Pub Date : 2025-03-29DOI: 10.1109/TSTE.2025.3574806
Hexiang Peng;Kai Liao;Jianwei Yang;Bo Pang;Zhengyou He
Coordinating Volt/Var control (VVC) across multiple timescales in distribution networks is challenging due to the diverse response characteristics of control devices. This paper proposes a novel bi-level data-driven multi-timescale VVC method to achieve coordinated control. The method integrates short-timescale control of continuous devices, such as photovoltaics, with longer-timescale control of discrete devices, including capacitor banks, and network reconfiguration. The VVC problem is formulated as a bi-level partially observable Markov decision process (POMDP). Inner-level control employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for continuous devices, while outer-level control uses the Deep Double Q-Network (DDQN) algorithm for discrete devices and network reconfiguration. Collaborative training is achieved by aligning reward signals and providing inner-level agent actions as state information to outer-level agents. To mitigate over-exploration caused by network reconfiguration, graph neural networks (GNNs) are utilized to identify representative topologies, simplifying the reconfiguration space. The proposed method is validated on the IEEE 33-bus and PG&E 69-bus systems, demonstrating superior VVC performance and enhanced robustness to topological variations.
{"title":"Deep Reinforcement Learning Based Multi-Timescale Volt/Var Control in Distribution Networks Considering Network Reconfiguration","authors":"Hexiang Peng;Kai Liao;Jianwei Yang;Bo Pang;Zhengyou He","doi":"10.1109/TSTE.2025.3574806","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3574806","url":null,"abstract":"Coordinating Volt/Var control (VVC) across multiple timescales in distribution networks is challenging due to the diverse response characteristics of control devices. This paper proposes a novel bi-level data-driven multi-timescale VVC method to achieve coordinated control. The method integrates short-timescale control of continuous devices, such as photovoltaics, with longer-timescale control of discrete devices, including capacitor banks, and network reconfiguration. The VVC problem is formulated as a bi-level partially observable Markov decision process (POMDP). Inner-level control employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for continuous devices, while outer-level control uses the Deep Double Q-Network (DDQN) algorithm for discrete devices and network reconfiguration. Collaborative training is achieved by aligning reward signals and providing inner-level agent actions as state information to outer-level agents. To mitigate over-exploration caused by network reconfiguration, graph neural networks (GNNs) are utilized to identify representative topologies, simplifying the reconfiguration space. The proposed method is validated on the IEEE 33-bus and PG&E 69-bus systems, demonstrating superior VVC performance and enhanced robustness to topological variations.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"2948-2958"},"PeriodicalIF":10.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183964","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 : 2025-03-21DOI: 10.1109/TSTE.2025.3547404
{"title":"IEEE Transactions on Sustainable Energy Information for Authors","authors":"","doi":"10.1109/TSTE.2025.3547404","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3547404","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"C4-C4"},"PeriodicalIF":8.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10936639","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667580","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 : 2025-03-21DOI: 10.1109/TSTE.2025.3547402
{"title":"IEEE Industry Applications Society Information","authors":"","doi":"10.1109/TSTE.2025.3547402","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3547402","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"C3-C3"},"PeriodicalIF":8.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10936640","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667743","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 : 2025-03-21DOI: 10.1109/TSTE.2025.3553209
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TSTE.2025.3553209","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3553209","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1487-1487"},"PeriodicalIF":8.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10936646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667744","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 : 2025-03-21DOI: 10.1109/TSTE.2025.3547400
{"title":"IEEE Transactions on Sustainable Energy Publication Information","authors":"","doi":"10.1109/TSTE.2025.3547400","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3547400","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675954","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 : 2025-03-12DOI: 10.1109/TSTE.2025.3548931
Zechuan Lin;Xuanrui Huang;Yifei Han;Xi Xiao;John V. Ringwood
Centralized control of wave energy converter (WEC) arrays for grid-scale generation can achieve higher energy production than decentralized (independent) control, due to its capability of fully exploiting mutual radiation effects. However, the state-of-the-art centralized model predictive control (CMPC) is significantly more computationally challenging than decentralized MPC (DMPC), since the number of control moves to be optimized grows in proportion to the number of WECs. In this paper, a fast CMPC controller is proposed, whose idea is to optimize only the first few control moves while rolling out future system trajectories using a fixed controller. A linear, two-degree-of-freedom (2-DoF) controller with a sea-state-dependent control coefficient tuning strategy is further proposed to serve as the rollout controller. It is shown that the proposed rollout-based CMPC (R-CMPC) can maintain almost the same energy production as conventional CMPC under a wide range of sea states, while significantly reducing the optimization dimension (in the studied case, by a factor of 6), enabling ultra-fast online computation (about 40 times faster than conventional CMPC).
