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YIN Jiamin,ZHENG Yun,YANG Jin.Research on Capacity Optimization of Generator-storage Combined Frequency Regulation System[J].Southern Energy Construction,2020,07(04):11-17. doi:  10.16516/j.gedi.issn2095-8676.2020.04.002
Citation: YIN Jiamin,ZHENG Yun,YANG Jin.Research on Capacity Optimization of Generator-storage Combined Frequency Regulation System[J].Southern Energy Construction,2020,07(04):11-17. doi:  10.16516/j.gedi.issn2095-8676.2020.04.002

Research on Capacity Optimization of Generator-storage Combined Frequency Regulation System

doi: 10.16516/j.gedi.issn2095-8676.2020.04.002
  • Received Date: 2020-07-16
  • Rev Recd Date: 2020-10-12
  • Publish Date: 2020-12-25
  •   Introduction   The paper aims to establish the profit model of generator-storage combined frequency regulation system and give the basis for battery storage power selection to determine the optimal capacity of battery storage.   Method  Based on the historical operation data of a power plant and market price in Guangdong Province, we simulated the participation of battery storage in frequency regulation and analyzed the influence of battery storage power selection on the performance and income of combined frequency regulation.  Result  The simulation results show that the battery storage system can greatly improve the frequency regulation income by enhancing the performance of the power unit and the mileage. With the increase of the battery storage power, the frequency regulation income gradually increases and then tends to be stable.  Conclusion  The impact of battery storage power on the performance and income is different due to the unit performance differences and operating conditions. This work provides that it is reasonable and feasible to determine the battery storage capacity through simulation calculation, which can provide reference for the optimization of battery storage capacity in subsequent projects.
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    SUNG H,WANGX H,CHENY Z,et al. Research on economic benefits of frequency modulation for energy storage combined generating units [J/OL]. Journal of Power Supply, 2020, 18(4):151-156. http://kns.cnki.net/kcms/detail/12.1420.TM.20190528.1549.008.html. http://kns.cnki.net/kcms/detail/12.1420.TM.20190528.1549.008.html
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Research on Capacity Optimization of Generator-storage Combined Frequency Regulation System

doi: 10.16516/j.gedi.issn2095-8676.2020.04.002

Abstract:    Introduction   The paper aims to establish the profit model of generator-storage combined frequency regulation system and give the basis for battery storage power selection to determine the optimal capacity of battery storage.   Method  Based on the historical operation data of a power plant and market price in Guangdong Province, we simulated the participation of battery storage in frequency regulation and analyzed the influence of battery storage power selection on the performance and income of combined frequency regulation.  Result  The simulation results show that the battery storage system can greatly improve the frequency regulation income by enhancing the performance of the power unit and the mileage. With the increase of the battery storage power, the frequency regulation income gradually increases and then tends to be stable.  Conclusion  The impact of battery storage power on the performance and income is different due to the unit performance differences and operating conditions. This work provides that it is reasonable and feasible to determine the battery storage capacity through simulation calculation, which can provide reference for the optimization of battery storage capacity in subsequent projects.

YIN Jiamin,ZHENG Yun,YANG Jin.Research on Capacity Optimization of Generator-storage Combined Frequency Regulation System[J].Southern Energy Construction,2020,07(04):11-17. doi:  10.16516/j.gedi.issn2095-8676.2020.04.002
Citation: YIN Jiamin,ZHENG Yun,YANG Jin.Research on Capacity Optimization of Generator-storage Combined Frequency Regulation System[J].Southern Energy Construction,2020,07(04):11-17. doi:  10.16516/j.gedi.issn2095-8676.2020.04.002
  • 近年来,随着风力发电以及光伏发电等新能源发电方式的不断并网,增加了电网系统的运行调控难度,电网对调频资源的需求也越来越高。同时,传统火电机组占比降低,电网可用的调频资源减少,调频容量不足的问题凸显。此外,由于传统火电机组的旋转惯性,对有功功率的调节响应速度较慢1-2,寻求新型调频手段辅助传统火电机组提升电网整体调频性能成为当前研究的热点3-4

    以广东为例,随着大批量海上风电接入,电网中风电装机容量不断增加,加上西电东送占比逐年增大,电网调频难度加剧。广东地区电源结构以大型燃煤机组为主,占总装机容量47.1%,调频依靠火电机组时调节任务繁重。燃煤机组长期承担繁重的调节任务,会造成发电机组设备磨损严重,增加燃料损耗,超净排放目标难以实现等一系列负面影响5-6。快速调频资源主要以联合循环电厂、抽蓄电厂和水电为主,其装机容量占全省装机容量30.4%,快速调节资源稀缺,调频的形势相对紧张。

    储能应用在调频领域,由于具备有功功率双向调节、响应速度快、调节精度高的特性,调频效果远好于常规发电机组。近几年来,在我国火电机组中采用电池储能系统联合进行调频,已经在工程中逐渐得到应用7-9。相关文献[10-15]对储能联合调频的经济效益进行了计算。

