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微电网的电流均衡/电压恢复自适应动态规划策略研究

王睿 孙秋野 张化光

王睿, 孙秋野, 张化光. 微电网的电流均衡/电压恢复自适应动态规划策略研究. 自动化学报, 2021, x(x): 1−13 doi: 10.16383/j.aas.c210015
引用本文: 王睿, 孙秋野, 张化光. 微电网的电流均衡/电压恢复自适应动态规划策略研究. 自动化学报, 2021, x(x): 1−13 doi: 10.16383/j.aas.c210015
Wang Rui, Sun Qiu-Ye, Zhang Hua-Guang. Research on current sharing/voltage recovery based adaptive dynamic programming control strategy of microgrids. Acta Automatica Sinica, 2021, x(x): 1−13 doi: 10.16383/j.aas.c210015
Citation: Wang Rui, Sun Qiu-Ye, Zhang Hua-Guang. Research on current sharing/voltage recovery based adaptive dynamic programming control strategy of microgrids. Acta Automatica Sinica, 2021, x(x): 1−13 doi: 10.16383/j.aas.c210015

微电网的电流均衡/电压恢复自适应动态规划策略研究

doi: 10.16383/j.aas.c210015
基金项目: 国家自然科学基金(U20A20190, 62073065), 国家重点研发计划(2018YFA0702200)资助
详细信息
    作者简介:

    王睿:东北大学信息科学与工程学院博士研究生. 主要研究方向为能源互联网中分布式电源的协同优化及其电磁时间尺度稳定性分析. E-mail: 1610232@stu.neu.edu.cn

    孙秋野:东北大学信息科学与工程学院教授. 主要研究方向为网络控制技术, 分布式控制技术, 分布式优化分析及其在能源互联网, 微网, 配电网等领域相关应用. 本文通信作者. E-mail: sunqiuye@mail.neu.edu.cn

    张化光:东北大学信息科学与工程学院教授. 主要研究方向为自适应动态规划, 模糊控制, 网络控制, 混沌控制. E-mail: zhanghuaguang@mail.neu.edu.cn

Research on Current Sharing/Voltage Recovery Based Adaptive Dynamic Programming Control Strategy of Microgrids

Funds: Supported by National Natural Science Foundation of P. R. China (U20A20190, 62073065), National Key Research and Development Program of China(2018YFA0702200)
More Information
    Author Bio:

    WANG Rui Ph.D. candidate at the School of Information Science and Engineering, Northeastern University. His research interest covers collaborative optimization of distributed generation and its stability analysis of electromagnetic timescale in energy internet

    SUN Qiu-Ye Professor at the School of Information Science and Engineering, Northeastern University. His research interest covers network control technology, distributed control technology, distributed optimization analysis and various applications in energy internet, microgrid, power distribution network. Corresponding author of this paper

    ZHANG Hua-Guang Professor at the School of Information Science and Engineering, Northeastern University. His research interest covers adaptive dynamic programming, fuzzy control, network control, and chaos control

  • 摘要: 含多类型分布式电源的微电网已经成为了未来电力系统的重要发展方向, 其中风能和光能在降低化石能源消耗和二氧化碳排放等方面有着极大优势, 考虑二者之间强互补性的协同调度已被广泛研究. 但风/光协同调度的微电网多关注分钟级的调度或优化问题而非风/光波动下秒级的实时电流按容量比例精准分担, 简称电流均衡, 而精准电流均衡有助于可再生能源的高比例消纳. 因此, 本文提出了基于自适应动态规划的微电网电流均衡和电压恢复控制策略. 首先, 构建包含风电整流型电能变换器和光电升压型电能变换器的广义风光拓扑同胚升压变换器模型, 其提供了后续控制器设计的模型基础. 其次, 本文将电流均衡和电压恢复问题转化为最优控制问题, 基于此, 每个能源主体的目标函数转化为获取最优控制变量和最小电压/电流控制偏差, 进而转化为求解哈密顿-雅克比-贝尔曼(Hamilton-Jacobi-Bellman, HJB)方程问题. 基于此, 提出了基于贝尔曼准则的分布式自适应动态规划控制策略以求取HJB方程的数值解, 最终实现电流均衡和电压恢复. 最后仿真结果验证了所提分布式自适应动态规划控制策略的有效性.
  • 图  1  微电网系统拓扑图

    Fig.  1  The typical circuit of the microgrid system

    图  2  风力发电装置和直流母线间的电能变换器

    Fig.  2  The interface converter between wind energy generator and DC bus.

