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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Li, Jiarong Lin, Jin Song, Yonghua Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing China Department of Electrical Engineering Department of Electrical and Computer Engineering Beijing China

ISBN: (纸本)9781728155081

Power to hydrogen is a promising method to consume surplus renewable energy existing in the world. However, due to bottlenecks in hydrogen storage and transportation, converting hydrogen into ammonia which is a kind of hydrogen carrier with satisfied quality in storage and transportation is considered. Industrial ammonia synthesis reactors always work in constant conditions with constant flow rate of the feed gas, while hydrogen output of electrolyzers may vary sharply across renewable energy, thus hydrogen buffer tanks are required between electrolyzers and reactors to eliminate the fluctuation to some extent. The capacity of the buffer needs to be optimized due to relatively expensive cost of tanks, and the optimal capacity of the buffer actually depends on the operation flexibility of the reactor which is rarely discussed in existing literatures. This paper studies operation flexibility of the reactor first and establishes operation model of ammonia synthesis system under variable working conditions, on this basis the capacity optimization model is proposed and verified by a P2A system. At last the operation performance and the economy of P2A system are discussed, and case studies show that about 24% of total investment cost can be reduced with the consideration of operation flexibility of the reactor. © 2020 IEEE.

关键词： Ammonia

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长江流域城市水污染治理成效的系统评估与解析

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Engineering 2023年第2期21卷 135-142,M0006页

作者：齐维晓王旭康瑾柏耀辉边睿薛弘涛陈莉关傲梅潘艺蓉刘会娟曲久辉 Center for Water and Ecology State Key Joint Laboratory of Environment Simulation and Pollution ControlSchool of EnvironmentTsinghua UniversityBeijing 100084China State Key Laboratory of Urban Water Resource and Environment School of Civil and Environmental EngineeringHarbin Institute of TechnologyShenzhenShenzhen 518055China Centre for Water Systems College of EngineeringMathematics and Physical SciencesUniversity of ExeterExeter EX44QFUK Hubei Provincial Research Institute of Environmental Science Wuhan 430072China Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China

如何实现人类社会经济的可持续发展是本世纪的重大挑战之一。近几十年来,中国经济实力大幅提升,国际影响力显著增强。然而,经济社会高速发展可能会给生态环境造成诸多不利影响,其中之一就是人类活动污水排放总量的快速增长。长江经济带... 详细信息

如何实现人类社会经济的可持续发展是本世纪的重大挑战之一。近几十年来,中国经济实力大幅提升,国际影响力显著增强。然而,经济社会高速发展可能会给生态环境造成诸多不利影响,其中之一就是人类活动污水排放总量的快速增长。长江经济带作为中国高质量发展的重点区域,科学评估和揭示沿江城市群污水治理成效及影响因素至关重要。本研究核算了2007-2017年间长江流域主要污染物的浓度变化趋势,利用数据驱动型技术对影响污染物通量变化的关键因子及其贡献开展了系统解耦和量化分析,揭示了污染物排放强度的时空异质性,解析了沿江城镇污水系统发展对污染物通量削减的作用效果。研究发现,长江流域水污染治理成效总体向好,长江经济带国内生产总值和沿江人类活动污水排放强度之间的强耦合在2013年之后呈现脱钩趋势,这主要得益于城镇排水和污水厂基础设施建设的快速发展。研究提出,长江流域城镇污水治理如何实现以水载资源再生利用为导引的“绿色零碳模式”转变,对于全面改善长江流域的水环境质量具有关键作用,同时也能为区域生态环境保护与经济高质量发展提供有益启示和借鉴。

关键词：国内生产总值长江经济带资源再生利用系统评估治理成效时空异质性中国经济实力城镇排水

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Evaluation of information value for solar power plants in market environment 4

Evaluation of information value for solar power plants in ma...

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4th IEEE Conference on Energy Internet and Energy System Integration, EI2 2020

作者： Wang, Bohong Guo, Qinglai Yang, Tianyu Sun, Hongbin Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments Tsinghua University Department of Electrical Engineering Beijing100084 China

ISBN: (纸本)9781728196060

Uncertainty is not negligible in power systems, it affects the generation, transmission and utilization of electricity. Especially in electricity markets, the randomness and variability of generation or load will cause notable forecast errors and lead to economic losses of market participants. Due to the data explosion in recent few years, data have gradually become valuable information that can be mined, analyzed, and applied. Meanwhile, the utility that more abundant and accurate information could eliminate uncertainty is emerging. In this paper, solar power plants (SPPs) are selected as the object, the general mechanism in electricity markets is briefly introduced and the corresponding market optimization model is constructed. With the optimization model analytically solved, the impact of uncertainty is analyzed and the expressions of information value are derived. Besides, uniform, normal, logistic, and Laplace distribution are used in the evaluation of information value and compared. Finally, the case study is conducted to verify the theoretical results of information value and illustrate its significance. © 2020 IEEE.

