Pub Date : 2025-02-10DOI: 10.1109/TSTE.2025.3540253
Lei Xu;Chunxia Dou;Dong Yue;Yudi Zhang;Bo Zhang;Houjun Li;Xiande Bu
The aggregation and control of massive electric vehicles (EVs) are crucial for grid frequency regulation (FR). However, challenges such as disordered charging, high computational and communication burdens need to be addressed. To this end, a hierarchical hybrid modeling and switching control method for EV aggregation (EVA) is proposed. For modeling, a hybrid state set for EVs comprising three discrete states and one dynamic state is established at the local level. The dynamic state's flexibility allows EVs to charge orderly while considering user demands. At the aggregation level, a Markov-based EVA state space model is designed, integrating the user's willingness-to-pay (WTP) index and hybrid state. It estimates the EVA's FR capacity (FRC) with a lower communication burden and reduces computational burden by simplifying control dimensions. For control, a model predictive control (MPC)-based state switching method is designed at the aggregation level, considering user's FR willingness and power cancellation issue. Furthermore, a predictive compensation mechanism is designed to address model parameter errors resulting from asynchronous control cycles. At the local level, a probabilistic response method is proposed for responding to dispatched control signals, which reduces battery degradation through the state of charge (SOC) based response probability generation. Simulation results validate the method's effectiveness.
{"title":"Hybrid Modeling and Switching Control of Electric Vehicle Aggregation for Frequency Regulation","authors":"Lei Xu;Chunxia Dou;Dong Yue;Yudi Zhang;Bo Zhang;Houjun Li;Xiande Bu","doi":"10.1109/TSTE.2025.3540253","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3540253","url":null,"abstract":"The aggregation and control of massive electric vehicles (EVs) are crucial for grid frequency regulation (FR). However, challenges such as disordered charging, high computational and communication burdens need to be addressed. To this end, a hierarchical hybrid modeling and switching control method for EV aggregation (EVA) is proposed. For modeling, a hybrid state set for EVs comprising three discrete states and one dynamic state is established at the local level. The dynamic state's flexibility allows EVs to charge orderly while considering user demands. At the aggregation level, a Markov-based EVA state space model is designed, integrating the user's willingness-to-pay (WTP) index and hybrid state. It estimates the EVA's FR capacity (FRC) with a lower communication burden and reduces computational burden by simplifying control dimensions. For control, a model predictive control (MPC)-based state switching method is designed at the aggregation level, considering user's FR willingness and power cancellation issue. Furthermore, a predictive compensation mechanism is designed to address model parameter errors resulting from asynchronous control cycles. At the local level, a probabilistic response method is proposed for responding to dispatched control signals, which reduces battery degradation through the state of charge (SOC) based response probability generation. Simulation results validate the method's effectiveness.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1860-1873"},"PeriodicalIF":8.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331636","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}
In this letter, a novel coordinated control is proposed to achieve integrated power generation of solid oxide fuel cell-gas turbine (SOFC-GT) systems. The integrated system is equipped with both grid following (GFL) and grid forming (GFM) capabilities, which represent an extended controllability compared with the conventional SOFC/GT that operates independently. Further, an adaptive power allocation strategy is developed to regulate the Hydrogen-Electricity conversion that couples the operation of SOFC and GT, which ensures the system's safe and efficient operation under various scenarios. Detailed control algorithms and validations are provided.
