Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.02720
Lirong Deng;Xuan Zhang;Tianshu Yang;Hongbin Sun;Yang Fu;Qinglai Guo;Shmuel S. Oren
In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arbitrage value. However, this price-smoothing effect can result in significant external welfare changes by reducing consumer costs and producer revenues, which is not negligible for the community with energy storage systems. As such, we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare. To incorporate market interaction into the SDP format, we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices. Then we present an analytical SDP algorithm that does not require state discretization. Apart from computational efficiency, another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value. Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage. The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.
{"title":"Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming","authors":"Lirong Deng;Xuan Zhang;Tianshu Yang;Hongbin Sun;Yang Fu;Qinglai Guo;Shmuel S. Oren","doi":"10.17775/CSEEJPES.2023.02720","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.02720","url":null,"abstract":"In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arbitrage value. However, this price-smoothing effect can result in significant external welfare changes by reducing consumer costs and producer revenues, which is not negligible for the community with energy storage systems. As such, we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare. To incorporate market interaction into the SDP format, we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices. Then we present an analytical SDP algorithm that does not require state discretization. Apart from computational efficiency, another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value. Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage. The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375969","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In a power system, when extreme events occur, such as ice storm, large scale blackouts may be unavoidable. Such small probability but high risk events have huge impact on power systems. Most resilience research in power systems only considers faults on the physical side, which would lead to overly idealistic results. This paper proposes a two-stage cyber-physical resilience enhancement method considering energy storage (ES) systems. The first stage calculates optimal planning of ES systems, and the second stage assesses resilience and enhancement of ES systems during the disaster. In the proposed model, cyber faults indirectly damage the system by disabling monitoring and control function of control center. As a result, when detection and response process of physical faults are blocked by cyber failures, serious load shedding occurs. Such a cyber-physical coupling mechanism of fault, response, restoration process is demonstrated in the modified IEEE Reliable Test System-79 (RTS-79). Simulation results show compared with the physical-only system, the cyber-physical system has a more accurate but degraded resilient performance. Besides, ES systems setting at proper place effectively enhance resilience of the cyber-physical transmission system with less load Shedding.
在电力系统中，当发生冰风暴等极端事件时，大规模停电可能不可避免。这种小概率但高风险的事件会对电力系统产生巨大影响。大多数电力系统复原力研究只考虑物理方面的故障，这将导致过于理想化的结果。本文提出了一种考虑到储能（ES）系统的两阶段网络物理弹性增强方法。第一阶段计算 ES 系统的最优规划，第二阶段评估 ES 系统在灾难期间的恢复能力和增强能力。在所提出的模型中，网络故障会使控制中心的监控功能失效，从而间接损害系统。因此，当物理故障的检测和响应过程被网络故障阻断时，就会出现严重的甩负荷现象。这种故障、响应和恢复过程的网络-物理耦合机制在修改后的 IEEE 可靠性测试系统-79（RTS-79）中得到了验证。仿真结果表明，与纯物理系统相比，网络物理系统具有更高的准确性，但弹性性能有所下降。此外，在适当位置设置 ES 系统可有效提高网络物理输电系统的恢复能力，减少甩负荷。
{"title":"Cyber-Physical Resilience Enhancement for Power Transmission Systems with Energy Storage Systems","authors":"Wenhao Zhang;Dongyang Rui;Weihong Wang;Yang Guo;Zhaoxia Jing;Wenhu Tang","doi":"10.17775/CSEEJPES.2022.07570","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.07570","url":null,"abstract":"In a power system, when extreme events occur, such as ice storm, large scale blackouts may be unavoidable. Such small probability but high risk events have huge impact on power systems. Most resilience research in power systems only considers faults on the physical side, which would lead to overly idealistic results. This paper proposes a two-stage cyber-physical resilience enhancement method considering energy storage (ES) systems. The first stage calculates optimal planning of ES systems, and the second stage assesses resilience and enhancement of ES systems during the disaster. In the proposed model, cyber faults indirectly damage the system by disabling monitoring and control function of control center. As a result, when detection and response process of physical faults are blocked by cyber failures, serious load shedding occurs. Such a cyber-physical coupling mechanism of fault, response, restoration process is demonstrated in the modified IEEE Reliable Test System-79 (RTS-79). Simulation results show compared with the physical-only system, the cyber-physical system has a more accurate but degraded resilient performance. Besides, ES systems setting at proper place effectively enhance resilience of the cyber-physical transmission system with less load Shedding.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2023-12-28DOI: 10.17775/CSEEJPES.2023.06400
Qing-Chang Zhong;Marcio Stefanello
In this paper, a compact mathematical model having an elegant structure, together with a generic control framework, are proposed for generic power systems dominated by power converters that are interconnected through a passive transmission and distribution (T&D) grid, by adopting the port-Hamiltonian (pH) systems theory and the fundamental circuit theory. The models of generic T&D lines are developed and then the model of a generic T&D grid is established. With the proposed control framework, the controlled converters are proven to be passive and Input-to-State Stable (ISS). The compact mathematical model is scalable and can be applied to power systems with multiple power electronic converters with generic passive controllers, passive local loads, and different types of passive T&D lines connected in a meshed configuration without self-loops, so it is very generic. Moreover, the resulting power system is proven to be ISS as well. The analysis is carried out without assumptions on constant frequency/voltage, constant loads, and/or lossless networks, except the need of passivity for all parts involved, and without using the Clarke/Park transformations or the graph theory. To simplify the presentation, three-phase balanced systems are adopted but the results can be easily adapted for single-phase or unbalanced three-phase systems.
