Pub Date : 2024-08-21DOI: 10.1109/TSTE.2024.3447023
Keunju Song;Minsoo Kim;Hongseok Kim
In recent years, power systems integrated with distributed energy resources (DERs) have been considered to mitigate climate change. However, this makes power systems even more uncertain and complex, so uncertainty-aware accurate forecasting needs to be considered for the massive penetration of renewable energy. To this end, we propose a scalable and missing-insensitive framework for probabilistic multi-site photovoltaic (PV) power forecasting, specifically focused on large-scale PV sites and space-time missing data. By leveraging the graph neural network (GNN), the proposed scalable graph learning mechanism with random coarse graph attention and probabilistic spatio-temporal learning performs efficiently for large-scale PV sites in terms of forecasting accuracy and model training complexity. At the same time, our framework adaptively imputes the missing PV data in the space and time domain, respectively. Ablation study results demonstrate that our framework is effective for extracting complex spatial-temporal features across large-scale PV sites. Under extensive experiments, our framework shows 7 $-$ 10% and 6 $-$ 25% improvement on average for over 1600 PV sites and three types of space-time missing data, which ensures accurate and stable forecasting.
{"title":"Graph-Based Large Scale Probabilistic PV Power Forecasting Insensitive to Space-Time Missing Data","authors":"Keunju Song;Minsoo Kim;Hongseok Kim","doi":"10.1109/TSTE.2024.3447023","DOIUrl":"10.1109/TSTE.2024.3447023","url":null,"abstract":"In recent years, power systems integrated with distributed energy resources (DERs) have been considered to mitigate climate change. However, this makes power systems even more uncertain and complex, so uncertainty-aware accurate forecasting needs to be considered for the massive penetration of renewable energy. To this end, we propose a scalable and missing-insensitive framework for probabilistic multi-site photovoltaic (PV) power forecasting, specifically focused on large-scale PV sites and space-time missing data. By leveraging the graph neural network (GNN), the proposed scalable graph learning mechanism with random coarse graph attention and probabilistic spatio-temporal learning performs efficiently for large-scale PV sites in terms of forecasting accuracy and model training complexity. At the same time, our framework adaptively imputes the missing PV data in the space and time domain, respectively. Ablation study results demonstrate that our framework is effective for extracting complex spatial-temporal features across large-scale PV sites. Under extensive experiments, our framework shows 7\u0000<inline-formula><tex-math>$-$</tex-math></inline-formula>\u000010% and 6\u0000<inline-formula><tex-math>$-$</tex-math></inline-formula>\u000025% improvement on average for over 1600 PV sites and three types of space-time missing data, which ensures accurate and stable forecasting.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"160-173"},"PeriodicalIF":8.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219459","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 : 2024-08-16DOI: 10.1109/TSTE.2024.3443117
Weihan Lin;Xiaofan Li;Lei Zuo
The shape of the floating buoy of a point absorber wave energy converter (WEC) plays a crucial role in both wave energy harvesting and current drag reduction. In this study, an approach to optimizing the buoy hull geometry with a neural network that replaces the hydrodynamic analysis software is presented, aimed at reducing the ocean current drag force while improving wave energy captured. A new parametric model is introduced to describe the complex shape of the buoy by utilizing the control points of non-uniform rational b-splines. A neural network is developed to significantly reduce the computational time compared to traditional hydrodynamic simulation methods. The optimal hull shape of the buoy is determined by solving an optimization problem using a genetic algorithm, a global optimization technique. The results of the case studies show that the optimal buoy hull shape reduces 68.7% and 71.1% of the current drag, and 50% of mooring line forces compared to the cylinder-shaped buoy and the optimal-power-shaped hull from literature. The optimal buoy hull shape increases the wave energy extraction by 46.1% compared to the thin-ship-shaped buoy but performs 21.1% worse than the optimal-power-shaped hull from the literature.