{"title":"Coordinated Control of the Integrated SOFC-GT Generation System for Microgrid Applications","authors":"Hanbin Dang;Changyue Li;Yuhua Du;Zhipeng Li;Fei Gao;Yigeng Huangfu","doi":"10.1109/TSTE.2025.3539894","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3539894","url":null,"abstract":"In this letter, a novel coordinated control is proposed to achieve integrated power generation of solid oxide fuel cell-gas turbine (SOFC-GT) systems. The integrated system is equipped with both grid following (GFL) and grid forming (GFM) capabilities, which represent an extended controllability compared with the conventional SOFC/GT that operates independently. Further, an adaptive power allocation strategy is developed to regulate the Hydrogen-Electricity conversion that couples the operation of SOFC and GT, which ensures the system's safe and efficient operation under various scenarios. Detailed control algorithms and validations are provided.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2259-2262"},"PeriodicalIF":8.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331634","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 : 2025-02-05DOI: 10.1109/TSTE.2025.3538682
Xinyu Liu;Jierui Huang;Di Zheng;Huanhai Xin;Tianshu Bi
Temporary overvoltage (TOV) severely restricts the development and utilization of renewable power resources (RPRs), especially when RPRs are delivered through the line commutated converter-based high voltage direct current (LCC-HVDC) system. To reveal the TOV mechanism for the sending system during commutation failures (CFs), the transient process of the system is partitioned into different stages, where the evolution of the system trajectories is analyzed. On this basis, the variation of AC voltage and DC current considering complex dynamic interactions between LCC-HVDC and renewable energy Plants (REPs) during repetitive CFs (RCFs) is clearly quantified. After revealing the impact of control parameters of both REPs and the LCC-HVDC on the TOV during RCFs, a collaborative optimization method for control parameters is proposed for TOV suppression. Moreover, when the blocking after the RCF tends to be inevitable, the optimal blocking moment is determined to inhibit the TOV caused by HVDC blocking. The accuracy and effectiveness of the proposed methods are verified with EMT simulations of a typical benchmark system.
{"title":"Analysis and Suppression for Temporary Overvoltage Considering Dynamic Interactions Between LCC-HVDC and Renewable Energy Plants","authors":"Xinyu Liu;Jierui Huang;Di Zheng;Huanhai Xin;Tianshu Bi","doi":"10.1109/TSTE.2025.3538682","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3538682","url":null,"abstract":"Temporary overvoltage (TOV) severely restricts the development and utilization of renewable power resources (RPRs), especially when RPRs are delivered through the line commutated converter-based high voltage direct current (LCC-HVDC) system. To reveal the TOV mechanism for the sending system during commutation failures (CFs), the transient process of the system is partitioned into different stages, where the evolution of the system trajectories is analyzed. On this basis, the variation of AC voltage and DC current considering complex dynamic interactions between LCC-HVDC and renewable energy Plants (REPs) during repetitive CFs (RCFs) is clearly quantified. After revealing the impact of control parameters of both REPs and the LCC-HVDC on the TOV during RCFs, a collaborative optimization method for control parameters is proposed for TOV suppression. Moreover, when the blocking after the RCF tends to be inevitable, the optimal blocking moment is determined to inhibit the TOV caused by HVDC blocking. The accuracy and effectiveness of the proposed methods are verified with EMT simulations of a typical benchmark system.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1849-1859"},"PeriodicalIF":8.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331631","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 : 2025-02-03DOI: 10.1109/TSTE.2025.3537622
Shoji Nishikata;Fujio Tatsuta
The output power of a wind farm composed of current-source series-connected wind turbine/generators with thyristor rectifier circuits that does not require offshore substation is studied. The steady-state operating characteristics for a single wind turbine/generator are examined first for the IEA 15MW offshore reference wind turbine. Then, dynamic performances for a single wind turbine/generator as well as for a wind farm (WF) consisting of 36 wind turbines are simulated for an average wind speed of 8.65 m/s considering offshore wind turbulence. The simulation results show that the ratio of the standard deviation of the output fluctuation to the average output of single wind turbine is 39.38%, while that of WF is 6.24%, confirming that output leveling effect is achieved.
