Pub Date : 2024-07-15DOI: 10.35833/MPCE.2023.000730
Weihang Yan;Vahan Gevorgian;Przemyslaw Koralewicz;S M Shafiul Alam;Emanuel Mendiola
Battery energy storage systems (BESSs) are an important asset for power systems with high integration levels of renewable energy, and they can be controlled to provide various critical services to the power grid. This paper presents the real-world experience of using a megawatt-scale BESS with grid-following (GFL) and grid-forming (GFM) controls and a run-of-river (ROR) hydropower plant to restore a regional power system. To demonstrate this, we carry out power-hardware-in-the-loop experiments integrating an actual GFL- or GFM-controlled BESS and a load bank. Both the simulation and experimental results presented in this paper show the different roles of GFL- or GFM-controlled BESS in power system black starts. The results provide further insight for system operators on how GFL- or GFM-controlled BESS can enhance grid stability and how an ROR hydropower plant can be converted into a black-start-capable unit with the support of a small-capacity BESS. The results show that an ROR hydropower plant combined with a BESS has the potential of becoming one of enabling elements to perform bottom-up black-start schemes as opposed to conventional bottom-down method, thus enhancing the system resiliency and robustness.
{"title":"Regional Power System Black Start with Run-of-River Hydropower Plant and Battery Energy Storage","authors":"Weihang Yan;Vahan Gevorgian;Przemyslaw Koralewicz;S M Shafiul Alam;Emanuel Mendiola","doi":"10.35833/MPCE.2023.000730","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000730","url":null,"abstract":"Battery energy storage systems (BESSs) are an important asset for power systems with high integration levels of renewable energy, and they can be controlled to provide various critical services to the power grid. This paper presents the real-world experience of using a megawatt-scale BESS with grid-following (GFL) and grid-forming (GFM) controls and a run-of-river (ROR) hydropower plant to restore a regional power system. To demonstrate this, we carry out power-hardware-in-the-loop experiments integrating an actual GFL- or GFM-controlled BESS and a load bank. Both the simulation and experimental results presented in this paper show the different roles of GFL- or GFM-controlled BESS in power system black starts. The results provide further insight for system operators on how GFL- or GFM-controlled BESS can enhance grid stability and how an ROR hydropower plant can be converted into a black-start-capable unit with the support of a small-capacity BESS. The results show that an ROR hydropower plant combined with a BESS has the potential of becoming one of enabling elements to perform bottom-up black-start schemes as opposed to conventional bottom-down method, thus enhancing the system resiliency and robustness.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599368","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324364","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}
The power flow (PF) calculation for AC/DC hybrid systems based on voltage source converter (VSC) plays a crucial role in the operational analysis of the new energy system. The fast and flexible holomorphic embedding (FFHE) PF method, with its non-iterative format founded on complex analysis theory, exhibits superior numerical performance compared with traditional iterative methods. This paper aims to extend the FF-HE method to the PF problem in the VSC-based AC/DC hybrid system. To form the AC/DC FFHE PF method, an AC/DC FF-HE model with its solution scheme and a sequential AC/DC PF calculation framework are proposed. The AC/DC FFHE model is established with a more flexible form to incorporate multiple control strategies of VSC while preserving the constructive and deterministic properties of original FFHE to reliably obtain operable AC/DC solutions from various initializations. A solution scheme for the proposed model is provided with specific recursive solution processes and accelerated Padé approximant. To achieve the overall convergence of AC/DC PF, the AC/DC FF-HE model is integrated into the sequential calculation framework with well-designed data exchange and control mode switching mechanisms. The proposed method demonstrates significant efficiency improvements, especially in handling scenarios involving control mode switching and multiple recalculations. In numerical tests, the superiority of the proposed method is confirmed through comparisons of accuracy and efficiency with existing methods, as well as the impact analyses of different initializations.