    虽然储能可以显著提高火电厂的AGC性能,带来调频收益的提升,但由于其成本较高,储能容量并不是越大越好。目前国内火电联合调频项目大多根据工程经验,按照1.5%~3.0%的机组容量配置储能16-17,区域电网对调频资源的实际需求考虑不足,也没有考虑到机组性能的差异对储能容量选型的影响。

    本文建立储能联合调频的收益模型,并以广东某电厂为研究对象,对机组和储能联合调频进行仿真计算,研究不同储能功率下的联合调频性能和调频收益,选择合适的储能功率,并与工程经验进行比较。

  • 图1所示,电网调度AGC指令下发到机组,储能系统同时获取该AGC指令,由于火电机组响应速度较慢(min级),储能系统利用自身响应速度快(s级)的特性先弥补短时间内机组出力与AGC指令间的功率差值。等机组响应跟上之后,储能系统出力可以逐渐降低,以确保储能系统和机组联合出力与AGC指令保持一致,并准备下一次AGC指令响应。

    Figure 1.  Principle of generator-storage combined frequency regulation

  • 根据《广东调频辅助服务市场交易规则(试行)》,广东调频市场补偿费用分为调频里程补偿和AGC容量补偿。发电单元日AGC补偿费用计算公式如下:

    R=R+RAGC ((1))
  • 中标发电单元在广东调频市场中提供调频服务可以获得相应的调频里程补偿。发电单元的日调频里程补偿计算公式如下:

    R=i=1n(Di×Qi×Ki) ((2))

    式中:n为每日调频市场总的交易周期数;Di为发电单元在第i个交易周期提供的调频里程(MW);Qi为第i个交易周期的里程计算价格(元/MW);Ki为发电单元在第i个交易周期的综合调频性能指标平均值。

  • 发电单元AGC容量为发电单元当前出力点在5分钟内向上可调容量与向下可调容量之和。发电单元日AGC容量补偿计算公式如下:

    RAGC=j=1m(Cj×Tj×s) ((3))

    式中:m为每日总调度时段数;Cj为发电单元在第j个调度时段的发电单元AGC容量(MW);Tj为发电单元在第j个调度时段的调频服务时长(h);s为AGC容量补偿标准(元/MWh)。

  • 调频里程是指发电单元响应AGC控制指令后结束时的实际出力值与响应指令时的出力值之差的绝对值。某时间段内的总调频里程为该时段内发电单元响应AGC控制指令的调频里程之和。总调频里程计算公式如下:

    D=j=1nDj ((4))

    式中:Dj为发电单元第j次的调频里程。

  • 综合调节性能指标指发电单元响应AGC控制指令的综合性能表现,计算公式如下:

    k=0.25×2×k1+k2+k3 ((5))

    式中:调节速率k1指发电单元响应AGC控制指令的速率;响应时间k2指发电单元响应AGC 控制指令的时间延迟;调节精度k3指发电单元机组响应AGC控制指令的精准度。

    k1= AGC  ((6))
    k2=1-(/5 min) ((7))
    k3=1- ((8))
  • 以广东某燃煤电厂#2机组和#4机组为储能联合调频改造对象,单机容量330 MW。考虑设置一套储能系统,储能单元同时接入#2机组和#4机组,采用一拖二方式运行,储能可在两台机组间切换,配合电厂的单机AGC运行模式。

    对电厂机组和储能系统分别建模,并基于机组7天(Day1~Day7)的实际历史运行数据和市场出清价格,对不同储能容量P(MW)进行联合调频仿真模拟运算。储能电池的放电功率按2C考虑,储能功率区间设置为机组容量的1%~5%。

  • 仿真结果如图2~图3所示,储能系统能大幅提升机组的k1值。随着储能功率的增加,调节速率k1几乎成比例增加,当储能功率增加到约9 MW以后,k1增加很少,基本维持在一个稳定水平。这是因为储能系统响应速度快,当收到AGC指令后,可以快速通过充、放电,迅速跟踪指令需求,远高于常规火电机组的调节速度。

    Figure 2.  Curves of regulation rate VS. battery power(#2 unit)

    Figure 3.  Curves of regulation rate VS. battery power(#4 unit)

  • 图4~图5所示,储能系统能小幅增加机组的k2值,但随着储能功率的继续增加,k2几乎不变。这是因为机组本身k2指标已较好,k2的提升空间较小,且仿真策略中为了电网的稳定,对储能的输出进行了速率限值。考虑到k2k值提升的贡献度只有25%,故储能系统用于提升k2的意义不大。

    Figure 4.  Curves of response time VS. battery power(#2 unit)

    Figure 5.  Curves of response time VS. battery power(#4 unit)

  • 图6~图7所示,储能系统对调节精度k3的影响不大,在部分情况下,甚至出现调节精度k3会随储能容量的增加而稍微降低。这是由于AGC精度计算算法以及储能运行策略导致,因为增加储能后会使机组调节速度增加,并较早结束调节,当调节结束后储能会退出运行,导致开始进行精度计算的时间提早,进而造成精度降低,若适当降低储能退出的门槛值,精度则会提高。

