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分布式多区域多能微网群协同AGC算法

席磊 周礼鹏

席磊, 周礼鹏. 分布式多区域多能微网群协同 AGC算法. 自动化学报, 2020, 46(9): 1818−1830 doi: 10.16383/j.aas.c200105
引用本文: 席磊, 周礼鹏. 分布式多区域多能微网群协同 AGC算法. 自动化学报, 2020, 46(9): 1818−1830 doi: 10.16383/j.aas.c200105
Xi Lei, Zhou Li-Peng. Coordinated AGC algorithm for distributed multi-region multi-energy micro-network group. Acta Automatica Sinica, 2020, 46(9): 1818−1830 doi: 10.16383/j.aas.c200105
Citation: Xi Lei, Zhou Li-Peng. Coordinated AGC algorithm for distributed multi-region multi-energy micro-network group. Acta Automatica Sinica, 2020, 46(9): 1818−1830 doi: 10.16383/j.aas.c200105

分布式多区域多能微网群协同AGC算法

doi: 10.16383/j.aas.c200105
基金项目: 国家自然科学基金(51707102)资助
详细信息
    作者简介:

    席磊:三峡大学副教授. 2016年于华南理工大学获得博士学位. 主要研究方向为电力系统运行与控制, 自动发电控制, 智能控制方法. 本文通信作者. E-mail: xilei2014@163.com

    周礼鹏:三峡大学硕士研究生. 主要研究方向为自动发电控制. E-mail: zlp197@126.com

Coordinated AGC Algorithm for Distributed Multi-region Multi-energy Micro-network Group

Funds: Supported by National Natural Science Foundation of China (51707102)
  • 摘要: 综合能源多区域协同是电网发展趋势, 而核心问题是采用何种方法对多区域进行协同. 本文基于Q ( $\sigma $ )融入了资格迹及双重Q学习, 提出一种面向多区域多能微网群的多智能体协同控制算法, 即DQ ( $\sigma ,\lambda $ ), 避免传统强化学习动作探索值高估的同时, 来获取分布式多区域的协同. 通过对改进的IEEE两区域负荷频率控制模型及三区域多能微网群自动发电控制(Automatic generation control, AGC)模型仿真, 结果表明, 与传统方法相比, 所提算法具有快速收敛性和更优动态性能, 能获得分布式多区域多能微网群的协同.
  • 图  1  多能微网群多区域协同控制架构

    Fig.  1  Multi-energy microgrid group multi-region cooperative control architecture

    图  2  DQ ( $\sigma,\lambda $ )的算法流程

    Fig.  2  Algorithm flow of DQ ( $\sigma,\lambda$ )

    图  3  BESS仿真模型

    Fig.  3  BESS simulation model

    图  4  改进的IEEE标准两区域负荷频率控制模型

    Fig.  4  Improved IEEE standard two-area load frequency control model

    图  5  两区域预学习效果及收敛效果

    Fig.  5  Pre-learning and convergence effect in two area

    图  6  阶跃负荷扰动下不同算法的性能指标

    Fig.  6  Performance index of different algorithms under step load disturbance

    图  7  随机白噪声扰动下不同算法的控制性能

    Fig.  7  Control performance of different algorithms under stochastic white noise disturbance

    图  8  分布式3区域多能微网群协同AGC模型

    Fig.  8  Coordinated AGC model of a distributed three-area multi-energy microgrid group

    图  9  多算法输出效果

    Fig.  9  Multi algorithm output effect

    图  10  多算法频率曲线

    Fig.  10  Multi algorithm frequency curve

    图  11  联络线交换功率偏差

    Fig.  11  Exchange power deviation of tie line

    表  1  模型传递函数的参数

    Table  1  Parameters of the model transfer function

    机组 参数 数值
    小水电机组 二次时延TSH 3
    伺机电动机时间常数TP 0.04
    伺机增益KS 5
    永态转差系数RP 1
    复位时间TR 0.3
    暂态转差系数RT 1
    闸门最大开启率Rmaxopen/(pu/s) 0.16
    闸门最大关闭率Rmaxclose/(pu/s) 0.16
    机组启动时间TWH 1
    生物发电机组 二次时延TSB 10
    调速器的时间常数TGB 0.08
    蒸汽启动时间TWB 5
    机械启动时间TMB 0.3
    微型燃气轮机机组 二次时延TSM 5
    燃油系统滞后时间常数T1 0.8
    燃油系统滞后时间常数T2 0.3
    负荷限制时间常数T3 3
    温度控制环路增益KT 1
    负荷限制Lmax 1.2
    燃料电池机组 二次时延TSF 2
    调速器的时间常数TF 10.056
    逆变器增益KF 9.205
    柴油发电储能机组 二次时延TSD 7
    调速器的时间常数TGD 2
    蒸汽启动时间TWF 1
    机械启动时间TMD 3
    下载: 导出CSV

    表  2  AGC机组参数

    Table  2  AGC unit parameters

    区域 类型 机组序号 $\Delta P_{\rm{in}}^{\max }$
    (kW/s)
    $\Delta P_{\rm{in}}^{\min }$
    (kW/s)
    $\Delta P_{\rm{in}}^{\rm{rate }+ }$
    (kW/s)
    $\Delta P_{\rm{in}}^{\rm{rate} - }$
    (kW/s)
    区域1和区域3 小水电 G1 250 − 250 15 − 15
    G2 250 − 250 15 − 15
    G3 150 − 150 8 − 8
    G4 150 − 150 8 − 8
    G5 150 − 150 8 − 8
    G6 100 − 100 7 − 7
    G7 100 − 100 7 − 7
    微型燃气轮机 G8 100 − 100 1.2 − 1.2
    G9 100 − 100 1.2 − 1.2
    G10 150 − 150 1.8 − 1.8
    G11 150 − 150 1.8 − 1.8
    燃料电池 G12 200 − 200 7 − 7
    G13 200 − 200 7 − 7
    G14 150 − 150 6 − 6
    G15 150 − 150 6 − 6
    区域2 小水电 G1 250 − 250 15 − 15
    G2 250 − 250 15 − 15
    G3 150 − 150 8 − 8
    G4 150 − 150 8 − 8
    G5 150 − 150 8 − 8
    G6 100 − 100 7 − 7
    柴油发电机储 G7 250 − 250 2 − 2
    G8 250 − 250 2 − 2
    G9 120 − 120 1 − 1
    G10 120 − 120 1 − 1
    生物质能 G11 200 − 200 3 − 3
    G12 200 − 200 3 − 3
    G13 200 − 200 3 − 3
    G14 200 − 200 3 − 3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-05
  • 录用日期:  2020-04-27
  • 网络出版日期:  2020-09-28
  • 刊出日期:  2020-09-28

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