Pub Date : 2025-03-04DOI: 10.1109/TSTE.2025.3547561
Qin Wang;Miguel Ortega-Vazquez;Aidan Tuohy;Erik Ela;Mobolaji Bello;Daniel Kirk-Davidoff;William B. Hobbs;Vijay Kumar
Probabilistic forecasting is becoming pivotal in utilities' decision-making processes, offering an accurate portrayal of plausible forecast deviations as opposed to deterministic forecasting which only focuses on the expected forecasted variables. Two methods, dynamic reserve and stochastic optimization, have been used to integrate probabilistic forecasts into power system operational planning. Dynamic reserve predicts system reserve requirements based on observed (from historical observations) or expected (from probabilistic forecasts) uncertainty spreads. This approach has a low computational burden, but it is commitment and dispatch agnostic. Stochastic optimization, on the other hand, considers multiple scenarios simultaneously (from probabilistic forecasts), allocating recourse across the commitment and dispatch variables, but demanding high computational resources and time. The selection between these methods depends on utility requirements and specific situations. This paper conducts a comprehensive evaluation of both methods using a calibrated real-size system representing the Southern Company for medium and high solar penetration levels. Additionally, it proposes a hybrid dynamic reserve and stochastic optimization approach with a risk evaluation pre-scheduling procedure to enhance decision-making.
{"title":"Assessing Dynamic Reserves vs. Stochastic Optimization for Effective Integration of Operating Probabilistic Forecasts","authors":"Qin Wang;Miguel Ortega-Vazquez;Aidan Tuohy;Erik Ela;Mobolaji Bello;Daniel Kirk-Davidoff;William B. Hobbs;Vijay Kumar","doi":"10.1109/TSTE.2025.3547561","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3547561","url":null,"abstract":"Probabilistic forecasting is becoming pivotal in utilities' decision-making processes, offering an accurate portrayal of plausible forecast deviations as opposed to deterministic forecasting which only focuses on the expected forecasted variables. Two methods, dynamic reserve and stochastic optimization, have been used to integrate probabilistic forecasts into power system operational planning. Dynamic reserve predicts system reserve requirements based on observed (from historical observations) or expected (from probabilistic forecasts) uncertainty spreads. This approach has a low computational burden, but it is commitment and dispatch agnostic. Stochastic optimization, on the other hand, considers multiple scenarios simultaneously (from probabilistic forecasts), allocating recourse across the commitment and dispatch variables, but demanding high computational resources and time. The selection between these methods depends on utility requirements and specific situations. This paper conducts a comprehensive evaluation of both methods using a calibrated real-size system representing the Southern Company for medium and high solar penetration levels. Additionally, it proposes a hybrid dynamic reserve and stochastic optimization approach with a risk evaluation pre-scheduling procedure to enhance decision-making.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2132-2143"},"PeriodicalIF":8.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing penetration of renewable energy sources, with intermittent and uncertain nature, brings new challenges to the secure and efficient operation of power systems. Expanding transmission networks and utilizing energy storage (ES) have been introduced as effective solutions to address these challenges. This paper presents a minimax regret robust co-planning model with mixed integer recourse for transmission and ES systems, designed from the perspective of a central planner. The model considers a polyhedral uncertainty set for future peak load growth, while uncertainties in wind farm expansion are addressed through internal scenario analysis. This approach will guarantee the robustness of investment decisions and provide the central planner with a clear picture of the maximum regret among all possible scenarios. Furthermore, the proposed minimax regret framework facilitates strategic planning for ES installation after the resolution of long-term uncertainties. In this paper, we reformulate the model into a standard min-max-min problem, in which the maximization level is only over uncertainties. Subsequently, a five-level solution strategy based on a modified nested column and constraint generation decomposition technique is represented to deal with the intractability and complexity of the problem caused by binary variables of transmission lines and ES blocks. The model is finally evaluated through comprehensive simulation studies to verify its tractability, practicality, and effectiveness.
