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含光伏和混合储能的同相牵引供电系统日前优化调度

刘元立 李群湛

刘元立, 李群湛. 含光伏和混合储能的同相牵引供电系统日前优化调度[J]. 西南交通大学学报, 2023, 58(1): 30-39. doi: 10.3969/j.issn.0258-2724.20200534
引用本文: 刘元立, 李群湛. 含光伏和混合储能的同相牵引供电系统日前优化调度[J]. 西南交通大学学报, 2023, 58(1): 30-39. doi: 10.3969/j.issn.0258-2724.20200534
LIU Yuanli, LI Qunzhan. Day-Ahead Optimal Scheduling of Co-phase Traction Power Supply System with Photovoltaic and Hybrid Energy Storage[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 30-39. doi: 10.3969/j.issn.0258-2724.20200534
Citation: LIU Yuanli, LI Qunzhan. Day-Ahead Optimal Scheduling of Co-phase Traction Power Supply System with Photovoltaic and Hybrid Energy Storage[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 30-39. doi: 10.3969/j.issn.0258-2724.20200534

含光伏和混合储能的同相牵引供电系统日前优化调度

doi: 10.3969/j.issn.0258-2724.20200534
基金项目: 国家自然科学基金(51877182);中国铁路总公司科技研究开发计划(N2018G024)
详细信息
    作者简介:

    刘元立(1995—),男,助理工程师,研究方向为牵引供电系统以及数值优化,E-mail:liuylcrfsdi@163.com

  • 中图分类号: TM922.3

Day-Ahead Optimal Scheduling of Co-phase Traction Power Supply System with Photovoltaic and Hybrid Energy Storage

  • 摘要:

    既有牵引供电系统中以负序为主的电能质量问题以及电分相环节严重制约了其安全、高效运行,目前理想的解决方案是基于对称补偿理论的同相供电技术. 通过同相补偿装置中的直流母线接入光伏发电系统以及混合储能装置,进一步实现再生回馈能量利用和牵引负荷削峰填谷,提高光伏渗透率. 因此,建立了一种同相牵引供电系统优化运行模型,该模型以同相牵引变电所日运行成本最低为目标,以混合储能装置充放电策略、光伏出力以及潮流控制器功率为决策变量,尤其考虑了电网侧三相电压不平衡度约束;进一步将原始优化模型中非线性约束进行线性化处理,得到混合整数线性规划模型,并利用商业规划求解器CPLEX进行求解. 算例分析结果表明:接入光伏与混合储能装置后日运行成本可节省36.45%,且三相电压不平衡度满足国标上限2%的要求.

     

  • 图 1  同相牵引供电系统示意

    Figure 1.  Structure of co-phase traction power supply system

    图 2  PFC视在功率约束线性化

    Figure 2.  Linearization of PFC apparent power constraint

    图 3  牵引负荷及最大光伏出力日前预测数据

    Figure 3.  Day-ahead forecast values of traction load and maximum photovoltaic output

    图 4  同相牵引变电所日前优化调度结果

    Figure 4.  Scheduling results of co-phase traction substation

    图 5  方案Ⅰ与方案Ⅱ对比

    Figure 5.  Comparison of scheme Ⅰ and scheme Ⅱ

    图 6  反馈电能计费方案 a、b 和 c 结果对比

    Figure 6.  Comparison of feedback power billing schemes a, b and c

    图 7  不同储能容量下的运行成本节省效果

    Figure 7.  Cost savings with different energy storage capacities

    表  1  模型输入参数

    Table  1.   Input parameters of model

    项目参数参数取值
    电网 US/kV220.0
    Scap/(MV·A)750
    $ {{\overline \varepsilon _{\text{U}}}} $/%2
    牵引变压器 N14
    N24$/ {\sqrt 3 }$
    UT/kV27.5
    潮流控制器 Uα/kV27.5
    Uβ/kV27.5
    Sα,cap/(MV·A)10
    Sβ,cap/ (MV·A)10
    混合储能装置电池超级电容
    SOC 范围[0.20, 0.80][0.05, 0.95]
    初始 SOC0.50.5
    效率(充/放电)0.80/0.800.95/0.95
    额定容量/(MW·h)5.000.25
    额定功率/MW210
    日最大循环数/次15不限
    电价峰时平时谷时
    电度/ (元·(kW·h)−11.2520.7820.370
    需量/
    (元·(kW·月−1−1
    42.00042.00042.000
    时间段8:00—11:00,
    18:00—21:00
    7:00—8:00,
    12:00—17:00
    0:00—6:00,
    22:00—0:00
    反馈电能
    计费方案
    方案 acfed = 0
    方案 bcfed = cbuy
    方案 ccfed = −0.8cbuy
    下载: 导出CSV

    表  2  方案Ⅰ与方案Ⅱ优化结果对比

    Table  2.   Comparison of scheme Ⅰ and scheme Ⅱ

    指标方案Ⅰ方案Ⅱ优化率/%
    经济电度电费/元67 077.8144 674.8533.40
    需量电费/元17 129.9010 489.8538.76
    回馈电能计费/元14 759.5826 36.1382.14
    光伏运维费用/元012 56.80
    储能运维费用/元03 833.72
    总成本/元98 967.2962 891.3536.45
    技术制动能量
    利用率/%
    080.2780.27
    最大电压
    不平衡度/%
    2.792.0028.32
    下载: 导出CSV

    表  3  反馈电能计费方案a、b和c优化结果对比

    Table  3.   Optimal result comparison of feedback power billing schemes a, b and c

    项目方案 a方案 b方案 c
    总弃光量/(MW·h)1.431.470
    电池日循环数15156
    电度电费/元44 674.8544 674.8553 149.54
    需量电费/元10 489.8510 489.8510 489.85
    回馈电能收费/元02 636.13−12 704.86
    光伏运维费用/元1 261.251 256.801 409.51
    储能运维费用/元3 770.443 833.721 713.61
    总成本/元60 196.3962 891.355 4057.66
    总成本节省率
    (相较方案Ⅰ) /%
    28.5136.4525.33
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-14
  • 修回日期:  2020-11-21
  • 网络出版日期:  2022-10-17
  • 刊出日期:  2021-04-07

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