The complexity and uncertainty in power systems cause great challenges to controlling power grids. As a popular data-driven technique, deep reinforcement learning (DRL) attracts attention in the control of power grids. However, DRL has some inherent drawbacks in terms of data efficiency and explainability. This paper presents a novel hierarchical task planning (HTP) approach, bridging planning and DRL, to the task of power line flow regulation. First, we introduce a three-level task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes (TP-MDPs). Second, we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units. In addition, we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP. Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization, a state-of-the-art deep reinforcement learning (DRL) approach, improving efficiency by 26.16% and 6.86% on both systems.
{"title":"Hierarchical Task Planning for Power Line Flow Regulation","authors":"Chenxi Wang;Youtian Du;Yanhao Huang;Yuanlin Chang;Zihao Guo","doi":"10.17775/CSEEJPES.2023.00620","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00620","url":null,"abstract":"The complexity and uncertainty in power systems cause great challenges to controlling power grids. As a popular data-driven technique, deep reinforcement learning (DRL) attracts attention in the control of power grids. However, DRL has some inherent drawbacks in terms of data efficiency and explainability. This paper presents a novel hierarchical task planning (HTP) approach, bridging planning and DRL, to the task of power line flow regulation. First, we introduce a three-level task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes (TP-MDPs). Second, we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units. In addition, we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP. Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization, a state-of-the-art deep reinforcement learning (DRL) approach, improving efficiency by 26.16% and 6.86% on both systems.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"29-40"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695065","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}
Hybrid energy storage system (HESS) is an effective way to mitigate wind power fluctuations on multi-time scale, and can improve influence of large-scale grid-connected wind power on stability and reliability of power system. A novel methodology named zero-phase controlled auto-regressive integrated moving-average (CARIMA) filter is proposed to integrate HESS to smooth wind power fluctuations. First, a design method for zero-phase CARIMA filter is provided, and then used to determine grid-connected power for a wind storage system and size HESS. The reasons, direct current (DC) component caused by energy storage efficiency and grid-connected power delay caused by phase shift, for causing superfluous energy storage configuration are revealed. In addition, a nonlinear programming scheduling strategy considering battery degradation is proposed. Power imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is addressed. Furthermore, thermostatically controlled loads (TCLs) are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy storage. Finally, effectiveness of the proposed strategy is verified by a case study.
{"title":"Zero-Phase CARIMA Filtering and Application in Wind-Storage System Sizing and Power Dispatch Optimization","authors":"Wei Wang;Peng Chen;Guorui Ren;Jizhen Liu;Fang Fang;Zhe Chen","doi":"10.17775/CSEEJPES.2022.06930","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.06930","url":null,"abstract":"Hybrid energy storage system (HESS) is an effective way to mitigate wind power fluctuations on multi-time scale, and can improve influence of large-scale grid-connected wind power on stability and reliability of power system. A novel methodology named zero-phase controlled auto-regressive integrated moving-average (CARIMA) filter is proposed to integrate HESS to smooth wind power fluctuations. First, a design method for zero-phase CARIMA filter is provided, and then used to determine grid-connected power for a wind storage system and size HESS. The reasons, direct current (DC) component caused by energy storage efficiency and grid-connected power delay caused by phase shift, for causing superfluous energy storage configuration are revealed. In addition, a nonlinear programming scheduling strategy considering battery degradation is proposed. Power imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is addressed. Furthermore, thermostatically controlled loads (TCLs) are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy storage. Finally, effectiveness of the proposed strategy is verified by a case study.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 6","pages":"2283-2295"},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859063","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.2023.05250
Yunyang Zou;Yan Xu
In a deregulated Var market, market power issue is more serious than in an energy market since reactive power cannot be transmitted over long distances. This letter designs a multi-timescale Var market framework, where market power that may arise in the hourly-ahead Var support service market due to system configuration deficiency and market structure flaws can be eliminated by day-ahead contract-based Var reserve service market. Settlement of day-ahead Var reserve contract is formulated as a two-stage robust optimization (TSRO) model considering worst case of uncertainty realization and potential market power that may arise in hourly-ahead market. TSRO with integer recourses is then solved by a new column and constraint generation algorithm. Results show a robust Var reserve contract can fully eliminate market power, and prevent suppliers from manipulating market prices.