    Figure 6.  Curves of adjustment accuracy VS. battery power(#2 unit)

    Figure 7.  Curves of adjustment accuracy VS. battery power(#4 unit)

  • 仿真结果如图8~图9所示,储能系统能大幅提升机组的k值。随着储能功率的增加,k值逐渐增加,当储能功率增加到约9 MW以后,k值基本维持在一个稳定水平。这是由于调节速率k1k值计算的贡献度最大,达50%,因此k1的大幅提升必然带来k值的明显提高。同时,储能功率的增加对响应时间k2和调节精度k3的影响不大,且k2k3k值计算的贡献度本身就较小。

    Figure 8.  Curves of performance index k VS. battery power(#2 unit)

    Figure 9.  Curves of performance index k VS. battery power(#4 unit)

    以7天的仿真运行数据来看,按平均值计算,当储能功率为9 MW、10 MW和12 MW时,#2机组的k值可由1.01分别提升至2.39、2.42和2.43,#4机组的k值可由1.17分别提升至2.44、2.47和2.48。

  • 仿真结果如图10~图11所示,储能系统在一定程度上能增加机组的调频里程。随着储能功率的增加,D值逐渐增加,但增速放缓。这是由于储能联合调频后,不仅由于调节性能指标的提升,更容易在调频市场中中标,而且在同样中标的情况下,由于增加了储能,可以在响应AGC指令后结束时,减少实际出力和AGC指令的差值,也即增加调频里程。

    Figure 10.  Curves of regulation mileage VS. battery power(#2 unit)

    Figure 11.  Curves of regulation mileage VS. battery power(#4 unit)

    以7天的仿真运行数据来看,按平均值计算,当储能功率为9 MW、10 MW和12 MW时,#2机组的D值可由1.539 GW分别增加至2.223 GW、2.260 GW和2.317 GW,#4机组的D值可由2.074 GW分别增加至2.756 GW、2.792 GW和2.845 GW。

  • 仿真结果如图12~图13所示,储能系统能大幅提升机组日调频收益。随着储能功率的增加,日调频收益R逐渐增加,但增速放缓。

    Figure 12.  Curves of income VS. battery power(#2 unit)

    Figure 13.  Curves of income VS. battery power(#4 unit)

    以7天的仿真运行数据来看,按平均值计算,当储能功率为9 MW、10 MW和12 MW时,#2机组的R值可由22 148元增加至74 417元、76 663元和78 791元,分别提升236%、246%和256%,#4机组的R值可由37 243元增加至103 163元、105 764元和107 981元,分别提升177%、184%和190%。

  • 不同储能功率下联合调频的性能和收益见表1。与储能功率为9 MW相比,当储能功率为10 MW和12 MW时,日调频收益的提升很小。实际工程中,建议储能容量可按9 MW考虑,与联合调频项目经验按照3.0%的机组容量配置储能基本相符。

    机组号储能容量调节性能k日调频收益R/元
    绝对值相对值/%绝对值相对值/%
    #29 MW(基准)2.3974 417
    10 MW2.42101.376 663103.0
    12 MW2.43101.778 791105.9
    #49 MW(基准)2.44103 163
    10 MW2.47101.2105 764102.5
    12 MW2.48101.6107 981104.7

    Table 1.  Table of performance and income

    此外,由表1图1~图13可见:

    1)对于不同的机组,即使处于同一调频控制区且机组容量相同,由于机组本身性能的差异,储能对机组性能提升的影响也不相同。

    2)由于每日的机组运行状况和电网调频需求不同,再加上调频市场中博弈的存在,对于同一台机组,日调频收益均不相同,储能对机组调频收益的提升也不相同。

  • 本文建立了储能联合调频的收益模型,并基于广东某电厂2台机组7天的历史运行数据,对机组和储能联合调频进行仿真计算,分析储能功率选择对联合调频性能的影响,并充分考虑调频市场出清价格的波动,以实际的市场出清价格进行调频收益计算,分析储能功率对调频收益的影响,仿真结果更具有参考意义。

    结果表明:

    1)储能系统能大幅提升机组的调频性能。随着储能功率的增加,调频性能的改善越来越明显,而后逐渐趋于稳定。

    2)储能系统可以增加机组的调频里程。随着储能功率的增加,调频里程逐渐增加,但增速不大且逐渐放缓。

    3)储能系统能大幅提升机组日调频收益。随着储能功率的增加,日调频收益逐渐增加,当储能功率增加到一定值后,日调频收益增速不明显。

    4)仿真结果证实了在储能联合调频项目中按照3.0%的机组容量配置储能基本是合理的,但同时也表明由于机组性能的差异和运行状况的不同,储能对机组性能和调频收益提升的影响不同。

    研究结果可以应用于后续储能联合调频项目的容量优化,通过基于实际的历史运行数据和市场数据,能够更准确地分析储能对机组联合调频性能和收益的影响,为后续储能联合调频项目合理选型提供参考。

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