Pub Date : 2025-03-16DOI: 10.17775/CSEEJPES.2024.05770
Ruizhi Yu;Wei Gu;Yijun Xu;Shuai Lu;Suhan Zhang
As an effective emulator of ill-conditioned power flow, continuous Newton methods (CNMs) have been extensively investigated using explicit and implicit numerical integration algorithms. However, explicit CNMs often suffer from non-convergence due to their limited stability region, while implicit CNMs require additional iterative loops to solve nonlinear equations. To address this, we propose a semi-implicit version of CNM. We formulate the power flow equations as a set of differential algebraic equations (DAEs), and solve the DAEs with the stiffly accurate Rosenbrock type method (SARM). The proposed method succeeds the numerical robustness from the implicit CNM framework while prevents the iterative solution of nonlinear systems, hence revealing higher convergence speed and computation efficiency. We develop a novel 4-stage, 3rd-order hyper-stable SARM with an embedded 2nd-order formula for adaptive step size control. This design enhances convergence through damping adjustment. Case studies on ill-conditioned systems verify the alleged performance. An algorithm extension for MATPOWER is made available on Github for benchmarking.
{"title":"Semi-Implicit Continuous Newton Method for Power Flow Analysis","authors":"Ruizhi Yu;Wei Gu;Yijun Xu;Shuai Lu;Suhan Zhang","doi":"10.17775/CSEEJPES.2024.05770","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.05770","url":null,"abstract":"As an effective emulator of ill-conditioned power flow, continuous Newton methods (CNMs) have been extensively investigated using explicit and implicit numerical integration algorithms. However, explicit CNMs often suffer from non-convergence due to their limited stability region, while implicit CNMs require additional iterative loops to solve nonlinear equations. To address this, we propose a semi-implicit version of CNM. We formulate the power flow equations as a set of differential algebraic equations (DAEs), and solve the DAEs with the stiffly accurate Rosenbrock type method (SARM). The proposed method succeeds the numerical robustness from the implicit CNM framework while prevents the iterative solution of nonlinear systems, hence revealing higher convergence speed and computation efficiency. We develop a novel 4-stage, 3rd-order hyper-stable SARM with an embedded 2nd-order formula for adaptive step size control. This design enhances convergence through damping adjustment. Case studies on ill-conditioned systems verify the alleged performance. An algorithm extension for MATPOWER is made available on Github for benchmarking.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 4","pages":"1957-1961"},"PeriodicalIF":5.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831952","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}
The integration of photovoltaic and energy storage in industrial parks enhances economic benefits. However, uncertainties in photovoltaic output and future electricity prices pose challenges to optimal configuration. To address these issues, this paper develops a credibility copula-based robust multistage plan. Firstly, it addresses endogenous uncertainties in electricity pricing and exogenous uncertainties in photovoltaic output. Meanwhile, the copula function is used to couple endogenous uncertainties of the time-of-use, on-grid and demand power prices. Secondly, based on credibility theory, a fuzzy chance constraint model of endogenous uncertainties of future electricity prices and exogenous uncertainties of the PV output is derived. Finally, the method transforms fuzzy chance constraints into deterministic robust optimization through clear equivalence classes. Simulation analyses using data from an industrial park validate the applicability and effectiveness of the proposed approach.