在放松管制的 Var 市场中，由于无功功率不能远距离传输，市场力量问题比能源市场更为严重。本文设计了一个多时间尺度的无功市场框架，通过基于日前合同的无功储备服务市场，可以消除由于系统配置缺陷和市场结构缺陷而可能在小时前无功支持服务市场中产生的市场支配力。考虑到不确定性实现的最坏情况和小时前市场可能出现的潜在市场支配力，将日前变量储备合同的结算制定为两阶段稳健优化（TSRO）模型。然后通过一种新的列和约束生成算法求解了具有整数资源的 TSRO。结果表明，稳健的 Var 储备合同可以完全消除市场力量，防止供应商操纵市场价格。
{"title":"Design of Robust Var Reserve Contract for Enhancing Reactive Power Ancillary Service Market Efficiency","authors":"Yunyang Zou;Yan Xu","doi":"10.17775/CSEEJPES.2023.05250","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05250","url":null,"abstract":"In a deregulated Var market, market power issue is more serious than in an energy market since reactive power cannot be transmitted over long distances. This letter designs a multi-timescale Var market framework, where market power that may arise in the hourly-ahead Var support service market due to system configuration deficiency and market structure flaws can be eliminated by day-ahead contract-based Var reserve service market. Settlement of day-ahead Var reserve contract is formulated as a two-stage robust optimization (TSRO) model considering worst case of uncertainty realization and potential market power that may arise in hourly-ahead market. TSRO with integer recourses is then solved by a new column and constraint generation algorithm. Results show a robust Var reserve contract can fully eliminate market power, and prevent suppliers from manipulating market prices.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 2","pages":"767-771"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375974","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351441","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}
Fossil fuel depletion and environmental pollution problems promote development of renewable energy (RE) globally. With increasing penetration of RE, operation security and economy of power systems (PS) are greatly impacted by fluctuation and intermittence of renewable power. In this paper, information gap decision theory (IGDT) is adapted to handle uncertainty of wind power generation. Based on conventional IGDT method, linear regulation strategy (LRS) and robust linear optimization (RLO) method are integrated to reformulate the model for rigorously considering security constraints. Then a robustness assessment method based on hybrid RLO-IGDT approach is proposed for analyzing robustness and economic performance of PS. Moreover, a risk-averse linearization method is adapted to convert the proposed assessment model into a mixed integer linear programming (MILP) problem for convenient optimization without robustness loss. Finally, results of case studies validate superiority of proposed method in guaranteeing operation security rigorously and effectiveness in assessment of RSR for PS without overestimation.
{"title":"Robustness Assessment of Wind Power Generation Considering Rigorous Security Constraints for Power System: A Hybrid RLO-IGDT Approach","authors":"Lianyong Zuo;Shengshi Wang;Yong Sun;Shichang Cui;Jiakun Fang;Xiaomeng Ai;Baoju Li;Chengliang Hao;Jinyu Wen","doi":"10.17775/CSEEJPES.2023.05980","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05980","url":null,"abstract":"Fossil fuel depletion and environmental pollution problems promote development of renewable energy (RE) globally. With increasing penetration of RE, operation security and economy of power systems (PS) are greatly impacted by fluctuation and intermittence of renewable power. In this paper, information gap decision theory (IGDT) is adapted to handle uncertainty of wind power generation. Based on conventional IGDT method, linear regulation strategy (LRS) and robust linear optimization (RLO) method are integrated to reformulate the model for rigorously considering security constraints. Then a robustness assessment method based on hybrid RLO-IGDT approach is proposed for analyzing robustness and economic performance of PS. Moreover, a risk-averse linearization method is adapted to convert the proposed assessment model into a mixed integer linear programming (MILP) problem for convenient optimization without robustness loss. Finally, results of case studies validate superiority of proposed method in guaranteeing operation security rigorously and effectiveness in assessment of RSR for PS without overestimation.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 2","pages":"518-529"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351468","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.2022.01080
Yating Zhao;Zhi Wu;Wei Gu;Jingxuan Wang;Fujue Wang;Zhoujun Ma;Minqiang Hu
Industrial parks (IPs) play a crucial role in facilitating economic efficiency and comprehensive energy utilization in the industrial age. At the same time, multi-energy coupling and management of various types of energy in IP have become serious challenges. In this paper, combined heat and power unit (CHP) model considering operation mode switching characteristics is formulated by exploring its internal composition to improve output flexibility of the energy supply side. Then, heat and electricity integrated energy system (HE-IES) optimal dispatch and pricing model are established, taking electricity and heat demand response strategy and steam thermal inertia property into account. Based on the above models, a mixed-integer bilinear programming framework is designed to coordinate the day-ahead operation and pricing strategy of the HE-IES in the IP. The scenario study is carried out on a practical industrial park in Southern China. Numerical results indicate the proposed mechanism can effectively improve IP's energy utilization and economic efficiency.
