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Robust Expansion Planning of Electric Vehicle Charging System and Distribution Networks

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CSEE Journal of Power and Energy Systems 2023年第6期10卷 2457-2469页

作者： Yue Xiang Ping Xue Yanliang Wang Lixiong Xu Wang Ma Miadreza Shafie-khah Junlong Li Junyong Liu College of Electrical Engineering Sichuan University Chengdu China state grid sichuan technical training center Sichuan Electric Vocational and Technical College Chengdu China School of Technology and Innovations University of Vaasa Vaasa Finland Department of Electronic and Electrical Engineering University of Bath Bath UK

Large integration of electric vehicles (EVs) brings uncertainties and challenges for distribution networks. Reliability and allocation of charging systems (charging stations and piles) need strong support from the distribution network, whose expansion planning would be also greatly impacted by the incremental load from the charging system. To address this issue, a collaborative planning scheme of the EV charging system and distribution networks that combines economic and reliability benefits is proposed, which is formulated as a two-stage distributionally robust optimization model considering source-load uncertainties. First, a candidate EVCS planning scheme is obtained using queuing theory. Then, system reliability is quantified by adopting the average energy not supplied index and correlated integrated into the two-stage mixed-integer linear programming model. Finally, it is solved to perform distributionally robust expansion planning by the column and constraint generation algorithm. Effectiveness and scalability of the proposed planning method are illustrated by numerical case studies. Results indicate the planning scheme that considers economic and reliability benefit can perform better than traditional optimization methods.

关键词： Planning Reliability Distribution networks Electric vehicle charging Collaboration Transportation Power system reliability

Bi-GRU Relation Extraction Model Based on Keywords Attention

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Data Intelligence 2022年第3期4卷 552-572页

作者： Yuanyuan Zhang Yu Chen Shengkang Yu Xiaoqin Gu Mengqiong Song Yu Peng Jianxia Chen Qi Liu technical training center of state grid Hubei Electric Power Co. Ltd.Wuhan 430070China Hubei University of Technology School of Computer ScienceWuhan 430068China

Relational extraction plays an important role in the field of natural language processing to predict semantic relationships between entities in a sentence. Currently, most models have typically utilized the natural language processing tools to capture high-level features with an attention mechanism to mitigate the adverse effects of noise in sentences for the prediction results. However, in the task of relational classification, these attention mechanisms do not take full advantage of the semantic information of some keywords which have information on relational expressions in the sentences. Therefore, we propose a novel relation extraction model based on the attention mechanism with keywords, named Relation Extraction Based on Keywords Attention(REKA). In particular, the proposed model makes use of bi-directional GRU(Bi-GRU) to reduce computation, obtain the representation of sentences, and extracts prior knowledge of entity pair without any NLP tools. Besides the calculation of the entity-pair similarity, Keywords attention in the REKA model also utilizes a linear-chain conditional random field(CRF) combining entity-pair features, similarity features between entity-pair features, and its hidden vectors, to obtain the attention weight resulting from the marginal distribution of each word. Experiments demonstrate that the proposed approach can utilize keywords incorporating relational expression semantics in sentences without the assistance of any high-level features and achieve better performance than traditional methods.

关键词： Relation extraction Bi-GRU CRF keywords attention Hidden similarity

Review of Service Restoration for Distribution Networks

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Journal of Modern Power Systems and Clean Energy 2020年第1期8卷 1-14页

作者： Feifan Shen Qiuwei Wu Yusheng Xue center for Electric Power and Energy Department of Electrical EngineeringTechnical University of DenmarkLyngbyDenmark state grid Electric Power Research Institute NanjingChina

With the rapid deployment of the advanced metering infrastructure(AMI)and distribution automation(DA),selfhealing has become a key factor to enhance the resilience of distribution *** a permanent fault occurrence,the distribution network operator(DNO)implements the selfhealing scheme to locate and isolate the fault and to restore power supply to out-of-service *** an essential component of self-healing,service restoration has attracted considerable *** paper mainly reviews the service restoration approaches of distribution networks,which requires communication *** service restoration approaches can be classified as centralized,distributed,and hierarchical approaches according to the communication *** these approaches,different techniques are used to obtain service restoration solutions,including heuristic rules,expert systems,metaheuristic algorithms,graph theory,mathematical programming,and multi-agent ***,future research areas of service restoration for distribution networks are discussed.

