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基于MVTV Copula方法的多风电场电力系统经济调度分析

李奇 潘俞如 邱宜彬 陈维荣

李奇, 潘俞如, 邱宜彬, 陈维荣. 基于MVTV Copula方法的多风电场电力系统经济调度分析[J]. 西南交通大学学报, 2021, 56(2): 339-346. doi: 10.3969/j.issn.0258-2724.20190459
引用本文: 李奇, 潘俞如, 邱宜彬, 陈维荣. 基于MVTV Copula方法的多风电场电力系统经济调度分析[J]. 西南交通大学学报, 2021, 56(2): 339-346. doi: 10.3969/j.issn.0258-2724.20190459
LI Qi, PAN Yuru, QIU Yibin, CHEN Weirong. Economic Dispatch for Power Systems of Multiple Wind Farms Based on MVTV Copula Method[J]. Journal of Southwest Jiaotong University, 2021, 56(2): 339-346. doi: 10.3969/j.issn.0258-2724.20190459
Citation: LI Qi, PAN Yuru, QIU Yibin, CHEN Weirong. Economic Dispatch for Power Systems of Multiple Wind Farms Based on MVTV Copula Method[J]. Journal of Southwest Jiaotong University, 2021, 56(2): 339-346. doi: 10.3969/j.issn.0258-2724.20190459

基于MVTV Copula方法的多风电场电力系统经济调度分析

doi: 10.3969/j.issn.0258-2724.20190459
基金项目: 国家自然科学基金(51977181);四川省科技计划(19YYJC0698);霍英东教育基金会高等院校青年教师基金(171104)
详细信息
    作者简介:

    李奇(1984—),男,教授,博士生导师,研究方向为分布式发电并网技术、智能信息处理等,E-mail: liqi0800@163.com

  • 中图分类号: TM614

Economic Dispatch for Power Systems of Multiple Wind Farms Based on MVTV Copula Method

  • 摘要: 针对多维风电出力数据间相关性对含有多风电场的电力系统经济调度影响问题,提出了MVTV Copula (mix vine time-varying Copula)方法,以此构建多风电场出力数据随机场景,并建立基于机组燃料费用及再调度费用最小为目标的电力系统经济调度模型. 为验证所述方法的有效性,采用IEEE-30节点系统,并以我国某地3个相邻风电场的实际出力为例对所述方法进行了仿真验证. 仿真结果表明:不考虑风电出力相关性的经济调度费用仅为实际调度费用的43.6%,而应用所述MTVT Copula方法建模后的再调度费用则更加接近实际调度费用,为实际调度费用的82%,验证了本文所提方法的有效性.

     

  • 图 1  仿真系统拓扑

    Figure 1.  Topology of simulation system

    图 2  各风电场实际出力曲线及预测出力曲线

    Figure 2.  Curve of actual output and forecast output

    图 3  DBI指标与场景数关系

    Figure 3.  Relationship between DBI index and scenario number

    图 4  基于MVTV Copula模型的典型随机场景

    Figure 4.  Typical random scenarios based on MVTV Copula model

    图 5  不考虑相关性的典型随机场景

    Figure 5.  Typical random scenarios ignoring dependence of wind outputs

    图 6  负载曲线及各场景下机组和风电总出力曲线

    Figure 6.  Load curve and total output curve of unit and wind power output under different scenarios

    表  1  各场景下最优藤结构及最优参数

    Table  1.   Optimal vine structure and optimal parameters for different scenarios

    场景 藤结构 最优 Copula 函数 AIC 值
    1 C {tv-gumbeltv-SJCtv-gumbel} 0.384
    2 D {tv-gumbeltv-normaltv-gumbel} 0.195
    3 C { tv-SJCtv-gumbeltv-gumbel} 0.119
    4 D {tv-SJCtv-SJCtv-gumbel} 0.097
    5 D {tv-normaltv-SJCtv-gumbel} 0.062
    注:tv-SJCtv-gumbeltv-normal 分别为时变SJC Copula函数、时变gumbel copula函数、时变normal函数.
    下载: 导出CSV

    表  2  采用混合藤与孤立藤结构的AIC值对比

    Table  2.   AIC values of mix vine and isolated vine structures

    藤结构 C 藤 D 藤 混合藤
    AIC 值 0.197 0.217 0.129
    下载: 导出CSV

    表  3  两类典型随机场景情况调度费用对比

    Table  3.   Economic dispatch results of two typical random scenarios USD

    场景 预测场景
    煤耗费用
    再调度费用 总费用
    不考虑相关性 9 969.16 1 544.45 11 513.61
    考虑相关性 9 969.16 3 540.64 13 509.80
    实际出力 9 969.16 4 320.77 14 289.93
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-05-20
  • 修回日期:  2020-03-29
  • 网络出版日期:  2021-01-18
  • 刊出日期:  2021-04-15

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