在工业时代，工业园区（IP）在促进经济效益和能源综合利用方面发挥着至关重要的作用。与此同时，工业园区的多能耦合和各类能源的管理也成为严峻的挑战。本文通过探讨热电联产机组的内部组成，建立了考虑运行模式切换特性的热电联产机组模型，以提高能源供应端的输出灵活性。然后，考虑电力和热力需求响应策略以及蒸汽热惯性特性，建立了热电综合能源系统（HE-IES）优化调度和定价模型。在上述模型的基础上，设计了一个混合整数双线性规划框架，以协调 IP 中热电综合能源系统的日前运行和定价策略。在中国南方的一个实际工业园区进行了情景研究。数值结果表明，所提出的机制能有效提高工业园的能源利用率和经济效益。
{"title":"Optimal Dispatch and Pricing of Industrial Parks Considering CHP Mode Switching and Demand Response","authors":"Yating Zhao;Zhi Wu;Wei Gu;Jingxuan Wang;Fujue Wang;Zhoujun Ma;Minqiang Hu","doi":"10.17775/CSEEJPES.2022.01080","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.01080","url":null,"abstract":"Industrial parks (IPs) play a crucial role in facilitating economic efficiency and comprehensive energy utilization in the industrial age. At the same time, multi-energy coupling and management of various types of energy in IP have become serious challenges. In this paper, combined heat and power unit (CHP) model considering operation mode switching characteristics is formulated by exploring its internal composition to improve output flexibility of the energy supply side. Then, heat and electricity integrated energy system (HE-IES) optimal dispatch and pricing model are established, taking electricity and heat demand response strategy and steam thermal inertia property into account. Based on the above models, a mixed-integer bilinear programming framework is designed to coordinate the day-ahead operation and pricing strategy of the HE-IES in the IP. The scenario study is carried out on a practical industrial park in Southern China. Numerical results indicate the proposed mechanism can effectively improve IP's energy utilization and economic efficiency.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 5","pages":"2174-2185"},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408829","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.2021.09640
Yiwei Qiu;Jin Lin;Zhipeng Zhou;Ningyi Dai;Feng Liu;Yonghua Song
Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent studies to remarkably improve performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate daily SDE model for PV power be obtained that reflects its weather-dependent and non-Gaussian uncertainty in operation, especially when high-resolution numerical weather prediction (NWP) or sky imager is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed only using the data from low-resolution public weather reports. Specifically, for each day, an hourly parameterized Jacobi diffusion process recreates temporal patterns of PV volatility. Its parameters are mapped from the day's public weather reports to reflect varying weather conditions using a simple learning model. The SDE model jointly captures intraday and intrahour volatility. Statistical examination shows that the proposed approach outperforms a selection of the latest deep learning-based time series models on real-world data collected in Macau.
