Pub Date : 2025-06-11DOI: 10.1109/TSTE.2025.3578889
Quan Sui;Huashen He;Jing Liang;Zhongwen Li;Chengguo Su
Transporting hydrogen by vessels may be more cost-effective than hydrogen trailers and hydrogen tankers, but it is also more sensitive to environmental factors (e.g., river levels). In order to capitalize on the advantages of based-vessel waterway hydrogen chains, a new short-term scheduling strategy of integrated electric-hydrogen-Thermal systems considering the hydroelectric power plant peaking for hydrogen vessel (HV) navigation is proposed in this paper. First, a temporal-spatial operational model of waterway hydrogen chains is developed. In this model, the relationship between the electrolysis temperature, hydrogen production efficiency, and maximum available operational power of the reversible solid oxide fuel cell (RSOC) is modelled. The impact of the hydroelectric power plant underflow on HV transfer is also evaluated. On this basis, a flexible multi-day collaborative scheduling strategy of the electric-hydrogen integrated system is designed, where the main power source, i.e., thermoelectric plant (TEP), is allowed to operate in pure power generation mode or cogeneration mode to release the operation flexibility. This scheduling model is first linearized as a mixed-integer second-order conic programming (MISOCP) problem and then solved efficiently through a two-layer method. Finally, case studies on a modified IEEE 118-node power system verify the effectiveness of the proposed strategy.
{"title":"Short-Term Scheduling of Integrated Electric-Hydrogen-Thermal Systems Considering Hydroelectric Power Plant Peaking for Hydrogen Vessel Navigation","authors":"Quan Sui;Huashen He;Jing Liang;Zhongwen Li;Chengguo Su","doi":"10.1109/TSTE.2025.3578889","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3578889","url":null,"abstract":"Transporting hydrogen by vessels may be more cost-effective than hydrogen trailers and hydrogen tankers, but it is also more sensitive to environmental factors (e.g., river levels). In order to capitalize on the advantages of based-vessel waterway hydrogen chains, a new short-term scheduling strategy of integrated electric-hydrogen-Thermal systems considering the hydroelectric power plant peaking for hydrogen vessel (HV) navigation is proposed in this paper. First, a temporal-spatial operational model of waterway hydrogen chains is developed. In this model, the relationship between the electrolysis temperature, hydrogen production efficiency, and maximum available operational power of the reversible solid oxide fuel cell (RSOC) is modelled. The impact of the hydroelectric power plant underflow on HV transfer is also evaluated. On this basis, a flexible multi-day collaborative scheduling strategy of the electric-hydrogen integrated system is designed, where the main power source, i.e., thermoelectric plant (TEP), is allowed to operate in pure power generation mode or cogeneration mode to release the operation flexibility. This scheduling model is first linearized as a mixed-integer second-order conic programming (MISOCP) problem and then solved efficiently through a two-layer method. Finally, case studies on a modified IEEE 118-node power system verify the effectiveness of the proposed strategy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3082-3094"},"PeriodicalIF":10.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183900","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}
To address the challenges of low efficiency, poor economic performance, and limited adaptability in renewable energy–coupled alkaline water electrolysis (AWE) systems, this study proposes a power–state rolling optimization strategy (PSROS) based on a two-stage optimization framework. First, the large-scale AWE system is divided into multiple modules to reduce the variable dimension of the optimization problem. Then, a simplified module-level optimal efficiency model is developed based on the efficiency characteristics of AWE units. Subsequently, multi-objective optimization models are constructed at the module and unit levels, comprehensively considering hydrogen production volume, lifespan degradation, and utilization balancing. Finally, a finite-horizon rolling optimization mechanism is introduced to solve the two-stage optimization problem, improving the continuity and rationality of scheduling decisions at the end of each optimization horizon. Annual case study results demonstrate that, under the non-battery scenario, PSROS improves system efficiency by 9.92%, 11.12%, and 3.81%, and reduces the levelized cost of hydrogen (LCOH) by 4.14, 5.43, and 2.35 CNY/kg compared with the simple start-stop strategy (SSSS), array rotation strategy (ARS), and rolling optimization strategy (ROS), respectively. With battery integration, the system efficiency is further improved by 0.77%, and the LCOH is further reduced by 0.49 CNY/kg.