{"title":"Minimax Regret Robust Co-Planning of Transmission and Energy Storage Systems With Mixed Integer Recourse","authors":"Ehsan Barkom;Hossein Saber;Moein Moeini-Aghtaie;Mehdi Ehsan;Mohammad Shahidehpour","doi":"10.1109/TSTE.2025.3547836","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3547836","url":null,"abstract":"The growing penetration of renewable energy sources, with intermittent and uncertain nature, brings new challenges to the secure and efficient operation of power systems. Expanding transmission networks and utilizing energy storage (ES) have been introduced as effective solutions to address these challenges. This paper presents a minimax regret robust co-planning model with mixed integer recourse for transmission and ES systems, designed from the perspective of a central planner. The model considers a polyhedral uncertainty set for future peak load growth, while uncertainties in wind farm expansion are addressed through internal scenario analysis. This approach will guarantee the robustness of investment decisions and provide the central planner with a clear picture of the maximum regret among all possible scenarios. Furthermore, the proposed minimax regret framework facilitates strategic planning for ES installation after the resolution of long-term uncertainties. In this paper, we reformulate the model into a standard min-max-min problem, in which the maximization level is only over uncertainties. Subsequently, a five-level solution strategy based on a modified nested column and constraint generation decomposition technique is represented to deal with the intractability and complexity of the problem caused by binary variables of transmission lines and ES blocks. The model is finally evaluated through comprehensive simulation studies to verify its tractability, practicality, and effectiveness.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2144-2156"},"PeriodicalIF":8.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-03DOI: 10.1109/TSTE.2025.3546996
Ammar Atif Abdalla;Mohamed Shawky El Moursi;Tarek H. M. El-Fouly;Khalifa Hassan Al Hosani
In pursuit of a carbon-neutral future, the integration of photovoltaic (PV) power plants into the electrical power grid is expanding. Although beneficial, this expansion presents challenges due to weather-induced variability, which destabilizes the grid and causes voltage and frequency deviations. A viable solution is the use of Battery Energy Storage Systems (BESS) alongside PV power plants. However, conventional controllers, which lead to uniform and frequent charging cycles, accelerate degradation and reduce efficiency in BESS. To address this, this paper proposes segmenting the BESS units into distinct charging and discharging groups, effectively minimizing battery cycling and enhancing their lifespan. The controller dynamically assigns batteries to each group based on power fluctuation forecasts using a power-sharing model. This model manages battery activation, enables inter-group support, and balances degradation by monitoring BESS charge levels and assessing battery health through an online system. This controller, coupled with a degradation balancing layer, strategically prioritizes units based on their cycling age. The proposed technique was rigorously tested and experimentally validated, demonstrating that it significantly reduces battery degradation to a maximum of 0.099%, in stark contrast to the up to 4.41% observed with conventional controllers.
{"title":"Online Monitoring of Battery Degradation for Enhanced Power Smoothing of PV Power Plants","authors":"Ammar Atif Abdalla;Mohamed Shawky El Moursi;Tarek H. M. El-Fouly;Khalifa Hassan Al Hosani","doi":"10.1109/TSTE.2025.3546996","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3546996","url":null,"abstract":"In pursuit of a carbon-neutral future, the integration of photovoltaic (PV) power plants into the electrical power grid is expanding. Although beneficial, this expansion presents challenges due to weather-induced variability, which destabilizes the grid and causes voltage and frequency deviations. A viable solution is the use of Battery Energy Storage Systems (BESS) alongside PV power plants. However, conventional controllers, which lead to uniform and frequent charging cycles, accelerate degradation and reduce efficiency in BESS. To address this, this paper proposes segmenting the BESS units into distinct charging and discharging groups, effectively minimizing battery cycling and enhancing their lifespan. The controller dynamically assigns batteries to each group based on power fluctuation forecasts using a power-sharing model. This model manages battery activation, enables inter-group support, and balances degradation by monitoring BESS charge levels and assessing battery health through an online system. This controller, coupled with a degradation balancing layer, strategically prioritizes units based on their cycling age. The proposed technique was rigorously tested and experimentally validated, demonstrating that it significantly reduces battery degradation to a maximum of 0.099%, in stark contrast to the up to 4.41% observed with conventional controllers.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2096-2113"},"PeriodicalIF":8.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The large-scale integration of Photovoltaic (PV) sources may reduce system inertia and power quality, resulting in increased frequency fluctuations and diminished system stability due to lack of the primary frequency regulation (FR) capability. To address these challenges, this paper proposes a unified strategy for frequency regulating and Maximum Power Point Tracking (MPPT) for PV sources to provide ancillary services to the power grid. The strategy employs a specifically designed active power control (APC) method to enable rapid and flexible power adjustments of PV sources, with which further FR function may be achieved. The presented APC algorithm adopts an iterative technique with a novel three-parameter PV characteristic curve, making it possible to reconstruct the real-time PV generation model, clarify the relationship between the system frequency, output power, and operating voltage. Its high control accuracy, fast convergence rate, and strong explainability offer significant practical value. Additionally, this adaptive control strategy features autonomous switch between the FR and MPPT modes adapting to real-time irradiation changes, without the need for additional irradiation or temperature sensors. The integration enhances both solar utilization efficiency and the FR capability, while eliminating the controller transitions during operating mode switches. Hardware-in-the-loop tests validate the feasibility and effectiveness of the proposed strategy.
