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摘要: 本文针对光伏-电池-超级电容直流微电网系统中光伏发电间歇性造成的功率失配, 提出了一种基于事件触发的无差拍预测控制(Event-triggered deadbeat predictive control, ETDPC)方法, 实现有效的能量管理. ETDPC控制方法结合事件触发控制策略和无差拍预测控制策略的优点, 该方法根据微电网的拓扑结构构建状态空间模型, 用于设计适用于微电网能量管理的触发条件: 当ETDPC的触发条件满足时, ETDPC中无差拍预测控制模块被激活, 可以在一个控制周期内产生最优控制信号, 实现对于扰动的快速响应, 减小母线电压纹波; 当系统状态不满足ETDPC中的触发条件时, 无差拍预测控制模块被挂起, 从而消除非必要运算, 以减轻实现能量管理的运算负担. 因此, 基于电池-超级电容器混合储能系统, ETDPC控制能够缓解间歇性光伏发电同负荷需求之间的功率失衡, 以稳定母线电压. 最后, 数字仿真和硬件在环实验结果表明, 相较于传统事件触发无差拍控制方法, 运算负担减小了50.63%, 母线电压纹波小于0.73%, 验证了ETDPC控制方法的有效性与性能优势, 为直流微电网的能量管理提供了一种参考.Abstract: This paper presents an event-triggered deadbeat predictive control (ETDPC) method for the mitigation of power mismatch in a photovoltaic(PV)-battery-supercapacitor microgrid. The proposed ETDPC control method combines the event-triggered control strategy and the deadbeat predictive control strategy and inherits their advantages accordingly. Based on the topology of the DC microgrid, the state-space model can be built for the design of the triggering condition for the energy management: when the triggering condition of ETDPC is activated, the deadbeat control block of ETDPC will be conducted and the optimal control signal can be generated within one control cycle, so that the DC bus voltage ripple can be reduced based on the fast response to the disturbance; When the state of the DC microgrid cannot satisfy the triggering condition, the deadbeat control block of ETDPC will be suspended to eliminate the redundant computations, so that the computational burden of the DC microgrid energy management can be reduced. Therefore, ETDPC can fully utilize of battery-supercapacitor hybrid energy storage system to mitigate the power unbalance between load demand the intermittent photovoltaic power generation and stabilize the bus voltage. To validate the effectiveness of the method, various simulation and hardware-in-loop (HIL) experiments are conducted based on a digital simulation system and the HIL platform, which shows that the computational burden is reduced by 50.63% compared to conventional deadbeat predictive control and the voltage ripple is regulated less than 0.73% of the reference. This work provides a reference of the control strategy for microgrid energy management.
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Key words:
- Microgrid /
- photovoltaic /
- energy storage system /
- event-triggered control /
- deadbeat predictive control
1) 收稿日期 2021-06-28 录用日期 2021-11-02 Manuscript received June 28, 2021; accepted November 2, 2021 国家自然科学基金项目 (52172350, 51775565) 深圳市科技计划资助 (RCBS20200714114920122) 的资助 Supported by National Natural Science Foundation of P. R. China (52172350, 51775565), Shenzhen Science and Technology Program (RCBS20200714114920122) 本文责任编委 梅生伟 Recommended by Associate Editor MEI Sheng-Wei 1. 中山大学 深圳 智能工程学院 深圳 518000 2. 中山大学 广2) 东省智能交通系统重点实验室 广州 510275 3. 南洋理工大学 新加坡 308232 4. 东北大学 流程工业综合自动化国家重点实验室沈阳 110004 1. School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shen Zhen, 518000 2. Guangdong Provincial Key Laboratory of Intelligent Transport System, Sun Yat-sen University, Guangzhou 510275 3. Nanyang Technological University, Singapore, 3082324 4. State Key Laboratory of Synthetical Automation for Industrial Process, Northeastern University, Shenyang 110004 -
表 1 仿真参数表
Table 1 Parameters for the simulation studies
参数 数值 双向半桥变换器 vbus 300 V C 4700 μF L (Lb, Lsc) 47 mH 混合储能系统 电池 vbat 200 V Capacity (容量) 65 Ah 超级电容 vsc 200 V Capacitance (容值) 50 F 光伏电池单元 voc (开路电压) 30.2 V isc (短路电流) 5.0 A 控制方法时间步长 ts 100 μs tet 100 μs 表 2 运算执行次数统计表
Table 2 Operation times of simulation studies
时间 (s) 100 200 300 执行次数 (万次) DPC 100 200 300 ETDPC 48.2 98.1 148.2 时间 (s) 400 500 600 执行次数 (万次) DPC 400 500 600 ETDPC 197.8 247.2 297.4 参数 平均执行次数 (万次/百秒) 纹波(V) DPC 100 1.8 ETDPC 49.37 2.2 表 3 硬件在环运算执行次数统计表
Table 3 Operation times of the HIL experiments
时间 (s) 100 200 300 400 500 执行次数 (万次) DPC 100 200 300 400 500 ETDPC 57.9 108.1 158.2 207.6 257.2 参数 平均执行次数 (万次/百秒) 纹波(V) DPC 100 1.5 ETDPC 52.6 2.0 -
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