{"title":"Credibility Copula-Based Robust Multistage Plan for Industrial Parks Under Exogenous and Endogenous Uncertainties","authors":"Zehao Shi;Jiajia Chen;Yanxin Wang;Yanlei Zhao;Bingyin Xu","doi":"10.17775/CSEEJPES.2024.09120","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.09120","url":null,"abstract":"The integration of photovoltaic and energy storage in industrial parks enhances economic benefits. However, uncertainties in photovoltaic output and future electricity prices pose challenges to optimal configuration. To address these issues, this paper develops a credibility copula-based robust multistage plan. Firstly, it addresses endogenous uncertainties in electricity pricing and exogenous uncertainties in photovoltaic output. Meanwhile, the copula function is used to couple endogenous uncertainties of the time-of-use, on-grid and demand power prices. Secondly, based on credibility theory, a fuzzy chance constraint model of endogenous uncertainties of future electricity prices and exogenous uncertainties of the PV output is derived. Finally, the method transforms fuzzy chance constraints into deterministic robust optimization through clear equivalence classes. Simulation analyses using data from an industrial park validate the applicability and effectiveness of the proposed approach.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"987-998"},"PeriodicalIF":6.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243747","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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.06280
Yan Xie;Shiying Ma;Jiakai Shen;Xiaojun Tang;Jianmiao Ren;Liwen Zheng
The construction of UHV AC/DC hybrid power grids and the integration of large-scale renewable energy have led to significant frequency stability issues. To enhance the frequency regulation capacity within areas and fully utilize the mutual supportive capacity among areas, active frequency control (AFC) theory has been proposed and developed based on the concept of active feed-forward control. However, existing AFC methods have limitations in computational solutions and control effects in large-scale, wide-area interconnected power systems. To address these shortcomings, this paper proposes an improved AFC method based on multi-step-size model predictive control (MSS-MPC). Through multi-step-size discretization, an improved model predictive control algorithm for AFC is derived based on the iterative solution of the multi-step-size linear quadratic regulator model, which ensures control accuracy through precise prediction and enhances the control performance through additional prediction. Based on the collaboration of two-level dispatching agencies and the consideration of differences in control objectives among areas at different stages, an improved three-stage AFC strategy is proposed, which aims to suppress additional frequency disturbances after control model switching by proposing the active-passive switching strategy. Case studies demonstrate that, compared with the existing AFC method, the proposed AFC method with the MSS-MPC algorithm has a better control effect and better computational performance.
{"title":"Wide-Area Active Frequency Control with Multi-Step-Size MPC","authors":"Yan Xie;Shiying Ma;Jiakai Shen;Xiaojun Tang;Jianmiao Ren;Liwen Zheng","doi":"10.17775/CSEEJPES.2024.06280","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.06280","url":null,"abstract":"The construction of UHV AC/DC hybrid power grids and the integration of large-scale renewable energy have led to significant frequency stability issues. To enhance the frequency regulation capacity within areas and fully utilize the mutual supportive capacity among areas, active frequency control (AFC) theory has been proposed and developed based on the concept of active feed-forward control. However, existing AFC methods have limitations in computational solutions and control effects in large-scale, wide-area interconnected power systems. To address these shortcomings, this paper proposes an improved AFC method based on multi-step-size model predictive control (MSS-MPC). Through multi-step-size discretization, an improved model predictive control algorithm for AFC is derived based on the iterative solution of the multi-step-size linear quadratic regulator model, which ensures control accuracy through precise prediction and enhances the control performance through additional prediction. Based on the collaboration of two-level dispatching agencies and the consideration of differences in control objectives among areas at different stages, an improved three-stage AFC strategy is proposed, which aims to suppress additional frequency disturbances after control model switching by proposing the active-passive switching strategy. Case studies demonstrate that, compared with the existing AFC method, the proposed AFC method with the MSS-MPC algorithm has a better control effect and better computational performance.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"972-986"},"PeriodicalIF":6.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006428","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243861","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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.07190
Yuegong Li;Guorong Zhu;Jianghua Lu;Hua Geng
Generally, voltage support at the point of common coupling (PCC) of a wind farm is achieved through centralized static var generators (SVGs). Since the reactive power requirements occupy their capacity in a steady state, the reactive power support capacity of the SVG is limited during high voltage ride through (HVRT) or low voltage ride through (LVRT). While wind turbines can provide voltage support in accordance with the grid code, their responses are usually delayed due to communication and transmission lags. To enhance the dynamic performance of wind farms during fault ride-through, a reactive power substitution (RPS) control strategy is proposed in this paper. In a steady state, this RPS control method preferentially utilizes the remaining capacity of wind turbines to substitute for the output of the SVG. Considering differences in terminal voltage characteristics and operating conditions, this RPS control method employs a particle swarm optimization (PSO) algorithm to ensure that wind turbines can provide their optimal reactive power support capacity. When the grid voltage swells or drops, the SVG has a sufficient reactive power reserve to support the grid quickly. This paper utilizes a regional power grid incorporating two wind farms connected to different buses as a case study to validate this RPS control strategy.