{"title":"Study on Output Power of Wind Farm Composed of Current-Source Series-Connected Wind Turbines","authors":"Shoji Nishikata;Fujio Tatsuta","doi":"10.1109/TSTE.2025.3537622","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3537622","url":null,"abstract":"The output power of a wind farm composed of current-source series-connected wind turbine/generators with thyristor rectifier circuits that does not require offshore substation is studied. The steady-state operating characteristics for a single wind turbine/generator are examined first for the IEA 15MW offshore reference wind turbine. Then, dynamic performances for a single wind turbine/generator as well as for a wind farm (WF) consisting of 36 wind turbines are simulated for an average wind speed of 8.65 m/s considering offshore wind turbulence. The simulation results show that the ratio of the standard deviation of the output fluctuation to the average output of single wind turbine is 39.38%, while that of WF is 6.24%, confirming that output leveling effect is achieved.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1827-1836"},"PeriodicalIF":8.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331778","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 : 2025-02-03DOI: 10.1109/TSTE.2025.3534589
Feihu Hu;Xuan Feng;Huaiwen Xu;Xinhao Liang;Xuanyuan Wang
Taking into account the orientation and distance characteristics of wind turbine stations in wind farms can improve the accuracy of wind power prediction. This paper proposed a deep learning spatio-temporal prediction method named orthogonal wind direction transformation spatio-temporal gradient Regression Activation Mapping (OWT-STGrad-RAM) for wind speed prediction. The model encodes the wind farm using an image, and each wind turbine is encoded as a point in the image. The spatio-temporal data related to wind turbines, such as wind speed, temperature, and air pressure, are integrated into fusion features through spatio-temporal fusion convolutional networks model for pre training to obtain a feature dataset. OWT is used to eliminate the effects of different prevailing winds, and STGrad-RAM is used to characterize the orientation and distance between wind turbine nodes and make the spatial features interpretable. The feature dataset is used for wind speed prediction. The experimental results show that the proposed method has achieved a significant improvement in wind speed prediction accuracy compared to the comparative models.
{"title":"Ultra-Short-Term Spatio-Temporal Wind Speed Prediction Based on OWT-STGradRAM","authors":"Feihu Hu;Xuan Feng;Huaiwen Xu;Xinhao Liang;Xuanyuan Wang","doi":"10.1109/TSTE.2025.3534589","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3534589","url":null,"abstract":"Taking into account the orientation and distance characteristics of wind turbine stations in wind farms can improve the accuracy of wind power prediction. This paper proposed a deep learning spatio-temporal prediction method named orthogonal wind direction transformation spatio-temporal gradient Regression Activation Mapping (OWT-STGrad-RAM) for wind speed prediction. The model encodes the wind farm using an image, and each wind turbine is encoded as a point in the image. The spatio-temporal data related to wind turbines, such as wind speed, temperature, and air pressure, are integrated into fusion features through spatio-temporal fusion convolutional networks model for pre training to obtain a feature dataset. OWT is used to eliminate the effects of different prevailing winds, and STGrad-RAM is used to characterize the orientation and distance between wind turbine nodes and make the spatial features interpretable. The feature dataset is used for wind speed prediction. The experimental results show that the proposed method has achieved a significant improvement in wind speed prediction accuracy compared to the comparative models.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1816-1826"},"PeriodicalIF":8.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331735","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 : 2025-02-03DOI: 10.1109/TSTE.2025.3537612
Shoujiang Li;Jianzhou Wang;Hui Zhang;Yong Liang
Accurate and reliable solar photovoltaic (PV) power forecasting are crucial for cost-effective resource planning and stable operation of smart grids. However, current methods are affected by the intermittent, non-stationary and stochastic nature of solar energy and thus cannot satisfy the requirement of high-precision forecasting. To this end, we propose a fuzzy cognitive map (FCM) forecasting method based on bubble entropy and smoothly clipped absolute deviation (SCAD) regularization, called BesFCM. This method first utilizes bubble entropy to fuse two mode decomposition methods to improve the representation of PV data to capture effective features with significant stability and discriminative ability, then employs a FCM with a combination of fuzzy logic, neural networks, and expert systems to model solar PV power generation, and finally develops a high order FCM learning method based on SCAD regularization to alleviate the overfitting problem, enhancing the robustness and generalization ability of forecasting. Experimental results demonstrate that the BesFCM achieves the best overall performance on PV power datasets from multiple sampling intervals in multiple regions of Belgium compared to multiple state-of-the-art baselines, validating the effectiveness for solar power generation forecasting, providing support and reference for improving the quality of smart grid dispatch and reducing spare capacity reserves.