{"title":"Collaborative Operation of Renewable Energy Hydrogen Production Systems Considering Balanced Utilization and Extended Lifespan of Multi-Electrolyzers","authors":"Shibo Wang;Lingguo Kong;Chao Liu;Chuang Liu;Guowei Cai;Shaobang Zhang;Shi You;Hanwen Zhang;Zhe Chen","doi":"10.1109/TSTE.2025.3578190","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3578190","url":null,"abstract":"To address the challenges of low efficiency, poor economic performance, and limited adaptability in renewable energy–coupled alkaline water electrolysis (AWE) systems, this study proposes a power–state rolling optimization strategy (PSROS) based on a two-stage optimization framework. First, the large-scale AWE system is divided into multiple modules to reduce the variable dimension of the optimization problem. Then, a simplified module-level optimal efficiency model is developed based on the efficiency characteristics of AWE units. Subsequently, multi-objective optimization models are constructed at the module and unit levels, comprehensively considering hydrogen production volume, lifespan degradation, and utilization balancing. Finally, a finite-horizon rolling optimization mechanism is introduced to solve the two-stage optimization problem, improving the continuity and rationality of scheduling decisions at the end of each optimization horizon. Annual case study results demonstrate that, under the non-battery scenario, PSROS improves system efficiency by 9.92%, 11.12%, and 3.81%, and reduces the levelized cost of hydrogen (LCOH) by 4.14, 5.43, and 2.35 CNY/kg compared with the simple start-stop strategy (SSSS), array rotation strategy (ARS), and rolling optimization strategy (ROS), respectively. With battery integration, the system efficiency is further improved by 0.77%, and the LCOH is further reduced by 0.49 CNY/kg.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3064-3081"},"PeriodicalIF":10.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-09DOI: 10.1109/TSTE.2025.3578278
Yonghao Gui;Hong Wang;Xiaoran Zha;Yaosuo Xue
The stochastic nature of renewable energy sources (RESs) necessitates treating power system frequency response as a random process with a nonstationary probability density function (PDF). Based upon the stochastic distribution control theory originated by the second author, this paper proposes a novel stochastic controller to improve the frequency PDF in power grids when integrating a large amount of RESs, thereby minimizing the effects of uncertainties and enhancing overall system stability. The key idea is to manipulate the controllable power generation resources so that the frequency PDF is make to follow a target PDF by using the stochastic distribution control theory originated by the second author. The proposed method can easily be plugged into existing automatic generation controls for multi-area transmission grids. The proposed method is validated via a modified Kundar’s two area system and 240-bus Western Electricity Coordinating Council systems. The simulation results show that the proposed control shapes the frequency PDF narrower and sharper, leading to a notable improvement toward minimizing the effects of randomness and uncertainty during grid operation.
{"title":"Probability Density Function Control of Frequency Fluctuations in Renewable-Rich Power Systems","authors":"Yonghao Gui;Hong Wang;Xiaoran Zha;Yaosuo Xue","doi":"10.1109/TSTE.2025.3578278","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3578278","url":null,"abstract":"The stochastic nature of renewable energy sources (RESs) necessitates treating power system frequency response as a random process with a nonstationary probability density function (PDF). Based upon the stochastic distribution control theory originated by the second author, this paper proposes a novel stochastic controller to improve the frequency PDF in power grids when integrating a large amount of RESs, thereby minimizing the effects of uncertainties and enhancing overall system stability. The key idea is to manipulate the controllable power generation resources so that the frequency PDF is make to follow a target PDF by using the stochastic distribution control theory originated by the second author. The proposed method can easily be plugged into existing automatic generation controls for multi-area transmission grids. The proposed method is validated via a modified Kundar’s two area system and 240-bus Western Electricity Coordinating Council systems. The simulation results show that the proposed control shapes the frequency PDF narrower and sharper, leading to a notable improvement toward minimizing the effects of randomness and uncertainty during grid operation.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3048-3063"},"PeriodicalIF":10.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-06DOI: 10.1109/TSTE.2025.3577286
Ziyang Chen;Tingna Shi;Yanfei Cao;Peng Song
This study addresses the critical challenge of constant power control for large-scale wind energy conversion system under the combined effects of pitch actuator degradation and multiple disturbances. In the paper, a novel fault-tolerant control strategy based on error-based active rejection control (E-ADRC) is proposed. The approach incorporates a composite control architecture, comprising a disturbance rejection tracking loop and a fault-tolerant compensation loop. Within the tracking loop, an enhanced E-ADRC algorithm is suggested which not only retains the robustness and ease of implementation of traditional E-ADRC but also significantly improves the attenuation of low-frequency wind disturbances—the turbine’s primary disruption. The fault-tolerant compensation loop applies independent control signals, derived from pitch angle residuals, to each faulty actuator, mitigating the extra fault disturbances in rotor speed tracking dynamics. This dual-loop structure enables the turbine to restore high-stability power output after a fault. Furthermore, the fault-tolerant compensation mechanism ensures that, even in cases of part of the three actuators failure, the previously misaligned pitch angles are synchronized, effectively suppressing the detrimental aerodynamic imbalance and reducing adverse loads. The superiority of this approach in enhancing power output stability and reducing structure fatigue damage have been validated through a refined hardware-in-the-loop test.