{"title":"Shape Optimization of a Point Absorber Wave Energy Converter for Reduced Current Drag and Improved Wave Energy Capture Using Neural Networks and Genetic Algorithms","authors":"Weihan Lin;Xiaofan Li;Lei Zuo","doi":"10.1109/TSTE.2024.3443117","DOIUrl":"10.1109/TSTE.2024.3443117","url":null,"abstract":"The shape of the floating buoy of a point absorber wave energy converter (WEC) plays a crucial role in both wave energy harvesting and current drag reduction. In this study, an approach to optimizing the buoy hull geometry with a neural network that replaces the hydrodynamic analysis software is presented, aimed at reducing the ocean current drag force while improving wave energy captured. A new parametric model is introduced to describe the complex shape of the buoy by utilizing the control points of non-uniform rational b-splines. A neural network is developed to significantly reduce the computational time compared to traditional hydrodynamic simulation methods. The optimal hull shape of the buoy is determined by solving an optimization problem using a genetic algorithm, a global optimization technique. The results of the case studies show that the optimal buoy hull shape reduces 68.7% and 71.1% of the current drag, and 50% of mooring line forces compared to the cylinder-shaped buoy and the optimal-power-shaped hull from literature. The optimal buoy hull shape increases the wave energy extraction by 46.1% compared to the thin-ship-shaped buoy but performs 21.1% worse than the optimal-power-shaped hull from the literature.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2758-2768"},"PeriodicalIF":8.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219462","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}
Heating, ventilation, and air conditioning (HVAC) systems constitute a large proportion of building energy consumption and provide considerable potential for power grid regulation. While the HVAC power consumption forecasting task is generally straightforward with sufficient historical data, it becomes challenging when dealing with scarce data. Such situation is common in cases of intermittent data collection or early system implementations, where precise forecasting is required despite limited data available. Considering accessible datasets from nearby or similar HVAC systems through energy management systems, this paper proposes an adaptive transfer learning framework to tackle this issue. Specifically, the framework leverages diverse source domains, employing model-level regularizers to quantify domain discrepancies and an adaptive parameter regulation mechanism to dynamically align source domains with the target domain. Embedded within the framework, a unique deep learning architecture with attention mechanisms is proposed, capable of identifying complex temporal patterns and hierarchical features in HVAC systems. Experiments on public HVAC datasets demonstrate the generalization, accuracy and robustness of our methodology under diverse data-scarce scenarios.
{"title":"An Adaptive Transfer Learning Framework for Data-Scarce HVAC Power Consumption Forecasting","authors":"Yanan Zhang;Gan Zhou;Zhan Liu;Li Huang;Yucheng Ren","doi":"10.1109/TSTE.2024.3444689","DOIUrl":"10.1109/TSTE.2024.3444689","url":null,"abstract":"Heating, ventilation, and air conditioning (HVAC) systems constitute a large proportion of building energy consumption and provide considerable potential for power grid regulation. While the HVAC power consumption forecasting task is generally straightforward with sufficient historical data, it becomes challenging when dealing with scarce data. Such situation is common in cases of intermittent data collection or early system implementations, where precise forecasting is required despite limited data available. Considering accessible datasets from nearby or similar HVAC systems through energy management systems, this paper proposes an adaptive transfer learning framework to tackle this issue. Specifically, the framework leverages diverse source domains, employing model-level regularizers to quantify domain discrepancies and an adaptive parameter regulation mechanism to dynamically align source domains with the target domain. Embedded within the framework, a unique deep learning architecture with attention mechanisms is proposed, capable of identifying complex temporal patterns and hierarchical features in HVAC systems. Experiments on public HVAC datasets demonstrate the generalization, accuracy and robustness of our methodology under diverse data-scarce scenarios.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2815-2825"},"PeriodicalIF":8.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219460","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}
With the increasing integration of renewable energy sources (RES) and single-phase loads, three-phase imbalance and transformer lightly/heavily loaded operation issues has become more prominent in low-voltage distribution networks (LDN). However, existing research on interconnected systems of LDN (ISLDN) rarely addresses the comprehensive management of both three-phase imbalance and lightly/heavily loaded operation issues. To this end, a comprehensive control strategy for three-phase imbalance and lightly/heavily loaded operation in ISLDN based on a four-leg soft open point (F-SOP) is proposed in this paper. Firstly, the comprehensive loss characteristics model of ISLDN including the F-SOP and the transformer are established, revealing that both imbalance and load rate can affect the equipment efficiency. Secondly, considering the objective of minimizing power loss, an optimization dispatch strategy with constraints of three-phase imbalance and the optimal economic operation area is proposed to obtain the dispatch power commands for the F-SOP. Furthermore, an improved peer-to-peer control for F-SOP is proposed to ensure the conduct of the optimization dispatch strategy and enhance stability performance of system. Finally, the effectiveness and feasibility of the proposed comprehensive control strategy are validated by the simulations and experiments. The results show that comprehensive control of lightly/heavily loaded operation and three-phase imbalance in the ISLDN can significantly reduce daily power loss by 62%, improving system operation reliability and economy.