{"title":"Fast Centralized Model Predictive Control for Wave Energy Converter Arrays Based on Rollout","authors":"Zechuan Lin;Xuanrui Huang;Yifei Han;Xi Xiao;John V. Ringwood","doi":"10.1109/TSTE.2025.3548931","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3548931","url":null,"abstract":"Centralized control of wave energy converter (WEC) arrays for grid-scale generation can achieve higher energy production than decentralized (independent) control, due to its capability of fully exploiting mutual radiation effects. However, the state-of-the-art centralized model predictive control (CMPC) is significantly more computationally challenging than decentralized MPC (DMPC), since the number of control moves to be optimized grows in proportion to the number of WECs. In this paper, a fast CMPC controller is proposed, whose idea is to optimize only the first few control moves while rolling out future system trajectories using a fixed controller. A linear, two-degree-of-freedom (2-DoF) controller with a sea-state-dependent control coefficient tuning strategy is further proposed to serve as the rollout controller. It is shown that the proposed rollout-based CMPC (R-CMPC) can maintain almost the same energy production as conventional CMPC under a wide range of sea states, while significantly reducing the optimization dimension (in the studied case, by a factor of 6), enabling ultra-fast online computation (about 40 times faster than conventional CMPC).","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2224-2235"},"PeriodicalIF":8.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330320","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 : 2025-03-12DOI: 10.1109/TSTE.2025.3546294
Yunda Xu;Ruifeng Yan;Tapan Kumar Saha
As renewable energy integration increases, ensuring stability of Inverter-Based Resources (IBRs) in weak grids is crucial, as grid-following (GFL) converters often become unstable under such conditions. Integrating virtual synchronous generator (VSG) batteries has shown potential to improve GFL stability, but determining the optimal size of the VSG required for stability remains an open question. Existing research typically relies on small-signal or impedance models for stability analysis, which are only valid at a single operating point and do not consider the full range of operating conditions, including various dispatch scenarios and grid strengths. This paper addresses this gap by proposing a novel methodology to visualize the system's stable operating region, offering insights into stability boundaries across various real power and grid impedance variations. Additionally, it introduces an optimal VSG battery sizing strategy that accounts for these variations, ensuring stability while minimizing VSG capacity. The strategy's effectiveness is validated through comprehensive PSCAD simulations, demonstrating its reliability across a wide range of real power and grid impedance operating points.
{"title":"Optimal VSG BESS Sizing for Improving Grid-Following Converter Stability Under Various Dispatch Scenarios and Grid Strengths","authors":"Yunda Xu;Ruifeng Yan;Tapan Kumar Saha","doi":"10.1109/TSTE.2025.3546294","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3546294","url":null,"abstract":"As renewable energy integration increases, ensuring stability of Inverter-Based Resources (IBRs) in weak grids is crucial, as grid-following (GFL) converters often become unstable under such conditions. Integrating virtual synchronous generator (VSG) batteries has shown potential to improve GFL stability, but determining the optimal size of the VSG required for stability remains an open question. Existing research typically relies on small-signal or impedance models for stability analysis, which are only valid at a single operating point and do not consider the full range of operating conditions, including various dispatch scenarios and grid strengths. This paper addresses this gap by proposing a novel methodology to visualize the system's stable operating region, offering insights into stability boundaries across various real power and grid impedance variations. Additionally, it introduces an optimal VSG battery sizing strategy that accounts for these variations, ensuring stability while minimizing VSG capacity. The strategy's effectiveness is validated through comprehensive PSCAD simulations, demonstrating its reliability across a wide range of real power and grid impedance operating points.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2210-2223"},"PeriodicalIF":8.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331633","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 : 2025-03-12DOI: 10.1109/TSTE.2025.3550563
Ahmed Shaban Omar;Ramadan El-Shatshat
This paper proposes a hybrid mixed-integer quadratic programming-constrained deep reinforcement learning (MIQP-CDRL) framework for energy management of multi-energy communities. The framework employs a hierarchical two-layer structure: the MIQP layer handles day-ahead scheduling, minimizing operational costs while ensuring system constraint satisfaction, while the CDRL agent makes real-time adjustments. The goal of this framework is to combine the strengths of CDRL in addressing sequential decision-making problems in stochastic systems with the advantages of a mathematical programming model to guide the agent's exploration during the training and reduce the dependency on opaque policies during real-time operation. The system dynamics are modeled as a constrained Markov decision process (CMDP), which is solved by a model-free CDRL agent built upon the constrained policy optimization (CPO) algorithm. Practical test results demonstrate the effectiveness of this framework in improving the optimality and feasibility of the real-time solutions compared to existing stand-alone DRL approaches.
