Pub Date : 2024-08-08DOI: 10.1109/TSTE.2024.3440331
Ting Yang;Jilin Lang;Hao Li
This paper presents a semi-peer coordination control strategy to ensure the bus voltage stability and effectively constrain the power trajectory, thereby mitigating safety concerns arising from excessive unit power and communication failures in distributed battery energy storage systems (DBESS) based DC microgrids. Firstly, the primary controller is employed a saturated feedforward design to maintain bus voltage stability and address the excessive part of power allocation with droop control. The saturation results enable flexible switching of the reference state, allowing energy storage units (ESUs) to autonomously transition between voltage tracking and power tracking modes. Secondly, the dual-dynamic power allocation strategy is introduced with distributed consensus and saturation allocation.The power allocation with distributed consensus aims to achieve synchronous proportional charging and discharging for SOC balancing. For the offline ESUs of communication failures, saturation power allocation is designed with arrived operation point to avoid the over-utilization of offline ESUs. To address potential communication failures in offline ESUs, the saturation power allocation strategy based on the current operational point is devised to mitigate the risk of over-utilization of offline ESUs. Finally, simulations and experimental results verify the effectiveness of the proposed method.
{"title":"Semi-Peer-to-Peer Safety Coordination Control for Distributed Battery Energy Storage System in DC Microgrids via Saturated Limitation","authors":"Ting Yang;Jilin Lang;Hao Li","doi":"10.1109/TSTE.2024.3440331","DOIUrl":"10.1109/TSTE.2024.3440331","url":null,"abstract":"This paper presents a semi-peer coordination control strategy to ensure the bus voltage stability and effectively constrain the power trajectory, thereby mitigating safety concerns arising from excessive unit power and communication failures in distributed battery energy storage systems (DBESS) based DC microgrids. Firstly, the primary controller is employed a saturated feedforward design to maintain bus voltage stability and address the excessive part of power allocation with droop control. The saturation results enable flexible switching of the reference state, allowing energy storage units (ESUs) to autonomously transition between voltage tracking and power tracking modes. Secondly, the dual-dynamic power allocation strategy is introduced with distributed consensus and saturation allocation.The power allocation with distributed consensus aims to achieve synchronous proportional charging and discharging for SOC balancing. For the offline ESUs of communication failures, saturation power allocation is designed with arrived operation point to avoid the over-utilization of offline ESUs. To address potential communication failures in offline ESUs, the saturation power allocation strategy based on the current operational point is devised to mitigate the risk of over-utilization of offline ESUs. Finally, simulations and experimental results verify the effectiveness of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2733-2744"},"PeriodicalIF":8.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943724","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 : 2024-08-07DOI: 10.1109/TSTE.2024.3438457
Hongyuan Liang;Zhigang Li;Mohammad Shahidehpour;Nianjie Tian;Youquan Jiang;J. H. Zheng;Jisong Zhu
Energy storage is becoming increasingly important in power and energy systems. However, its strongly nonconvex complementarity constraints, which prevent simultaneous charging or discharging behavior, hinder its application in optimization-based decision making. One remedy is to relax these constraints, but the existing relaxation methods are specific to power system applications with limited universality. To bridge this gap, we provide a methodology to derive the general form of sufficient conditions for the exact relaxation of a general energy storage-concerned optimization problem (ESCOP). Specific sufficient conditions for a wide range of ESCOPs can be easily accessed via the proposed methodology. This paper provides mathematical proofs and analyses of the proposed conditions, where sufficient conditions obtained from specific forms of ESCOPs are numerically validated to guarantee exact relaxation and significantly improve the ESCOP solution efficiency.