{"title":"Achieving an Accurate Random Process Model for PV Power Using Cheap Data: Leveraging the SDE and Public Weather Reports","authors":"Yiwei Qiu;Jin Lin;Zhipeng Zhou;Ningyi Dai;Feng Liu;Yonghua Song","doi":"10.17775/CSEEJPES.2021.09640","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2021.09640","url":null,"abstract":"Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent studies to remarkably improve performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate daily SDE model for PV power be obtained that reflects its weather-dependent and non-Gaussian uncertainty in operation, especially when high-resolution numerical weather prediction (NWP) or sky imager is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed only using the data from low-resolution public weather reports. Specifically, for each day, an hourly parameterized Jacobi diffusion process recreates temporal patterns of PV volatility. Its parameters are mapped from the day's public weather reports to reflect varying weather conditions using a simple learning model. The SDE model jointly captures intraday and intrahour volatility. Statistical examination shows that the proposed approach outperforms a selection of the latest deep learning-based time series models on real-world data collected in Macau.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 1","pages":"124-135"},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430405","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}
Traditional analytical approaches for stability assessment of inverter-based resources (IBRs), often requiring detailed knowledge of IBR internals, become impractical due to IBRs' proprietary nature. Admittance measurements, relying on electromagnetic transient simulation or laboratory settings, are not only time-intensive but also operationally inflexible, since various non-linear control loops make IBRs' admittance models operating-point dependent. Therefore, such admittance measurements must be performed repeatedly when operating point changes. To avoid time-consuming and cumbersome measurements, admittance estimation for arbitrary operating points is highly desirable. However, existing admittance estimation algorithms usually face challenges in versatility, data demands, and accuracy. Addressing this challenge, this letter presents a simple and efficient admittance estimation method for black-boxed IBRs, by utilizing a minimal set of seven operating points to solve a homogeneous linear equation system. Case studies demonstrate this proposed method ensures high accuracy across various types of IBRs. Estimation accuracy is satisfying even when non-negligible measurement errors exist.
{"title":"An Efficient Method to Estimate Admittance of Black-boxed Inverter-based Resources for Varying Operating Points","authors":"Weihua Zhou;Bin Liu;Nabil Mohammed;Behrooz Bahrani","doi":"10.17775/CSEEJPES.2023.07090","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.07090","url":null,"abstract":"Traditional analytical approaches for stability assessment of inverter-based resources (IBRs), often requiring detailed knowledge of IBR internals, become impractical due to IBRs' proprietary nature. Admittance measurements, relying on electromagnetic transient simulation or laboratory settings, are not only time-intensive but also operationally inflexible, since various non-linear control loops make IBRs' admittance models operating-point dependent. Therefore, such admittance measurements must be performed repeatedly when operating point changes. To avoid time-consuming and cumbersome measurements, admittance estimation for arbitrary operating points is highly desirable. However, existing admittance estimation algorithms usually face challenges in versatility, data demands, and accuracy. Addressing this challenge, this letter presents a simple and efficient admittance estimation method for black-boxed IBRs, by utilizing a minimal set of seven operating points to solve a homogeneous linear equation system. Case studies demonstrate this proposed method ensures high accuracy across various types of IBRs. Estimation accuracy is satisfying even when non-negligible measurement errors exist.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"421-426"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695088","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}
Dependence of distributed generation (DG) outputs and load plays an essential role in renewable energy accommodation. This paper presents a novel DG hosting capacity (DGHC) evaluation method for distribution networks considering high-dimensional dependence relations among solar radiation, wind speed, and various load types (i.e., commercial, residential, and industrial). First, an advanced dependence modeling method called regular vine (R-vine) is applied to capture the complex dependence structure of solar radiation, wind speed, commercial loads, industrial loads, and residential loads. Then, a chance-constrained DGHC evaluation model is employed to figure out maximum hosting capacity of each DG and its optimal allocation plan with different operational risks. Finally, a Benders decomposition algorithm is also employed to reduce computational burden. The proposed approaches are validated using a set of historical data from China. Results show dependence among different DGs and loads has significant impact on hosting capacity. Results also suggest using the R-vine model to capture dependence among distributed energy resources (DERs) and load. This finding provides useful advice for distribution networks in installing renewable energy generations.
