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史洁
发布日期:2019/12/26    阅览:

姓名:

史洁

性别:

学历学位:

研究生,博士

职称:

讲师

联系电话:

15966051089

电子邮件:

sps_shij@ujn.edu.cn

通讯地址:

山东省济南市济微路106号,新澳2025年精准资料库入口,250022

招生方向:

物理学

研究简介

研究新能源并网机理及技术,在风电、光伏功率预测原理、高比例新能源发电并网、风光储多能互补机理及应用等方面有较好的科研基础。  

个人简介

博士毕业于华北电力大学(北京)新能源电力系统国家重点实验室,获得教育部博士研究生国家奖学金、华北电力大学优秀博士论文、中国可再生能源协会风能专业委员会优秀硕士论文和博士论文,北京市优秀博士毕业生、华北电力大学优秀毕业生等荣誉。攻读博士期间赴**德克萨斯州立大学能源系统研究中心(ESRC)学习2年,在风电、光伏功率预测机理、高比例新能源发电并网、风光储多能互补机理及应用等方面有较好的科研基础,以第一作者发表论文22篇,其中SCI索引6篇,包含IEEE Transactions on Smart GridApplied   Energy一区论文,ESI全球1%高被引论文1篇。申请发明专利3项,已授权1项。主持国家自然科学基金青年项目1项,属山东省高校优秀创新团队核心成员,参与国家自然科学基金项目面上项目和863重点研发项目。与山东大学、华北电力大学、电子科技大学共同承办IEEE I&CPS Asia国际会议。

科研主要成果

    主持及参与科研项目(仅列重要部分):

    2017.01-2019.12    National   Science Foundation of ChinaNo.51606085

?    The Study on Mechanism of Wind Power Forecasting to   Very-short-term Wind Storage Combined Power Generation System Scheduling

?  Research   for wind power output forecasting, and establish generation scheduling model   of wind storage combined system

    2014.03-2017.03     Talent   Training Program in BeijingNo. 2014000020124G095

?    Research for short-term wind power forecasting of   integrated wind farm based on weighted combined algorithm

?    Study for improving forecasting accuracy of wind power   output

    2007.12-2011.12     National   High Technology Development Plan

?    Research and development of short-term wind power   forecasting system

?  Design   system structure and optimize forecasting model based on historical data   analysis

    2007.11-2010.06    Program   of North China Electric Power Design and Research Institute

?    System design and development of Post-evaluation

?    Design system structure and hierarchical modeling

    2009.03-2010.06     National   Energy Administration Research Program

?    Wind power development problems and countermeasures   research

?    According to the demand analysis, the framework   analysis and design of the prediction system function, structure, participate   in the technical agreement formulation

?  Based   on historical data, the algorithm design, parameters and prediction results   of the short-term forecasting statistical model of wind farms complete the   main functions of training, testing and prediction forecasting

    2011.06-2011.12     The  United     States      Center      for    the        Commercialization of     Electric Technologies (CCET)/IEEE

?  Virtual photovoltaic power station and energy storage   units are installed in west Texas at United States

?  The   economic feature   of the installed stations are evaluated and analyzed based on the wind farm   capacity and power output, along with Local Marginal Price (LMP) in   electricity market

    SCI学术论文(仅列重要部分):

(1) Hybrid Energy   Storage System (HESS) optimization enabling very short-term wind power   generation scheduling based on output feature extraction. Applied Energy.   Dec. 2019.

(2) Generation   Scheduling Optimization of Wind-Energy Storage System Based on Wind Power   Output Fluctuation Features. 2018. IEEE Transactions on Industry   Applications, 54 (1): 10-17.

(3) Hybrid   Forecasting Model for Very-short Term Wind Power Forecasting Based on Grey   Relational Analysis and Wind Speed Distribution Features. 2014. IEEE   Transactions on Smart Grid, 5 (1): 521-526.

(4) Forecasting Power   Output of Photovoltaic System Based on Weather Classification and Support   Vector Machine. 2012. IEEE Transactions on Industry Applications, 48(3):   1064-1069.

(5) Short-term Wind   Power Prediction Based on Wavelet Transform-Support Vector Machine and   Statistic Characteristics Analysis. 2012. IEEE Transactions on Industry   Applications, 48(4) 1136   -1141.

(6) Piecewise Support   Vector Machine Model for Short Term Wind Power Prediction.2009. International   Journal of Green Energy, 6 (5): 479-489.

(7) Model   optimization for very-short-term wind power forecasting using Hilbert-Huang   Transform. 2016. International Conference on Smart Grid and Clean Energy   Technologies. Chengdu, China.

(8) Weighted Parallel   Algorithm to Improve the Performance of Short-term Wind Power Forecasting.   2012 IEEE PES General Meeting, 22 - 26 July 2012, San Diego, CA, USA.

(9) Multistage Model   for Short Term Wind Power Forecasting, the  ASME 2011 Power Conference   Co-located with International Conference on Power Engineering-2011. July   12-14, 2011, Denver, Colorado, USA.

(10)Uncertainty   Analysis Of Short Term Wind Power Forecasting Based on Error Characteristics   Statistics, ACTA ENERGIAE SOLARIS SINICA. 33(12): 2179-2184, 2012.

(11) Genetic   Algorithm-Piecewise Support Vector Machine Model for Short Term Wind Power   Prediction. Proceeding of IEEE 8th World Congress on Intelligent Control and   Automation. July 7-92010JinanChina.

(12) Demand Response   - An Assessment of Load Participation in the ERCOT Nodal Market. 2012 IEEE   PES General Meeting, 22 - 26 July 2012, San Diego, CA, USA.

教研主要成果

    主讲课程:

风能发电原理,工程流体力学、大数据与新能源的美丽邂逅(新生研讨课)、风资源测量与评估、专业导论等。

    教研项目:

教育部协同育人项目:以产学为导向的新能源科学与工程专业实践教学体系改革与建设

 

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