CN113690882A - 基于***热备用的主动配电网优化调度方法 - Google Patents

基于***热备用的主动配电网优化调度方法 Download PDF

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CN113690882A
CN113690882A CN202110939555.2A CN202110939555A CN113690882A CN 113690882 A CN113690882 A CN 113690882A CN 202110939555 A CN202110939555 A CN 202110939555A CN 113690882 A CN113690882 A CN 113690882A
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曹宏基
刘道兵
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China Three Gorges University CTGU
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Abstract

基于***热备用主动配电网优化调度方法,搭建含储能、新能源发电、微型燃气轮机、与上级电网交互、负荷削减的主动配电网,作为***热备用容量的测试***;搭建主动配电网的***热备用容量模型,作为***的基本热备用容量;建立主动配电网的***爬坡降幅模型,进一步提高***热备用容量;构建基于***爬坡降幅、调度成本的多目标优化函数,通过权重因子确定调度成本、储能、微型燃气轮机、新能源的爬坡降幅重要程度。发明提供方法使用基本备用容量为主动配电网留下固定的热备用容量,同时建立***爬坡降幅模型,综合调度成本、***爬坡降幅对主动配电网热备用进行优化,并且该方法提供了针对五种***爬坡降幅的控制策略,使其可以根据各调度资源的重要程度确定权重因子,完成多目标优化。