{"title":"A Comprehensive Control Strategy for F-SOP Considering Three-Phase Imbalance and Economic Operation in ISLDN","authors":"Xin Wang;Qi Guo;Chunming Tu;Liang Che;Zhong Xu;Fan Xiao;Tianlin Li;Leiqi Chen","doi":"10.1109/TSTE.2024.3444794","DOIUrl":"10.1109/TSTE.2024.3444794","url":null,"abstract":"With the increasing integration of renewable energy sources (RES) and single-phase loads, three-phase imbalance and transformer lightly/heavily loaded operation issues has become more prominent in low-voltage distribution networks (LDN). However, existing research on interconnected systems of LDN (ISLDN) rarely addresses the comprehensive management of both three-phase imbalance and lightly/heavily loaded operation issues. To this end, a comprehensive control strategy for three-phase imbalance and lightly/heavily loaded operation in ISLDN based on a four-leg soft open point (F-SOP) is proposed in this paper. Firstly, the comprehensive loss characteristics model of ISLDN including the F-SOP and the transformer are established, revealing that both imbalance and load rate can affect the equipment efficiency. Secondly, considering the objective of minimizing power loss, an optimization dispatch strategy with constraints of three-phase imbalance and the optimal economic operation area is proposed to obtain the dispatch power commands for the F-SOP. Furthermore, an improved peer-to-peer control for F-SOP is proposed to ensure the conduct of the optimization dispatch strategy and enhance stability performance of system. Finally, the effectiveness and feasibility of the proposed comprehensive control strategy are validated by the simulations and experiments. The results show that comprehensive control of lightly/heavily loaded operation and three-phase imbalance in the ISLDN can significantly reduce daily power loss by 62%, improving system operation reliability and economy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"149-159"},"PeriodicalIF":8.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219461","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 : 2024-08-15DOI: 10.1109/TSTE.2024.3444474
Shiyi Liu;Razvan Gabriel Cirstea;Heng Wu;Theo Bosma;Xiongfei Wang
This paper explores the impact of back-to-back converter control strategies on the torsional dynamics of grid-forming permanent magnet synchronous generator wind turbines (GFM-WTs). Two general converter control methods for GFM-WTs, distinguished by the placement of dc-link voltage control (DVC)–either in the machine-side converter or the grid-side converter, are evaluated through the complex torque coefficient method to offer a theoretical basis. It reveals that a negative damping is introduced when the DVC is with the machine-side converter. Then, to further characterize the parametric impacts of electrical and mechanical system constants and controller gains, the sensitivity analysis is performed by employing the partial derivative algorithm, which is based on the feedforward neural network training. It is found that the converter control has a limited impact on the damped frequency in both GFM converter control methods. Finally, electromagnetic simulations based on full-order nonlinear models of GFM-WTs are carried out to confirm the theoretical findings.
{"title":"Comparative Evaluation of Converter Control Impact on Torsional Dynamics of Type-IV Grid-Forming Wind Turbines","authors":"Shiyi Liu;Razvan Gabriel Cirstea;Heng Wu;Theo Bosma;Xiongfei Wang","doi":"10.1109/TSTE.2024.3444474","DOIUrl":"10.1109/TSTE.2024.3444474","url":null,"abstract":"This paper explores the impact of back-to-back converter control strategies on the torsional dynamics of grid-forming permanent magnet synchronous generator wind turbines (GFM-WTs). Two general converter control methods for GFM-WTs, distinguished by the placement of dc-link voltage control (DVC)–either in the machine-side converter or the grid-side converter, are evaluated through the complex torque coefficient method to offer a theoretical basis. It reveals that a negative damping is introduced when the DVC is with the machine-side converter. Then, to further characterize the parametric impacts of electrical and mechanical system constants and controller gains, the sensitivity analysis is performed by employing the partial derivative algorithm, which is based on the feedforward neural network training. It is found that the converter control has a limited impact on the damped frequency in both GFM converter control methods. Finally, electromagnetic simulations based on full-order nonlinear models of GFM-WTs are carried out to confirm the theoretical findings.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2803-2814"},"PeriodicalIF":8.6,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219463","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}
This study presents a novel approach for the precise monitoring and prognosis of photovoltaic (PV) inverter status, which is crucial for the proactive maintenance of PV systems. It addresses the gaps in traditional model-based methods, which tend to neglect the overall reliability of inverters, and the limitations of data-driven approaches that largely depend on simulated data. This research presents a robust solution applicable to real-world scenarios. The proposed data-driven model for PV inverter failure prognosis employs actual inverter measurements, integrating various operational and weather-related factors based on domain knowledge. This approach effectively represents inverter stressors and operational status. Utilizing an Enhanced Siamese Convolutional Neural Network (ESCNN), the model merges operational data with domain knowledge features, redefining the prognosis challenge as a classification task. Furthermore, the paper discusses an ESCNN-based real-time inverter failure monitoring method developed on the well-trained model. The proposed models are rigorously trained and tested with real inverter data and a novel filtering method is included to address accidental failures in practical scenarios. The results validate the model's efficacy, and the directions for future research are also outlined.
