CN112874368A - 一种基于qpso算法的电动汽车充电策略优化方法 - Google Patents
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Abstract
一种基于QPSO算法的电动汽车充电策略优化方法,涉及一种电动汽车充电策略优化技术,为了解决电动汽车集中充电和快速充电具有不确定性,而对电网的安全运行带来威胁的问题;同时,通过为电动汽车充电,解决光伏就地消纳问题。本发明通过建立电动汽车充电的微电网模型;并定义微电网模型的优化目标函数;从而根据定义的优化目标函数,得到电动汽车充电策略;进而基于QPSO算法对电动汽车充电策略进行优化。有益效果为对电动汽车做出更合理的充电策略,提高微电网中光伏就地消纳能力,并保证了尽可能小的对电网冲击,采用了QPSO算法对电动汽车充电策略进行优化,解决了PSO算法优化维度高导致目标函数不收敛的问题。
Description
技术领域
本发明涉及一种电动汽车充电策略优化技术。
背景技术
随着经济的发展,我国用电量与日俱增;环境污染及化石能源紧缺已经成为世界性的热点问题;大力发展新能源发电以及电动汽车代替燃油汽车已经成为节能减排的重要手段;然而光伏和风电等可再生能源发电功率具有波动性和间歇性特点,大规模及高渗透率上网会给电网带来不小的冲击,严重威胁到电力***的安全性和稳定性;此外,从负荷角度来看,电动汽车集中充电和快速充电具有不确定性和大功率的特点,也对电网的安全运行带来威胁。
发明内容
本发明的目的是为了通过电动汽车集中充电解决光伏发电***就地消纳的问题,同时也降低了电动汽车集中充电对电网的安全运行带来的威胁,因此提出了一种基于QPSO算法的电动汽车充电策略优化方法。
本发明所述的一种基于QPSO算法的电动汽车充电策略优化方法包括以下步骤:
步骤一、建立电动汽车充电的微电网模型;
步骤二、定义微电网模型的优化目标函数;
步骤三、根据步骤二定义的优化目标函数,得到电动汽车充电策略;
步骤四、基于QPSO算法对步骤三得到的电动汽车充电策略进行优化。
本发明的有益效果是:本发明建立了光伏发电的微电网模型,为了不影响电网的安全稳定性,尽可能少的实现大电网与微电网模型的电能传输;本发明考虑到了电网分时电价及光伏就地消纳的问题,对电动汽车做出更合理的充电策略,提高微电网中光伏就地消纳能力,并保证了尽可能小的对电网冲击;本发明采用了QPSO算法对电动汽车充电策略进行优化,解决了PSO(粒子群)算法优化维度高导致目标函数不收敛的问题。
附图说明
图1为具体实施方式一所述的一种基于QPSO算法的电动汽车充电策略优化方法流程图;
图2为具体实施方式二中微电网模型的结构框图;
图3为具体实施方式五中光伏发电功率与预测发电功率的曲线图;
图4为具体实施方式六中基于QPSO算法对电动汽车充电策略进行优化的具体方法流程图;
图5为具体实施方式六中优化后电动汽车充电策略的恒功率充电方式微电网功率曲线图;
图6为具体实施方式六中优化后电动汽车充电策略的微电网功率曲线图;
图7为具体实施方式六中优化后电动汽车充电策略的电动汽车与储能SOC变化曲线图。
具体实施方式
具体实施方式一:结合图1说明本实施方式,本实施方式所述的一种基于QPSO算法的电动汽车充电策略优化方法包括以下步骤:
步骤一、建立电动汽车充电的微电网模型;
步骤二、定义微电网模型的优化目标函数;
步骤三、根据步骤二定义的优化目标函数,得到电动汽车充电策略;
步骤四、基于QPSO算法对步骤三得到的电动汽车充电策略进行优化。
具体实施方式二:结合图2说明本实施方式,本实施方式是对具体实施方式一所述的一种基于QPSO算法的电动汽车充电策略优化方法进一步限定,在本实施方式中,步骤一中微电网模型包括储能***、光伏发电***、并网***、能量管理***和电动汽车充电***。
在本实施方式中,建立了含有光伏发电***、储能***、并网***、能量管理***和电动汽车充电***的微电网模型;其中,电动汽车充电***为:企业员工提供上下班接送服务的通勤电动汽车;通勤电动汽车电池容量为160kW·h,企业上班时间为8:30-16:30,根据通勤车往返行驶路程和单位耗能量得到通勤车在下班之前要充电到SOC不小于80%,才能满足16:30之后送员工回家并返回单位的需求,SOC为电池荷电状态;
具体实施方式三:本实施方式是对具体实施方式二所述的一种基于QPSO算法的电动汽车充电策略优化方法进一步限定,在本实施方式中,步骤二中的优化目标函数包括:电动汽车充电价格和电动汽车充电***消耗总电量中光伏发电***提供电量所占比例。
在本实施方式中,在电动汽车充电功率不变的情况下,充电时间与充电功率的关系如公式所示:
