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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,122cc太阳集成游戏,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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