{"title":"DG Hosting Capacity Assessment Considering Dependence Among Wind Speed, Solar Radiation, and Load Demands","authors":"Junyi Yang;Jiangmin Bao;Yuhan Hou;Han Wu;Qiang Li;Yue Yuan","doi":"10.17775/CSEEJPES.2021.07270","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2021.07270","url":null,"abstract":"Dependence of distributed generation (DG) outputs and load plays an essential role in renewable energy accommodation. This paper presents a novel DG hosting capacity (DGHC) evaluation method for distribution networks considering high-dimensional dependence relations among solar radiation, wind speed, and various load types (i.e., commercial, residential, and industrial). First, an advanced dependence modeling method called regular vine (R-vine) is applied to capture the complex dependence structure of solar radiation, wind speed, commercial loads, industrial loads, and residential loads. Then, a chance-constrained DGHC evaluation model is employed to figure out maximum hosting capacity of each DG and its optimal allocation plan with different operational risks. Finally, a Benders decomposition algorithm is also employed to reduce computational burden. The proposed approaches are validated using a set of historical data from China. Results show dependence among different DGs and loads has significant impact on hosting capacity. Results also suggest using the R-vine model to capture dependence among distributed energy resources (DERs) and load. This finding provides useful advice for distribution networks in installing renewable energy generations.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 3","pages":"1011-1025"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304023","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.2023.05960
Jian Hao;Jingwen Zhang;Wenyu Ye;Ruijing Liao;Lijun Yang
Use of traditional mineral oil (MO) as a liquid insulation in transformers has spanned more than 130 years. However, MO has poor heat resistance, a low ignition point, and is a non-renewable resource, which does not meet development requirements for high-performance and environmentally friendly insulation oil. Consequently, researchers have explored alternatives such as natural ester (NE) and synthetic ester (SE) oils, as well as mixed insulation oils. Mixed insulating oil is a blend of diverse insulating oil types, with optimal performance achieved by adjusting proportions of base oils. This article summarizes the innovative achievements and development of mixed insulation oil in terms of development of mixed ratio, basic physical chemical properties, electrical properties, thermal stability, and application including operation and maintenance technology. Through these efforts, this article aims to provide recommendations for future development of mixed insulating oils to advance liquid dielectric research based on enhancement mechanisms.
{"title":"Development of Mixed Insulation Oil as Alternative Liquid Dielectric: A Review","authors":"Jian Hao;Jingwen Zhang;Wenyu Ye;Ruijing Liao;Lijun Yang","doi":"10.17775/CSEEJPES.2023.05960","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05960","url":null,"abstract":"Use of traditional mineral oil (MO) as a liquid insulation in transformers has spanned more than 130 years. However, MO has poor heat resistance, a low ignition point, and is a non-renewable resource, which does not meet development requirements for high-performance and environmentally friendly insulation oil. Consequently, researchers have explored alternatives such as natural ester (NE) and synthetic ester (SE) oils, as well as mixed insulation oils. Mixed insulating oil is a blend of diverse insulating oil types, with optimal performance achieved by adjusting proportions of base oils. This article summarizes the innovative achievements and development of mixed insulation oil in terms of development of mixed ratio, basic physical chemical properties, electrical properties, thermal stability, and application including operation and maintenance technology. Through these efforts, this article aims to provide recommendations for future development of mixed insulating oils to advance liquid dielectric research based on enhancement mechanisms.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 3","pages":"1242-1258"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375972","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304110","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 high renewable penetrated power system has severe frequency regulation problems. Distributed resources can provide frequency regulation services but are limited by communication time delay. This paper proposes a communication resources allocation model to reduce communication time delay in frequency regulation service. Communication device resources and wireless spectrum resources are allocated to distributed resources when they participate in frequency regulation. We reveal impact of communication resources allocation on time delay reduction and frequency regulation performance. Besides, we study communication resources allocation solution in high renewable energy penetrated power systems. We provide a case study based on the HRP-38 system. Results show communication time delay decreases distributed resources' ability to provide frequency regulation service. On the other hand, allocating more communication resources to distributed resources' communication services improves their frequency regulation performance. For power systems with renewable energy penetration above 70%, required communications resources are about five times as many as 30% renewable energy penetrated power systems to keep frequency performance the same.
{"title":"Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System","authors":"Hongjie He;Ning Zhang;Chongqing Kang;Song Ci;Fei Teng;Goran Strbac","doi":"10.17775/CSEEJPES.2023.07630","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.07630","url":null,"abstract":"The high renewable penetrated power system has severe frequency regulation problems. Distributed resources can provide frequency regulation services but are limited by communication time delay. This paper proposes a communication resources allocation model to reduce communication time delay in frequency regulation service. Communication device resources and wireless spectrum resources are allocated to distributed resources when they participate in frequency regulation. We reveal impact of communication resources allocation on time delay reduction and frequency regulation performance. Besides, we study communication resources allocation solution in high renewable energy penetrated power systems. We provide a case study based on the HRP-38 system. Results show communication time delay decreases distributed resources' ability to provide frequency regulation service. On the other hand, allocating more communication resources to distributed resources' communication services improves their frequency regulation performance. For power systems with renewable energy penetration above 70%, required communications resources are about five times as many as 30% renewable energy penetrated power systems to keep frequency performance the same.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 2","pages":"468-480"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351489","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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