式中,TEV_cha为通勤电动汽车的充电时间,SOCEV_0为电动汽车初始荷电状态(本实施中取15%),SOCEV_end为电动汽车充电结束时电池荷电状态,Pch为电动汽车充电功率,WEV为电动汽车电池容量。
由于通勤电动汽车充电的电能来源不同,电价不同,光伏电价根据当地补贴后电价计算,从大电网取用的电能根据分时电价计算充电价格。储能电池价格根据为其充电的电源价格费用来计算;目标函数一为电动汽车充电单价,如公式所示:
式中,CEV为电动汽车单位千瓦时充电价格,CPV为光伏电价,QPV为光伏***充电电量,Cpeak为大电网尖峰电价,Qpeak为大电网尖峰电量,Clow为大电网低估电价,Qlow为大电网低谷充电电量,QEV为下班之前通勤车充电电量。
表1光伏与电网电价
为了尽可能让微电网中光伏发电就地消纳,目标函数二为电动汽车充电***消耗总电量中光伏发电***提供电量所占比例(The Percentage of New Energy in Total EVCharging Energy,PNTC);
为了减小微电网对电力***的影响,微电网与并网***交互功率不宜过大;Pgrid为大电网功率,Pgrid_limit为电网交互功率极值(本实施方式中取10kW);
|Pgrid|≤Pgrid_limit
为了降低储能电池寿命损耗,对储能电池的SOC范围约束为:
SOCB_min≤SOCB≤SOCB_max
式中,SOCB为储能电池的SOC,SOCB_min为储能电池SOC最小值(本实施例取20%),SOCB_max为储能电池SOC最大值(本实施例取90%);储能***容量为30kW·h。
根据电动汽车充电过程功率平衡关系,可得:
Pgrid+PPV+PB=PEV
式中,PB为储能***传输功率,充放电最大值为30kW,PPV为光伏发电功率,PEV为电动汽车充电功率,充电最大值为30kW。
具体实施方式四:本实施方式是对具体实施方式三所述的一种基于QPSO算法的电动汽车充电策略优化方法进一步限定,在本实施方式中,电动汽车充电***消耗总电量中光伏发电***提供电量所占比例用公式(1)表示;
所述公式(1)为:
式中,PNTC为电动汽车充电***消耗总电量中光伏发电***提供电量所占比例,QPV为光伏发电***产生的电能用于对电动汽车充电***充电的电量,QEV为电动汽车充电***消耗电能的总电量。
具体实施方式五:结合图3说明本实施方式,本实施方式是对具体实施方式三所述的一种基于QPSO算法的电动汽车充电策略优化方法进一步限定,在本实施方式中,步骤三中得到电动汽车充电策略的具体方法为:
步骤三一、根据日前光伏发电功率预测结合电动汽车充电需求,确定微电网模型中储能***初始SOC;
当日光伏发电功率与预测发电功率曲线如图3所示;通过日前光伏发电量预测与通勤车用电需求量对比,确定储能电池是否需要在电网用电低谷时期提前充电。根据历史发电数据及天气情况得到日前光伏发电预测,如光伏出力大于通勤车用电量,多余的光伏发电量可存入储能电池或输入电网;如光伏出力小于通勤车用电量,缺额电量可由提前充电的储能电池或大电网提供。本实施例选取了典型晴天,光伏发电量与电动车充电量无差额,储能***初始SOC为20%。
步骤三二、设置微电网模型中QPSO算法的粒子维度;
依据步骤一所建立的微电网模型,充电策略优化时间间隔为15分钟;设在一个64维的搜索空间里,每个种群由100个粒子组成,即X={x1,...xi,...x100};64维粒子设置为x=(Pgrid(1),...,Pgrid(32),PEV(33),...,PEV(64))T;1-32维粒子为8:30-16:30每隔15分钟的大电网功率,33-64维粒子是8:30-16:30每隔15分钟的电动汽车充电功率。微电网中,根据64维粒子,PB、SOCB等变量均可确定;
步骤三三、计算QPSO算法的吸引子;
式中,pbest_i是当前迭代中的第i个粒子历史最好位置;gbest是当前全局最优粒子;pi是吸引子,用于第i个粒子位置的更新;
步骤三四:对QPSO算法中粒子的位置进行更新,并设置创新参数;
粒子位置更新公式为:
步骤三五:得到电动汽车充电策略。
具体实施方式六:结合图4至图7说明本实施方式,本实施方式是对具体实施方式三所述的一种基于QPSO算法的电动汽车充电策略优化方法进一步限定,在本实施方式中,步骤四中基于QPSO算法对电动汽车充电策略进行优化的具体方法为:
步骤四一、获取光伏发电***发电量的预测曲线;
步骤四二、通过光伏发电***的发电量与电动汽车充电***用电量进行对比,并得到两者的缺额,根据两者的缺额确定储能***的初始SOC;其中储能***的初始SOC为20%;
步骤四三、在微电网模型中输入光伏发电***的功率及电动汽车充电***的初始SOC,其中电动汽车充电***的初始SOC为15%;
步骤四四、初始化粒子群参数和种群位置;