{"title":"Power Flow Calculation for VSC-Based AC/DC Hybrid Systems Based on Fast and Flexible Holomorphic Embedding","authors":"Peichuan Tian;Yexuan Jin;Ning Xie;Chengmin Wang;Chunyi Huang","doi":"10.35833/MPCE.2024.000185","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000185","url":null,"abstract":"The power flow (PF) calculation for AC/DC hybrid systems based on voltage source converter (VSC) plays a crucial role in the operational analysis of the new energy system. The fast and flexible holomorphic embedding (FFHE) PF method, with its non-iterative format founded on complex analysis theory, exhibits superior numerical performance compared with traditional iterative methods. This paper aims to extend the FF-HE method to the PF problem in the VSC-based AC/DC hybrid system. To form the AC/DC FFHE PF method, an AC/DC FF-HE model with its solution scheme and a sequential AC/DC PF calculation framework are proposed. The AC/DC FFHE model is established with a more flexible form to incorporate multiple control strategies of VSC while preserving the constructive and deterministic properties of original FFHE to reliably obtain operable AC/DC solutions from various initializations. A solution scheme for the proposed model is provided with specific recursive solution processes and accelerated Padé approximant. To achieve the overall convergence of AC/DC PF, the AC/DC FF-HE model is integrated into the sequential calculation framework with well-designed data exchange and control mode switching mechanisms. The proposed method demonstrates significant efficiency improvements, especially in handling scenarios involving control mode switching and multiple recalculations. In numerical tests, the superiority of the proposed method is confirmed through comparisons of accuracy and efficiency with existing methods, as well as the impact analyses of different initializations.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324320","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 : 2024-04-24DOI: 10.35833/MPCE.2023.000613
Xi Lu;Xinzhe Fan;Haifeng Qiu;Wei Gan;Wei Gu;Shiwei Xia;Xiao Luo
In this paper, an operation model for distribution systems with energy storage (ES) is proposed and solved with the aid of machine learning. The model considers ES applications with uncertainty realizations. It also considers ES applications for economy and security purposes. Considering the special features of ES operations under day-ahead decision mechanisms of distribution systems, an ES operation scheme is designed for transferring uncertainties to later hours through ES to ensure the secure operation of distribution system. As a result, uncertainties from different time intervals are assembled and may counteract each other, thereby alleviating the uncertainties. As different ES applications rely on ES flexibility (in terms of charging and discharging) and interact with each other, by coordinating different ES applications, the proposed operation model achieves efficient exploit of ES flexibility. To shorten the computation time, a long short-term memory recurrent neural network is used to determine the binary variables corresponding to ES status. The proposed operation model then becomes a convex optimization problem and is solved precisely. Thus, the solving efficiency is greatly improved while ensuring the satisfactory use of ES flexibility in distribution system operation.
本文提出了一种带储能（ES）的配电系统运行模型，并在机器学习的帮助下进行了求解。该模型考虑了具有不确定性的 ES 应用。它还考虑了以经济和安全为目的的 ES 应用。考虑到配电系统日前决策机制下 ES 运行的特殊性，设计了一种 ES 运行方案，通过 ES 将不确定性转移到较晚时段，以确保配电系统的安全运行。这样，不同时间段的不确定性被集合在一起，可以相互抵消，从而缓解不确定性。由于不同的 ES 应用依赖于 ES 的灵活性（在充电和放电方面）并相互影响，通过协调不同的 ES 应用，所提出的运行模型实现了对 ES 灵活性的有效利用。为了缩短计算时间，采用了长短期记忆递归神经网络来确定与 ES 状态相对应的二进制变量。这样，所提出的运行模型就变成了一个凸优化问题，并得到精确求解。因此，在确保配电系统运行中充分发挥 ES 灵活性的同时，大大提高了求解效率。
{"title":"Machine Learning Based Uncertainty-Alleviating Operation Model for Distribution Systems with Energy Storage","authors":"Xi Lu;Xinzhe Fan;Haifeng Qiu;Wei Gan;Wei Gu;Shiwei Xia;Xiao Luo","doi":"10.35833/MPCE.2023.000613","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000613","url":null,"abstract":"In this paper, an operation model for distribution systems with energy storage (ES) is proposed and solved with the aid of machine learning. The model considers ES applications with uncertainty realizations. It also considers ES applications for economy and security purposes. Considering the special features of ES operations under day-ahead decision mechanisms of distribution systems, an ES operation scheme is designed for transferring uncertainties to later hours through ES to ensure the secure operation of distribution system. As a result, uncertainties from different time intervals are assembled and may counteract each other, thereby alleviating the uncertainties. As different ES applications rely on ES flexibility (in terms of charging and discharging) and interact with each other, by coordinating different ES applications, the proposed operation model achieves efficient exploit of ES flexibility. To shorten the computation time, a long short-term memory recurrent neural network is used to determine the binary variables corresponding to ES status. The proposed operation model then becomes a convex optimization problem and is solved precisely. Thus, the solving efficiency is greatly improved while ensuring the satisfactory use of ES flexibility in distribution system operation.