Average Current Control with Internal Model Control and Real-time Frequency Decoupling for Hybrid Energy Storage Systems in Microgrids

Among hybrid energy storage systems(HESSs),battery-ultracapacitor systems in active topology use DC/DC power converters for their operations.HESSs are part of the solutions designed to improve the operation of power systems in different applications.In the residential microgrid applications,a multilevel control system is required to manage the available energy and interactions among the microgrid components.For this purpose,a rule-based power management system is designed,whose operation is validated in the simulation,and the performances of different controllers are compared to select the best strategy for the DC/DC converters.The average current control with internal model control and real-time frequency decoupling is proposed as the most suitable controller according to the contemplated performance parameters,allowing voltage regulation values close to 1%.The results are validated using real-time hardware-in-the-loop(HIL).These systems can be easily adjusted for other applications such as electric vehicles.

运行工况自适应的风储系统双层功率平滑策略

针对风电并网带来的功率波动问题,提出一种运行工况自适应的风电-混合储能系统双层功率平滑策略。在上层,考虑风储系统并网功率波动和储能运行状态,利用无模型自适应控制动态跟踪经滑动平均算法得到的并网参考功率,生成混合储能总功率任务;在下层,考虑储能介质的技术特性,利用模糊控制动态调节经变分模态分解确定的混合储能总功率任务的频率分界点,完成功率的自适应分配。算例分析表明:所提策略可以自适应平抑多工况下的风电功率波动,实现混合储能各介质间功率的合理分配。

基于EWT的分布式光储PCC功率波动自适应平抑方法

分布式光伏在交流侧公共连接点(point of common coupling,PCC)汇流的功率有较大的随机性与波动性,影响电网的稳定运行。为此,提出了基于经验小波变换(empirical wavelet transform,EWT)的分布式光储PCC功率自适应平抑方法。首先,针对混合储能(hybrid energy storage system,HESS)与分布式光伏接入PCC的典型场景,在分析EWT自适应处理波形的特点后,结合功率波动率与储能元件的响应特性,对PCC的光伏原始汇流功率进行EWT分解与优化修正,实现HESS的功率初级分配。之后为避免HESS的荷电状态(state of charge,SOC)频繁越限,提出了一种主动功率补偿的SOC控制策略,通过主动改变储能的参考信号使其SOC在安全范围内工作。结合实际数据的仿真验证表明,该平抑方法能够自适应地实现光伏出力的合理分解与功率分配,在延长储能使用寿命的同时有效满足并网功率波动的要求,为平抑光伏输出功率波动提供了新思路。

基于自适应粒子群优化算法的串联复合涡轮储能优化策略

针对发动机串联复合涡轮发电系统储能困难等问题,提出了一种基于自适应粒子群优化(SAPSO)算法的最大功率点追踪(MPPT)方法,增强发电系统功率的捕获能力。此外,采用混合储能系统(HESS)替代单一蓄电池储能,实现电能的高效、稳定存储。通过Matlab/Simulink软件,建立了基于发动机串联复合涡轮发电的储能优化控制仿真模型,对比分析了不同控制方法在设定工况下的功率追踪性能以及混合储能系统的储能特性。仿真结果表明,相较于传统扰动观测法(P&O)控制方法,在所提的SAPSO-MPPT方法下,发电功率提高了190 W,响应时间缩短了0.15 s。同时,HESS能够有效追踪母线上的需求功率,电能回收效率高达95.3%。最后,基于Y24型改装发动机台架搭建了串联复合涡轮发电系统实验平台,对所提储能优化控制策略的节油潜力进行了实验验证。结果表明,SAPSO-MPPT+HESS储能优化策略能够有效提高排气能量回收效率,优化后系统总热效率比原发动机提高了提高0.53个百分点。

混合超级电容器应用于光伏储能的研究

光伏发电具有波动性和间歇性的特点,无法直接向负载提供稳定的电能,而混合超级电容器组成的储能系统能提供适中的能量密度和较高的功率密度,且使用寿命长,能更好消纳光伏发电,提高储能效率和电能质量。搭建混合超级电容器(HSC)光伏储能系统和锂离子电池(LIB)光伏储能系统,比较不同天气条件下的电压波动以及电量变化。在晴天条件下,HSC模组的充电电量比LIB模组提高了7.8%,而两模组的电压波动均不大。在多云条件下,HSC模组的充电电量比LIB模组提高了10.4%,且HSC模组的电压波动程度比LIB模组小。在阴天条件下,HSC模组的充电电量比LIB模组提高了15.4%,两模组的电压波动都较小,但LIB的充电电压出现下降趋势。