{"title":"A Unified Strategy for Frequency Regulating and MPPT for Photovoltaic Sources Based on a Novel Three-Parameter Characteristic Curve","authors":"Yihao Zhu;Hongda Cai;Pengcheng Yang;Yongzhi Zhou;Yanghong Xia;Wei Wei","doi":"10.1109/TSTE.2025.3546706","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3546706","url":null,"abstract":"The large-scale integration of Photovoltaic (PV) sources may reduce system inertia and power quality, resulting in increased frequency fluctuations and diminished system stability due to lack of the primary frequency regulation (FR) capability. To address these challenges, this paper proposes a unified strategy for frequency regulating and Maximum Power Point Tracking (MPPT) for PV sources to provide ancillary services to the power grid. The strategy employs a specifically designed active power control (APC) method to enable rapid and flexible power adjustments of PV sources, with which further FR function may be achieved. The presented APC algorithm adopts an iterative technique with a novel three-parameter PV characteristic curve, making it possible to reconstruct the real-time PV generation model, clarify the relationship between the system frequency, output power, and operating voltage. Its high control accuracy, fast convergence rate, and strong explainability offer significant practical value. Additionally, this adaptive control strategy features autonomous switch between the FR and MPPT modes adapting to real-time irradiation changes, without the need for additional irradiation or temperature sensors. The integration enhances both solar utilization efficiency and the FR capability, while eliminating the controller transitions during operating mode switches. Hardware-in-the-loop tests validate the feasibility and effectiveness of the proposed strategy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2084-2095"},"PeriodicalIF":8.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1109/TSTE.2025.3546203
Aldo Barrueto;Hector Chavez;Karina Barbosa
Modern power systems may experience decrease in stability due to the increased integration of variable generation sources that depend on power electronics converters. A common control strategy is to incorporate synthetic inertia from wind turbines, typically using state-feedback control in a single-area power system model that assumes uniform frequency. As power systems become more interconnected, different frequency behaviors can emerge in multiple areas, casting doubt on current methods that do not consider multi-area stability. Furthermore, most single-area synthetic inertia methods ignore the limitations of communication systems in real power systems. This paper proposes a decentralized synthetic inertia control strategy for a two-area power system with wind power. This approach accounts for the actual behavior of power systems in different areas and the limitations of communication systems in real scenarios. Numerical results, derived from dynamic models using actual operating data from the Chilean Power System, demonstrate that the decentralized control performs comparably to centralized control in maintaining power system stability and optimizing frequency nadir. However, the decentralized control has the advantage of relying solely on local variables, eliminating the need for communication links between areas during operation.
