CN113572181A - 一种基于分时电价的电气化铁路储能系统双层容量配置优化方法 - Google Patents
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Abstract
本发明公开了一种基于分时电价的电气化铁路储能系统双层容量配置优化方法。所述一种基于分时电价的电气化铁路储能系统双层模型同时兼顾削峰填谷及低储高发双应用功能。在本发明中,建立考虑分时电价的锂电池储能电气化铁路双层优化配置模型。采用粒子群(PSO)和灰狼优化(GWO)算法求解该双层模型,从而得出解决双重应用功能前提下的最佳容量配置及储能充放电功率调度,使得电气化铁路中的储能系统得到了更加充分的利用,有效地解决了电气化铁路配置储能的高成本问题,具有广泛的应用性及有效性。
Description
技术领域
本发明涉及储能技术领域,尤其涉及一种基于分时电价的电气化铁路储能系统双层容量配置优化方法。
背景技术
目前在电气化铁路方面已经出现了广泛且效果不错的储能技术应用,储能系统的充电放电双重功能应用于牵引负荷的削峰和填谷上,将具有“削峰填谷”作用的储能装置接入电气化铁路将会产生经济跟技术两方面的效益,从经济层面来看,它能够减少牵引变压器的容量以及最大需量,可以降低建设施工成本;从技术层面来看,由于储能系统具有削除负荷峰值的特性,可以起到弱化负序的积极影响。
各地的电网公司大多出台了相应的分时电价政策,同时,电气化铁路作为大宗工业用户,大工业用户可根据自身的用电特性申请采用分时电价进行电费计量,在储能装置对牵引负荷充放电完成削峰填谷的同时,各时段电价的差距可能会使得储能接入电气化铁路后产生一个相较于单一电价政策下的峰谷电价套利收益。
发明内容
本发明考虑储能装置在对牵引负荷进行削峰填谷的同时,可以考虑兼顾分时电价政策下低储高发套利,实现储能装置的对电气化铁路的双重应用。在上述的双重应用的背景下,针对如何协调削峰填谷和低储高发套利对储能装置的竞争性需要和系统经济效益最大化的目标,建立了考虑分时电价的锂电池储能电气化铁路双层优化配置模型,结合储能装置荷电状态信息,设计两种运行模式的协调运行策略。
1储能系统双层优化配置模型
1.1双层优化模型
本文对牵引供电系统的BESS配置时,打破了规划与运行之间的隔阂,在规划模型中嵌入运行过程,以获得更符合实际的方案,规划BESS容量时要考虑到储能峰谷套利值,而储能峰谷套利值需要得到储能系统24h出力后才能计算;并且运行时BESS出力的确定又需要以规划的容量为前提,所以本文研究需要二者互相迭代才能计算,与双层规划模型相匹配。
在双层规划模型中,上层规划结果作用于下层目标函数和约束条件,下层规划以最优值反馈到上层,实现上下层之间的互相作用。一般的双层数学模型可表示为
式中:F(x,w)为上层的目标函数;G(x)为上层的约束条件;f(x,y)为下层的目标函数;w为下层目标值;g(x,y)为下层约束条件;x为上层决策变量;y为下层决策变量。
本文采用双层规划模型对BESS优化配置。上层规划以日综合效益最大为目标函数,优化变量为储能容量,功率,削峰线。下层规划根据分时电价的低储高发和削峰填谷多重应用指导,逐步迭代得到满足要求的BESS运行控制策略,下层规划结果用于计算上层目标函数。本文采用的双层规划模型可以化简为
1.2上层规划数学模型
优化的目标是在系统安全稳定的基础上实现综合经济效益最大化,具体目标函数为
max f(P1,En,Pn)=C3+C4+C5+C6-C1-C2 (3)
式中P1,En,Pn分别为削峰线,配置储能的容量,配置储能的功率;C1为BESS初始投资成本;C2为BESS运行维护成本;C3为储能低储高发套利收益;C4为牵引变压器投资成本收益;C5为牵引变压器维护成本收益;C6为储能削峰后基本电费收益。各分量计算如下所述。