SOC自适应调节的独立电网混合储能优化配置方法

针对高比例新能源接入独立电网后容易出现的频率稳定和功率波动问题,提出一种荷电状态自适应调节的混合储能优化配置方法。首先,基于对高比例新能源系统动态频率特性的分析,从频率稳定方面计算储能最小支援功率;然后,考虑系统对新能源出力波动的平抑需求,以经济性最低为目标函数,建立储能全寿命周期优化配置模型,并根据功率支援需求对荷电状态下限进行自适应调节;最后,以某高风电渗透率独立电网为例,验证所提策略的正确性和有效性。结果表明,所提方法兼顾系统频率稳定性与平滑新能源出力的需求,显著降低储能配置成本,具有更优的技术经济性。

基于遗传算法的电动汽车HESS功率优化分配

针对电动汽车锂离子电池和超级电容的混合储能系统不同工作模式的功率最优分配困难的问题,提出基于遗传算法的混合储能系统能量功率的最优分配策略。对储能系统的不同工作模式进行分类管理,建立能量管理系统输出功率与需求功率误差最小的目标函数。采用遗传算法求解目标函数,获得锂离子电池和超级电容的出力系数,进而优化功率分配。最后,搭建混合储能系统仿真模型和实验台进行仿真和实验,结果表明,在EUDS和UDDS路况下,与传统滞环控制相比,采用遗传算法优化的能量管理总损耗降低1.2%和0.9%。

燃料电池船舶复合供能系统控制策略设计与仿真

针对燃料电池船舶复合供能系统中的燃料电池功率波动问题和储能单元电池荷电状态(State of Charge, SOC)极端分化问题,依据系统拓扑结构,提出基于负载功率频率分解与模糊逻辑控制法相结合的复合供能系统控制策略设计。采用实例仿真验证该设计的优势。结果表明,该设计可有效保持燃料电池输出功率平滑,对储能单元SOC具有良好的均衡控制效果。

基于事件触发的直流微电网无差拍预测控制

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.

提高双向调节能力的混合储能平滑风电控制策略

为有效平滑风电出力,避免电池频繁充放电,提出了基于模型预测控制-模糊控制的并网功率平滑控制策略。首先采用模型预测控制获取风电目标出力与混合储能总输出参考功率;然后,设计了基于超级电容荷电状态的模糊自适应时间常数的一阶低通滤波法,对超级电容与锂电池实现自适应功率分配;接着基于双储能系统的充放电不平衡指标设计了模糊荷电状态优化控制,同时设计了改进双储能工作模式及相应切换规则以避免荷电状态越限;最后在Matlab/Simulink平台上建模仿真,验证了该控制策略的有效性。结果表明,所提控制策略不仅可以有效平滑风电并网功率,减小储能容量与功率配置,还可以减小锂电池的充放电切换次数,提高系统的双向调节能力。

基于卡尔曼滤波和模型预测控制的混合储能平抑风电功率波动策略

针对混合储能平抑风电功率波动时储能系统成本过高的问题,提出一种基于卡尔曼滤波和模型预测控制的风电波动平抑控制策略。该方法基于风储联合发电系统,在满足风电平抑需求的基础上,通过预设截止频率以储能容量变化最小与功率波动最低为多目标,利用遗传算法求解卡尔曼滤波自适应参数获得最优储能目标功率。为提高混合储能系统协调运行能力,考虑调节储能荷电状态(state of charge,SOC)通过模型预测控制实现计及电池运行寿命与超级电容SOC变化的动态功率分配。最后,结合实际风电功率数据进行仿真验证。结果表明,所提策略能够有效改善电池SOC、降低超级电容容量,符合储能平抑风电功率需求,能充分考虑两种储能设备的特性差异,提高功率分配的合理性,改善储能系统经济性。