{"title":"Error-Based Active Disturbance Rejection Power Control for Large-Scale Wind Turbines Under Pitch Actuator Performance Degradation Failure","authors":"Ziyang Chen;Tingna Shi;Yanfei Cao;Peng Song","doi":"10.1109/TSTE.2025.3577286","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3577286","url":null,"abstract":"This study addresses the critical challenge of constant power control for large-scale wind energy conversion system under the combined effects of pitch actuator degradation and multiple disturbances. In the paper, a novel fault-tolerant control strategy based on error-based active rejection control (E-ADRC) is proposed. The approach incorporates a composite control architecture, comprising a disturbance rejection tracking loop and a fault-tolerant compensation loop. Within the tracking loop, an enhanced E-ADRC algorithm is suggested which not only retains the robustness and ease of implementation of traditional E-ADRC but also significantly improves the attenuation of low-frequency wind disturbances—the turbine’s primary disruption. The fault-tolerant compensation loop applies independent control signals, derived from pitch angle residuals, to each faulty actuator, mitigating the extra fault disturbances in rotor speed tracking dynamics. This dual-loop structure enables the turbine to restore high-stability power output after a fault. Furthermore, the fault-tolerant compensation mechanism ensures that, even in cases of part of the three actuators failure, the previously misaligned pitch angles are synchronized, effectively suppressing the detrimental aerodynamic imbalance and reducing adverse loads. The superiority of this approach in enhancing power output stability and reducing structure fatigue damage have been validated through a refined hardware-in-the-loop test.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3015-3030"},"PeriodicalIF":10.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-06DOI: 10.1109/TSTE.2025.3577568
Xinquan Chen;Siqi Bu;Ilhan Kocar
The penetration of inverter-based resources (IBRs) into the grid is experiencing significant growth. Their control behavior during unbalanced grid conditions can impact system stability and protection. This paper evaluates the performance of prevalent grid-forming control-based IBRs (GFM-IBRs) under unbalanced grid conditions and proposes novel insights in a novel framework. First, we investigate the potential impacts of grid-forming control-based IBRs (GFM-IBRs) on system dynamics, protection, and fault ride-through (FRT) capability under unbalanced grid conditions based on extensive literature review and through EMT simulations in benchmark systems with IBRs. To discover these impacts and accommodate unbalanced grid conditions, we implement a generic control structure for full converter-based GFM-IBRs under FRT mode, then perform a comparative analysis of existing solutions that include sequence decomposition methods, positive sequence current-limiting methods, negative sequence controls, and current coordination methods, to identify their capabilities and limitations through literature review and EMT simulations in a large-scale power system. Finally, key challenges and solutions are discussed, highlighting prospects for future research.