{"title":"Deriving Sufficient Conditions for Exact Relaxation of Complementarity Constraints in Optimization Problems With Energy Storage","authors":"Hongyuan Liang;Zhigang Li;Mohammad Shahidehpour;Nianjie Tian;Youquan Jiang;J. H. Zheng;Jisong Zhu","doi":"10.1109/TSTE.2024.3438457","DOIUrl":"10.1109/TSTE.2024.3438457","url":null,"abstract":"Energy storage is becoming increasingly important in power and energy systems. However, its strongly nonconvex complementarity constraints, which prevent simultaneous charging or discharging behavior, hinder its application in optimization-based decision making. One remedy is to relax these constraints, but the existing relaxation methods are specific to power system applications with limited universality. To bridge this gap, we provide a methodology to derive the general form of sufficient conditions for the exact relaxation of a general energy storage-concerned optimization problem (ESCOP). Specific sufficient conditions for a wide range of ESCOPs can be easily accessed via the proposed methodology. This paper provides mathematical proofs and analyses of the proposed conditions, where sufficient conditions obtained from specific forms of ESCOPs are numerically validated to guarantee exact relaxation and significantly improve the ESCOP solution efficiency.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2690-2702"},"PeriodicalIF":8.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943726","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 : 2024-08-06DOI: 10.1109/TSTE.2024.3439512
Jie Wang;Xiaolong Jin;Hongjie Jia;Marcos Tostado-Véliz;Yunfei Mu;Xiaodan Yu;Shuo Liang
This paper proposes a joint electricity and carbon sharing framework with photovoltaic (PV) and energy storage system (ESS) for deep decarbonization, allowing distributed PV prosumers to participate in a sharing network established by aggregator of prosumers (AOP). The ESS-equipped AOP plays multiple roles as a carbon aggregator, an ESS operator, and an energy-sharing provider at the same time. First, a demand response (DR)-based model that incorporates the multi-strategy of ESS is proposed to optimize energy-carbon transaction. A low-carbon DR with consideration of electricity-carbon coupling is developed to incentivize prosumers to adjust consumption behavior for costs and emissions reduction. Second, a joint optimization based on Stackelberg game is proposed, where AOP is leader, and prosumers act as followers. A dynamic pricing mechanism is proposed for AOP to determine the electricity-carbon coupled selling and buying prices simultaneously. Meanwhile, prosumers would adjust their energy consumption as response to different sharing prices. In addition, a distributed optimization algorithm with interactions is used to reach the Stackelberg game equilibrium. Finally, through a practical testing case, the effectiveness of the method is validated in terms of economic benefits and PV sharing enhancement, as well as the reduction of carbon emissions.
{"title":"Joint Electricity and Carbon Sharing With PV and Energy Storage: A Low-Carbon DR-Based Game Theoretic Approach","authors":"Jie Wang;Xiaolong Jin;Hongjie Jia;Marcos Tostado-Véliz;Yunfei Mu;Xiaodan Yu;Shuo Liang","doi":"10.1109/TSTE.2024.3439512","DOIUrl":"10.1109/TSTE.2024.3439512","url":null,"abstract":"This paper proposes a joint electricity and carbon sharing framework with photovoltaic (PV) and energy storage system (ESS) for deep decarbonization, allowing distributed PV prosumers to participate in a sharing network established by aggregator of prosumers (AOP). The ESS-equipped AOP plays multiple roles as a carbon aggregator, an ESS operator, and an energy-sharing provider at the same time. First, a demand response (DR)-based model that incorporates the multi-strategy of ESS is proposed to optimize energy-carbon transaction. A low-carbon DR with consideration of electricity-carbon coupling is developed to incentivize prosumers to adjust consumption behavior for costs and emissions reduction. Second, a joint optimization based on Stackelberg game is proposed, where AOP is leader, and prosumers act as followers. A dynamic pricing mechanism is proposed for AOP to determine the electricity-carbon coupled selling and buying prices simultaneously. Meanwhile, prosumers would adjust their energy consumption as response to different sharing prices. In addition, a distributed optimization algorithm with interactions is used to reach the Stackelberg game equilibrium. Finally, through a practical testing case, the effectiveness of the method is validated in terms of economic benefits and PV sharing enhancement, as well as the reduction of carbon emissions.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2703-2717"},"PeriodicalIF":8.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943732","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 : 2024-08-02DOI: 10.1109/TSTE.2024.3437210
Yijie Yang;Jian Shi;Dan Wang;Chenye Wu;Zhu Han
Buildings produce a significant share of greenhouse gas (GHG) emissions, making homes and businesses a major factor in climate change. To address this critical challenge, this paper explores achieving net-zero emission through the carbon-aware optimal scheduling of the multi-energy building integrated energy systems (BIES). We integrate advanced technologies and strategies, such as the carbon capture system (CCS), power-to-gas (P2G), carbon tracking, and emission allowance trading, into the traditional BIES scheduling problem. The proposed model enables accurate accounting of carbon emissions associated with building energy systems and facilitates the implementation of low-carbon operations. Furthermore, to address the challenge of accurately assessing uncertainty sets related to forecasting errors of loads, generation, and carbon intensity, we develop a learning-based robust optimization approach for BIES that is robust in the presence of uncertainty and guarantees statistical feasibility. The proposed approach comprises a shape learning stage and a shape calibration stage to generate an optimal uncertainty set that ensures favorable results from a statistical perspective. Numerical studies conducted based on both synthetic and real-world datasets have demonstrated that the approach yields up to 8.2% cost reduction, compared with conventional methods, in assisting buildings to robustly reach net-zero emissions.