    图  3  光伏发电装置和直流母线间的电能变换器

    Fig.  3  The interface converter between solar energy generator and DC bus.

    图  4  风力发电装置与直流母线的等效电能变换器

    Fig.  4  The equivalent interface converter between wind energy generator and DC bus.

    图  5  风力发电装置等效拓扑同胚电能变换器控制框图

    Fig.  5  Control block diagram for realizing the equivalent topological homeomorphism system of the wind energy interface converter.

    图  6  基于广义升压变换器的微电网系统

    Fig.  6  The typical circuit of the microgrid system based on generalized boost converter.

    图  7  自适应动态规划结构

    Fig.  7  The adaptive dynamic programming structure.

    图  8  微电网系统实时电流

    Fig.  8  Real-time current of distributed generators in the microgrid system.

    图  9  微电网系统分布式电源实时电压

    Fig.  9  Real-time voltage of distributed generators in the microgrid system.

    图  10  微电网系统直流母线实时电压

    Fig.  10  Real-time voltage of DC bus in the microgrid system.

    图  11  控制输入曲线

    Fig.  11  Control input curve.

    图  12  值函数曲线

    Fig.  12  Value function curve.

    图  13  基于文献[24]的微电网系统实时电流

    Fig.  13  Real-time current of distributed generators in the microgrid system based on [24].

    图  14  基于本文方法的微电网系统实时电流

    Fig.  14  Real-time current of distributed generators in the microgrid system based on this paper.

    符号含义说明
    $D{G_i}$第i个分布式电源(风机或光伏)
    N可再生能源的数目
    M负载的数量
    ${L_{rec}}$风机电能变换器的输入电感
    ${L_{boo}}$光伏电能变换器的输入电感
    ${C_{dc}}$风/光电能变换器的输出电容
    ${V_{dc}}$风/光电能变换器的输出电压
    ${e_d}$风机电能变换器d轴电源电压
    ${e_q}$风机电能变换器q轴电源电压
    ${i_d}$风机电能变换器d轴输入电流
    ${i_q}$风机电能变换器q轴输入电流
    ${V_d}$风机电能变换器d轴输入电压
    ${V_q}$风机电能变换器q轴输入电压
    ${d_d}$风机电能变换器d轴开关函数
    ${d_q}$风机电能变换器q轴开关函数
    $\omega $风机电能变换器交流转速
    $d$风机电能变换器等效占空比
    ${m_i}$可再生能源$D{G_i}$的比例系数
    ${V_{ref}}$直流母线的参考电压
    $h$采样/控制时间间隔
    ${u_i}\left( k \right)$在时刻${t_k}$的控制变量
    ${R_i}$$D{G_i}$与直流母线间的传输阻抗
    ${R_{Lj}}$微电网中第j个负载
    ${E_{Ii}}(k)$电流分担偏差
    ${E_v}(k)$电压恢复偏差
    $\alpha $折扣因子
    $a,b,c$三个正值权重
    $J_i^\# $最优值函数
    $u_i^\# $最优策略
    HJB哈密顿-雅克比-贝尔曼
    ${w_i}$期望控制器
    ${l_c}$评价网络的学习率
    $p$迭代次数编号
    $W_a^1(k)$输入层到隐含层的权重参数
    $W_a^2(k)$隐含层到输出层的权重参数
    ${l_a}$执行网络的学习率
    下载: 导出CSV
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  • 收稿日期:  2021-01-05
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