{"title":"Voltage Support Capacity Improvement for Wind Farms with Reactive Power Substitution Control","authors":"Yuegong Li;Guorong Zhu;Jianghua Lu;Hua Geng","doi":"10.17775/CSEEJPES.2024.07190","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.07190","url":null,"abstract":"Generally, voltage support at the point of common coupling (PCC) of a wind farm is achieved through centralized static var generators (SVGs). Since the reactive power requirements occupy their capacity in a steady state, the reactive power support capacity of the SVG is limited during high voltage ride through (HVRT) or low voltage ride through (LVRT). While wind turbines can provide voltage support in accordance with the grid code, their responses are usually delayed due to communication and transmission lags. To enhance the dynamic performance of wind farms during fault ride-through, a reactive power substitution (RPS) control strategy is proposed in this paper. In a steady state, this RPS control method preferentially utilizes the remaining capacity of wind turbines to substitute for the output of the SVG. Considering differences in terminal voltage characteristics and operating conditions, this RPS control method employs a particle swarm optimization (PSO) algorithm to ensure that wind turbines can provide their optimal reactive power support capacity. When the grid voltage swells or drops, the SVG has a sufficient reactive power reserve to support the grid quickly. This paper utilizes a regional power grid incorporating two wind farms connected to different buses as a case study to validate this RPS control strategy.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"999-1017"},"PeriodicalIF":6.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243749","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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.03030
Qi Chen;Gang Mu;Hongbo Liu;Changgang Wang
The data acquisition technologies used in power systems have been continuously improving, thus laying the solid foundation for data-driven operation analysis of power systems. However, existing methods for analyzing the relationship between operational variables mainly depend on the mathematical model and element parameters of the power system. Therefore, a thorough data-based analysis method is required to investigate the spatiotemporal characteristics of power system operation, especially for new types of power systems. The causal inference method, which has been successfully applied in many fields, is a powerful tool for investigating the interaction of data variables. In this study, a causal inference method is proposed based on supervisory control and data acquisition (SCADA) data for investigating the spatiotemporal causal relationships in power systems. Initially, a multiple data-sequence regression model is proposed to analyze the relationship of operation data variables. Next, the linear non-Gaussian acyclic model (LiNGAM) is used to calculate the causal index of the operational variables, and its limitations are analyzed. Furthermore, a new causal index of “full variable amplitude LiNGAM (FVA-LiNGAM)” is proposed by incorporating prior causal direct knowledge and considering the effect of real variable amplitude. Using the FVA-LiNGAM causal index, the causal relationship of operation variables can be investigated with higher spatiotemporal accuracy than that of the original LiNGAM index. Taking a real SCADA data subset of a provincial power system as an example, the validity of the FVA-LiNGAM causal index is verified. The variation patterns in spatiotemporal causality are explored using actual SCADA data sequences. The result shows that there indeed exists some spatiotemporal causality variation patterns between the operating variables of the power system.
{"title":"Data-Sequence Modeling Based Causal Evaluation Method for Power Systems and Spatiotemporal Causality Variation Patterns","authors":"Qi Chen;Gang Mu;Hongbo Liu;Changgang Wang","doi":"10.17775/CSEEJPES.2024.03030","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.03030","url":null,"abstract":"The data acquisition technologies used in power systems have been continuously improving, thus laying the solid foundation for data-driven operation analysis of power systems. However, existing methods for analyzing the relationship between operational variables mainly depend on the mathematical model and element parameters of the power system. Therefore, a thorough data-based analysis method is required to investigate the spatiotemporal characteristics of power system operation, especially for new types of power systems. The causal inference method, which has been successfully applied in many fields, is a powerful tool for investigating the interaction of data variables. In this study, a causal inference method is proposed based on supervisory control and data acquisition (SCADA) data for investigating the spatiotemporal causal relationships in power systems. Initially, a multiple data-sequence regression model is proposed to analyze the relationship of operation data variables. Next, the linear non-Gaussian acyclic model (LiNGAM) is used to calculate the causal index of the operational variables, and its limitations are analyzed. Furthermore, a new causal index of “full variable amplitude LiNGAM (FVA-LiNGAM)” is proposed by incorporating prior causal direct knowledge and considering the effect of real variable amplitude. Using the FVA-LiNGAM causal index, the causal relationship of operation variables can be investigated with higher spatiotemporal accuracy than that of the original LiNGAM index. Taking a real SCADA data subset of a provincial power system as an example, the validity of the FVA-LiNGAM causal index is verified. The variation patterns in spatiotemporal causality are explored using actual SCADA data sequences. The result shows that there indeed exists some spatiotemporal causality variation patterns between the operating variables of the power system.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 4","pages":"1429-1441"},"PeriodicalIF":5.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831643","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 : 2025-03-16DOI: 10.17775/CSEEJPES.2024.08620