关键词： Power markets

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Optimization of Heterogeneous Resources Combination using Virtual Power Plant based on Modern Portfolio Theory

Optimization of Heterogeneous Resources Combination using Vi...

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2020 IEEE Sustainable Power and Energy Conference, iSPEC 2020

作者： Yun, Qiuchen Tian, Liting Zhang, Fang Cheng, Lin School of Electrical Engineering Beijing Jiaotong University Beijing China Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China

ISBN: (纸本)9781728191645

In recent years, with the development of smart grid technologies, wind turbines, photovoltaics and other renewable energy sources as well as demand-side loads have participated in grid dispatching, resulting in increased complexity of energy flows in the grid. Distributed energy resources (DERs) differ in many dimensions, such as technical attributes, operating modes, and business objectives, and its management authority also belongs to different entities, making it more difficult for grids to manage DERs. A management mode, virtual power plant (VPP), being enabled by information and communication technology, could effectively overcome the challenges with decentralized DERs and help realize the aggregation of DERs. In this paper, the interaction between customers and VPP is modeled, where VPP divides the owners of DERs into three categories according to the willingness and responsiveness of customers and balances budget costs and risks based on the modern portfolio theory. simulation with the CIGRE 20kV distribution network benchmark demonstrates that VPP could greatly meet the power consumption demand of multiple participants during operation. This paper also provides a basis for the selection of DERs aggregated by VPP.11This paper is sponsored by the National Key Research and Development Program (No.2017YFB0903300) and National Natural Science Foundation of China (No.51807107) 22This paper is sponsored by the Young Talents Support Project of the Chinese Society for Electrical Engineering (CSEE) titled 'Research on key technologies for optimal operation and control of transportation energy systems'. © 2020 IEEE.

关键词： Virtual Power Plants

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highMLR: An open-source package for R with machine learning for feature selection in high dimensional cancer clinical genome time to event data

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Expert systems with Applications 2022年第0期210卷

作者： Bhattacharjee, Atanu Vishwakarma, Gajendra K. Banerjee, Souvik Pashchenko, Alexander F. Section of Biostatistics Centre for Cancer Epidemiology Tata Memorial Centre India Homi Bhabha National Institute Mumbai India Department of Mathematics & Computing Indian Institute of Technology Dhanbad Dhanbad India Laboratory of Intellectual Control Systems and simulation V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Russia

Machine learning techniques, popularly used as a tool for dimensionality reduction and pattern recognition of features, have been utilized extensively in data mining. In survival analysis, where the primary outcome is the time until a specific event occurs, identifying relevant features for building an efficient prediction model is essential. This is where machine learning can be a suitable option. However, there is an existing gap in utilizing machine learning techniques in high-dimensional survival data due to the non-availability of convenient programming functions and packages. In this article, we have developed an efficient machine learning procedure for analyzing survival data associated with high-dimensional gene expressions. Though there are several R libraries available for performing machine learning, no package support is available to implement machine learning with classification on high-dimensional survival data. highMLR, our developed R package, is capable of implementing machine learning methods on high dimensional survival data and provides a way of feature selection based on the logarithmic loss function. Several statistical methods for survival analysis have been incorporated into this machine learning algorithm. A high-dimensional gene expression dataset has been analyzed using the proposed R library to show its efficacy in feature selection. © 2022 Elsevier Ltd

关键词： Gene expression

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Nonlinear E-field simulation of HVDC Converter Transformer Busing and Its Structure Optimization With RBF Neural Network and NSGA-II Algorithm

Nonlinear E-field Simulation of HVDC Converter Transformer B...

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IEEE International Conference on Power and Energy systems (ICPES)

作者： Yongsheng He Shiling Zhang Zongxiang Lu Pengbo Wang State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute Chongqing China State Key Lab of Control and Simulation of Power Systems and Generation Equipment Haidian China Dept name of State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing China

Static DC and PR are typical operation conditions of HVDC converter transformer bushing outlet device, moreover, the electrical parameters of RIP, transformer oil and OIP have the nonlinear relationship with E-field stress and temperature. Under the temperature gradient, the electrical parameters will modify the static DC and PR E-field distribution of the composite insulation. In order to simulate static DC and PR electric field of the composite insulation structure, this paper established the full model of the converter transformer outlet. Then, the structure parameters of converter transformer outlet have been optimized using combined RBF neural network and NSGA-II algorithm. Moreover, electric field verification calculation has been carried out under AC and PR conditions. The results show that under uniform temperature condition, E-field strength is larger than that under iso-thermal condition. The overshoot coefficient of the bushing end and barrier reach 65% and 45%, respectively. The study can provide reference for the design of outlet structure of the converter transformer.

