期刊文献+

消纳大规模风电的多时间尺度协调的有功调度系统设计 被引量:239

Design of a Multi-time Scale Coordinated Active Power Dispatching System for Accommodating Large Scale Wind Power Penetration
在线阅读 下载PDF
导出
摘要 大规模风电的接入对电网调度模式和技术提出了重大挑战。通过分析风电预测精度随时间尺度逐级提高的特性和有功调度的固有特点,提出了多时间尺度协调的有功调度模式及其关键技术。这种调度模式是基于"多级协调、逐级细化"的思路,将上一级遗留的偏差由下一级来修正。文中首先介绍了该系统的整体构架和总体思路;然后,分析了发电态势分析的几个基本问题:理想发电模型、扩展短期预测和超短期预测以及负荷特性分解;接着,提出滚动计划优化模型及其与日前计划的关系,以及以弃风最小为目标的实时调度模型及其与风电场调度的关系。最后,给出了一个实际系统的应用效果。 Large scale wind power has posed a great challenge for the power system dispatching mode and technology. By analyzing the characteristics of the predicting accuracy of wind power that increases level by level with different time scales and the inherent features of active power dispatch, a multi-time scale coordinated automatic power dispatching mode and its key technologies are proposed. In this mode, the deviations left over by the 'preceding level are corrected by the next level based on the idea of "multi-level coordination, level by level refining". First, the framework and general idea of this system is described. Second, the methods of evaluating the generation trend are proposed including such problems as the ideal generation output model, extended short-term forecasting, ultra short-term forecasting and decomposition of the load curve. Third, the online rolling power scheduling model and its relationship with the day-ahead schedule, as well as the real-time power dispatching model using the waste wind minimum as the objective, and its relationship with the wind farm dispatch are presented. Finally, the application effects of an actual system are illustrated.
出处 《电力系统自动化》 EI CSCD 北大核心 2011年第1期1-6,共6页 Automation of Electric Power Systems
基金 国家自然科学基金资助项目(50707013) 国家电网公司科技项目(20100086)~~
关键词 风电接入 有功调度 多时间尺度协调 实时调度 电力系统调度 wind power penetration active power dispatch multi-time scale power system dispatch
  • 相关文献

参考文献21

  • 1MIRANDA M S, DUNN R W. One-hour-ahead wind speed prediction using a Bayesian methodology[C]// Proceedings of IEEE Power Engineering Society General Meeting, June 18-22, 2006, Montreal, Canada.
  • 2EI-FOULY T H M, EI-SAADANY E F, SALAMA M M A. One day ahead prediction of wind speed using annual trends [C]// Proceedings of IEEE Power Engineering Society General Meeting, June 18-22, 2006, Montreal, Canada.
  • 3SENJYU T, YONA A, URASAKI N, et al. Application of recurrent neural network to long-term-ahead generating power forecasting for wind power generator[C]// Proceedings of IEEE Power Systems Conference and Exposition, October 29- November 1, 2006, Atlanta, GA, USA: 1260-1265.
  • 4LI S. Wind power prediction using recurrent multilayer perceptron neural networks[C]// Proceedings of IEEE Power Engineering Society General Meeting, July 13-17, 2003, Toronto, Canada: 2325 -2330.
  • 5FAN S S, LIAO J J R, YOKOYAMA R R, et al. Forecasting the wind generation using a two-stage network based on meteorological information [ J ]. IEEE Trans on Energy Conversion, 2009, 24(2): 474-482.
  • 6SIDERATOS G, HATZIARGYRIOU N D. An advanced statistical method for wind power forecasting[J].IEEE Trans on Power Systems, 2007, 22(1):258-265.
  • 7ACKERMANN T, ABBAD J R, DUDURYCH I M, et al. European balancing act[J]. IEEE Power and Energy Magazine, 2007, 5(6): 90 -103.
  • 8GUOVEIA E M, MATOS M A. Operational reserve of a power system with a large amount of wind power[C]// Proceedings of International Conference on Probabilistic Method Applied to Power Systems, September 12 16, 2004, Ames, IA, USA: 717- 722.
  • 9SODER L. Reserve margin planning in a wind-hydro thermal power system[J]. IEEETrans on Power Systems, 199:3, 8(2) 564-571.
  • 10DANY G. Power reserve in interconnected system with high wind power production[C]// Proceedings of IEEE Power Tech Conference, September 10-13, 2001, Porto, Portugal: 4-6.

二级参考文献46

共引文献78

同被引文献2353

引证文献239

二级引证文献4347

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部