{"title":"Net-Zero Scheduling of Multi-Energy Building Energy Systems: A Learning-Based Robust Optimization Approach With Statistical Guarantees","authors":"Yijie Yang;Jian Shi;Dan Wang;Chenye Wu;Zhu Han","doi":"10.1109/TSTE.2024.3437210","DOIUrl":"10.1109/TSTE.2024.3437210","url":null,"abstract":"Buildings produce a significant share of greenhouse gas (GHG) emissions, making homes and businesses a major factor in climate change. To address this critical challenge, this paper explores achieving net-zero emission through the carbon-aware optimal scheduling of the multi-energy building integrated energy systems (BIES). We integrate advanced technologies and strategies, such as the carbon capture system (CCS), power-to-gas (P2G), carbon tracking, and emission allowance trading, into the traditional BIES scheduling problem. The proposed model enables accurate accounting of carbon emissions associated with building energy systems and facilitates the implementation of low-carbon operations. Furthermore, to address the challenge of accurately assessing uncertainty sets related to forecasting errors of loads, generation, and carbon intensity, we develop a learning-based robust optimization approach for BIES that is robust in the presence of uncertainty and guarantees statistical feasibility. The proposed approach comprises a shape learning stage and a shape calibration stage to generate an optimal uncertainty set that ensures favorable results from a statistical perspective. Numerical studies conducted based on both synthetic and real-world datasets have demonstrated that the approach yields up to 8.2% cost reduction, compared with conventional methods, in assisting buildings to robustly reach net-zero emissions.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2675-2689"},"PeriodicalIF":8.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881464","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 : 2024-08-02DOI: 10.1109/TSTE.2024.3424932
Yuhao Li;Han Wang;Jie Yan;Chang Ge;Shuang Han;Yongqian Liu
Ultra-short-term wind power forecasting plays a vital role in real-time scheduling, frequency regulation, and intraday market transactions. Due to the complexity of weather systems, unit aging, wind farm control strategies, etc., the temporal dependency relationship in wind power series changes from time to time (known as concept drift), which leads to the low forecasting accuracy of the commonly used offline modeling methods. Online modeling can effectively deal with concept drift by utilizing the latest information in the flow data and capturing the latest concepts during the modeling process. However, the existing online modeling methods cannot meet the timeliness requirements of the power grid for ultra-short-term wind power forecasting. Therefore, a strategy of “dynamic matching and online modeling” for ultra-short-term wind power forecasting is proposed in this paper. Training samples are dynamically selected according to the characteristic similarity of amplitude and fluctuation, aiming to improve the representativeness of samples and reduce the training time simultaneously. In addition to historical power, Numerical Weather Prediction wind speed is also introduced in the process of “dynamic matching” to improve the forecasting accuracy. Operation data from three wind farms in China is used to validate the effectiveness and robustness of the proposed method. The results show that the forecasting accuracy can be improved by 1.18%–4.32% for 4 hours in advance.