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10508288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324305","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 : 2024-04-23DOI: 10.35833/MPCE.2023.000648
Shangning Tan;Junliang Liu;Xiong Du;Jingyuan Su;Lijuan Fan
The voltage source converter based multi-terminal high-voltage direct current (VSC-MTDC) system has attracted much attention because it can achieve the interconnection between AC grids. However, the initial phases and short-circuit ratios (SCRs) of the interconnected AC grids cause the steady-state phases (SSPs) of AC ports in the VSC-MTDC system to be different. This can lead to issues such as mismatches in multiple converter reference frame systems, potentially causing inaccuracies in stability analysis when this phenomenon is disregarded. To address the aforementioned issues, a multi-port network model of the VSC-MTDC system, which considers the SSPs of the AC grids and AC ports, is derived by multiplying the port models of different subsystems (SSs). The proposed multi-port network model can accurately describe the transmission characteristics between the input and output ports of the system. Additionally, this model facilitates accurate analysis of the system stability. Furthermore, it identifies the key factors affecting the system stability. Ultimately, the accuracy of the proposed multi-port network model and the analysis of key factors are verified by time-domain simulations.
{"title":"Multi-Port Network Modeling and Stability Analysis of VSC-MTDC Systems","authors":"Shangning Tan;Junliang Liu;Xiong Du;Jingyuan Su;Lijuan Fan","doi":"10.35833/MPCE.2023.000648","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000648","url":null,"abstract":"The voltage source converter based multi-terminal high-voltage direct current (VSC-MTDC) system has attracted much attention because it can achieve the interconnection between AC grids. However, the initial phases and short-circuit ratios (SCRs) of the interconnected AC grids cause the steady-state phases (SSPs) of AC ports in the VSC-MTDC system to be different. This can lead to issues such as mismatches in multiple converter reference frame systems, potentially causing inaccuracies in stability analysis when this phenomenon is disregarded. To address the aforementioned issues, a multi-port network model of the VSC-MTDC system, which considers the SSPs of the AC grids and AC ports, is derived by multiplying the port models of different subsystems (SSs). The proposed multi-port network model can accurately describe the transmission characteristics between the input and output ports of the system. Additionally, this model facilitates accurate analysis of the system stability. Furthermore, it identifies the key factors affecting the system stability. Ultimately, the accuracy of the proposed multi-port network model and the analysis of key factors are verified by time-domain simulations.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10507191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328376","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}
An nonlinear model predictive controller (NMPC) is proposed in this paper for compensations of single line-to-ground (SLG) faults in resonant grounded power distribution networks (RGPDNs), which reduces the likelihood of power line bushfire due to electric faults. Residual current compensation (RCC) inverters with arc suppression coils (ASCs) in RGPDNs are controlled using the proposed NMPC to provide appropriate compensations during SLG faults. The proposed NMPC is incorporated with the estimation of ASC inductance, where the estimation is carried out based on voltage and current measurements from the neutral point of the distribution network. The compensation scheme is developed in the discrete time using the equivalent circuit of RGPDNs. The proposed NMPC for RCC inverters ensures that the desired current is injected into the neutral point during SLG faults, which is verified through both simulations and control hardware-in-the-loop (CHIL) validations. Comparative results are also presented against an integral sliding mode controller (ISMC) by demonstrating the capability of power line bushfire mitigation.
