modeling Framework of Hybrid Power Plants Providing Secondary Frequency Regulation","authors":"Yuxin Deng;Xin Fang;Ningchao Gao;Jin Tan","doi":"10.1109/OAJPE.2024.3504835","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3504835","url":null,"abstract":"Hybrid power plants (HPPs) present a promising solution to address the significant challenges posed by the rapid integration of variable renewable energy sources (VREs) into power systems, particularly in maintaining power balance and frequency stability. Therefore, there is a pressing need for system operators and HPP owners to effectively manage both the energy and regulation services of HPPs within the current system operational framework. Existing studies on HPP modeling often separate dynamic control from steady-state scheduling and lack coordinated integration of self-scheduling of HPPs with the system-level scheduling, leading to over/under estimation of the flexibility of HPPs. To address this challenge, this paper presents a generic modeling framework for HPPs that integrates steady-state optimization with dynamic control across multiple timescales, enabling seamless HPP participation in day-ahead and real-time markets and real-time control. Additionally, the framework facilitates comprehensive economic and frequency performance evaluations. Case studies on a modified IEEE 39-bus system demonstrate the framework’s ability to ensure frequency performance with flexible HPP operation modes, align BESS state-of-charge (SOC) with dispatch targets, and optimize reliability and economic outcomes under various scenarios.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"595-609"},"PeriodicalIF":3.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10764748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777750","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 : 2024-11-18DOI: 10.1109/OAJPE.2024.3501575
Musharrat Sabah;Aaron M. Cramer;Yuan Liao
Market-based control is a control approach that can be used to organize resource control problems by establishing an artificial market economy for the allocation of these resources. In such system, the set of market-clearing prices is the set of prices that result in an equilibrium between demanded and supplied resources throughout the system. In this paper, a new method has been proposed for solving the market-clearing problem, the problem of determining the market-clearing prices. The algorithm is applied on a complex representative power system and three simplified power systems based on the representative system under different operational scenarios. The proposed method is compared with existing reference root-finding algorithms. The comparison illustrates the proposed algorithm’s ability to address the numerical challenges the market-clearing problem poses. Dynamic simulation has been used to demonstrate the efficacy of the proposed algorithm in clearing the market in a wide range of dynamic conditions.
{"title":"Numerical Algorithm for Solving Market Clearing Problem in Power Electronics-Based Power Distribution Systems","authors":"Musharrat Sabah;Aaron M. Cramer;Yuan Liao","doi":"10.1109/OAJPE.2024.3501575","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3501575","url":null,"abstract":"Market-based control is a control approach that can be used to organize resource control problems by establishing an artificial market economy for the allocation of these resources. In such system, the set of market-clearing prices is the set of prices that result in an equilibrium between demanded and supplied resources throughout the system. In this paper, a new method has been proposed for solving the market-clearing problem, the problem of determining the market-clearing prices. The algorithm is applied on a complex representative power system and three simplified power systems based on the representative system under different operational scenarios. The proposed method is compared with existing reference root-finding algorithms. The comparison illustrates the proposed algorithm’s ability to address the numerical challenges the market-clearing problem poses. Dynamic simulation has been used to demonstrate the efficacy of the proposed algorithm in clearing the market in a wide range of dynamic conditions.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"665-675"},"PeriodicalIF":3.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10756704","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875108","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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