{"title":"Energy Management of Multi-Energy Communities: A Hierarchical MIQP-Constrained Deep Reinforcement Learning Approach","authors":"Ahmed Shaban Omar;Ramadan El-Shatshat","doi":"10.1109/TSTE.2025.3550563","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3550563","url":null,"abstract":"This paper proposes a hybrid mixed-integer quadratic programming-constrained deep reinforcement learning (MIQP-CDRL) framework for energy management of multi-energy communities. The framework employs a hierarchical two-layer structure: the MIQP layer handles day-ahead scheduling, minimizing operational costs while ensuring system constraint satisfaction, while the CDRL agent makes real-time adjustments. The goal of this framework is to combine the strengths of CDRL in addressing sequential decision-making problems in stochastic systems with the advantages of a mathematical programming model to guide the agent's exploration during the training and reduce the dependency on opaque policies during real-time operation. The system dynamics are modeled as a constrained Markov decision process (CMDP), which is solved by a model-free CDRL agent built upon the constrained policy optimization (CPO) algorithm. Practical test results demonstrate the effectiveness of this framework in improving the optimality and feasibility of the real-time solutions compared to existing stand-alone DRL approaches.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2236-2250"},"PeriodicalIF":8.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331734","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 : 2025-03-07DOI: 10.1109/TSTE.2025.3549225
Ziwen Gu;Yatao Shen;Zijian Wang;Yaqun Jiang;Chun Huang;Peng Li
Accurate photovoltaic power (PVP) prediction is a prerequisite for the efficient and stable operation of new power systems. While existing research has extensively explored the relationship between global factors such as temperature, irradiance, and photovoltaic power, the local dynamic impacts of these factors are often overlooked, which may reduce the accuracy of predictions. To address this issue, this paper considers the dynamic interrelationships among multiple factors and proposes a dynamic locally featured embedding-based broad learning system (DLFE-BLS) algorithm for PVP prediction. Firstly, a novel dynamic phase space reconstruction method (DPSR) is proposed to characterize the dynamic properties of multivariate data. Furthermore, a dynamic local featured embedding (DLFE) algorithm is introduced to extract local dynamic features from multivariate data. Finally, by integrating the dynamic reconstruction and dynamic feature extraction processes into the broad learning system (BLS) framework, we propose the DLFE-BLS algorithm to improve the accuracy of PVP prediction. Case studies have shown that DLFE-BLS outperforms other models in terms of prediction accuracy. Additionally, it has the highest accuracy when applied to transfer prediction.
{"title":"Photovoltaic Power Prediction Considering Multifactorial Dynamic Effects: A Dynamic Locally Featured Embedding-Based Broad Learning System","authors":"Ziwen Gu;Yatao Shen;Zijian Wang;Yaqun Jiang;Chun Huang;Peng Li","doi":"10.1109/TSTE.2025.3549225","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3549225","url":null,"abstract":"Accurate photovoltaic power (PVP) prediction is a prerequisite for the efficient and stable operation of new power systems. While existing research has extensively explored the relationship between global factors such as temperature, irradiance, and photovoltaic power, the local dynamic impacts of these factors are often overlooked, which may reduce the accuracy of predictions. To address this issue, this paper considers the dynamic interrelationships among multiple factors and proposes a dynamic locally featured embedding-based broad learning system (DLFE-BLS) algorithm for PVP prediction. Firstly, a novel dynamic phase space reconstruction method (DPSR) is proposed to characterize the dynamic properties of multivariate data. Furthermore, a dynamic local featured embedding (DLFE) algorithm is introduced to extract local dynamic features from multivariate data. Finally, by integrating the dynamic reconstruction and dynamic feature extraction processes into the broad learning system (BLS) framework, we propose the DLFE-BLS algorithm to improve the accuracy of PVP prediction. Case studies have shown that DLFE-BLS outperforms other models in terms of prediction accuracy. Additionally, it has the highest accuracy when applied to transfer prediction.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2197-2209"},"PeriodicalIF":8.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331780","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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