{"title":"Decentralized Synthetic Inertia Control for Two-Area Power Systems With Wind Integration","authors":"Aldo Barrueto;Hector Chavez;Karina Barbosa","doi":"10.1109/TSTE.2025.3546203","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3546203","url":null,"abstract":"Modern power systems may experience decrease in stability due to the increased integration of variable generation sources that depend on power electronics converters. A common control strategy is to incorporate synthetic inertia from wind turbines, typically using state-feedback control in a single-area power system model that assumes uniform frequency. As power systems become more interconnected, different frequency behaviors can emerge in multiple areas, casting doubt on current methods that do not consider multi-area stability. Furthermore, most single-area synthetic inertia methods ignore the limitations of communication systems in real power systems. This paper proposes a decentralized synthetic inertia control strategy for a two-area power system with wind power. This approach accounts for the actual behavior of power systems in different areas and the limitations of communication systems in real scenarios. Numerical results, derived from dynamic models using actual operating data from the Chilean Power System, demonstrate that the decentralized control performs comparably to centralized control in maintaining power system stability and optimizing frequency nadir. However, the decentralized control has the advantage of relying solely on local variables, eliminating the need for communication links between areas during operation.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2073-2083"},"PeriodicalIF":8.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-25DOI: 10.1109/TSTE.2025.3545467
Lin Xue;Tao Niu;Nan Feng;Sidun Fang;Yuyao Feng;Hung Dinh Nguyen;Guanhong Chen
Transient security-constrained optimal power flow (TSCOPF) is an important class of problems for system operation. Several challenges arise when dealing with bulk power grids, including the large size and complex transient voltage behaviors. This paper aims to address such hurdles by proposing a dynamic dimensionality reduction matrix adaptive correction (DDR-MAC) algorithm, which can effectively evaluate proper Volt/Var levels to guarantee secure system operation. First, this paper performs dimensionality reduction processing at the bus and device levels to obtain a low-dimensional model with dominant modes, which solves the problems of high-order and large computational volumes of differential equations. Moreover, a dimensionality reduction error assessment model is established to ensure reduced-order accuracy. Then, the reduced-order TSCOPF model is equivalently decomposed into a mixed-integer linear optimization model and a combined coefficient correction model for system dynamic constraints and steady-state nonlinear constraints. Furthermore, a secant/tangent sensitivity adaptive correction method is presented to achieve fast computation. The DDR-MAC approach is verified across differently scaled IEEE test systems and the Nordic test system and can improve computational efficiency by 49.07% while offering higher accuracy than traditional computation methods.
{"title":"Matrix Adaptive Correction-Based Dynamic Dimensionality Reduction Method for Voltage-Related TSCOPF in Bulk Power Systems With High Wind Power Penetration","authors":"Lin Xue;Tao Niu;Nan Feng;Sidun Fang;Yuyao Feng;Hung Dinh Nguyen;Guanhong Chen","doi":"10.1109/TSTE.2025.3545467","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3545467","url":null,"abstract":"Transient security-constrained optimal power flow (TSCOPF) is an important class of problems for system operation. Several challenges arise when dealing with bulk power grids, including the large size and complex transient voltage behaviors. This paper aims to address such hurdles by proposing a dynamic dimensionality reduction matrix adaptive correction (DDR-MAC) algorithm, which can effectively evaluate proper Volt/Var levels to guarantee secure system operation. First, this paper performs dimensionality reduction processing at the bus and device levels to obtain a low-dimensional model with dominant modes, which solves the problems of high-order and large computational volumes of differential equations. Moreover, a dimensionality reduction error assessment model is established to ensure reduced-order accuracy. Then, the reduced-order TSCOPF model is equivalently decomposed into a mixed-integer linear optimization model and a combined coefficient correction model for system dynamic constraints and steady-state nonlinear constraints. Furthermore, a secant/tangent sensitivity adaptive correction method is presented to achieve fast computation. The DDR-MAC approach is verified across differently scaled IEEE test systems and the Nordic test system and can improve computational efficiency by 49.07% while offering higher accuracy than traditional computation methods.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2058-2072"},"PeriodicalIF":8.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-25DOI: 10.1109/TSTE.2025.3544247
Dong Wang;Yajun Guo;Yunhui Huang;Keliang Zhou;Shuo Wang
The deployment of inertia control in wind turbines (WTs) is required by grid operators to support system frequency stability, while its addition also strengthens the dynamic coupling of WT's mechanical system with the electrical gird, potentially exacerbating torsional vibrations in drivetrains. Such side effect has been reported for the supplementary inertia control in the current grid-following (GFL) control frame, while for its alternative, i.e., the so-called virtual inertia control, this effect has rarely been concerned. To fill this gap, this article conducts a comprehensive study on the torsional stability for virtual-synchronous-controlled (VSynC) DFIG-based WTs. A reduced-order small-signal model considering different wind speed operating regions is firstly proposed. Then, how the paths of different controls, such as virtual inertia control, pitching control, etc., affecting the torsional mechanical/electrical torque are sorted out, and correlated loop shaping on torsional damping are investigated in detail. Through a comparative analysis with GFL scheme, we confirm that VSynC-DFIG suffers more sever torsional stability challenges due to its curtailed electrical damping contribution. Furthermore, we identify the operating conditions that are prone to instability and reveal the mechanisms by which critical control factors impact stability. Finally, hardware-in-the-loop simulations are conducted to validate the correctness of the analyses.