1.3下层规划数学模型
下层模型用以求解上层配置规划下最佳的BESS调度策略,制定BESS的调度兼顾削峰填谷和峰谷套利。
故设定本层优化目标函数如下:
max C3 (4)
附图说明
图1为BESS加入后前后对比图
具体实施方式
为使本发明的上述目的、特征和优点能够更加明显易懂,下面结合说明书附图对本发明的具体实施方式做详细的说明。
在下面的描述中阐述了很多具体细节以便于充分理解本发明,本领域技术人员可以在不违背本发明内涵的情况下做类似推广,因此本发明不受下面公开的具体实施案例的限制。
双层优化模型分为上下两层,其中上层决策者为电气化铁路储能系统,其优化目标为总效益最大,其决策变量为削峰线,储能的额定容量和功率,同时考虑了储能可用容量约束、充放电电量平衡约束、充放电状态约束、充放电功率约束、不可调度时段约束、充放电次数约束;下层模型以峰谷套利值最大化为目标,其决策变量为峰、谷、平三种时段的分时电价以及储能充放电功率,同时考虑了峰谷特性约束,充放电次数约束。在构建本实施案例所述的考虑分时电价电气化铁路储能系统的双层优化模型前,还需要对电气化铁路牵引负荷的特性进行分析,
上层模型如式(3)所示
(1)BESS初始投资成本
C1=ceEn+cpPn (5)
式中:ce为储能单位容量成本;cp为储能单位功率成本;En为储能额定功率;Pn为储能容量。
(2)BESS运行维护成本
式中:cm为储能单位运行成本;ir为通货膨胀率;dr为贴现率;N为储能使用寿命。
(3)储能低储高发套利收益
式中:Pref(t)为储能充放电功率;Ytime(t)为实时电价。
(4)牵引变压器投资成本收益
C4=cv(Pmax-P1) (8)
式中:cv为牵引变压器单位成本;Pmax为牵引负荷峰值;P1为目标削峰线。
(5)牵引变压器维护成本收益
式中:ct为牵引变压器单位维护成本。
(6)基本电费收益
式中:Ybase为基本电费单价。
下层模型如式(4)所示
下层制定多运行模式协调控制策略
模式一:调峰模式
模式二:低储高发套利模式
负荷差值△P(t)作为两种运行模式的判据
△P(t)≥0时,进入调峰模式
△P(t)<0时,进入低储高发套利模式
Ⅰ区域处于谷时电价段,低储高发模式充电至SOCmax,调峰模式中放电完成削峰,最终SOC保持在SOCmax
Ⅱ区域处于平时电价段,低储高发模式中储能充电至SOCmax,调峰模式中放电完成削峰,最终SOC保持在SOCmax
Ⅲ区域处于峰时电价段,低储高发模式中储能放电至SOC3时,停止放电,调峰模式中放电完成削峰,最终SOC保持在SOCmin
Ⅳ区域处于平时电价段,低储高发模式中储能充电至SOCmax,调峰模式中放电完成削峰,最终SOC保持在SOCmax
Ⅴ区域处于峰时电价段,低储高发模式中储能放电至SOC5时,停止放电,调峰模式中放电完成削峰,最终SOC保持在SOCmin
调峰模式中:
遇到调峰任务,储能放电削峰,其余时段不工作,储能放电功率为Pref=-△P(t)
低储高发套利模式中:
1)当处于谷时电价时,储能充电至SOCmax获得低价电量,充满后保持谷时电价段储能SOC最大
2)当处于平时电价时,储能保持SOC处于SOCmax,以迎接随时出现的调峰任务或者高价时刻放电套利任务
3)当处于峰时电价时,储能的荷电状态在SOC3或SOC5以上时售电套利和调峰同时进行(调峰优先),在储能的荷电状态在SOC3或SOC5以下时只进行调峰。
约束条件
在外层优化模型中,BESS的规划应综合考虑系统运行要求及储能技术现状,即蓄电池的容量、功率和荷电状态均应在合理范围之内,如下所示:
Pref≤Pn (11)