抑制输水工程中风光功率波动的混合储能控制策略研究

【目的】在输水工程中,风电、光伏等新能源具有不稳定性,若直接接入电网会对可靠运行的电能质量造成严重的影响。为了抑制输水工程中的风光功率波动问题,有必要对此进行研究。【方方法法】提出了一种基于模糊控制的混合储能控制策略,分别利用能量型储能和功率型储能平移不同时间尺度的功率脉动。【结果】考虑将风光功率波动变化态势、储能荷电状态作为模糊控制器的输入,调节功率型储能与能量型储能为系统出力,并考虑储能电池的一致性,有效地保障储能电池的循环使用寿命。【结论】通过算例验证了混合储能控制策略抑制风光功率波动的有效性,充分发挥混合电池储能系统优势,减少能量型储能使用频次,实现价值最大化。

基于SOC实时反馈的HESS平抑光伏功率波动策略

采用铅酸电池和超级电容器构成的混合储能系统(HESS)平抑光伏功率波动,并根据铅酸电池与超级电容器特性的不同,提出基于小波包分解的HESS控制策略,实现原始功率的初级分配。针对上述分量波动大、正负变化频繁的问题,采用模糊控制对储能装置的功率参考指令进行修正。基于卡尔曼滤波实时监测储能装置的荷电状态(SOC),合理控制储能装置的充放电,延长储能装置使用寿命。在Matlab/Simulink中搭建光伏混合储能系统仿真模型,对提出的协调控制策略进行仿真验证。仿真结果表明,所提协调控制策略可行。

Realization and Analysis of Good Fuel Economy and Kinetic Performance of a Low-cost Hybrid Electric Vehicle

By using high-power and high-efficiency propulsion systems,current hybrid electric vehicles（HEVs） in market can achieve excellent fuel economy and kinetic performance.However,it is the cost of current HEVs that hinders HEVs coming into widespread use.A novel hybrid electric propulsion system is designed to balance HEV cost and performance for developing markets.A battery/supercapacitor-based hybrid energy storage system（HESS） is used to improve energy conversion efficiency and reduce battery size and cost.An all-in-one-controller（AIOC） which integrates engine electronic control unit（ECU）,motor ECU,and HESS management system is developed to save materials and energy,and reduce the influence of distribution parameters on circuit.As for the powertrain configuration,four schemes are presented：belt-driven starter generator（BSG） scheme,four-wheel drive HEV scheme,full HEV scheme,and ranger-extender electric vehicle（EV） scheme.Component selection and parameter matching for the propulsion system are performed,and an energy management strategy is developed based on powertrain configuration and selected components.Forward-facing simulation models are built,comprehending the control strategy based on the optimal engine torque for the low-cost hybrid electric propulsion system.Co-simulation of AVL CRUISE and Matlab/Simulink is presented and the best scheme is selected.The simulation results indicate that,for the best design,fuel consumption in urban driving condition is 4.11 L/（100 km） and 0-50 km/h accelerating time is 10.95 s.The proposed research can realize low-cost concept for HEV while achieving satisfactory fuel economy and kinetic performance,and help to improve commercialization of HEVs.

Energy management strategy for a parallel hybrid electric vehicle equipped with a battery/ultra-capacitor hybrid energy storage system

To solve the low power density issue of hybrid electric vehicular batteries, a combination of batteries and ultracapacitors (UCs) could be a solution. The high power density feature of UCs can improve the performance of battery/UC hybrid energy storage systems (HESSs). This paper presents a parallel hybrid electric vehicle (HEV) equipped with an internal combus- tion engine and an HESS. An advanced energy management strategy (EMS), mainly based on fuzzy logic, is proposed to improve the fuel economy of the HEV and the endurance of the HESS. The EMS is capable of determining the ideal distribution of output power among the internal combustion engine, battery, and UC according to the propelling power or regenerative braking power of the vehicle. To validate the effectiveness of the EMS, numerical simulation and experimental validations are carried out. The results indicate that EMS can effectively control the power sources to work within their respective efficient areas. The battery load can be mitigated and prolonged battery life can be expected. The electrical energy consumption in the HESS is reduced by 3.91% compared with that in the battery only system. Fuel consumption of the HEV is reduced by 24.3% compared with that of the same class conventional vehicles under Economic Commission of Europe driving cycle.