{"title":"Torsional Vibration Analysis of Virtual-Synchronous-Controlled DFIG-Based Wind Turbines","authors":"Dong Wang;Yajun Guo;Yunhui Huang;Keliang Zhou;Shuo Wang","doi":"10.1109/TSTE.2025.3544247","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3544247","url":null,"abstract":"The deployment of inertia control in wind turbines (WTs) is required by grid operators to support system frequency stability, while its addition also strengthens the dynamic coupling of WT's mechanical system with the electrical gird, potentially exacerbating torsional vibrations in drivetrains. Such side effect has been reported for the supplementary inertia control in the current grid-following (GFL) control frame, while for its alternative, i.e., the so-called virtual inertia control, this effect has rarely been concerned. To fill this gap, this article conducts a comprehensive study on the torsional stability for virtual-synchronous-controlled (VSynC) DFIG-based WTs. A reduced-order small-signal model considering different wind speed operating regions is firstly proposed. Then, how the paths of different controls, such as virtual inertia control, pitching control, etc., affecting the torsional mechanical/electrical torque are sorted out, and correlated loop shaping on torsional damping are investigated in detail. Through a comparative analysis with GFL scheme, we confirm that VSynC-DFIG suffers more sever torsional stability challenges due to its curtailed electrical damping contribution. Furthermore, we identify the operating conditions that are prone to instability and reveal the mechanisms by which critical control factors impact stability. Finally, hardware-in-the-loop simulations are conducted to validate the correctness of the analyses.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2044-2057"},"PeriodicalIF":8.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-25DOI: 10.1109/TSTE.2025.3545342
Gabriel Miguel Gomes Guerreiro;Ranjan Sharma;Frank Martin;Nikolaus Goldenbaum;Guangya Yang
This letter presents a field-verified passive method to estimate the equivalent fundamental frequency grid impedance. The method requires reactive power, active power, and root-mean-square voltage measurements as inputs and is based on a mathematical derivation independent of the angle and frequency estimation. A thorough verification process was adopted, comprising simulations in EMT utilizing a wind turbine with actual control code, field wind turbine measurements, and an operational wind power plant where the transmission system operator agreed to change grid configurations to allow testing of the algorithm for two grid scenarios. Results show good agreement between expectations and estimation from the proposed method.
{"title":"Passive Method for Estimating Fundamental Frequency Grid Impedance: Including Model and Field Verification","authors":"Gabriel Miguel Gomes Guerreiro;Ranjan Sharma;Frank Martin;Nikolaus Goldenbaum;Guangya Yang","doi":"10.1109/TSTE.2025.3545342","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3545342","url":null,"abstract":"This letter presents a field-verified passive method to estimate the equivalent fundamental frequency grid impedance. The method requires reactive power, active power, and root-mean-square voltage measurements as inputs and is based on a mathematical derivation independent of the angle and frequency estimation. A thorough verification process was adopted, comprising simulations in EMT utilizing a wind turbine with actual control code, field wind turbine measurements, and an operational wind power plant where the transmission system operator agreed to change grid configurations to allow testing of the algorithm for two grid scenarios. Results show good agreement between expectations and estimation from the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2263-2266"},"PeriodicalIF":8.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1109/TSTE.2025.3544640
Leidong Yuan;Qingguang Yu;Xin Yao;Min Guo
The aggregation of electric vehicles (EVs) can take full advantage of the power flexibility of EV clusters, based on the aggregation feasible region (AFR) of EVs. However, existing aggregation techniques of conventional distributed energy resources (DERs), exhibit significant inaccuracies or require excessive computational efforts when applied to EVs with high power and small capacity. To solve this problem, an iterative method based on the high dimensional polyhedron projection principle is proposed to derive the exact AFR formula for the full power spectrum of EVs, and the limitations of the existing analytical AFR are revealed. The results reveal that the exact AFR is a set of linear inequalities, whose coefficients can be represented in binary code, and the complexity of the right side is linear with the number of EVs, making it highly conducive to programming calculations. Moreover, this paper proposes a simplified practical AFR based on the exact AFR, which reduces computational complexity. Numerical simulation cases demonstrate that the accuracy of this simplified approach is still higher than that of existing methods.