{"title":"Learning a Robust Fuzzy Cognitive Map Based on Bubble Entropy Fusion With SCAD Regularization for Solar Power Generation","authors":"Shoujiang Li;Jianzhou Wang;Hui Zhang;Yong Liang","doi":"10.1109/TSTE.2025.3537612","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3537612","url":null,"abstract":"Accurate and reliable solar photovoltaic (PV) power forecasting are crucial for cost-effective resource planning and stable operation of smart grids. However, current methods are affected by the intermittent, non-stationary and stochastic nature of solar energy and thus cannot satisfy the requirement of high-precision forecasting. To this end, we propose a fuzzy cognitive map (FCM) forecasting method based on bubble entropy and smoothly clipped absolute deviation (SCAD) regularization, called BesFCM. This method first utilizes bubble entropy to fuse two mode decomposition methods to improve the representation of PV data to capture effective features with significant stability and discriminative ability, then employs a FCM with a combination of fuzzy logic, neural networks, and expert systems to model solar PV power generation, and finally develops a high order FCM learning method based on SCAD regularization to alleviate the overfitting problem, enhancing the robustness and generalization ability of forecasting. Experimental results demonstrate that the BesFCM achieves the best overall performance on PV power datasets from multiple sampling intervals in multiple regions of Belgium compared to multiple state-of-the-art baselines, validating the effectiveness for solar power generation forecasting, providing support and reference for improving the quality of smart grid dispatch and reducing spare capacity reserves.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1837-1848"},"PeriodicalIF":8.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329500","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 : 2025-01-30DOI: 10.1109/TSTE.2025.3536099
Yiqian Wang;Qi Zhao;Wen Zhang;Tingting Zhang;Xianzhuo Sun;Mingkui Wei;Li Shen;Hua Ye
With the increasing integration of photovoltaics (PV) into power systems, the low-voltage ride-through (LVRT) control of PV grid-connected systems is drawing significant attention. This paper presents a multi-objective bi-level LVRT control strategy for the two-stage PV grid-connected system to maximize the positive and negative sequence voltage support capability while ensuring safe operation under asymmetrical faults. The AC level controls the grid side inverter, while the DC level regulates the boost converter. The grid voltage support control strategy is implemented at the AC level to support the positive and negative sequence voltage of the point of common coupling. Considering there is an inherent contradiction between grid voltage support with the overcurrent of inverter and DC voltage oscillation, the current references are automatically adjusted to facilitate the maximum positive and negative voltage support while limiting the overcurrent and oscillation of DC-link voltage. Based on the power reference shared from the AC level, the DC level regulates the boost converter to stabilize the DC-link voltage speedily by utilizing the compensation current. Finally, simulations and experiments demonstrate the voltage support capability and fast dynamic response characteristics of DC-link voltage in different scenarios.
{"title":"A Multi-Objective Bi-Level LVRT Control Strategy for Two-Stage PV Grid-Connected System Under Asymmetrical Faults","authors":"Yiqian Wang;Qi Zhao;Wen Zhang;Tingting Zhang;Xianzhuo Sun;Mingkui Wei;Li Shen;Hua Ye","doi":"10.1109/TSTE.2025.3536099","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3536099","url":null,"abstract":"With the increasing integration of photovoltaics (PV) into power systems, the low-voltage ride-through (LVRT) control of PV grid-connected systems is drawing significant attention. This paper presents a multi-objective bi-level LVRT control strategy for the two-stage PV grid-connected system to maximize the positive and negative sequence voltage support capability while ensuring safe operation under asymmetrical faults. The AC level controls the grid side inverter, while the DC level regulates the boost converter. The grid voltage support control strategy is implemented at the AC level to support the positive and negative sequence voltage of the point of common coupling. Considering there is an inherent contradiction between grid voltage support with the overcurrent of inverter and DC voltage oscillation, the current references are automatically adjusted to facilitate the maximum positive and negative voltage support while limiting the overcurrent and oscillation of DC-link voltage. Based on the power reference shared from the AC level, the DC level regulates the boost converter to stabilize the DC-link voltage speedily by utilizing the compensation current. Finally, simulations and experiments demonstrate the voltage support capability and fast dynamic response characteristics of DC-link voltage in different scenarios.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1467-1482"},"PeriodicalIF":8.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667385","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 : 2025-01-27DOI: 10.1109/TSTE.2025.3535224
Nan Zhang;Zheren Zhang;Zheng Xu
The extensive integration of renewable energy resources inevitably gives rise to the complex and uncertain power system, where the somber matter of frequency instability becomes apparent. This article presents a coordinated adaptive radial basis function neural network (RBFNN)-based sliding mode control (CAR-SMC) to reduce the frequency deviation and oscillation of the uncertain power system comprising multiple wind farms. Firstly, the SMC is aimed at establishing the upper layer control law of the frequency regulation controllers. Then, the uncertainties are represented with RBFNN, and an adaptive law is employed to estimate the uncertainties online rapidly and realize the free-chattering of SMC. Furthermore, since a single SMC is only capable of handling a single control input system, a power distribution law based on momentum is proposed to implement the multiple control inputs of the AR-SMC, and also coordinate the frequency regulation abilities of wind turbines and energy storage systems (ESSs). Eventually, the proposed CAR-SMC is validated on a modified IEEE 39-bus system. The simulation results demonstrate that CAR-SMC can enhance the frequency stability in the presence of disturbances and uncertainties during steady-state operation, as well as in under-frequency and over-frequency scenarios.