{"title":"Deep Learning-Based Failure Prognostic Model for PV Inverter Using Field Measurements","authors":"Liming Liu;Yi Luo;Zhaoyu Wang;Feng Qiu;Shijia Zhao;Murat Yildirim;Rajarshi Roychowdhury","doi":"10.1109/TSTE.2024.3443234","DOIUrl":"10.1109/TSTE.2024.3443234","url":null,"abstract":"This study presents a novel approach for the precise monitoring and prognosis of photovoltaic (PV) inverter status, which is crucial for the proactive maintenance of PV systems. It addresses the gaps in traditional model-based methods, which tend to neglect the overall reliability of inverters, and the limitations of data-driven approaches that largely depend on simulated data. This research presents a robust solution applicable to real-world scenarios. The proposed data-driven model for PV inverter failure prognosis employs actual inverter measurements, integrating various operational and weather-related factors based on domain knowledge. This approach effectively represents inverter stressors and operational status. Utilizing an Enhanced Siamese Convolutional Neural Network (ESCNN), the model merges operational data with domain knowledge features, redefining the prognosis challenge as a classification task. Furthermore, the paper discusses an ESCNN-based real-time inverter failure monitoring method developed on the well-trained model. The proposed models are rigorously trained and tested with real inverter data and a novel filtering method is included to address accidental failures in practical scenarios. The results validate the model's efficacy, and the directions for future research are also outlined.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2789-2802"},"PeriodicalIF":8.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219464","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 : 2024-08-14DOI: 10.1109/TSTE.2024.3443228
Marco Rosati;John V. Ringwood
Among the various wave energy technologies, oscillating water columns (OWCs) have shown some of the greatest promise, due to their simplicity of operation and possibility for shore mounting, with consequent ease of access and integration with other infrastructure, such as breakwaters. To minimize the levelized cost of energy, OWC energy capture must be maximized. To date, most focus has been on maximizing air turbine efficiency, while neglecting other aspects of the system. This paper presents an integrated wave-to-wire optimal control approach, considering the OWC hydrodynamics, turbine characteristics, and generator. The approach is based on a receding-horizon pseudospectral formulation, which transcribes the continuous-time optimal control problem into a finite-dimensional nonlinear program. The results show optimal exploitation of the hydrodynamic, aerodynamic, and electric subsystem efficiency characteristics, surpassing the electric energy production available through a specific focus on turbine efficiency.