{"title":"Nonlinear Model Predictive Controller for Compensations of Single Line-to-Ground Fault in Resonant Grounded Power Distribution Networks","authors":"Warnakulasuriya Sonal Prashenajith Fernando;Mostafa Barzegar-Kalashani;Md Apel Mahmud;Shama Naz Islam;Nasser Hosseinzadeh","doi":"10.35833/MPCE.2023.000065","DOIUrl":"10.35833/MPCE.2023.000065","url":null,"abstract":"An nonlinear model predictive controller (NMPC) is proposed in this paper for compensations of single line-to-ground (SLG) faults in resonant grounded power distribution networks (RGPDNs), which reduces the likelihood of power line bushfire due to electric faults. Residual current compensation (RCC) inverters with arc suppression coils (ASCs) in RGPDNs are controlled using the proposed NMPC to provide appropriate compensations during SLG faults. The proposed NMPC is incorporated with the estimation of ASC inductance, where the estimation is carried out based on voltage and current measurements from the neutral point of the distribution network. The compensation scheme is developed in the discrete time using the equivalent circuit of RGPDNs. The proposed NMPC for RCC inverters ensures that the desired current is injected into the neutral point during SLG faults, which is verified through both simulations and control hardware-in-the-loop (CHIL) validations. Comparative results are also presented against an integral sliding mode controller (ISMC) by demonstrating the capability of power line bushfire mitigation.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10505131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769404","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 : 2024-04-18DOI: 10.35833/MPCE.2023.000652
Kunyu Zuo;Lei Wu
The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals, i. e., sharing disturbance mitigation among all controllable assets to even their burden. However, limited by neighboring communication, the time-consuming peer-to-peer coordination of the droop-free control slows down the nodal convergence to global consensus, reducing the power-sharing efficiency as the number of nodes increases. To this end, this paper first proposes a local power-sharing droop-free control scheme to contain disturbances within nearby nodes, in order to reduce the number of nodes involved in the coordination and accelerate the convergence speed. A hybrid local-global power-sharing scheme is then put forward to leverage the merits of both schemes, which also enables the autonomous switching between local and global power-sharing modes according to the system states. Systematic guidance for key control parameter designs is derived via the optimal control methods, by optimizing the power-sharing distributions at the steady-state consensus as well as along the dynamic trajectory to consensus. System stability of the hybrid scheme is proved by the eigenvalue analysis and Lyapunov direct method. Moreover, simulation results validate that the proposed hybrid local-global power-sharing scheme performs stably against disturbances and achieves the expected control performance in local and global power-sharing modes as well as mode transitions. Moreover, compared with the classical global power-sharing scheme, the proposed scheme presents promising benefits in convergence speed and scalability.
{"title":"Hybrid Local-Global Power-Sharing Scheme for Droop-Free Controlled Microgrids","authors":"Kunyu Zuo;Lei Wu","doi":"10.35833/MPCE.2023.000652","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000652","url":null,"abstract":"The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals, i. e., sharing disturbance mitigation among all controllable assets to even their burden. However, limited by neighboring communication, the time-consuming peer-to-peer coordination of the droop-free control slows down the nodal convergence to global consensus, reducing the power-sharing efficiency as the number of nodes increases. To this end, this paper first proposes a local power-sharing droop-free control scheme to contain disturbances within nearby nodes, in order to reduce the number of nodes involved in the coordination and accelerate the convergence speed. A hybrid local-global power-sharing scheme is then put forward to leverage the merits of both schemes, which also enables the autonomous switching between local and global power-sharing modes according to the system states. Systematic guidance for key control parameter designs is derived via the optimal control methods, by optimizing the power-sharing distributions at the steady-state consensus as well as along the dynamic trajectory to consensus. System stability of the hybrid scheme is proved by the eigenvalue analysis and Lyapunov direct method. Moreover, simulation results validate that the proposed hybrid local-global power-sharing scheme performs stably against disturbances and achieves the expected control performance in local and global power-sharing modes as well as mode transitions. Moreover, compared with the classical global power-sharing scheme, the proposed scheme presents promising benefits in convergence speed and scalability.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10505130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324296","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 : 2024-04-18DOI: 10.35833/MPCE.2023.000565
Zifeng Zhang;Yuntao Ju
Networked microgrids (NMGs) are critical in the accommodation of distributed renewable energy. However, the existing centralized state estimation (SE) cannot meet the demands of NMGs in distributed energy management. The current estimator is also not robust against bad data. This study introduces the concepts of relative error to construct an improved robust SE (IRSE) optimization model with mixed-integer nonlinear programming (MINLP) that overcomes the disadvantage of inaccurate results derived from different measurements when the same tolerance range is considered in the robust SE (RSE). To improve the computation efficiency of the IRSE optimization model, the number of binary variables is reduced based on the projection statistics and normalized residual methods, which effectively avoid the problem of slow convergence or divergence of the algorithm caused by too many integer variables. Finally, an embedded consensus alternating direction of multiplier method (ADMM) distribution algorithm based on outer approximation (OA) is proposed to solve the IRSE optimization model. This algorithm can accurately detect bad data and obtain SE results that communicate only the boundary coupling information with neighbors. Numerical tests show that the proposed algorithm effectively detects bad data, obtains more accurate SE results, and ensures the protection of private information in all microgrids.
