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摘要: 针对光伏(Photovoltaic, PV)−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问题, 提出一种基于事件触发的无差拍预测控制(Event-triggered deadbeat predictive control, ETDPC)方法, 以实现有效的能量管理. ETDPC方法结合事件触发控制策略和无差拍预测控制策略(Deadbeat predictive control, DPC)的优点, 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, 对于电池−超级电容器混合储能系统(Hybrid energy storage system, HESS), ETDPC能够缓解间歇性光伏发电与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(Hardware-in-loop, HIL)实验结果表明, 相较于传统无差拍控制方法, 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 method combines the event-triggered control strategy and the deadbeat predictive control (DPC) 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 be fully utilized for battery-supercapacitor hybrid energy storage system (HESS) to mitigate the power unbalance between the load demand and the intermittent photovoltaic power generation and stabilize the bus voltage. To validate the effectiveness of the method, various simulations and hardware-in-loop (HIL) experiments are conducted based on a digital simulation system and the HIL platform, which show that the computational burden is reduced by 50.63% compared to the 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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图 2 基于事件触发无差拍控制的微电网能量管理策略框图
Fig. 2 Diagram of ETDPC-based energy management strategy for microgrid
图 4 光伏和负载跳变时微电网仿真波形,包括$v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $和$i_{sc} $
Fig. 4 The simulation results of microgrid under step changes of PV and load, including the waveforms of $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $, and $i_{sc} $
图 5 光伏和负载跳变时电池与超级电容电流$i_{bat} $和$i_{sc} $仿真波形及其对应参考值波形$i_{bat,ref}$和$i_{sc,ref}$
Fig. 5 The simulation results of $i_{bat} $ and $i_{sc} $, and the corresponding reference $i_{bat,ref}$ and $i_{sc,ref}$ respectively under step changes of PV and load
图 6 $v_{bus,ref} $跳变时微电网仿真结果,包括$v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $和$i_{sc} $波形
Fig. 6 The simulation results of microgrid under step changes of $v_{bus,ref}$, including the waveforms of $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $, and $i_{sc} $
图 7 $i_{bus,ref}$跳变时$i_{bat} $和$i_{sc} $仿真结果及其对应参考值$i_{bat,ref} $和$i_{sc,ref }$
Fig. 7 The simulation results of $i_{bat} $ and $i_{sc} $, and the corresponding reference $i_{bat,ref}$ and $i_{sc,ref}$ under step changes of $i_{bus,ref}$
图 8 电流$i_h $以及观测所得电流$i_{ob} $对比
Fig. 8 The comparison between the current ${i_{h}} $ and the observed current ${i_{ob}} $
图 9 传统无差拍与事件触发无差拍控制信号对比
Fig. 9 Comparison of traditional deadbeat and event-triggered deadbeat control signals
图 12 基于ETDPC硬件在环波形: $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $和$i_{sc} $
Fig. 12 The HIL waveforms of ETDPC method: $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $, and $i_{sc} $
图 13 基于DPC硬件在环波形: $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $和$i_{sc} $
Fig. 13 The HIL waveforms of DPC method: $v_{bus} $, $i_R $, $i_{pv} $, $i_{bat} $, and $i_{sc} $
图 14 基于ETDPC硬件在环功率波形:$P_{pv} $, $P_{bat} $, $P_{sc} $和$P_{R} $
Fig. 14 The HIL power waveforms of ETDPC method: $P_{pv} $, $P_{bat} $, $P_{sc} $, and $P_{R} $
图 15 基于DPC硬件在环功率波形: $P_{pv} $, $P_{bat} $, $P_{sc} $和$P_{R} $
Fig. 15 The HIL power waveforms of DPC method: $P_{pv} $, $P_{bat} $, $P_{sc} $, and $P_{R} $
表 1 仿真参数表
Table 1 Parameters for the simulation studies
类别 参数名称 数值 双向
半桥
变换器$v_{bus }$ 300 V $C $ 4 700 μF $L\,(L_{bat},\;L_{sc})$ 47 mH 混合储能系统 电池 $v_{bat }$ 200 V Capacity (容量) 65 Ah 超级
电容$v_{sc} $ 200 V Capacitance (容值) 50 F 光伏电池单元 $v_{pv }$ (开路电压) 30.2 V $i_{pv} $ (短路电流) 5.0 A 控制方法时间步长 $t_s $ 100 μs $t_{et} $ 100 μs 
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表 2 运算执行次数统计表
Table 2 Statistics table of the number of operation times
时间 (s) 执行次数 (万次) DPC ETDPC 100 100 48.2 200 200 98.1 300 300 148.2 400 400 197.8 500 500 247.2 600 600 297.4 平均执行次数 (万次/百秒) 100 49.37 纹波(V) 1.8 2.2
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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) 执行次数 (万次) DPC ETDPC 100 100 57.9 200 200 108.1 300 300 158.2 400 400 207.6 500 500 257.2 平均执行次数 (万次/百秒) 100 52.6 纹波(V) 1.5 2.0
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