步骤四五、更新粒子位置,并判断选择粒子是否符合微电网模型,如果不符合,则执行步骤四六;如果符合,则执行步骤四七;
步骤四六、设置罚函数,并对适应度值进行约束,然后执行步骤四七;
步骤四七、评估适应度值,并更新粒子最优位置;
步骤四八、判断前种群的最优值是否大于当前种群的全局最优值,如果前种群的最优值大于当前种群的全局最优值,则执行步骤四十;否则,执行步骤四九;即判断pbest是否大于gbest;如果pbest≤gbest,则执行步骤四九;如果pbest>gbest,则执行步骤四十,其中:pbest为当前种群的最优值,gbest为当前种群的全局最优值;
步骤四九、更新全局最优粒子位置,然后执行步骤四十;
步骤四十、判断迭代次数是否达到最大;如果否,则返回执行步骤四五;如果是,则得到全局最优的粒子,并输出最优充电策略,完成对电动汽车充电策略的优化;即判断itrtn是否大于Tmax,如果itrtn≥Tmax,则得到全局最优的粒子;如果itrtn<Tmax,则返回执行步骤四五,其中,itrtn为迭代次数,Tmax为最大迭代次数。
在本实施方式中,恒功率充电方式微电网功率曲线图如图5所示,从图中可以看到优化前电动汽车以30kW的恒功率充电,光伏功率不够时,用电网交互功率补充差额;加入QPSO优化算法后微电网功率曲线如图6所示。优化策略电动汽车与储能SOC变化曲线如图7所示。光伏多余电量由储能***吸收,降低微电网对电力***的影响。仿真以企业通勤电动汽车为背景,恒功率直充与采用QPSO优化算法得到的电动汽车充电策略效果对比如表2所示;从电动汽车充电单价和新能源就地消纳率来看,基于QPSO算法的电动汽车充电策略要优于无优化方案。
表2电动汽车充电效果对比
充电方式 | 单价(元/kW·h) | PNTC |
恒功率充电 | 0.569 | 64.93% |
优化策略 | 0.459 | 95.96% |
在本实施方式中,按照本实施方式所述的优化方法对电力***中微电网电动汽车充电策略进行优化,其结果为微电网中新能源直接就地消纳和电动汽车集中充电站运行方案提供了理论基础,同时也为后续电力***对充电站充电调度等工作提供了理论依据。
Claims (6)
1.一种基于QPSO算法的电动汽车充电策略优化方法,其特征在于,该充电策略优化方法包括以下步骤:
步骤一、建立电动汽车充电的微电网模型;
步骤二、定义微电网模型的优化目标函数;
步骤三、根据步骤二定义的优化目标函数,得到电动汽车充电策略;
步骤四、基于QPSO算法对步骤三得到的电动汽车充电策略进行优化。
2.根据权利要求1所述的一种基于QPSO算法的电动汽车充电策略优化方法,其特征在于,步骤一中微电网模型包括储能***、光伏发电***、并网***、能量管理***和电动汽车充电***。
3.根据权利要求2所述的一种基于QPSO算法的电动汽车充电策略优化方法,步骤二中的优化目标函数包括:电动汽车充电价格和电动汽车充电***消耗总电量中光伏发电***提供电量所占比例。
5.根据权利要求3所述的一种基于QPSO算法的电动汽车充电策略优化方法,其特征在于,步骤三中得到电动汽车充电策略的具体方法为:
步骤三一、根据日前光伏发电功率预测结合电动汽车充电需求,确定微电网模型中储能***初始SOC;
步骤三二、设置微电网模型中QPSO算法的粒子维度;
步骤三三、计算QPSO算法的吸引子;
步骤三四:对QPSO算法中粒子的位置进行更新,并设置创新参数;
步骤三五:得到电动汽车充电策略。
6.根据权利要求3所述的一种基于QPSO算法的电动汽车充电策略优化方法,其特征在于,步骤四中基于QPSO算法对电动汽车充电策略进行优化的具体方法为:
步骤四一、获取光伏发电***发电量的预测曲线;
步骤四二、通过光伏发电***的发电量与电动汽车充电***用电量进行对比,并得到两者的缺额,根据两者的缺额确定储能***的初始SOC;
步骤四三、在微电网模型中输入光伏发电***的功率及电动汽车充电***的初始SOC;
步骤四四、初始化粒子群参数和种群位置;
步骤四五、更新粒子位置,并判断选择粒子是否符合微电网模型,如果不符合,则执行步骤四六;如果符合,则执行步骤四七;
步骤四六、设置罚函数,并对适应度值进行约束,然后执行步骤四七;
步骤四七、评估适应度值,并更新粒子最优位置;
步骤四八、判断前种群的最优值是否大于当前种群的全局最优值,如果前种群的最优值大于当前种群的全局最优值,则执行步骤四十;否则,执行步骤四九;
步骤四九、更新全局最优粒子位置,然后执行步骤四十;
步骤四十、判断迭代次数是否达到最大;如果否,则返回执行步骤四五;如果是,则得到全局最优的粒子,并输出最优充电策略,完成对电动汽车充电策略的优化。
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