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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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
{"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":"https://doi.org/10.1109/tste.2024.3424932","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.8,"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}
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