Zhenglong Sun;Zhifeng He;Naiyuan Liu;Rui Zhang;Bo Wang;Guowei Cai
With the sustained increase in the penetration rate of grid-forming (GFM) converters in power systems, problems such as overcurrent and transient instability during system faults have drawn significant attention. This paper proposes an enhanced dynamic dq-axis current-limiting strategy (ED-CLiS) for GFM converters, which improves transient stability across all stages before and after current limiting. First, a dynamic dq-axis current-limiting strategy (D-CLiS) is proposed. This strategy regulates the power angle to prevent it from going out of control during current limiting, which is achieved by controlling the $d$- and $q$-axis current reference values to reshape the ${P}-delta^{prime}$ curve. Additionally, a large reverse acceleration area can induce excessive angle $delta^{prime}$ swings, causing the D-CLiS to be triggered repeatedly and resulting in converter instability after the control exit, even without faults. A reference power control structure (RPCS) is proposed, which suppresses the phenomenon by adjusting the power reference value to control the converter's reverse acceleration area. Finally, the control strategy is verified through simulations.
{"title":"Enhanced Transient Stability Strategy for Grid-Forming Converter Based on Current Limiting","authors":"Zhenglong Sun;Zhifeng He;Naiyuan Liu;Rui Zhang;Bo Wang;Guowei Cai","doi":"10.17775/CSEEJPES.2024.08620","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.08620","url":null,"abstract":"With the sustained increase in the penetration rate of grid-forming (GFM) converters in power systems, problems such as overcurrent and transient instability during system faults have drawn significant attention. This paper proposes an enhanced dynamic dq-axis current-limiting strategy (ED-CLiS) for GFM converters, which improves transient stability across all stages before and after current limiting. First, a dynamic dq-axis current-limiting strategy (D-CLiS) is proposed. This strategy regulates the power angle to prevent it from going out of control during current limiting, which is achieved by controlling the <tex>$d$</tex>- and <tex>$q$</tex>-axis current reference values to reshape the <tex>${P}-delta^{prime}$</tex> curve. Additionally, a large reverse acceleration area can induce excessive angle <tex>$delta^{prime}$</tex> swings, causing the D-CLiS to be triggered repeatedly and resulting in converter instability after the control exit, even without faults. A reference power control structure (RPCS) is proposed, which suppresses the phenomenon by adjusting the power reference value to control the converter's reverse acceleration area. Finally, the control strategy is verified through simulations.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"960-971"},"PeriodicalIF":6.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243859","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 : 2025-02-21DOI: 10.17775/CSEEJPES.2024.05670
Meiyi Li;Javad Mohammadi
The ever-increasing integration of stochastic renewable energy sources into power systems operation is making the supply-demand balance more challenging. While joint chance-constrained methods are equipped to model these complexities and uncertainties, solving these problems using traditional iterative solvers is often time-consuming, limiting their suitability for real-time applications. To overcome the shortcomings of today's solvers, we propose a fast, scalable, and explainable machine learning-based optimization proxy. Our solution, called Learning to Optimize the Optimization of Joint Chance-Constrained Problems $(mathcal{LOOP}-mathcal{JCCP})$, is iteration-free and solves the underlying problem in a single-shot. Our model uses a polyhedral reformulation of the original problem to manage constraint violations and ensure solution feasibility across various scenarios through customizable probability settings. To this end, we build on our recent deterministic solution $(mathcal{LOOP}-mathcal{LC} 2.0)$ by incorporating a set aggregator module to handle uncertain sample sets of varying sizes and complexities. Our results verify the feasibility of our near-optimal solutions for joint chance-constrained power dispatch scenarios. Additionally, our feasibility guarantees increase the transparency and interpretability of our method, which is essential for operators to trust the outcomes. We showcase the effectiveness of our model in solving the stochastic energy management problem of Virtual Power Plants (VPPs). Our theoretical analysis, supported by empirical evidence, reveals strong flexibility in parameter tuning, adaptability to diverse datasets, and significantly improved computational speed.