{"title":"Grid-Forming IBRs Under Unbalanced Grid Conditions: Challenges, Solutions, and Prospects","authors":"Xinquan Chen;Siqi Bu;Ilhan Kocar","doi":"10.1109/TSTE.2025.3577568","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3577568","url":null,"abstract":"The penetration of inverter-based resources (IBRs) into the grid is experiencing significant growth. Their control behavior during unbalanced grid conditions can impact system stability and protection. This paper evaluates the performance of prevalent grid-forming control-based IBRs (GFM-IBRs) under unbalanced grid conditions and proposes novel insights in a novel framework. First, we investigate the potential impacts of grid-forming control-based IBRs (GFM-IBRs) on system dynamics, protection, and fault ride-through (FRT) capability under unbalanced grid conditions based on extensive literature review and through EMT simulations in benchmark systems with IBRs. To discover these impacts and accommodate unbalanced grid conditions, we implement a generic control structure for full converter-based GFM-IBRs under FRT mode, then perform a comparative analysis of existing solutions that include sequence decomposition methods, positive sequence current-limiting methods, negative sequence controls, and current coordination methods, to identify their capabilities and limitations through literature review and EMT simulations in a large-scale power system. Finally, key challenges and solutions are discussed, highlighting prospects for future research.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3031-3047"},"PeriodicalIF":10.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-05DOI: 10.1109/TSTE.2025.3576928
Xuguang Wang;Wangjie Liu;Junhong Ni;Mi Zhang
Reliable short-term photovoltaic (PV) power forecasting is of crucial significance for the rational dispatching of power sources and the effective control of operating costs for the power grid. However, temporal misalignment and regression accuracy imbalance of PV power data pose significant challenges to the reliability of forecast results. In this study, multivariate PV power forecasting is investigated from the perspective of forecast model samples. Firstly, the extent of misalignment of a sample is parameterized by a time-delay vector. Subsequently, the sample-wise graph is defined to relate the time-delay vector with PV power data. Then, the time-delay vector is estimated by minimizing the smoothness metric of the sample-wise graph. Finally, a sample-wise graph-based sample weighting strategy is introduced to address the issue of regression accuracy imbalance. The efficiency of the proposed PV power forecasting scheme is validated through extensive experiments on real-world datasets. Comparison experiments suggest that the proposed scheme can achieve remarkably improved short-term PV power forecasting.
{"title":"Sample-Wise Graph-Based Multivariate Short-Term PV Power Forecasting","authors":"Xuguang Wang;Wangjie Liu;Junhong Ni;Mi Zhang","doi":"10.1109/TSTE.2025.3576928","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3576928","url":null,"abstract":"Reliable short-term photovoltaic (PV) power forecasting is of crucial significance for the rational dispatching of power sources and the effective control of operating costs for the power grid. However, temporal misalignment and regression accuracy imbalance of PV power data pose significant challenges to the reliability of forecast results. In this study, multivariate PV power forecasting is investigated from the perspective of forecast model samples. Firstly, the extent of misalignment of a sample is parameterized by a time-delay vector. Subsequently, the sample-wise graph is defined to relate the time-delay vector with PV power data. Then, the time-delay vector is estimated by minimizing the smoothness metric of the sample-wise graph. Finally, a sample-wise graph-based sample weighting strategy is introduced to address the issue of regression accuracy imbalance. The efficiency of the proposed PV power forecasting scheme is validated through extensive experiments on real-world datasets. Comparison experiments suggest that the proposed scheme can achieve remarkably improved short-term PV power forecasting.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3003-3014"},"PeriodicalIF":10.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-03DOI: 10.1109/TSTE.2025.3575520
Razieh Rastgoo;Nima Amjady;Shunfu Lin;S. M. Muyeen
The unpredictable nature of solar power generation, largely influenced by fluctuating cloud cover, poses a challenge to the stability of renewable energy systems. Considering this, accurate forecasting of solar power can lead to better grid management and operation. With the advent of deep learning models, various models have been suggested to enhance the ultra-short-term solar power forecasting performance. Given that cloud images offer more direct and comprehensive information about cloud patterns compared to the numerical weather prediction data, analyzing cloud images allows for more precise and efficient cloud change predictions, leading to a more accurate ultra-short-term solar power forecasting. In this way, aiming to enhance the forecasting performance, in this paper, we introduce a deep learning-based model, including three main blocks. In the first block, a Multi-Stream Video Vision Transformer (MS-ViViT) model is proposed for extracting different types of spatio-temporal features from the input image sequences. The output features from the first block are input to the second block, Fused Improved Reformer (Fused I-Reformer), including three Improved Reformer (I-Reformer) models equipped with a Fused Encoder as well as a new loss function for sequence learning. Finally, an Attentive Residual Fully Connected (ARFC) model is proposed for solar power value prediction. The comparison results with 36 comparative models on six real-world datasets using seven evaluation metrics confirm the effectiveness of the proposed ultra-short-term solar power forecasting model.
