2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)最新文献_第6页

Internal Characteristic Analysis and Optimal Application Scenario of 2nd Filter and C-type Filter 二型滤波器和c型滤波器的内部特性分析及优化应用场景

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783304

Liu Ke, Yan Han, Wang Xin, Wang Xuan, Yang Fangnan, Li Jianwu

引用次数: 0

Decentralized Coordination Strategy for Integrated Electricity-Hydrogen Market 电力-氢一体化市场的分散协调策略

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783397

Qi An, Jianxiao Wang, Asad Mujeeb, Chen Wu, Guiyuan Xue, Gengyin Li

{"title":"Decentralized Coordination Strategy for Integrated Electricity-Hydrogen Market","authors":"Qi An, Jianxiao Wang, Asad Mujeeb, Chen Wu, Guiyuan Xue, Gengyin Li","doi":"10.1109/CEEPE55110.2022.9783397","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783397","url":null,"abstract":"As a clean, carbon-free, flexible and efficient secondary energy, the application prospect of hydrogen is promising. In the existing hydrogen market, the reforming of fossil fuels is the most mainstream way to produce hydrogen. Under the background that water electrolysis into hydrogen will become the mainstream method of hydrogen production in the future, the existing operation mechanism will no longer be applicable to hydrogen market. With the deepening of the coupling between both the electricity and hydrogen market, it is necessary to study the decomposition and coordination mechanism of the electricity-hydrogen market. In this paper, a joint clearing model for the electricity-hydrogen market is established. The above two markets are connected by power-to-hydrogen (P2H) and fuel cell facilities. A decentralized algorithm based on optimality condition decomposition (OCD) is developed to decompose and coordinate the electricity market and the hydrogen market. Case study based on modified IEEE-30 bus system with Belgian-20-node gas system demonstrate the effectiveness and efficiency of the proposed mechanism and method.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132909263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Research on Carbon Emission Prediction Method Considering Data Preprocessing 考虑数据预处理的碳排放预测方法研究

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783389

Yin Wang, Jiahui Tian, Ligang He, Qianmao Zhang, Lijie Zhang, S. Miao

{"title":"Research on Carbon Emission Prediction Method Considering Data Preprocessing","authors":"Yin Wang, Jiahui Tian, Ligang He, Qianmao Zhang, Lijie Zhang, S. Miao","doi":"10.1109/CEEPE55110.2022.9783389","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783389","url":null,"abstract":"Achieving accurate carbon emission forecasts is an important prerequisite for formulating and evaluating low-carbon transformation and upgrading strategies. However, the existing carbon emission forecasting methods are vulnerable to abnormal data and social factors. Aiming at the above problems, this paper studies a carbon emission prediction model considering the data preprocessing method. First, establish the calculation model of 'converting carbon by electricity', and calculate the conversion coefficient of 'carbon-electricity'; Secondly, data preprocessing methods such as isolated forest algorithm and exponential smoothing method are used to eliminate abnormal data, and then the processed 'carbon-electricity' conversion coefficient is input into the established BA-LSTM prediction model, and the prediction result of carbon emissions is obtained by combining with electricity consumption calculation. Finally, a verification analysis is carried out with the data of the whole society's electricity consumption and energy consumption in a certain province in China. The results show that, compared with the traditional carbon emission prediction method, the calculation model combined with the data preprocessing method proposed in this paper has higher prediction accuracy.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131841955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated Scheduling for Power System with High Proportion of Renewable Energy Considering Multiple Time Resolution and Energy Storage System 考虑多时间分辨率和储能系统的高可再生能源电力系统综合调度

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783372

J. Guo, Mingqiang Wang, Jianan Liu, Jianying Li

{"title":"Integrated Scheduling for Power System with High Proportion of Renewable Energy Considering Multiple Time Resolution and Energy Storage System","authors":"J. Guo, Mingqiang Wang, Jianan Liu, Jianying Li","doi":"10.1109/CEEPE55110.2022.9783372","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783372","url":null,"abstract":"In the context of the clean and low-carbon energy revolution, high proportional renewable energy source (RES) will be one of the basic characteristics of the future power system. The traditional sequential scheduling module exposes serious problems. In order to solve these problems above, this paper establishes an integrated scheduling model, which determines unit reserve, base points (BPs) and participation factors (PFs) at the same time. It realizes the integrated decision-making of unit commitment (UC), economic dispatch (ED), and PFs of automatic generation control (AGC). To accurately capture the newly updated short-term wind power forecast, the finer time resolution is applied in the first few time intervals, such as 5, 15 and 30 min. Simultaneously, for the short-term wind power spike, energy storage system (ESS) as its countermeasure is used to deal with it. The entire model is regarded as a mixed integer linear programming (MILP) model, which is solved by CPLEX solver. The practicality and validity of the proposed model are verified by the IEEE-RTS system. In general, the integrated model proposed in this paper can accommodate more RES integration, relieve wind curtailment, reduce the total cost and improve the security of the power system, which has a wider application prospect in the power system scheduling field.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133859565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recyclable and High-Performance Thermosetting Polymers for Digital Light Processing 3D Printing 用于数字光处理3D打印的可回收和高性能热固性聚合物