{"title":"Electric Energy Maximization for Oscillating Water Column Wave Energy Systems Using a Receding-Horizon Pseudospectral Control Approach","authors":"Marco Rosati;John V. Ringwood","doi":"10.1109/TSTE.2024.3443228","DOIUrl":"10.1109/TSTE.2024.3443228","url":null,"abstract":"Among the various wave energy technologies, oscillating water columns (OWCs) have shown some of the greatest promise, due to their simplicity of operation and possibility for shore mounting, with consequent ease of access and integration with other infrastructure, such as breakwaters. To minimize the levelized cost of energy, OWC energy capture must be maximized. To date, most focus has been on maximizing air turbine efficiency, while neglecting other aspects of the system. This paper presents an integrated wave-to-wire optimal control approach, considering the OWC hydrodynamics, turbine characteristics, and generator. The approach is based on a receding-horizon pseudospectral formulation, which transcribes the continuous-time optimal control problem into a \u0000<italic>finite-dimensional</i>\u0000 nonlinear program. The results show optimal exploitation of the hydrodynamic, aerodynamic, and electric subsystem efficiency characteristics, surpassing the electric energy production available through a specific focus on turbine efficiency.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2769-2776"},"PeriodicalIF":8.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219466","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 : 2024-08-14DOI: 10.1109/TSTE.2024.3443230
M. Jafari Harandi;Mohammad Tavakoli Bina;M. Aliakbar Golkar;M. Reza J. Harandi;Mohammadreza Toulabi
It is a challenging task to affect suitably on the frequency response of a variable speed wind turbine (VSWT). The problem would be more crucial when the system is subjected to uncertainty in parameters as well as various external effects such as load changes and grid disturbances. Feedback linearization has already been applied to the VSWT, where two state variables experience instability. Hence, this paper presents a new methodology based on full-state feedback linearization in which by choosing an appropriate output, the closed-loop system is fully linearized, and the resulting linear system is stabilized by an optimal linear quadratic regulator (LQR). Since the parameters may be uncertain, the developed controller is augmented with an adaptive dynamic feedback such that all of the parameters are estimated while asymptotic stability is ensured by the Lyapunov method. Furthermore, robustness analysis is performed, and the effects of external disturbance are reduced by suitable selection of the gains. The analytical outcomes are verified through simulations, where these are compared with those of available work to show the improvement have been made by the suggested method.
{"title":"Inertial Frequency Response of Wind Turbines Using Adaptive Full Feedback Linearization Control: Stability and Robustness Analysis","authors":"M. Jafari Harandi;Mohammad Tavakoli Bina;M. Aliakbar Golkar;M. Reza J. Harandi;Mohammadreza Toulabi","doi":"10.1109/TSTE.2024.3443230","DOIUrl":"10.1109/TSTE.2024.3443230","url":null,"abstract":"It is a challenging task to affect suitably on the frequency response of a variable speed wind turbine (VSWT). The problem would be more crucial when the system is subjected to uncertainty in parameters as well as various external effects such as load changes and grid disturbances. Feedback linearization has already been applied to the VSWT, where two state variables experience instability. Hence, this paper presents a new methodology based on full-state feedback linearization in which by choosing an appropriate output, the closed-loop system is fully linearized, and the resulting linear system is stabilized by an optimal linear quadratic regulator (LQR). Since the parameters may be uncertain, the developed controller is augmented with an adaptive dynamic feedback such that all of the parameters are estimated while asymptotic stability is ensured by the Lyapunov method. Furthermore, robustness analysis is performed, and the effects of external disturbance are reduced by suitable selection of the gains. The analytical outcomes are verified through simulations, where these are compared with those of available work to show the improvement have been made by the suggested method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2777-2788"},"PeriodicalIF":8.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219465","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 : 2024-08-13DOI: 10.1109/TSTE.2024.3442834
Eihab E.E. Ahmed;Alpaslan Demirci;Gokturk Poyrazoglu;Saeed D. Manshadi
There are various active power curtailment (APC) approaches to mitigate overvoltage. In PV-rich networks, the overvoltage happens to be especially at the end of the distribution feeders. While APC helps maintain voltage within operational limits, it results in varying degrees of renewable curtailment for each prosumer. This curtailment increases as the distance from the transformer grows. Hence, these approaches introduce unfairness among prosumers. This study proposes an equitable APC (EAPC) based on the prosumer's self-consumption rate (SCR). The method calculates each prosumer's SCR, compares it with the precalculated critical SCR, and calculates a fair share of curtailment for each prosumer. Subsequently, leveraging the voltage sensitivity matrix obtained from the inverse of the Jacobian matrix, the new active power injection at the point of common coupling (PCC) is calculated to mitigate the overvoltage. To show the effectiveness of the proposed method, a comparison with three other methods is presented under various PV penetration levels. The proposed EAPC is less sensitive to the prosumer's location and improves fairness among prosumers. In addition, a battery deployment scenario is analysed considering the annual supply and demand balance to suppress the extra curtailment introduced by EAPC without increasing the battery capacity.