{"title":"Learning to Optimize Joint Chance-Constrained Power Dispatch Problems","authors":"Meiyi Li;Javad Mohammadi","doi":"10.17775/CSEEJPES.2024.05670","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.05670","url":null,"abstract":"The ever-increasing integration of stochastic renewable energy sources into power systems operation is making the supply-demand balance more challenging. While joint chance-constrained methods are equipped to model these complexities and uncertainties, solving these problems using traditional iterative solvers is often time-consuming, limiting their suitability for real-time applications. To overcome the shortcomings of today's solvers, we propose a fast, scalable, and explainable machine learning-based optimization proxy. Our solution, called Learning to Optimize the Optimization of Joint Chance-Constrained Problems <tex>$(mathcal{LOOP}-mathcal{JCCP})$</tex>, is iteration-free and solves the underlying problem in a single-shot. Our model uses a polyhedral reformulation of the original problem to manage constraint violations and ensure solution feasibility across various scenarios through customizable probability settings. To this end, we build on our recent deterministic solution <tex>$(mathcal{LOOP}-mathcal{LC} 2.0)$</tex> by incorporating a set aggregator module to handle uncertain sample sets of varying sizes and complexities. Our results verify the feasibility of our near-optimal solutions for joint chance-constrained power dispatch scenarios. Additionally, our feasibility guarantees increase the transparency and interpretability of our method, which is essential for operators to trust the outcomes. We showcase the effectiveness of our model in solving the stochastic energy management problem of Virtual Power Plants (VPPs). Our theoretical analysis, supported by empirical evidence, reveals strong flexibility in parameter tuning, adaptability to diverse datasets, and significantly improved computational speed.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"1060-1069"},"PeriodicalIF":6.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243744","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}
The integration of blockchain technology with energy community trading represents a promising frontier in the energy sector, offering innovative solutions to challenges in energy trading and management. This review conducts a systematic investigation of the potential benefits, applications and challenges of blockchain in facilitating multi-level energy trading for energy communities. Firstly, the background information of the blockchain and Internet of Things (IoT) is provided, along with an elucidation of their integration architecture for energy communities. Building on this foundation, the applications of blockchain in transactive energy communities are analyzed from three perspectives: community-level energy trading, regional-level energy trading, and grid-level energy trading. Following that, the currently known projects and pilots on blockchain-based trans active energy are comprehensively summarized. Finally, key challenges in implementing blockchain-based energy trading for local energy communities are discussed, providing guidance for future research.
{"title":"Exploring the Potential of IoT-Blockchain Integration Technology for Energy Community Trading: Opportunities, Benefits, and Challenges","authors":"Wenhu Tang;Xuehua Xie;Yunlin Huang;Tong Qian;Weiwei Li;Xiuzhang Li;Zhao Xu","doi":"10.17775/CSEEJPES.2024.02160","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.02160","url":null,"abstract":"The integration of blockchain technology with energy community trading represents a promising frontier in the energy sector, offering innovative solutions to challenges in energy trading and management. This review conducts a systematic investigation of the potential benefits, applications and challenges of blockchain in facilitating multi-level energy trading for energy communities. Firstly, the background information of the blockchain and Internet of Things (IoT) is provided, along with an elucidation of their integration architecture for energy communities. Building on this foundation, the applications of blockchain in transactive energy communities are analyzed from three perspectives: community-level energy trading, regional-level energy trading, and grid-level energy trading. Following that, the currently known projects and pilots on blockchain-based trans active energy are comprehensively summarized. Finally, key challenges in implementing blockchain-based energy trading for local energy communities are discussed, providing guidance for future research.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"521-537"},"PeriodicalIF":6.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860863","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 : 2025-02-21DOI: 10.17775/CSEEJPES.2024.06310
Hanchi Zhang;Hongyang Zhou;Filipe Faria da Silva;Claus Leth Bak
An increasing number of large-scale renewable plants are generating abundant electricity. As power-to-gas and power-to-x technologies become promising ways to utilize surplus electricity to enhance the flexibility of energy systems, an innovative gas-electricity integrated transmission system (GEITS) to co-transmit electricity and hydrogen or other gas products is proposed, with the foreseeable advantages of compact structure, lower installation cost, and larger energy capacity. This paper investigates the feasibility of GEITS, suggests a design guideline, and gives the operation technical parameters of GEITS in different application scenarios. The dimensions and operating pressures of GEITS benchmark natural gas pipelines and then the nominal voltages of GEITS are calculated based on the electrical strength of high-pressure hydrogen. The nominal ampacities of GEITS are evaluated by temperature-rising simulations, which are larger than those of other transmission lines. Furthermore, high-pressure flowing hydrogen acting as an electrical insulator is a novel topic, and it is investigated via experimental validation on a scale model. Although the effect of 0.4 m/s flowing hydrogen on discharge characteristics has not been observed compared to static hydrogen, the discharge is impaired in 2.4 m/s flowing nitrogen. Future works will investigate the electrical strength of high-pressure long-distance hydrogen gaps under lightning impulse tests and the discharge phenomenon in higher flowing-velocity hydrogen. Methane and methane blended with hydrogen with higher insulation performance can increase the nominal voltages.