关键词： Distribution networks fault detection fault isolation service restoration self-healing scheme

Prediction of DC Corona Onset Voltage for Rod-Plane Air Gaps by a Support Vector Machine

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Plasma Science and Technology 2016年第10期18卷 998-1004页

作者：金硕阮江军杜志叶朱琳舒胜文 School of Electrical Engineering Wuhan University Wuhan 430072 China state grid Hubei Electric Power Company Technology training center Wuhan 430079 China Electric Power Research Institute of state grid Fujian Electric Power Company Limited Fuzhou 350007 China

This paper proposes a new method to predict the corona onset voltage for a rod- plane air gap, based on the support vector machine （SVM）. Because the SVM is not limited by the size, dimension and nonlinearity of the samples, this method can realize accurate prediction with few training data. Only electric field features are chosen as the input; no geometric parameter is included. Therefore, the experiment data of one kind of electrode can be used to predict the corona onset voltages of other electrodes with different sizes. With the experimental data obtained by ozone detection technology, and experimental data provided by the reference, the efficiency of the proposed method is validated. Accurate predicted results with an average relative less than 3% are obtained with only 6 experimental data.

关键词： corona SVM prediction method electrodes electric fields

Behavioural analysis and dynamic simulation of the debris flow that occurred in Ganluo County(sichuan, China) on 30 August 2020

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Journal of Mountain Science 2022年第6期19卷 1495-1508页

作者： LI Nuo-dong LIU Wei ZHAO Jin-heng Chengdu Power Supply Company of state grid sichuan Electric Power Company Chengdu 610041China Key Laboratory of Mountain Hazards and Surface Process Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China University of Chinese Academy of Sciences Beijing 100049China state Key Laboratory of Hydraulics and Mountain River Engineering Sichuan UniversityChengdu 610065China Department of Water Hydropower and Construction Engineering Skill Training Center of State Grid Sichuan Electric Power CompanyChengdu 610000China

On August 30, 2020, a high-intensity storm that dropped 45.4 mm of rain in 5 hours hit the Heixiluo basin and triggered a landslide-generated debris flow event, causing fatalities and damage. The original source of the debris flow was a large slope collapse on a steep hillside. The fallen debris mass was enlarged through sediment entrainment and slope collapse and ultimately buried a bridge at the gully entrance. Approximately 6.9×10;m;of material, including sediments and collapsed slope deposits in the gullies, was entrained, and the maximum erosion depth reached 17 m. A geomorphological analysis was initially performed based on a detailed field investigation to recognize the liquid and solid sources of the debris flow and the areas subjected to deposition and erosion. A map of the erosiondeposition distribution was obtained based on preand post-event DEMs. Using the rainfall estimated by the nearest rain gauge and the solid source estimated by the DEMs, runoff and debris flow propagation was simulated using a liquid-solid two-phase model that considers the effects of runoff and entrainment. The similarity between the estimated and simulated deposition-erosion volumes was satisfactory. The behaviour of debris flows captured in the simulation is broadly in line with the main features of the observed event.

关键词： Debris flow Formation analysis Two-phase model Dynamic simulation

Transition towards higher penetration of renewables: an overview of interlinked technical, environmental and socio-economic challenges

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Journal of Modern Power Systems and Clean Energy 2019年第1期7卷 1-8页

作者： Xinyu CHEN Michael B.MCELROY Qiuwei WU Yinbiao SHU Yusheng XUE John A.Paulson School of Engineering and Applied Sciences Harvard University Department of Electrical Engineering Technical University of Denmark state grid Corporation of China state grid Electric Power Research Institute

Investment for renewables has been growing rapidly since the beginning of the new century, and the momentum is expected to sustain in order to mitigate the impact of anthropogenic climate *** towards higher renewable penetration in the power industry will not only confront technical challenges, but also face socio-economic *** connected between environment and energy systems are also tightened under elevated penetration of *** paper will provide an overview of some important challenges related to technical, environmental and socio-economic aspects at elevated renewable *** integrated analytical framework for interlinked technical, environmental and socio-economic systems will be presented at the end.