{"title":"Aggregation Feasible Region of Full Power Spectrum Electric Vehicles Based on Polyhedron Projection","authors":"Leidong Yuan;Qingguang Yu;Xin Yao;Min Guo","doi":"10.1109/TSTE.2025.3544640","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3544640","url":null,"abstract":"The aggregation of electric vehicles (EVs) can take full advantage of the power flexibility of EV clusters, based on the aggregation feasible region (AFR) of EVs. However, existing aggregation techniques of conventional distributed energy resources (DERs), exhibit significant inaccuracies or require excessive computational efforts when applied to EVs with high power and small capacity. To solve this problem, an iterative method based on the high dimensional polyhedron projection principle is proposed to derive the exact AFR formula for the full power spectrum of EVs, and the limitations of the existing analytical AFR are revealed. The results reveal that the exact AFR is a set of linear inequalities, whose coefficients can be represented in binary code, and the complexity of the right side is linear with the number of EVs, making it highly conducive to programming calculations. Moreover, this paper proposes a simplified practical AFR based on the exact AFR, which reduces computational complexity. Numerical simulation cases demonstrate that the accuracy of this simplified approach is still higher than that of existing methods.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2029-2043"},"PeriodicalIF":8.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-18DOI: 10.1109/TSTE.2025.3543420
Adnan Saeed;Chaoshun Li;Qiannan Zhu;Belal Ahmad
With rising wind power penetration into power systems obtaining wind speed forecasts with associated uncertainty becomes crucial for better planning and dispatch. This study proposes an enhanced multi quantile regression-based loss function specially tailored to train models to generate both deterministic forecast and the corresponding prediction intervals. Though the regression architecture of the model plays an important role in extracting precise forecasts, however, its efficiency is often ignored which may be a downside for short term forecasting scenarios where model training time may also be a significant factor. The present study therefore designed a multi-scale dilated convolution-based architecture for enhanced efficiency. The architecture generates predictions at different scales which are combined using particle swarm optimization to obtain optimal forecasts. The model is trained using the proposed loss function on datasets from both NREL simulations and operational Chinese state grid measurements across three different locations. The proposed model exhibits excellent forecasting performance in comparative experiments with both simulated and real-world operational datasets.
{"title":"Deterministic Forecasts and Prediction Intervals for Wind Speed Using Enhanced Multi-Quantile Loss Based Dilated Causal Convolutions","authors":"Adnan Saeed;Chaoshun Li;Qiannan Zhu;Belal Ahmad","doi":"10.1109/TSTE.2025.3543420","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3543420","url":null,"abstract":"With rising wind power penetration into power systems obtaining wind speed forecasts with associated uncertainty becomes crucial for better planning and dispatch. This study proposes an enhanced multi quantile regression-based loss function specially tailored to train models to generate both deterministic forecast and the corresponding prediction intervals. Though the regression architecture of the model plays an important role in extracting precise forecasts, however, its efficiency is often ignored which may be a downside for short term forecasting scenarios where model training time may also be a significant factor. The present study therefore designed a multi-scale dilated convolution-based architecture for enhanced efficiency. The architecture generates predictions at different scales which are combined using particle swarm optimization to obtain optimal forecasts. The model is trained using the proposed loss function on datasets from both NREL simulations and operational Chinese state grid measurements across three different locations. The proposed model exhibits excellent forecasting performance in comparative experiments with both simulated and real-world operational datasets.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2002-2014"},"PeriodicalIF":8.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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