{"title":"Feasibility Analysis and Design of Gas-Electricity Integrated Transmission System","authors":"Hanchi Zhang;Hongyang Zhou;Filipe Faria da Silva;Claus Leth Bak","doi":"10.17775/CSEEJPES.2024.06310","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.06310","url":null,"abstract":"An increasing number of large-scale renewable plants are generating abundant electricity. As power-to-gas and power-to-x technologies become promising ways to utilize surplus electricity to enhance the flexibility of energy systems, an innovative gas-electricity integrated transmission system (GEITS) to co-transmit electricity and hydrogen or other gas products is proposed, with the foreseeable advantages of compact structure, lower installation cost, and larger energy capacity. This paper investigates the feasibility of GEITS, suggests a design guideline, and gives the operation technical parameters of GEITS in different application scenarios. The dimensions and operating pressures of GEITS benchmark natural gas pipelines and then the nominal voltages of GEITS are calculated based on the electrical strength of high-pressure hydrogen. The nominal ampacities of GEITS are evaluated by temperature-rising simulations, which are larger than those of other transmission lines. Furthermore, high-pressure flowing hydrogen acting as an electrical insulator is a novel topic, and it is investigated via experimental validation on a scale model. Although the effect of 0.4 m/s flowing hydrogen on discharge characteristics has not been observed compared to static hydrogen, the discharge is impaired in 2.4 m/s flowing nitrogen. Future works will investigate the electrical strength of high-pressure long-distance hydrogen gaps under lightning impulse tests and the discharge phenomenon in higher flowing-velocity hydrogen. Methane and methane blended with hydrogen with higher insulation performance can increase the nominal voltages.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"595-606"},"PeriodicalIF":6.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860948","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 the field of power systems, insulating polymers have been found to have extensive applications due to their outstanding properties. However, these materials are susceptible to defects arising from various factors during production and operation, which may progress and potentially lead to safety incidents. This paper comprehensively reviews non-destructive testing (NDT) techniques for insulating polymers. Based on the physical principles underlying these methods, they are categorized into electrical testing methods, non-electrical passive testing methods, and non-electrical active testing methods. The paper offers a retrospective assessment of the applications of these methods in insulating polymers. Finally, evaluation of the applicability, advantages, and limitations of these diverse methods is systematically conducted, aiming to facilitate the targeted selection of the optimal NDT method in engineering applications.
{"title":"Review of Non-Destructive Testing Methods for Defects in Insulating Polymers","authors":"Liming Wang;Yanxin Tu;Bin Cao;Yuhao Liu;Hongwei Mei;Lishuai Liu;Fanghui Yin","doi":"10.17775/CSEEJPES.2023.08020","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.08020","url":null,"abstract":"In the field of power systems, insulating polymers have been found to have extensive applications due to their outstanding properties. However, these materials are susceptible to defects arising from various factors during production and operation, which may progress and potentially lead to safety incidents. This paper comprehensively reviews non-destructive testing (NDT) techniques for insulating polymers. Based on the physical principles underlying these methods, they are categorized into electrical testing methods, non-electrical passive testing methods, and non-electrical active testing methods. The paper offers a retrospective assessment of the applications of these methods in insulating polymers. Finally, evaluation of the applicability, advantages, and limitations of these diverse methods is systematically conducted, aiming to facilitate the targeted selection of the optimal NDT method in engineering applications.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"1380-1397"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838230","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243860","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}
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