SOCmax≤SOC(t)≤SOCmin (12)
在本实施案例中,选取湖南省某变电所某典型日负荷为实例,选用磷酸铁锂电池作为分析对象,储能装置采样间隔为1min,双层优化模型分为上下两层,利用粒子群(PSO)算法和灰狼优化(GWO)算法对该双层模型进行求解,安装BESS并控制BESS分别作用于只回收再生制动或只处理削峰的单一应用需求,以及本文提出的多重应用规划需求。得到各项经济参数如表1所示,图1为BESS加入后对比效果图,场景1为不安装BESS的情况,应用场景2为安装BESS只用于低储高发的情况,应用场景3为安装BESS只用于牵引负荷削峰填谷的情况,应用场景4为安装BESS既应用于牵引负荷削峰填谷,又应用于低储高发的情况。
由实验对比可以看出场景4在同时应用于负荷削峰填谷和低储高发的同时获得经济效益比单一应用更多的,同时提高了能源的利用率,获得一定的环境效益。
虽然当前BESS的配置成本比较高导致综合收益较小,但从长远角度看,储能式电气化铁路非常具有前景,随着高速列车的需求和发展,储能系统设备和控制设备的配置费用也随着技术发展在下降,因此BESS在今后配置成本下降的同时,安装BESS而产生的经济效益将会进一步提升。
本文提出了一种基于分时电价的电气化铁路储能系统双层容量配置优化方法,建立电气化铁路储能系统双层优化配置模型,使用PSO和GWO分别求解该双层模型,在不同应用场景进行实验对比分析,验证提出的方法的有效性和正确性。
最后,本申请的方法仅为较佳的实施方案,并非用于限定本发明的保护范围。凡在本发明的精神和原则之内,所做的任何修改、等同替换、改进等,均应包含本发明的保护范围之内。
Claims (4)
1.一种基于分时电价的电气化铁路储能系统双层容量配置优化方法,其特征在于,包括:收集牵引变电所的牵引负荷实测数据;基于分时电价时段的划分;基于削峰填谷的任务;建立考虑分时电价的锂电池储能电气化铁路双层优化配置模型,并将上述数据导入双层优化模型中;求解出以储能系统削峰填谷带来的总效益最大及分时套利最多为目标的储能系统充放电策略及最优储能容量配置。
2.根据权利要求1所述的储能充放电策略和双层优化方法,其特征在于:双层优化模型分为上下两层,其中上层决策者为电气化铁路储能系统,其优化目标为总效益最大,其决策变量为削峰线,储能的额定容量和功率,同时考虑了储能可用容量约束、充放电电量平衡约束、充放电状态约束、充放电功率约束、不可调度时段约束、充放电次数约束;下层模型以峰谷套利值最大化为目标,其决策变量为峰、谷、平三种时段的分时电价以及储能充放电功率,同时考虑了峰谷特性约束,充放电次数约束。在构建本实施案例所述的考虑分时电价电气化铁路储能系统的双层优化模型前,还需要对电气化铁路牵引负荷的特性进行分析。
3.根据权利要求1所述的储能充放电策略和双层优化方法,其特征在于:建立考虑分时电价的锂电池储能电气化铁路双层优化配置模型,上层其优化目标在削峰填谷下总效益最大:ce为储能单位容量成本;cp为储能单位功率成本;En为储能额定功率;Pn为储能容量;Pref(t)为储能充放电功率;Ytime(t)为实时电价;cv为牵引变压器单位成本;Pmax为牵引负荷峰值;P1为目标削峰线;ct为牵引变压器单位维护成本;Ybase为基本电费单价。下层优化目标为在分时电价及削峰填谷机制下的分时电价套利最大。
4.根据权利要求3所述充放电策略优化模型,其特征在于:求解双层优化模型过程中,采用KKT最优条件将双层模型转换成单层模型,引入拉格朗日乘子,将下层模型的约束条件和目标函数联系到一起,将双层模型模型转化为单层模型,然后通过PSO和GWO算法求解得到双层模型的最优解。
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