{"title":"Generic Modeling and Control Framework for Power Systems Dominated by Power Converters Connected Through a Passive Transmission and Distribution Grid","authors":"Qing-Chang Zhong;Marcio Stefanello","doi":"10.17775/CSEEJPES.2023.06400","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.06400","url":null,"abstract":"In this paper, a compact mathematical model having an elegant structure, together with a generic control framework, are proposed for generic power systems dominated by power converters that are interconnected through a passive transmission and distribution (T&D) grid, by adopting the port-Hamiltonian (pH) systems theory and the fundamental circuit theory. The models of generic T&D lines are developed and then the model of a generic T&D grid is established. With the proposed control framework, the controlled converters are proven to be passive and Input-to-State Stable (ISS). The compact mathematical model is scalable and can be applied to power systems with multiple power electronic converters with generic passive controllers, passive local loads, and different types of passive T&D lines connected in a meshed configuration without self-loops, so it is very generic. Moreover, the resulting power system is proven to be ISS as well. The analysis is carried out without assumptions on constant frequency/voltage, constant loads, and/or lossless networks, except the need of passivity for all parts involved, and without using the Clarke/Park transformations or the graph theory. To simplify the presentation, three-phase balanced systems are adopted but the results can be easily adapted for single-phase or unbalanced three-phase systems.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2023-12-28DOI: 10.17775/CSEEJPES.2023.03930
Yongheng Wang;Xinwei Shen;Yan Xu
This paper proposes a collaborative planning model for active distribution network (ADN) and electric vehicle (EV) charging stations that fully considers vehicle-to-grid (V2G) function and reactive power support of EVs in different regions. This paper employs a sequential decomposition method based on physical characteristics of the problem, breaking down the holistic problem into two sub-problems for solution. Subproblem I optimizes the charging and discharging behavior of autopilot electric vehicles (AEVs) using a mixed-integer linear programming (MILP) model. Subproblem II uses a mixed-integer second-order cone programming (MISOCP) model to plan ADN and retrofit or construct V2G charging stations (V2GCS), as well as multiple distributed generation resources (DGRs). The paper also analyzes the impact of bi-directional active-reactive power interaction of V2GCS on ADN planning. The presented model is tested in the 47-node ADN in Longgang District, Shenzhen, China, and the IEEE 33-node ADN, demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.
{"title":"Joint Planning of Active Distribution Network and EV Charging Stations Considering Vehicle-to-Grid Functionality and Reactive Power Support","authors":"Yongheng Wang;Xinwei Shen;Yan Xu","doi":"10.17775/CSEEJPES.2023.03930","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.03930","url":null,"abstract":"This paper proposes a collaborative planning model for active distribution network (ADN) and electric vehicle (EV) charging stations that fully considers vehicle-to-grid (V2G) function and reactive power support of EVs in different regions. This paper employs a sequential decomposition method based on physical characteristics of the problem, breaking down the holistic problem into two sub-problems for solution. Subproblem I optimizes the charging and discharging behavior of autopilot electric vehicles (AEVs) using a mixed-integer linear programming (MILP) model. Subproblem II uses a mixed-integer second-order cone programming (MISOCP) model to plan ADN and retrofit or construct V2G charging stations (V2GCS), as well as multiple distributed generation resources (DGRs). The paper also analyzes the impact of bi-directional active-reactive power interaction of V2GCS on ADN planning. The presented model is tested in the 47-node ADN in Longgang District, Shenzhen, China, and the IEEE 33-node ADN, demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Generator tripping scheme (GTS) is the most commonly used scheme to prevent power systems from losing safety and stability. Usually, GTS is composed of offline predetermination and real-time scenario match. However, it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system. To improve efficiency of predetermination, this paper proposes a framework of knowledge fusion-based deep reinforcement learning (KF-DRL) for intelligent predetermination of GTS. First, the Markov Decision Process (MDP) for GTS problem is formulated based on transient instability events. Then, linear action space is developed to reduce dimensionality of action space for multiple controllable generators. Especially, KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process. This can enhance the efficiency and learning process. Moreover, the graph convolutional network (GCN) is introduced to the policy network for enhanced learning ability. Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.