{"title":"A Coordinated Adaptive SMC Method for Frequency Regulation Control in Power Systems With Multiple Wind Farms","authors":"Nan Zhang;Zheren Zhang;Zheng Xu","doi":"10.1109/TSTE.2025.3535224","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3535224","url":null,"abstract":"The extensive integration of renewable energy resources inevitably gives rise to the complex and uncertain power system, where the somber matter of frequency instability becomes apparent. This article presents a coordinated adaptive radial basis function neural network (RBFNN)-based sliding mode control (CAR-SMC) to reduce the frequency deviation and oscillation of the uncertain power system comprising multiple wind farms. Firstly, the SMC is aimed at establishing the upper layer control law of the frequency regulation controllers. Then, the uncertainties are represented with RBFNN, and an adaptive law is employed to estimate the uncertainties online rapidly and realize the free-chattering of SMC. Furthermore, since a single SMC is only capable of handling a single control input system, a power distribution law based on momentum is proposed to implement the multiple control inputs of the AR-SMC, and also coordinate the frequency regulation abilities of wind turbines and energy storage systems (ESSs). Eventually, the proposed CAR-SMC is validated on a modified IEEE 39-bus system. The simulation results demonstrate that CAR-SMC can enhance the frequency stability in the presence of disturbances and uncertainties during steady-state operation, as well as in under-frequency and over-frequency scenarios.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1806-1815"},"PeriodicalIF":8.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502910","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 : 2025-01-27DOI: 10.1109/TSTE.2025.3533893
Kaiyuan Su;Xi Wang;Xiaorong Xie
To advance carbon reduction of the offshore oilfield power system (OOPS), the grid-forming undersea pumped storage system (GFM-UPSS) emerges as a promising solution. This paper introduces a novel framework for a 100% renewable OOPS utilizing the GFM-UPSS. Firstly, the control strategy of the GFM-UPSS is presented. It consists of the grid-side converter (GSC), machine-side converter (MSC), and reversible pump-turbine (RPT) to achieve frequency and voltage regulation. A steady-state model is then developed detailing the water head, power, and volume of the spherical shell. In addition, the paper explores the converter parameter impacts on the GFM-UPSS transient model and derives the closed-form solutions. With the steady-state model, an optimal sizing method is presented and economic advantages in the marine environment are studied for the GFM-UPSS. Finally, EMT simulations are conducted to assess the frequency & voltage stabilities and verify the effectiveness of the GFM-UPSS in enabling a 100% renewable OOPS. The optimal sizing results show that construction costs, mainly for OWP, are dominated and are influenced by sphere radius, placement depth, and start-stop cycles, while a 2.5 capacity ratio between OWP and GFM-UPSS consistently emerges as optimal. Moreover, analysis of transient stability shows that it improves with higher frequency & voltage modulation coefficient and lower virtual impedance. The impact of RPT and MSC, mainly on frequency regulation, is determined by the DC droop coefficient and turbine inertia.