联网微电网（NMGs）对于适应分布式可再生能源至关重要。然而，现有的集中式状态估计（SE）无法满足分布式能源管理中的 NMGs 需求。目前的估计器对坏数据也不具有鲁棒性。本研究引入了相对误差的概念，利用混合整数非线性编程（MINLP）构建了改进的鲁棒状态估计（IRSE）优化模型，克服了鲁棒状态估计（RSE）在考虑相同容差范围时不同测量结果不准确的缺点。为了提高 IRSE 优化模型的计算效率，基于投影统计和归一化残差方法减少了二进制变量的数量，有效避免了因整数变量过多而导致的算法收敛慢或发散的问题。最后，提出了一种基于外近似（OA）的嵌入式共识交替乘法（ADMM）分布算法来求解 IRSE 优化模型。该算法能准确检测出不良数据，并获得只与邻域传递边界耦合信息的 SE 结果。数值测试表明，所提出的算法能有效检测坏数据，获得更准确的 SE 结果，并确保所有微电网中私人信息的保护。
{"title":"An Embedded Consensus ADMM Distribution Algorithm Based on Outer Approximation for Improved Robust State Estimation of Networked Microgrids","authors":"Zifeng Zhang;Yuntao Ju","doi":"10.35833/MPCE.2023.000565","DOIUrl":"10.35833/MPCE.2023.000565","url":null,"abstract":"Networked microgrids (NMGs) are critical in the accommodation of distributed renewable energy. However, the existing centralized state estimation (SE) cannot meet the demands of NMGs in distributed energy management. The current estimator is also not robust against bad data. This study introduces the concepts of relative error to construct an improved robust SE (IRSE) optimization model with mixed-integer nonlinear programming (MINLP) that overcomes the disadvantage of inaccurate results derived from different measurements when the same tolerance range is considered in the robust SE (RSE). To improve the computation efficiency of the IRSE optimization model, the number of binary variables is reduced based on the projection statistics and normalized residual methods, which effectively avoid the problem of slow convergence or divergence of the algorithm caused by too many integer variables. Finally, an embedded consensus alternating direction of multiplier method (ADMM) distribution algorithm based on outer approximation (OA) is proposed to solve the IRSE optimization model. This algorithm can accurately detect bad data and obtain SE results that communicate only the boundary coupling information with neighbors. Numerical tests show that the proposed algorithm effectively detects bad data, obtains more accurate SE results, and ensures the protection of private information in all microgrids.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10505132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769405","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 : 2024-04-18DOI: 10.35833/MPCE.2023.000893
Xu Yang;Haotian Liu;Wenchuan Wu;Qi Wang;Peng Yu;Jiawei Xing;Yuejiao Wang
As numerous distributed energy resources (DERs) are integrated into the distribution networks, the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks (ADNs). Since accurate models are usually unavailable in ADNs, an increasing number of reinforcement learning (RL) based methods have been proposed for the optimal dispatch problem. However, these RL based methods are typically formulated without safety guarantees, which hinders their application in real world. In this paper, we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic (S3AC) for the optimal dispatch of DERs in ADNs, which not only minimizes the operational cost but also satisfies safety constraints during online execution. In the proposed S3AC, the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition (SCADA) system, effectively providing enhanced safety for executed actions. Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.