Wavelet-based power management for hybrid energy storage system

A wavelet-based power management system is proposed in this paper with a combination of the battery and ultracapacitor(UC)hybrid energy storage system(HESS).The wavelet filter serves as a frequency-based filter for distributing the power between the battery and UC.In order to determine the optimal level of wavelet decomposition as well as the optimal activation power of the wavelet controller,an optimization procedure is established.The proposed frequency-based power management system moderates the usage of battery current,consequently improving its lifetime.Compared with the conventional threshold-based power management systems,the proposed system has the advantage of enhanced battery and UC power management.A LiFePO4 battery is considered and its life loss is modeled.As a case study,an electric motorcycle is evaluated in the federal test procedure(FTP)driving cycle.Compared with a conventional energy storage system(ESS)and a state of available power(SoP)management systems,the results show an improvement for the battery lifetime by 115%and 3%,respectively.The number of battery replacements is increased,and the energy recovery is improved.The 10-year overall costs of the proposed HESS strategy using wavelet are1500 dollars lower,compared with the ESS.

A Model Predictive Control Method for Hybrid Energy Storage Systems

The traditional PI controller for a hybrid energy storage system(HESS)has certain drawbacks,such as difficult tuning of the controller parameters and the additional filters to allocate high-and low-frequency power fluctuations.This paper proposes a model predictive control(MPC)method to control three-level bidirectional DC/DC converters for grid-connections to a HESS in a DC microgrid.First,the mathematical model of a HESS consisting of a battery and ultra capacitor(UC)is established and the neutral point voltage imbalance of a three-level converter is solved by analyzing the operating modes of the converter.Secondly,for the control of the grid-connected converters,an MPC method is proposed for calculating steady-state reference values in the outer layer and the dynamic rolling optimization in the inner layer.The outer layer ensures the voltage regulation and establishes the current predictive model,while the inner layer,using the model predictive current control,makes the current follow the predictive value,thus reducing the system current ripple.This cascaded topology has two independent controllers and is free of filters to realize the high-and low-frequency power allocation for a HESS.Therefore,it allows two types of energy storage devices to independently regulate the voltage and realizes the power allocation of the battery and UC.Finally,simulation studies are conducted in PSCAD/EMTDC,and the effectiveness of the proposed HESS control strategy is verified in a case,such as a controller comparison and fault scenario.

一种基于低通滤波算法的车用HESS功率分配控制策略

在混合动力汽车行驶中,为了能让车载超级电容有效地为蓄电池提供能量缓冲,其荷电状态(SOC)需保持在一个安全范围内,以防止电容过充或能量不足。针对这一问题,基于车载超级电容的工作特性,改进了传统的车用混合储能系统滤波分配法,舍弃算法逻辑复杂、参数设计困难的逻辑门控制,使用简单的PI控制实现了电动汽车运行过程中超级电容电量的自保持,并通过基于TMS320F2812控制的混合储能装置和基于半实物仿真的永磁同步电机试验平台对所提出的方法进行了试验验证。结果表明,该方法在超级电容提供负载峰值功率,达到对蓄电池“削峰填谷”目标的同时,又使其稳态电压保持在一个稳定值,满足混合动力汽车对于车载超级电容的能量回收要求。

Peak Current Control Strategy with Extended-State Tracking Compensator for DC-DC in Hybrid Energy Storage System

Because variations of ultra-capacitor voltage and battery voltage generate subharmonic and chaotic behaviors in hybrid energy storage system (HESS) application when a DC-DC converter is under the peak current control, a novel digital control strategy, i.e., peak current control with extended-state tracking compensator, is introduced to deal with the stability. The gains of the control algorithm are selected based on pole locations formulated from the Bessel filter. The simulation results validate that under the peak current control strategy with compensator, the DC-DC converter does not have the subharmonic and chaotic behaviors. The response time under the peak current control with compensator is the same as that under the peak current control. The ripple voltage and ripple current of battery are less. The tracking error of inductor current tends to zero.

Optimal allocation of hybrid energy storage for microgrids based on multi-attribute utility theory

To satisfy the requirements of high energy density,high power density,quick response and long lifespan for energy storage systems(ESSs),hybrid energy storage systems(HESSs)have been investigated for their complementary characteristics of‘high energy density components’and‘high power density components’.To optimize HESS combinations,related indices such as annual cost,fluctuation smoothing ability as well as safety and environmental impact have to be evaluated.The multiattribute utility method investigated in this paper is aimed to draw an overall conclusion for HESS allocation optimization in microgrid.Building on multi-attribute utility theory,this method has significant advantages in solving the incommensurability and contradiction among multiple attributes.Instead of determining the weights of various attributes subjectively,when adopting the multi-attribute utility method,the characteristics of attributes and the relation among them can be investigated objectively.Also,the proper utility function and merging rules are identified to achieve the aggregate utility which can reflect comprehensive qualities of HESSs.