{"title":"Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework","authors":"Lingkang Zeng;Wei Yao;Ze Hu;Hang Shuai;Zhouping Li;Jinyu Wen;Shijie Cheng","doi":"10.17775/CSEEJPES.2022.08970","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.08970","url":null,"abstract":"Generator tripping scheme (GTS) is the most commonly used scheme to prevent power systems from losing safety and stability. Usually, GTS is composed of offline predetermination and real-time scenario match. However, it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system. To improve efficiency of predetermination, this paper proposes a framework of knowledge fusion-based deep reinforcement learning (KF-DRL) for intelligent predetermination of GTS. First, the Markov Decision Process (MDP) for GTS problem is formulated based on transient instability events. Then, linear action space is developed to reduce dimensionality of action space for multiple controllable generators. Especially, KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process. This can enhance the efficiency and learning process. Moreover, the graph convolutional network (GCN) is introduced to the policy network for enhanced learning ability. Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375964","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2023-12-28DOI: 10.17775/CSEEJPES.2022.05250
Hongyuan Liang;Zhigang Li;J. H. Zheng;Q. H. Wu
Line-commutated converter (LCC)-based high-voltage DC (HVDC) systems have been integrated with bulk AC power grids for interregional transmission of renewable power. The nonlinear LCC model brings additional nonconvexity to optimal power flow (OPF) of hybrid AC-DC power grids. A convexification method for the LCC station model could address such nonconvexity but has rarely been discussed. We devise an equivalent reformulation for classical LCC station models that facilitates second-order cone convex relaxation for the OPF of LCC-based AC-DC power grids. We also propose sufficient conditions for exactness of convex relaxation with its proof. Equivalence of the proposed LCC station models and properties, exactness, and effectiveness of convex relaxation are verified using four numerical simulations. Simulation results demonstrate a globally optimal solution of the original OPF can be efficiently obtained from relaxed model.
{"title":"Convexification of Hybrid AC-DC Optimal Power Flow with Line-Commutated Converters","authors":"Hongyuan Liang;Zhigang Li;J. H. Zheng;Q. H. Wu","doi":"10.17775/CSEEJPES.2022.05250","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.05250","url":null,"abstract":"Line-commutated converter (LCC)-based high-voltage DC (HVDC) systems have been integrated with bulk AC power grids for interregional transmission of renewable power. The nonlinear LCC model brings additional nonconvexity to optimal power flow (OPF) of hybrid AC-DC power grids. A convexification method for the LCC station model could address such nonconvexity but has rarely been discussed. We devise an equivalent reformulation for classical LCC station models that facilitates second-order cone convex relaxation for the OPF of LCC-based AC-DC power grids. We also propose sufficient conditions for exactness of convex relaxation with its proof. Equivalence of the proposed LCC station models and properties, exactness, and effectiveness of convex relaxation are verified using four numerical simulations. Simulation results demonstrate a globally optimal solution of the original OPF can be efficiently obtained from relaxed model.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2023-12-28DOI: 10.17775/CSEEJPES.2023.00440
Baoluo Li;Shiyun Xu;Huadong Sun;Zonghan Li;Lin Yu
Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.
{"title":"System Strength Assessment Based on Multi-task Learning","authors":"Baoluo Li;Shiyun Xu;Huadong Sun;Zonghan Li;Lin Yu","doi":"10.17775/CSEEJPES.2023.00440","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00440","url":null,"abstract":"Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2023-12-28DOI: 10.17775/CSEEJPES.2023.00190
Tianyun Zhang;Jun Zhang;Feiyue Wang;Peidong Xu;Tianlu Gao;Haoran Zhang;Ruiqi Si
In artificial intelligence (AI) based-complex power system management and control technology, one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution. However, there is, currently, nearly no standard technical framework for objective and quantitative intelligence evaluation. In this article, based on a parallel system framework, a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems, by resorting to human intelligence evaluation theories. On this basis, this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning (AutoRL) systems. A parallel system based quantitative assessment and self-evolution (PLASE) system for power grid corrective control AI is thereby constructed, taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results. Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent, and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results, effectively, as well as intuitively improving its intelligence level through self-evolution.
