• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2020, Vol. 41 ›› Issue (8): 120-128.doi: 10.12204/j.issn.1000-7229.2020.08.014

• 智能电网 • 上一篇    下一篇

基于误差前馈预测的多时空尺度风电集群有功功率分层控制策略

郑惠萍1,曾鹏2,刘新元1,程雪婷1,薄利明1,杨尉薇1,张一帆1,崔杨2   

  1. 1.国网山西省电力公司电力科学研究院,太原市 030001;2.现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林省吉林市 132012
  • 出版日期:2020-08-07 发布日期:2020-08-07
  • 作者简介:郑惠萍(1972),女,硕士,教授级高级工程师,主要研究方向为电力系统运行分析、新能源联网发电关键技术等; 曾鹏(1996),男,硕士研究生,通信作者,主要研究方向为综合能源系统的运行控制等; 刘新元(1986),男,硕士,高级工程师,主要研究方向为电力系统运行分析、新能源联网发电关键技术等; 程雪婷(1991),女,硕士,工程师,主要研究方向为电力系统运行分析、新能源联网发电关键技术等; 薄利明(1989),男,硕士,工程师,主要研究方向为电力系统运行分析与控制; 杨尉薇(1985),女,硕士,高级工程师,主要研究方向为电力系统运行分析与控制等; 张一帆(1989),女,硕士,工程师,主要研究方向为电力系统自动化; 崔杨(1980),男,博士,教授,主要研究方向为电力系统运行分析、新能源联网发电关键技术等。
  • 基金资助:
    国网山西省电力公司电力科学研究院科技项目“适应特高压交直流混联电网的新能源协同控制策略研究”(52053018000N)

Active Power Layered Control Strategy Based on Error Feedforward Prediction for Multiple Temporal and Spatial Scales Wind Power Cluster

ZHENG Huiping1,ZENG Peng2,LIU Xinyuan1,CHENG Xueting1,BO Liming1,YANG Weiwei1,ZHANG Yifan1,CUI Yang2   

  1. 1. State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China; 2. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin 132012, Jilin Province, China
  • Online:2020-08-07 Published:2020-08-07

摘要: 随着风电渗透率的提高,风电场存在的可预测性和可调度性差等问题已经凸显。针对上述因素导致风电消纳水平降低的问题,文章提出结合误差前馈预测的风电集群有功功率分层控制策略。首先,提出考虑风电变化趋势的误差前馈模型,将其与小波包分解和持续法模型相结合组成超短期功率预测模型,并根据历史数据的训练情况赋予误差前馈限值。其次,基于此预测模型提出一种多时空尺度的有功功率分层控制策略,该策略在已有调度指令的前提下,通过将控制层分为集群层、场群层和子场层,实现对各风电场的协调控制。最后,基于东北某风电基地的实际运行数据通过MATLAB和CPLEX进行仿真分析,结果证明所提方法改善了风电消纳水平和风电场储能协调出力。

关键词: 风电消纳, 误差前馈预测, 有功分层控制, 超短期功率预测

Abstract: With the increase of wind power penetration rate, the problems of predictability and poor schedulability of wind farms have become prominent. Aiming at the problem that the above-mentioned factors lead to the reduction of wind power consumption level, this paper proposes an active-power layered control strategy combined with error feedforward prediction for wind power clusters. Firstly, an error-feedforward prediction model considering wind power variation trend is proposed, which is combined with wavelet packet decomposition and persistence method prediction model to form an ultra-short-term power prediction model, and the error feedforward limit is given according to the training situation of historical data. Secondly, on the basis of this prediction model, a  multiple temporal and spatial scales  active-power layered control strategy is proposed. The strategy divides the control layer  into cluster layer, farm group layer and sub-farm layer under the premise of existing scheduling instructions.  It realizes coordinated control of various wind farms. Finally, on the basis of the actual operation data of a wind power in Northeast China, simulation analysis is carried out by MATLAB and CPLEX. The results show that the proposed method improves the wind power accommodation level and the coordination output of wind farms energy storage.

Key words: wind power accommodation, error feedforward prediction, active-power layered control, ultra-short-term power prediction

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