{"title":"Ultra-Short-Term Wind Power Forecasting Based on the Strategy of “Dynamic Matching and Online Modeling”","authors":"Yuhao Li;Han Wang;Jie Yan;Chang Ge;Shuang Han;Yongqian Liu","doi":"10.1109/TSTE.2024.3424932","DOIUrl":"10.1109/TSTE.2024.3424932","url":null,"abstract":"Ultra-short-term wind power forecasting plays a vital role in real-time scheduling, frequency regulation, and intraday market transactions. Due to the complexity of weather systems, unit aging, wind farm control strategies, etc., the temporal dependency relationship in wind power series changes from time to time (known as concept drift), which leads to the low forecasting accuracy of the commonly used offline modeling methods. Online modeling can effectively deal with concept drift by utilizing the latest information in the flow data and capturing the latest concepts during the modeling process. However, the existing online modeling methods cannot meet the timeliness requirements of the power grid for ultra-short-term wind power forecasting. Therefore, a strategy of “dynamic matching and online modeling” for ultra-short-term wind power forecasting is proposed in this paper. Training samples are dynamically selected according to the characteristic similarity of amplitude and fluctuation, aiming to improve the representativeness of samples and reduce the training time simultaneously. In addition to historical power, Numerical Weather Prediction wind speed is also introduced in the process of “dynamic matching” to improve the forecasting accuracy. Operation data from three wind farms in China is used to validate the effectiveness and robustness of the proposed method. The results show that the forecasting accuracy can be improved by 1.18%–4.32% for 4 hours in advance.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"107-123"},"PeriodicalIF":8.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881465","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 : 2024-08-02DOI: 10.1109/TSTE.2024.3437758
Jiaqing Zhai;Li Guo;Zhongguan Wang;Xialin Li;Yixin Liu;Chengshan Wang
Distributed energy resources (DERs) integrated in active distribution networks (ADNs) participating in primary frequency regulation (PFR) service can enhance frequency safety and stability of power systems. However, PFR service can result in power flow (PF) insecurity issues, especially in low and medium-voltage networks without accurate line parameters. To address the problem, this paper proposes a coordinated control architecture based on the Koopman data-driven power flow. The cluster model training layer uses Koopman operator theory to transform the original complex nonlinear PF model into a dimension-augmented linear PF model. The online PFR optimization layer constructs an optimization model of PFR based on the data-driven PF, considering security constraints of ADNs. The local frequency response layer responds to frequency change in real-time and ensures fast frequency support. This method is validated using a modified IEEE 82-node test case, which demonstrates that it has the advantages of fast online solving, and independence on model parameters. The proposed method can fully exploit PFR capability of ADN and achieve the optimal PF profiles while ensuring the aggregate PFR characteristics.
{"title":"Coordinated Frequency Regulation of Active Distribution Networks Considering Dimension-Augmented Power Flow Constraints","authors":"Jiaqing Zhai;Li Guo;Zhongguan Wang;Xialin Li;Yixin Liu;Chengshan Wang","doi":"10.1109/TSTE.2024.3437758","DOIUrl":"10.1109/TSTE.2024.3437758","url":null,"abstract":"Distributed energy resources (DERs) integrated in active distribution networks (ADNs) participating in primary frequency regulation (PFR) service can enhance frequency safety and stability of power systems. However, PFR service can result in power flow (PF) insecurity issues, especially in low and medium-voltage networks without accurate line parameters. To address the problem, this paper proposes a coordinated control architecture based on the Koopman data-driven power flow. The cluster model training layer uses Koopman operator theory to transform the original complex nonlinear PF model into a dimension-augmented linear PF model. The online PFR optimization layer constructs an optimization model of PFR based on the data-driven PF, considering security constraints of ADNs. The local frequency response layer responds to frequency change in real-time and ensures fast frequency support. This method is validated using a modified IEEE 82-node test case, which demonstrates that it has the advantages of fast online solving, and independence on model parameters. The proposed method can fully exploit PFR capability of ADN and achieve the optimal PF profiles while ensuring the aggregate PFR characteristics.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"138-148"},"PeriodicalIF":8.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881530","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 : 2024-08-02DOI: 10.1109/TSTE.2024.3434995
Ke Wang;Yixun Xue;Mohammad Shahidehpour;Xinyue Chang;Zening Li;Yue Zhou;Hongbin Sun
The inherent linkage between a power distribution system (PDS) and a district heating system (DHS) necessitates coordinated load restoration after natural disasters. To guarantee optimal load restoration during a recovery process, a coordinated dispatch strategy of the maintenance crew for the PDS/DHS considering the optimal reconfiguration of their respective networks is proposed in this paper. The proposed solution focuses on the intricate mutual interaction of the DHS and PDS and coordinates the fault isolation and service restoration stages. The proposed optimization is modeled as a mixed-integer second-order cone problem (MISOCP), which contains numerous integer variables. To lessen the computational burden, a two-stage acceleration algorithm is proposed, which divides the solution procedure into two stages based on two types of integer variables: load status variables and variables associated with the maintenance path and network topology. Then, the acceleration principles are proposed to determine the load status variables. The effectiveness and accuracy of the proposed model are validated by extensive cases, which demonstrate the performance of the coordinated maintenance and reconfiguration in integrated energy systems for fault recovery.