{"title":"Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control","authors":"Tianyun Zhang;Jun Zhang;Feiyue Wang;Peidong Xu;Tianlu Gao;Haoran Zhang;Ruiqi Si","doi":"10.17775/CSEEJPES.2023.00190","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00190","url":null,"abstract":"In artificial intelligence (AI) based-complex power system management and control technology, one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution. However, there is, currently, nearly no standard technical framework for objective and quantitative intelligence evaluation. In this article, based on a parallel system framework, a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems, by resorting to human intelligence evaluation theories. On this basis, this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning (AutoRL) systems. A parallel system based quantitative assessment and self-evolution (PLASE) system for power grid corrective control AI is thereby constructed, taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results. Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent, and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results, effectively, as well as intuitively improving its intelligence level through self-evolution.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emergency control is an essential means to help system maintain synchronism after fault clearance. Traditional “offline calculation, online matching” scheme faces significant challenges on adaptiveness and robustness problems. To address these challenges, this paper proposes a novel closed-loop framework of transient stability prediction (TSP) and emergency control based on Deep Belief Network (DBN). First, a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted, which takes into account accuracy and rapidity at the same time. Next, a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity. When impending instability of the system is foreseen, optimal emergency control strategy can be determined in time. Lastly, responses after emergency control are fed back to the TSP model. If prediction result is still unstable, an additional control strategy will be implemented. Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council (NPCC) 140-bus system. Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.
{"title":"Adaptive Emergency Control of Power Systems Based on Deep Belief Network","authors":"Junyong Wu;Baoqin Li;Liangliang Hao;Fashun Shi;Pengjie Zhao","doi":"10.17775/CSEEJPES.2022.00070","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.00070","url":null,"abstract":"Emergency control is an essential means to help system maintain synchronism after fault clearance. Traditional “offline calculation, online matching” scheme faces significant challenges on adaptiveness and robustness problems. To address these challenges, this paper proposes a novel closed-loop framework of transient stability prediction (TSP) and emergency control based on Deep Belief Network (DBN). First, a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted, which takes into account accuracy and rapidity at the same time. Next, a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity. When impending instability of the system is foreseen, optimal emergency control strategy can be determined in time. Lastly, responses after emergency control are fed back to the TSP model. If prediction result is still unstable, an additional control strategy will be implemented. Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council (NPCC) 140-bus system. Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375981","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2023-12-28DOI: 10.17775/CSEEJPES.2023.05240
Boyuan Yin;Xianwu Zeng;John Frederick Eastham;Emelie Nilsson;Jean-francois Rouquette;Jean Rivenc;Ludovic Ybanez;Xiaoze Pei
Hydrogen-powered electric aircraft have attracted significant interests aiming to achieve decarbonization targets. Onboard DC electric networks are facing great challenges in DC fault protection requirements. Vacuum interrupters are widely used in low voltage and medium voltage power systems due to being environmentally friendly with low maintenance. In this paper a moving coil actuator with compensation coils for a vacuum interrupter, as part of a hybrid direct current circuit breaker, is designed and experimentally tested. Compensation coils are used to improve operating speed compared with original moving coil actuator. Comparisons between four possible connections of compensation coils and original moving coil actuator are carried out. Experimental results show comparisons between different connections of actuator coils in terms of opening time and coil current with a range of pre-charged capacitor voltages. Dynamic performance of each actuator connection is also compared. The actuator with compensation coils is shown to have a higher current rising rate and achieve faster opening speed, which is a critical requirement for electric aircraft network protection. The parallel connection actuator achieves the highest opening speed within 3.5 ms with capacitor voltage of 50 V.
氢动力电动飞机在实现脱碳目标方面备受关注。机载直流电网在直流故障保护要求方面面临巨大挑战。真空灭弧室因其环保和低维护成本的特点，被广泛应用于低压和中压电力系统。本文设计了一种带补偿线圈的动圈传动装置，用于真空灭弧室，作为混合直流断路器的一部分，并进行了实验测试。与原来的动圈传动器相比，补偿线圈用于提高运行速度。对补偿线圈的四种可能连接方式和原始动圈推杆进行了比较。实验结果表明，在预充电容电压范围内，不同连接方式的致动器线圈在打开时间和线圈电流方面都有可比性。此外，还比较了每种致动器连接的动态性能。结果表明，带补偿线圈的致动器具有更高的电流上升率和更快的打开速度，而这正是飞机电网保护的关键要求。并联致动器在电容器电压为 50 V 时，可在 3.5 ms 内达到最高打开速度。
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