{"title":"Ultra-Short-Term Solar Power Prediction Using Sky Image Sequences by a Residual Vision Reformer","authors":"Razieh Rastgoo;Nima Amjady;Shunfu Lin;S. M. Muyeen","doi":"10.1109/TSTE.2025.3575520","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3575520","url":null,"abstract":"The unpredictable nature of solar power generation, largely influenced by fluctuating cloud cover, poses a challenge to the stability of renewable energy systems. Considering this, accurate forecasting of solar power can lead to better grid management and operation. With the advent of deep learning models, various models have been suggested to enhance the ultra-short-term solar power forecasting performance. Given that cloud images offer more direct and comprehensive information about cloud patterns compared to the numerical weather prediction data, analyzing cloud images allows for more precise and efficient cloud change predictions, leading to a more accurate ultra-short-term solar power forecasting. In this way, aiming to enhance the forecasting performance, in this paper, we introduce a deep learning-based model, including three main blocks. In the first block, a Multi-Stream Video Vision Transformer (MS-ViViT) model is proposed for extracting different types of spatio-temporal features from the input image sequences. The output features from the first block are input to the second block, Fused Improved Reformer (Fused I-Reformer), including three Improved Reformer (I-Reformer) models equipped with a Fused Encoder as well as a new loss function for sequence learning. Finally, an Attentive Residual Fully Connected (ARFC) model is proposed for solar power value prediction. The comparison results with 36 comparative models on six real-world datasets using seven evaluation metrics confirm the effectiveness of the proposed ultra-short-term solar power forecasting model.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"2972-2988"},"PeriodicalIF":10.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-03DOI: 10.1109/TSTE.2025.3576153
Sujay A. Kaloti;Badrul H. Chowdhury
The widely reported increase in the frequency of high impact, low probability extreme weather events pose significant challenges to the resilient operation of electric power systems. This paper explores strategies to enhance operational resilience that addresses the distribution network’s ability to adapt to changing operating conditions. We introduce a novel Dual Agent-Based framework for optimizing the scheduling of distributed energy resources (DERs) within a networked microgrid (N-MG) using the deep reinforcement learning (DRL) paradigm. This framework focuses on minimizing operational and environmental costs during normal operations while enhancing critical load supply indices (CSI) under emergency conditions. Additionally, we introduce a multi-temporal dynamic reward shaping structure along with the incorporation of an error coefficient to enhance the learning process of the agents. To appropriately manage loads during emergencies, we propose a load flexibility classification system that categorizes loads based on its criticality index. The scalability of the proposed approach is demonstrated through running multiple case-studies on a modified IEEE 123-node benchmark distribution network. Furthermore, validation of the method is provided by means of comparisons with two metaheuristic algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA).