{"title":"Resilience-Oriented Two-Stage Restoration Considering Coordinated Maintenance and Reconfiguration in Integrated Power Distribution and Heating Systems","authors":"Ke Wang;Yixun Xue;Mohammad Shahidehpour;Xinyue Chang;Zening Li;Yue Zhou;Hongbin Sun","doi":"10.1109/TSTE.2024.3434995","DOIUrl":"10.1109/TSTE.2024.3434995","url":null,"abstract":"The inherent linkage between a power distribution system (PDS) and a district heating system (DHS) necessitates coordinated load restoration after natural disasters. To guarantee optimal load restoration during a recovery process, a coordinated dispatch strategy of the maintenance crew for the PDS/DHS considering the optimal reconfiguration of their respective networks is proposed in this paper. The proposed solution focuses on the intricate mutual interaction of the DHS and PDS and coordinates the fault isolation and service restoration stages. The proposed optimization is modeled as a mixed-integer second-order cone problem (MISOCP), which contains numerous integer variables. To lessen the computational burden, a two-stage acceleration algorithm is proposed, which divides the solution procedure into two stages based on two types of integer variables: load status variables and variables associated with the maintenance path and network topology. Then, the acceleration principles are proposed to determine the load status variables. The effectiveness and accuracy of the proposed model are validated by extensive cases, which demonstrate the performance of the coordinated maintenance and reconfiguration in integrated energy systems for fault recovery.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"124-137"},"PeriodicalIF":8.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881463","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}
Accurate wind power prediction (WPP) is crucial to the secure and stable operation of large-scale power systems, and data-driven WPP methods have recently been widely studied and applied. However, existing data-driven methods cannot be applied to new wind farms due to the lack of operational data. This paper presents a novel Bayesian deep learning-based adaptive wind farm power prediction (BDL-AWFPP) method, which is the first time to utilize the computational fluid dynamics (CFD) simulation results as the prior of BDL-based method, thus avoiding the problem that data-driven approaches cannot be applied to newly constructed wind farms. Firstly, a CFD-based wind farm numerical simulation database and a wind turbine power curve database are established to construct a multi-source heterogeneous prior dataset. Then, the BDL-AWFPP model is proposed to utilize the multi-source heterogeneous prior dataset, which can be updated adaptively with newly acquired operational data and saved periodically throughout the life cycle. And an auxiliary aging assessment method for wind turbines is also developed according to the periodically-saved models. Finally, a stochastic variational inference (SVI)-based parameter updating algorithm is derived for the proposed BDL-AWFPP model. Case studies on an actual wind farm validate the effectiveness of the proposed method.
{"title":"A Bayesian Deep Learning-Based Adaptive Wind Farm Power Prediction Method Within the Entire Life Cycle","authors":"Xiaoming Liu;Jun Liu;Yu Zhao;Yongxin Nie;Jiacheng Liu;Tao Ding","doi":"10.1109/TSTE.2024.3435936","DOIUrl":"10.1109/TSTE.2024.3435936","url":null,"abstract":"Accurate wind power prediction (WPP) is crucial to the secure and stable operation of large-scale power systems, and data-driven WPP methods have recently been widely studied and applied. However, existing data-driven methods cannot be applied to new wind farms due to the lack of operational data. This paper presents a novel Bayesian deep learning-based adaptive wind farm power prediction (BDL-AWFPP) method, which is the first time to utilize the computational fluid dynamics (CFD) simulation results as the prior of BDL-based method, thus avoiding the problem that data-driven approaches cannot be applied to newly constructed wind farms. Firstly, a CFD-based wind farm numerical simulation database and a wind turbine power curve database are established to construct a multi-source heterogeneous prior dataset. Then, the BDL-AWFPP model is proposed to utilize the multi-source heterogeneous prior dataset, which can be updated adaptively with newly acquired operational data and saved periodically throughout the life cycle. And an auxiliary aging assessment method for wind turbines is also developed according to the periodically-saved models. Finally, a stochastic variational inference (SVI)-based parameter updating algorithm is derived for the proposed BDL-AWFPP model. Case studies on an actual wind farm validate the effectiveness of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2663-2674"},"PeriodicalIF":8.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866316","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 : 2024-07-26DOI: 10.1109/TSTE.2024.3434493
Derick Mathew;J. Prasanth Ram;Jihun Ha;Jung-Wook Park;Young-Jin Kim
The conventional de-load power tracking algorithm, utilizing a perturb and observe method, manifests deficiencies in terms of speed, stability, and efficacy in identifying operating points within the inverter's voltage range. In this article, the Adaptive Multi-Mode Single-Step Power Tracking (AMSPT) algorithm is introduced, showcasing rapid adaptability to varying solar irradiation conditions, while mitigating energy losses and enhancing overall operational stability. Its key innovation lies in efficiently pinpointing the operating point within the inverter's specified voltage range through a single step. Upon achieving the desired operating point, the algorithm promptly suppresses oscillatory behavior, expediting the settling process and minimizing deviations around the set-point. This article substantiates the superiority of the AMSPT algorithm over existing methods, showcasing remarkable advancements in tracking accuracy, power fluctuations, and energy discrepancies across diverse PV system case studies. Comprehensive validation through theoretical analysis, simulations, and experimental setups meticulously confirms the claimed benefits of the proposed method.