关键词： Wind power Solar power Integrated energy systems Missing money problem Low carbon policies

PSO-LSSVM-based Online SOC Estimation for Simulation Substation Battery

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Structural Durability & Health Monitoring 2022年第1期16卷 37-51页

作者： Qiang Zhang Xianguang Zha Jun Wu Liang Zhang Wei Dai Gang Ren Shiqian Li Ning Ji Xiangjun Zhu Fengwei Tian state grid Jiangsu Electric Power Co. Ltd.Nanjing210024China state grid Jiangsu Electric Power Company Research Institute Nanjing211103China Technican training center of state grid Jiangsu Electric Power Co. Ltd.Suzhou215004China

As the emergency power supply for a simulation substation,lead-acid batteries have a work pattern featuring noncontinuous operation,which leads to capacity ***,the accurate estimation of battery state of charge(SOC),a measurement of the amount of energy available in a battery,remains a hard nut to crack because of the non-stationarity and randomness of battery capacity *** paper has proposed a comprehensive method for lead-acid battery SOC estimation,which may aid in maintaining a reasonable charging schedule in a simulation substation and improving battery’s *** on the battery work pattern,an improved Ampere-hour method is used to calculate the SOC during constant current and constant voltage(CC/CV)charging and *** addition,the combined Particle Swarm Optimization(PSO)and Least Squares Support Vector Machine(LSSVM)model is used to estimate the SOC during non-CC *** results show that this method is workable in online SOC estimation of working batteries in a simulation substaion,with the maximum relative error standing at only 2.1%during the non-training period,indicating a high precision and wide applicability.

关键词： Simulation substation lead-acid batteries SOC PSO LSSVM

Uncertainty-averse TRANSCO planning for accommodating renewable energy in CO_(2)reduction environment

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Journal of Modern Power Systems and Clean Energy 2015年第1期3卷 24-32页

作者： Chunyu ZHANG Yi DING Qi WANG Yusheng XUE JacobØSTERGAARD center for Electric Power and Energy Technical University of Denmark2800 Kgs.LyngbyDenmark state grid Electric Power Research Institute(SGEPRI) Nanjing 210003China

The concern of the environment and energy sustainability requests a crucial target of CO_(2)abatement and results in a relatively high penetration of renewable energy generation in the transmission *** maintaining system reliability and security,the transmission company(TRANSCO)has to make strategic planning to handle the uncertainty challenges from the intermittent renewable energy *** this paper,a stochastic multi-period multi-objective transmission planning(MPMOTP)model is proposed to reduce correlated uncertainties from renewable energy generation,conventional generation,demand-side variations,market price volatility,and transmission *** objectives,*** CO_(2)reduction benefit,energy purchase and network expansion cost and power delivery profit,are optimized simultaneously by a developed two-phase multi-objective particle swarm optimization(MOPSO)*** feasibility and effectiveness of the proposed uncertainty-averse MPMOTP model have been verified by the IEEE 24-bus test system.

关键词： CO_(2)reduction Renewable energy Uncertainty Multi-objective planning TRANSCO MOPSO

Leveraging Continuous Prompt for Few-Shot Named Entity Recognition in Electric Power Domain with Meta-Learning

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Data Intelligence 2023年第2期5卷 494-509页

作者： Yang Yu Wei He Yu-meng Kang You-lang Ji Metrology center State Grid Jiangsu Market Service Center Nanjing Jiangsu 210019China

Conventional named entity recognition methods usually assume that the model can be trained with sufficient annotated data to obtain good recognition ***,in Chinese named entity recognition in the electric power domain,existing methods still face the challenges of lack of annotated data and new entities of unseen *** address these challenges,this paper proposes a meta-learning-based continuous cue adjustment method.A generative pre-trained language model is used so that it does not change its own model structure when dealing with new entity *** guide the pre-trained model to make full use of its own latent knowledge,a vector of learnable parameters is set as a cue to compensate for the lack of training *** order to further improve the model's few-shot learning capability,a meta-learning strategy is used to train the *** results show that the proposed approach achieves the best results in a few-shot electric Chinese power named entity recognition dataset compared to several traditional named entity approaches.

关键词： Named Entity Recognition Pre-trained model Prompt-Tuning Meta-Learning Few-Shot