关键词：

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72-Hour Real-Time Forecasting of Ambient Pm2.5 by Hybrid Graph Deep Neural Network with Aggregated Neighborhood Spatiotemporal Information

SSRN

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SSRN 2023年

作者： Teng, Mengfan Li, Siwei Xing, Jia Fan, Chunying Yang, Jie Wang, Shuo Song, Ge Ding, Yu Dong, Jiaxin Wang, Shansi Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Hubei Luojia Laboratory Wuhan University Wuhan430079 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China School of Systems Science Beijing Normal University Beijing100875 China

The observation-based air pollution forecasting method has high computational efficiency over traditional numerical models, but a poor ability in long-term (after 6 hours) forecasting due to lack of detailed representation of atmospheric processes associated with the pollution transport. To address such limitation, here we propose a novel real-time air pollution forecasting model that applies a hybrid graph deep neural network (GNN_LSTM) to dynamically capture the spatiotemporal correlations among neighborhood monitoring sites to better represent the physical mechanism of pollutant transport across the space. Such design substantially improves the model performance in 72-hour PM2.5 forecasting over the whole Beijing-Tianjin-Hebei region (overall R2 increases from 0.6 to 0.79), particularly for polluted episodes (PM2.5 concentration >55 µg/m3) with pronounced regional transport to be captured by GNN_LSTM model. Inclusion of AOD feature further enhances the model performance in predicting PM2.5 over the sites where the AOD can inform additional aloft PM2.5 pollution feature related to the regional transport. The importance of neighborhood site (particularly for those in the upwind flow pathway of target area) features for long-term PM2.5 forecast is demonstrated by the increased performance in predicting PM2.5 in target city (Beijing) with the inclusion of additional 128 neighborhood sites. Moreover, the newly developed GNN_LSTM model also implies the "source"-receptor relationship, as impacts from distanced sites associated with regional transport grows along with the forecasting time (from 0% to 38% in 72 hours) following the wind flow. Such results suggest great potentials of GNN_LSTM in long-term air quality forecasting and air pollution prevention. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Short-term Power Prediction Based on Improved NWP Data Considering Wind Speed Distribution Inner Wind Farm

Short-term Power Prediction Based on Improved NWP Data Consi...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Wei Ma Ying Qiao ZongXiang Lu ZhiFeng Liang JiaMing Li YuChen Hao State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments of Electrical Engineering Tsinghua University Beijing China State Grid Corporation of China Beijing China State Grid Jiangsu Power Grid Corporation Nanjing China

The wind farm has only one NWP data point, which cannot accurately reflect the wind resources inside the wind farm. To solve the problem of a poor match between short-term wind power prediction numerical weather forecast and wind farm measured weather. Put forward a kind of based on clustering and temporal characteristics of the modified liters of scale wind power prediction method, first of all, using EOF orthogonal decomposition method to extract the features of each fan, using hierarchical analysis method of the characteristics of the data matrix clustering, and then, each cluster sample selected representative of a fan, using the error transfer characteristic of the NWP data sequence is modified. Finally, The CNN prediction model is used for power prediction, and the weight is determined according to the cluster sample size. A wind farm in northern Hebei, China, is used to verify the effectiveness of the proposed method.

关键词： Fans Wind speed System integration Wind farms Wind power generation Predictive models Feature extraction

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Optimal Sizing of Photovoltaic and Battery Energy Storage of Electric Vehicle Charging Station Based on Two-part Electricity Pricing

Optimal Sizing of Photovoltaic and Battery Energy Storage of...

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2020 IEEE Sustainable Power and Energy Conference, iSPEC 2020

作者： Wang, Conghui Tian, Liting Zhang, Fang Cheng, Lin School of Electrical Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China

ISBN: (纸本)9781728191645

This paper focuses on optimal sizing of photovoltaic (PV) and battery energy storage system (BESS) of special-use charging station for electric taxi cabs. Aiming to minimize annual equivalent cost of the charging station under two-part electricity pricing mechanism, an optimal sizing algorithm of PV and BESS is established considering the randomness of PV output and the power constraint of the grid, as well as whether charging station participates in demand response (DR). The cooperative operation strategies of charging facilities, PV and BESS under typical scenarios are proposed. Considering the factors that affect the optimal results, this paper provides sensitivity analysis in a charging station. Finally, the proposed optimal sizing algorithm is verified in the charging station equipped with 24 EV charging piles, and the economic analysis of the optimal sizing scheme is carried out. Based on economical analysis of optimal sizing results, it is found out that the equivalent cost of EV charging station is lower when participating in demand response. The results show that the annual equivalent total cost of the charging station is significantly reduced under the new optimal sizing scheme. © 2020 IEEE.

关键词： Sensitivity analysis

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Front Cover Image

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Energy Science & Engineering 2023年第11期11卷

作者： Ali Nekoonam Morteza Montazeri-Gh Systems Simulation and Control Laboratory School of Mechanical Engineering Iran University of Science and Technology (IUST) Tehran Iran

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