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783401

Yingfan Zhang, Haohuan Wang, Senyuan Yang, Zhengyong Huang

引用次数: 0

Thermal State Prediction of Transformers Based on ISSA-LSTM 基于ISSA-LSTM的变压器热状态预测

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783331

Chao Wu, Junlian Lin, Zongchao Yu, Jiangwei Yang, Xuan Liu

引用次数: 0

Sensitivity Analysis of System Power Distribution to UHVAC Line Parameters 系统配电对特高压线路参数的敏感性分析

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783394

Shenghe Wang, Zhengyang Wu, Zhong Fu, Jinglei Deng, Fan Cheng

{"title":"Sensitivity Analysis of System Power Distribution to UHVAC Line Parameters","authors":"Shenghe Wang, Zhengyang Wu, Zhong Fu, Jinglei Deng, Fan Cheng","doi":"10.1109/CEEPE55110.2022.9783394","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783394","url":null,"abstract":"Variation of the line parameters yields the change of all bus voltages, which need to be considered when quantifying the sensitivity of the power distribution to the line parameter. For long-distance ultra-high voltage AC (UHVAC) lines, the conductance and susceptance parameters of its equivalent model cannot be separated, causing difficulties to calculate the corresponding sensitivity. In this paper, by combining the partial derivatives of all power constraints to the line parameter, the sensitivities of the node voltages to the line parameters are obtained through the matrix form. Considering the variation of the line parameters and bus voltages, the analytical expression of the sensitivity of the power distribution to line parameters is derived. For long-distance UHVAC line, the partial derivatives of the admittance to the line parameters are determined by comparing the errors between the actual values from the continuous parameter modified method and the calculated values with different slopes, and the corresponding sensitivities are obtained based on the partial derivatives. The modified two-region IEEE RTS- 96 test system is applied to validate the feasibility and accuracy of the proposed model.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114856027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Black Start Simulation Method with Energy Storage Assistance Systems in Wind Power Consumption 风电消纳中储能辅助系统的黑启动仿真方法

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783307

Hong Hu, Libin Wen, Jun Li, Zhiyuan Sun, Xuanren Meng, Kun Zheng

引用次数: 1

Circuit Simulation of MPPT Charge Regulator Based on Soft Switching 基于软开关的MPPT电荷调节器电路仿真

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783395

Ziwen Fang, Yahong Yang, Zhiqi Lu, Aiwei Yang, Xiaobin He

引用次数: 0

Carbon Emission Prediction of Thermal Power Plants Based on Machine Learning Techniques 基于机器学习技术的火电厂碳排放预测

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) Pub Date : 2022-04-22 DOI: 10.1109/CEEPE55110.2022.9783417

Chao Zhu, Peng Shi, Zhuang Li, Mingle Li, Hongji Zhang, Tao Ding

{"title":"Carbon Emission Prediction of Thermal Power Plants Based on Machine Learning Techniques","authors":"Chao Zhu, Peng Shi, Zhuang Li, Mingle Li, Hongji Zhang, Tao Ding","doi":"10.1109/CEEPE55110.2022.9783417","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783417","url":null,"abstract":"Since the magnificent goal of Peak Carbon Dioxide Emissions and Carbon Neutrality was put forward in 2020, carbon emission reduction has attracted unprecedented attention. The power industry must fulfill its carbon emission reduction obligations as soon as possible. Thermal power plants are the main source of carbon emissions in the power industry, so finding out the key influencing factors of thermal-power-plant carbon emission and making accurate predictions are important measures to promote the low-carbon development of the power industry. Although some precise models have been proposed, most power plants cannot obtain all the parameters required by the precise models in the actual production practice, which limits their application. Machine learning technology accepts numerical data as input and establishes the mapping relationship between variables automatically, which results in loose requirements on data. This paper summarizes several key influencing factors of carbon dioxide emissions of thermal power plants that are easy to observe and establishes a prediction model of carbon dioxide emissions of thermal power plants based on eXtreme Gradient Boosting. In addition, we compare our method with two machine learning methods proposed in previous research and obtain a satisfactory result.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125367784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1