{"title":"Adaptive Multi-Mode Single-Step Power Tracking for Microinverter-Based Photovoltaic System","authors":"Derick Mathew;J. Prasanth Ram;Jihun Ha;Jung-Wook Park;Young-Jin Kim","doi":"10.1109/TSTE.2024.3434493","DOIUrl":"10.1109/TSTE.2024.3434493","url":null,"abstract":"The conventional de-load power tracking algorithm, utilizing a perturb and observe method, manifests deficiencies in terms of speed, stability, and efficacy in identifying operating points within the inverter's voltage range. In this article, the Adaptive Multi-Mode Single-Step Power Tracking (AMSPT) algorithm is introduced, showcasing rapid adaptability to varying solar irradiation conditions, while mitigating energy losses and enhancing overall operational stability. Its key innovation lies in efficiently pinpointing the operating point within the inverter's specified voltage range through a single step. Upon achieving the desired operating point, the algorithm promptly suppresses oscillatory behavior, expediting the settling process and minimizing deviations around the set-point. This article substantiates the superiority of the AMSPT algorithm over existing methods, showcasing remarkable advancements in tracking accuracy, power fluctuations, and energy discrepancies across diverse PV system case studies. Comprehensive validation through theoretical analysis, simulations, and experimental setups meticulously confirms the claimed benefits of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2651-2662"},"PeriodicalIF":8.6,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780213","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 : 2024-07-25DOI: 10.1109/TSTE.2024.3433611
Haihan Ye;Wu Chen;Heng Wu
Under the scenario of offshore wind power trans- mission, the system the wind power plant connecting to a modular multilevel converter (WPP-MMC) under faults is investigated, the fault interactions are modeled, and its impact on the existence of stable equilibrium point (SEP) is revealed, as a prerequisite for maintaining synchronization during fault ride-through. Consider- ing that both WPP and MMC have their own control dynamics, e.g., reactive current limiting mode or low voltage ride-through mode for WPP and voltage source mode or current source mode for MMC, the WPP-MMC system may exhibit multiple operating conditions, based on which the fault interactions inside the system become more complex and diverse. Focusing on this phenomenon, this paper analyzes the existence of SEP under each operating condition, so that the optimal parameter settings for maintaining synchronization can be obtained. Moreover, this paper finds an interesting phenomenon that multiple SEPs may exist under the same fault, which brings fresh reflections on the application of classic theoretical tools in WPP-MMC systems. Finally, the correctness of the theoretical analysis and parameter settings is verified by simulation results.
{"title":"Analysis of the Existence of Stable Equilibrium Points in the WPP-MMC System Under Symmetrical AC Fault","authors":"Haihan Ye;Wu Chen;Heng Wu","doi":"10.1109/TSTE.2024.3433611","DOIUrl":"10.1109/TSTE.2024.3433611","url":null,"abstract":"Under the scenario of offshore wind power trans- mission, the system the wind power plant connecting to a modular multilevel converter (WPP-MMC) under faults is investigated, the fault interactions are modeled, and its impact on the existence of stable equilibrium point (SEP) is revealed, as a prerequisite for maintaining synchronization during fault ride-through. Consider- ing that both WPP and MMC have their own control dynamics, e.g., reactive current limiting mode or low voltage ride-through mode for WPP and voltage source mode or current source mode for MMC, the WPP-MMC system may exhibit multiple operating conditions, based on which the fault interactions inside the system become more complex and diverse. Focusing on this phenomenon, this paper analyzes the existence of SEP under each operating condition, so that the optimal parameter settings for maintaining synchronization can be obtained. Moreover, this paper finds an interesting phenomenon that multiple SEPs may exist under the same fault, which brings fresh reflections on the application of classic theoretical tools in WPP-MMC systems. Finally, the correctness of the theoretical analysis and parameter settings is verified by simulation results.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"95-106"},"PeriodicalIF":8.6,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780214","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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