{"title":"Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks","authors":"Xu Yang;Haotian Liu;Wenchuan Wu;Qi Wang;Peng Yu;Jiawei Xing;Yuejiao Wang","doi":"10.35833/MPCE.2023.000893","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000893","url":null,"abstract":"As numerous distributed energy resources (DERs) are integrated into the distribution networks, the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks (ADNs). Since accurate models are usually unavailable in ADNs, an increasing number of reinforcement learning (RL) based methods have been proposed for the optimal dispatch problem. However, these RL based methods are typically formulated without safety guarantees, which hinders their application in real world. In this paper, we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic (S3AC) for the optimal dispatch of DERs in ADNs, which not only minimizes the operational cost but also satisfies safety constraints during online execution. In the proposed S3AC, the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition (SCADA) system, effectively providing enhanced safety for executed actions. Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10505133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322029","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 : 2024-04-18DOI: 10.35833/MPCE.2023.000674
B. Vinod Kumar;Aneesa Farhan M A
The popularity of electric vehicles (EVs) has sparked a greater awareness of carbon emissions and climate impact. Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations (EVCSs), impacting radial distribution networks (RDNs). To reduce the impact of voltage drop, the increased power loss (PL), lower system interruption costs, and proper allocation and positioning of the EVCSs and capacitors are necessary. This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value (NPV), considering the reduction in energy losses and interruption costs. As a part of the analysis considering reliability, several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV. To locate the best nodes for EVCSs and capacitors, the hybrid of grey wolf optimization (GWO) and particle swarm optimization (PSO) (HGWO_PSO) and the hybrid of PSO and Cuckoo search (CS) (HPSO_CS) algorithms are proposed, forming a combination of GWO, PSO, and CS optimizations. The impact of EVCSs on NPV is also investigated in this paper. The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.
{"title":"Optimal Simultaneous Allocation of Electric Vehicle Charging Stations and Capacitors in Radial Distribution Network Considering Reliability","authors":"B. Vinod Kumar;Aneesa Farhan M A","doi":"10.35833/MPCE.2023.000674","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000674","url":null,"abstract":"The popularity of electric vehicles (EVs) has sparked a greater awareness of carbon emissions and climate impact. Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations (EVCSs), impacting radial distribution networks (RDNs). To reduce the impact of voltage drop, the increased power loss (PL), lower system interruption costs, and proper allocation and positioning of the EVCSs and capacitors are necessary. This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value (NPV), considering the reduction in energy losses and interruption costs. As a part of the analysis considering reliability, several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV. To locate the best nodes for EVCSs and capacitors, the hybrid of grey wolf optimization (GWO) and particle swarm optimization (PSO) (HGWO_PSO) and the hybrid of PSO and Cuckoo search (CS) (HPSO_CS) algorithms are proposed, forming a combination of GWO, PSO, and CS optimizations. The impact of EVCSs on NPV is also investigated in this paper. The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10505134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324270","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}
Fault diagnosis (FD) for offshore wind turbines (WTs) are instrumental to their operation and maintenance (O&M). To improve the FD effect in the very early stage, a condition monitoring based sample set mining method from super-visory control and data acquisition (SCADA) time-series data is proposed. Then, based on the convolutional neural network (CNN) and attention mechanism, an interpretable convolutional temporal-spatial attention network (CTSAN) model is proposed. The proposed CTSAN model can extract deep temporal-spatial features from SCADA time-series data sequentially by: ① a convolution feature extraction module to extract features based on time intervals; ② a spatial attention module to extract spatial features considering the weights of different features; and ③ a temporal attention module to extract temporal features considering the weights of intervals. The proposed CT-SAN model has the superiority of interpretability by exposing the deep temporal-spatial features extracted in a human-understandable form of the temporal-spatial attention weights. The effectiveness and superiority of the proposed CTSAN model are verified by real offshore wind farms in China.
{"title":"Fault Diagnosis Based on Interpretable Convolutional Temporal-Spatial Attention Network for Offshore Wind Turbines","authors":"Xiangjing Su;Chao Deng;Yanhao Shan;Farhad Shahnia;Yang Fu;Zhaoyang Dong","doi":"10.35833/MPCE.2023.000606","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000606","url":null,"abstract":"Fault diagnosis (FD) for offshore wind turbines (WTs) are instrumental to their operation and maintenance (O&M). To improve the FD effect in the very early stage, a condition monitoring based sample set mining method from super-visory control and data acquisition (SCADA) time-series data is proposed. Then, based on the convolutional neural network (CNN) and attention mechanism, an interpretable convolutional temporal-spatial attention network (CTSAN) model is proposed. The proposed CTSAN model can extract deep temporal-spatial features from SCADA time-series data sequentially by: ① a convolution feature extraction module to extract features based on time intervals; ② a spatial attention module to extract spatial features considering the weights of different features; and ③ a temporal attention module to extract temporal features considering the weights of intervals. The proposed CT-SAN model has the superiority of interpretability by exposing the deep temporal-spatial features extracted in a human-understandable form of the temporal-spatial attention weights. The effectiveness and superiority of the proposed CTSAN model are verified by real offshore wind farms in China.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10494233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324322","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}
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