{"title":"Dual Agent Framework for Scheduling Networked Microgrids Using DRL to Improve Resilience","authors":"Sujay A. Kaloti;Badrul H. Chowdhury","doi":"10.1109/TSTE.2025.3576153","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3576153","url":null,"abstract":"The widely reported increase in the frequency of high impact, low probability extreme weather events pose significant challenges to the resilient operation of electric power systems. This paper explores strategies to enhance operational resilience that addresses the distribution network’s ability to adapt to changing operating conditions. We introduce a novel Dual Agent-Based framework for optimizing the scheduling of distributed energy resources (DERs) within a networked microgrid (N-MG) using the deep reinforcement learning (DRL) paradigm. This framework focuses on minimizing operational and environmental costs during normal operations while enhancing critical load supply indices (CSI) under emergency conditions. Additionally, we introduce a multi-temporal dynamic reward shaping structure along with the incorporation of an error coefficient to enhance the learning process of the agents. To appropriately manage loads during emergencies, we propose a load flexibility classification system that categorizes loads based on its criticality index. The scalability of the proposed approach is demonstrated through running multiple case-studies on a modified IEEE 123-node benchmark distribution network. Furthermore, validation of the method is provided by means of comparisons with two metaheuristic algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA).","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"2989-3002"},"PeriodicalIF":10.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-02DOI: 10.1109/TSTE.2025.3575788
Jie Zhu;Yinliang Xu;Nengling Tai;Ye Guo;Hongbin Sun
This paper proposes a statistically feasible joint chance-constrained scheduling framework for integrated power distribution networks (PDN) and district heating systems (DHS). The proposed method constructs data-driven uncertainty sets directly from samples, eliminating the need for prior distribution assumptions. It integrates joint chance-constrained programming (JCCP) with robust optimization (RO) to reformulate the original problem. The resulting model is both tractable and computationally efficient. Additionally, we introduce a novel constraint-specific uncertainty set reconstruction technique. This technique refines the uncertainty set by incorporating optimization-relevant information. It significantly reduces conservatism while ensuring system violation probability requirements. Comparative studies with state-of-the-art uncertainty optimization methods demonstrate the advantages of our approach. The proposed method improves computational efficiency by two orders of magnitude. It also achieves more cost-effective solutions than the best-performing benchmark method.
{"title":"Statistically Feasible Joint Chance-Constrained Scheduling of Integrated Distribution Network and District Heating System","authors":"Jie Zhu;Yinliang Xu;Nengling Tai;Ye Guo;Hongbin Sun","doi":"10.1109/TSTE.2025.3575788","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3575788","url":null,"abstract":"This paper proposes a statistically feasible joint chance-constrained scheduling framework for integrated power distribution networks (PDN) and district heating systems (DHS). The proposed method constructs data-driven uncertainty sets directly from samples, eliminating the need for prior distribution assumptions. It integrates joint chance-constrained programming (JCCP) with robust optimization (RO) to reformulate the original problem. The resulting model is both tractable and computationally efficient. Additionally, we introduce a novel constraint-specific uncertainty set reconstruction technique. This technique refines the uncertainty set by incorporating optimization-relevant information. It significantly reduces conservatism while ensuring system violation probability requirements. Comparative studies with state-of-the-art uncertainty optimization methods demonstrate the advantages of our approach. The proposed method improves computational efficiency by two orders of magnitude. It also achieves more cost-effective solutions than the best-performing benchmark method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"2959-2971"},"PeriodicalIF":10.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1109/TSTE.2025.3564106
Haohui Ding;Qinran Hu;Yuze Wang;Cong Wang;Jia Su;Haizhou Liu
Fuel cell systems (FCS) are recognized as promising electric sources of power systems. However, existing FCS models are either nonconvex or inaccurate when the FCS is under heavy load. This letter proposes a mechanism-based FCS feasible operational area (FCSFOA) model, taking into account the decline in fuel cell efficiency and the dynamic power consumption of the auxiliary system. Therefore, the FCSFOA model is accurate both under light load and heavy load, and the average error is only 5.4% compared with the actual data. In contrast, the FCS linear model, the most commonly used in the dispatch of power systems, has an average error of 24.4% . Besides, the FCSFOA model is also convex, which is favorable for the dispatch of power systems.
{"title":"A Mechanism-Based Convex Model of Fuel Cell Systems Considering the Effect of Auxiliary System","authors":"Haohui Ding;Qinran Hu;Yuze Wang;Cong Wang;Jia Su;Haizhou Liu","doi":"10.1109/TSTE.2025.3564106","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3564106","url":null,"abstract":"Fuel cell systems (FCS) are recognized as promising electric sources of power systems. However, existing FCS models are either nonconvex or inaccurate when the FCS is under heavy load. This letter proposes a mechanism-based FCS feasible operational area (FCSFOA) model, taking into account the decline in fuel cell efficiency and the dynamic power consumption of the auxiliary system. Therefore, the FCSFOA model is accurate both under light load and heavy load, and the average error is only 5.4% compared with the actual data. In contrast, the FCS linear model, the most commonly used in the dispatch of power systems, has an average error of 24.4% . Besides, the FCSFOA model is also convex, which is favorable for the dispatch of power systems.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3124-3127"},"PeriodicalIF":10.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183969","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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