{"title":"Advanced Grid-Forming Undersea Pumped Storage to Enable 100% Renewable Offshore Oilfield Power Systems","authors":"Kaiyuan Su;Xi Wang;Xiaorong Xie","doi":"10.1109/TSTE.2025.3533893","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3533893","url":null,"abstract":"To advance carbon reduction of the offshore oilfield power system (OOPS), the grid-forming undersea pumped storage system (GFM-UPSS) emerges as a promising solution. This paper introduces a novel framework for a 100% renewable OOPS utilizing the GFM-UPSS. Firstly, the control strategy of the GFM-UPSS is presented. It consists of the grid-side converter (GSC), machine-side converter (MSC), and reversible pump-turbine (RPT) to achieve frequency and voltage regulation. A steady-state model is then developed detailing the water head, power, and volume of the spherical shell. In addition, the paper explores the converter parameter impacts on the GFM-UPSS transient model and derives the closed-form solutions. With the steady-state model, an optimal sizing method is presented and economic advantages in the marine environment are studied for the GFM-UPSS. Finally, EMT simulations are conducted to assess the frequency & voltage stabilities and verify the effectiveness of the GFM-UPSS in enabling a 100% renewable OOPS. The optimal sizing results show that construction costs, mainly for OWP, are dominated and are influenced by sphere radius, placement depth, and start-stop cycles, while a 2.5 capacity ratio between OWP and GFM-UPSS consistently emerges as optimal. Moreover, analysis of transient stability shows that it improves with higher frequency & voltage modulation coefficient and lower virtual impedance. The impact of RPT and MSC, mainly on frequency regulation, is determined by the DC droop coefficient and turbine inertia.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1791-1805"},"PeriodicalIF":8.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331701","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 : 2025-01-27DOI: 10.1109/TSTE.2025.3533929
Kuan Zhang;Yawen Xie;Nian Liu;Siqi Chen
This paper proposes a customized incentive compatible mean field game (MFG) method for virtual power plant (VPP) with a large number of self-interest heterogeneous distributed energy resources (DERs) to participate in the real-time peak regulation. Firstly, an optimal chance-constrained peak-regulation bidding model of VPP considering the stochastic power flexibility is formulated, where inscribed pyramid approximation method is utilized to form a compact and concise dispatch region. Secondly, a customized MFG method with dynamic granulation division is proposed for encouraging very large-scale DERs to spontaneously respond to the peak regulation instructions from VPP while achieving dynamic allocation of peak-regulation revenue. Brouwer fixed-point theorem and contraction mapping theorem are used to prove the existence and uniqueness of the mean field equilibrium (MFE) of the formulated MFG, and ϵ-Nash property of MFE is validated based on the Lipschitz continuity condition. Furthermore, an accelerated decentralized solution algorithm is developed to rapidly search MFE, exhibiting good scalability. Comparative studies have validated the superiority of the proposed methodology on incentive compatibility and decomposition efficiency of the VPP's peak-regulation instructions.
{"title":"Customized Mean Field Game Method of Virtual Power Plant for Real-Time Peak Regulation","authors":"Kuan Zhang;Yawen Xie;Nian Liu;Siqi Chen","doi":"10.1109/TSTE.2025.3533929","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3533929","url":null,"abstract":"This paper proposes a customized incentive compatible mean field game (MFG) method for virtual power plant (VPP) with a large number of self-interest heterogeneous distributed energy resources (DERs) to participate in the real-time peak regulation. Firstly, an optimal chance-constrained peak-regulation bidding model of VPP considering the stochastic power flexibility is formulated, where inscribed pyramid approximation method is utilized to form a compact and concise dispatch region. Secondly, a customized MFG method with dynamic granulation division is proposed for encouraging very large-scale DERs to spontaneously respond to the peak regulation instructions from VPP while achieving dynamic allocation of peak-regulation revenue. Brouwer fixed-point theorem and contraction mapping theorem are used to prove the existence and uniqueness of the mean field equilibrium (MFE) of the formulated MFG, and ϵ-Nash property of MFE is validated based on the Lipschitz continuity condition. Furthermore, an accelerated decentralized solution algorithm is developed to rapidly search MFE, exhibiting good scalability. Comparative studies have validated the superiority of the proposed methodology on incentive compatibility and decomposition efficiency of the VPP's peak-regulation instructions.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1453-1466"},"PeriodicalIF":8.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667583","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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