{"title":"An Equitable Active Power Curtailment Framework for Overvoltage Mitigation in PV-Rich Active Distribution Networks","authors":"Eihab E.E. Ahmed;Alpaslan Demirci;Gokturk Poyrazoglu;Saeed D. Manshadi","doi":"10.1109/TSTE.2024.3442834","DOIUrl":"10.1109/TSTE.2024.3442834","url":null,"abstract":"There are various active power curtailment (APC) approaches to mitigate overvoltage. In PV-rich networks, the overvoltage happens to be especially at the end of the distribution feeders. While APC helps maintain voltage within operational limits, it results in varying degrees of renewable curtailment for each prosumer. This curtailment increases as the distance from the transformer grows. Hence, these approaches introduce unfairness among prosumers. This study proposes an equitable APC (EAPC) based on the prosumer's self-consumption rate (SCR). The method calculates each prosumer's SCR, compares it with the precalculated critical SCR, and calculates a fair share of curtailment for each prosumer. Subsequently, leveraging the voltage sensitivity matrix obtained from the inverse of the Jacobian matrix, the new active power injection at the point of common coupling (PCC) is calculated to mitigate the overvoltage. To show the effectiveness of the proposed method, a comparison with three other methods is presented under various PV penetration levels. The proposed EAPC is less sensitive to the prosumer's location and improves fairness among prosumers. In addition, a battery deployment scenario is analysed considering the annual supply and demand balance to suppress the extra curtailment introduced by EAPC without increasing the battery capacity.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2745-2757"},"PeriodicalIF":8.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219467","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 : 2024-08-08DOI: 10.1109/TSTE.2024.3440322
Da Xu;Aoyu Hu;Chi-Seng Lam;Xiaodong Yang;Xiaolong Jin
Carbon capture, utilization, and storage (CCUS) can play critical roles in transitioning to global net-zero emissions. However, existing works only focus on small-scale or local CO 2 utilization. For the first time, this paper proposes a cooperative planning model of multi-energy system and CCUS considering the regional CO 2 availability. In this model, the multi-energy system and CCUS are coupled through interconnected energy hubs. To leverage its inherent operational dispatchability and flexibility, the physicochemical and thermo-electrochemical processes of CCUS are mathematically formulated with source-sink matching analysis. The multi-energy planning is a demanding optimization challenge owing to its inherent nonconvexities and substantial energy-interest couplings. The original problem is firstly relaxed as mixed integer second-order cone programming (MISOCP) to ensure satisfactory computational efficiency. A carbon-oriented bargaining problem can then be reformulated to share the cooperative surplus, which is further decomposed into a joint investment/operation subproblem and a cost-sharing subproblem. The proposed methodology is benchmarked over interconnected energy hub systems to show its effectiveness and superiority in technical, economic, and environmental aspects.
{"title":"Cooperative Planning of Multi-Energy System and Carbon Capture, Utilization and Storage","authors":"Da Xu;Aoyu Hu;Chi-Seng Lam;Xiaodong Yang;Xiaolong Jin","doi":"10.1109/TSTE.2024.3440322","DOIUrl":"10.1109/TSTE.2024.3440322","url":null,"abstract":"Carbon capture, utilization, and storage (CCUS) can play critical roles in transitioning to global net-zero emissions. However, existing works only focus on small-scale or local CO\u0000<sub>2</sub>\u0000 utilization. For the first time, this paper proposes a cooperative planning model of multi-energy system and CCUS considering the regional CO\u0000<sub>2</sub>\u0000 availability. In this model, the multi-energy system and CCUS are coupled through interconnected energy hubs. To leverage its inherent operational dispatchability and flexibility, the physicochemical and thermo-electrochemical processes of CCUS are mathematically formulated with source-sink matching analysis. The multi-energy planning is a demanding optimization challenge owing to its inherent nonconvexities and substantial energy-interest couplings. The original problem is firstly relaxed as mixed integer second-order cone programming (MISOCP) to ensure satisfactory computational efficiency. A carbon-oriented bargaining problem can then be reformulated to share the cooperative surplus, which is further decomposed into a joint investment/operation subproblem and a cost-sharing subproblem. The proposed methodology is benchmarked over interconnected energy hub systems to show its effectiveness and superiority in technical, economic, and environmental aspects.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2718-2732"},"PeriodicalIF":8.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141943725","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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