Research on Current Sharing/Voltage Recovery Based Adaptive Dynamic Programming Control Strategy of Microgrids
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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.Abstract: Microgrids consisting of different distributed generators (DGs) have become one popular direction for future power systems. Therein, wind energy and solar energy have been regarded as the most promising contributors to reduce fuel consumption and CO2 emission. Although wind energy and solar energy have been widely studied due to their strong complementarity, the microgrid with wind energy and solar energy coordination schedule system pays more attention on the minute level scheduling, rather than the second level current sharing under wind/solar disturbance. Therein, the accurate current sharing is useful for high proportion of renewable energy. To this end, the accurate real-time current sharing based on their capacity ratio for the microgrid is not achieved. Therefore, this paper proposes an accurate current sharing and voltage recovery control strategy based on adaptive dynamic programming approach in microgrids. Firstly, the generalized wind/solar topological homeomorphism boost converter model with the AC rectifier interface converter of the wind energy and DC boost interface converter of the solar energy is proposed to provide the model basis for subsequent controller design. Secondly, the accurate current sharing and voltage recovery problem is transformed into an optimal control problem. To this end the objective function of each source agent is switched to obtain the optimal controller and minimize the voltage/current control deviation, which is further switched to solve the Hamilton-Jacobi-Bellman (HJB) function. To this end, an adaptive dynamic programming control strategy based on the Bellman principle is proposed to solve the HJB function. The accurate current sharing and voltage recovery can be achieved. Finally, simulation results verify the effectiveness of the proposed distributed adaptive dynamic programming control strategy.1) 收稿日期 2021-01-05 录用日期 2021-04-16 Manuscript received January 5, 2021; accepted April 16, 2021 国家自然科学基金 (U20A20190, 62073065), 国家重点研发计划 (2018YFA0702200) 资助 Supported by National Natural Science Foundation of China (U20A20190, 62073065), and National Key Research and Develo-2) pment Program of China (2018YFA0702200) 本文责任编委 张俊 Recommended by Associate Editor ZHANG Jun 1. 东北大学信息科学与工程学院 沈阳 110819 1. College of Information Science and Engineering, Northeastern University, Shenyang 110819
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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 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 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 
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