Description

基于***热备用的主动配电网优化调度方法
技术领域
本发明涉及***热备用技术领域,具体涉及一种基于***热备用的主动配电网优化调度方法。
背景技术
主动配电网可以充分发挥分布式发电***效能,提高用户的供电可靠性,削弱分布式电源对电网的冲击和负面影响,具有良好的经济和社会效益。由于主动配电网在电源、能源转换单元、储能和运行状态等方面的多样性,以及分布式电源的间歇性和随机性与负荷预测误差相互叠加,主动配电网的热备用容量问题比传统大电网的热备用容量问题复杂得多。主要表现为:①分布式电源出力的波动;②负荷预测的不准确性;③储能、与上级电网交互功率、负荷削减、分布式电源共为***热备用。
目前,国内外针对主动配电网热备用的研究方法为:借鉴传统电网的热备用预留方案,将已有的***备用容量预留方法直接应用到主动配电网中。例如:针对负荷、新能源出力预测误差,预留微型燃气轮机的爬坡上、下功率,保证***的热备用容量。
上述实现方法没有全面考虑主动配电网可调度资源,且***热备用容量为定值,不可进行优化。国内外对全面考虑主动配电网特点的***热备用研究尚处于起步阶段。
基于上述现有技术中的不足,根据主动配电网可调度资源的特征,如储能、与上级电网交互功率、负荷削减、分布式电源等因素提出一种热备用可优化的主动配电网优化调度方法,是当前要解决的技术问题。
发明内容
本发明提供一种基于***热备用主动配电网优化调度方法,使用储能、与上级电网交互功率、负荷削减、分布式电源同时为***预留热备用,保证***的基本备用容量,同时建立基于该四种调度资源的***爬坡降幅模型,能够进一步提高***的热备用。并且该方法通过权重因子确定储能、微型燃气轮机、新能源的不可调节容量重要程度,完成多目标优化。相较于传统方法,本发明方法能够优化***热备用容量,提升***的抗干扰能力。
本发明采取的技术方案为:
基于***热备用主动配电网优化调度方法,包括以下步骤:
步骤一:搭建含储能、新能源发电、微型燃气轮机、与上级电网交互、负荷削减的主动配电网,作为***热备用容量的测试***;
步骤二:搭建主动配电网的***热备用容量模型,作为***的基本热备用容量;
步骤三:搭建主动配电网的***爬坡降幅模型,提高***热备用容量;
步骤四:构建基于***爬坡降幅、调度成本的多目标优化函数,通过权重因子确定储能、微型燃气轮机、新能源的爬坡降幅以及调度成本重要程度。
所述步骤二中,首先建立储能、负荷需求响应、与上级电网交互的备用容量模型,再通过改进的微型燃气轮机、新能源出力模型建立爬坡降幅约束,最后,建立***热备用容量模型:
(1)储能备用容量:
Figure BDA0003214240300000021
Figure BDA0003214240300000022
其中:Nn为主动配电网节点个数;PESSu,t是t时刻储能的正旋转备用容量,PESSl,t是t时刻储能的负旋转备用容量;ηc,j、ηd,j分别是节点j的储能的充、放电效率,PESSCj,max、PESSDj,max分别是节点j处储能的充、放电功率上限,SESSj,t是节点j的储能在t时刻的电量,SESSj,max是节点j的储能电量的上限,PESSC,j,t、PESSD,j,t分别是j节点储能在t时刻的充电、放电功率。
(2)负荷需求响应和其与上级电网交互功率的备用容量:
Figure BDA0003214240300000023
Figure BDA0003214240300000024
其中:Pxyu,t、Pxyl,t分别为t时刻需求响应负荷和与上级电网交互功率的正、负旋转备用容量,PCUTj,max是节点j的可控负荷的最大削减量,P0j,max是主动配电网节点j与上级电网交互的有功功率的上限,PCUTj,t是j节点t时刻负荷削减量,P0j,t是t时刻节点j与上级电网交互的功率。
(3)***的正、负旋转备用容量约束:
①、微型燃气轮机出力约束:
PMTj,t≤PMTj,max (5)
αj,t(PMTj,max-Kj,max)+(1-αj,t)PMTj,min≤PMTj,t (6)
PMTj,t≤(1-αj,t)(PMTj,max-Kj,max)+αj,tPMTj,max (7)
PMTj,t≤βj,t(PMTj,min-Kj,min)+(1-βj,t)PMTj,max (8)
(1-βj,t)(PMTj,min-Kj,min)+βj,tPMTj,min≤PMTj,t (9)
αj,tj,t≤1 (10)
αj,tj,t∈{0,1} (11)
Kj,min≤PMTj,t+1-PMTj,t≤Kj,max (12)
Figure BDA0003214240300000031
其中:PMTj,min、PMTj,max分别为节点j的微型燃气轮机有功出力的下限、上限;
Kj,min、Kj,max分别为节点j的微型燃气轮机有功出力的爬坡率下限(值大小为负)、上限(值大小为正);
αj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,max-Kj,max至PMTj,max的区间之内;
βj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,min至PMTj,min-Kj,min的区间之内,是时值为1,否时值为0;
QMTj,t是t时刻j节点的微型燃气轮机无功出力;SMTj是节点j的微型燃气轮机的装机容量;
PMTj,t是j节点微型燃气轮机在t时刻的有功。
②、风电厂出力约束:
PWTj,t≤δj,tPWTj,t,max+(1-δj,t)(1-F)PWTj,t,max (14)
PWTj,t≥δj,t(1-F)PWTj,t,max (15)
0≤QWTj,t≤QWTj,t,max (16)
δj,t∈{0,1} (17)
其中:PWTj,t是节点j的风机t时刻的有功出力,PWTj,t,max是节点j风机t时刻最大有功出力预测值。
QWTj,t,max、QWTj,t分别是t时刻节点j的风机无功出力最大出力值、风机无功出力;
F是风电预测误差率;
δj,t是t时刻节点j的风机有功出力PWTj,t是/否在(1-F)PWTj,t,max至PWTj,t,max的区间之内,是时值为1,否时值为0。
③、微型燃气轮机和风机爬坡降幅约束:
微型燃气轮机爬坡降幅约束:
PMTj,t-PMTj,max+Kj,max≤CEXTRA1j,t+M(1-αj,t) (18)
0≤CEXTRA1j,t+Mαj,t (19)
PMTj,min-Kj,min-PMTj,t≤CEXTRA2j,t+M(1-βj,t) (20)
0≤CEXTRA2j,t+Mβj,t (21)
风机爬坡降幅约束:
PWTj,t≤CEXTRA3j,t+Mδj,t (22)
(1-F)PWTj,t,max≤CEXTRA3j,t+M(1-δj,t) (23)
其中:PWTj,t是节点j的风机t时刻的有功出力,PWTj,t,max是节点j风机t时刻最大有功出力预测值;Kj,min、Kj,max分别为j节点的微型燃气轮机有功出力的爬坡率下限(值大小为负)、上限(值大小为正);αj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,max-Kj,max至PMTj,max的区间之内,βj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,min至PMTj,min-Kj,min的区间之内,是时值为1,否时值为0;PMTj,min、PMTj,max分别为j节点微型燃气轮机有功出力的下限、上限;δj,t是t时刻节点j的风机有功出力PWTj,t是/否在(1-F)PWTj,t,max至PWTj,t,max区间之内,是时值为1,否时值为0;
CEXTRA1j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡上限减小的爬坡降幅;CEXTRA2j,t是节点j的微型燃气轮机t时刻有功出力使得爬坡下限减小的爬坡降幅;
CEXTRA3j,t是节点j的风机在t时刻爬坡降幅,M是无穷大的实数。
由于风机出力在(1-F)PWTj,t,max至PWTj,t,max范围内的不稳定性,风机的最大正旋转备用容量为(1-F)PWTj,t,max,当风机出力在(1-F)PWTj,t,max以下时,可以增发功率作为***的正旋转备用容量,而风机弃风成本较大,因此其不作为***的负旋转备用容量。
④、***的正、负旋转备用容量约束:
Figure BDA0003214240300000051
Figure BDA0003214240300000052
式中:Pctu,t是t时刻需要正旋转备用容量的***预测误差,Nn为主动配电网节点个数,Kj,min、Kj,max分别为j节点的微型燃气轮机有功出力的爬坡率下限(值大小为负)、上限(值大小为正);CEXTRA1j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡上限减小的爬坡降幅,CEXTRA2j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡下限减小的爬坡降幅,CEXTRA3j,t是节点j的风机在t时刻爬坡降幅;PESSu,t是t时刻储能的正旋转备用容量,PESSl,t是t时刻储能的负旋转备用容量;Pxyu,t、Pxyl,t分别为t时刻符合削减和与上级电网交互功率的正、负旋转备用容量;Pctl,t是t时刻需要负旋转备用容量的***预测误差;PWTj,t,max是节点j风机t时刻最大有功出力预测值。
Pctl,t是t时刻需要负旋转备用容量的***预测误差,其计算公式:
Figure BDA0003214240300000053
Figure BDA0003214240300000054
式中:PLj,t是t时刻节点j的负荷的有功功率,L是负荷预测误差率。当风机有功出力预测上限向上波动时,此时不影响正、负旋转备用容量;当风机出力在(1-F)PWTj,t,max至PWTj,t,max的范围内时,PWTj,t-PWTj,t,max(1-F)的风电有功出力是不稳定的,需要***有增发功率来应对风电出力减少的能力;PWTj,t,max是节点j风机t时刻最大有功出力预测值,PWTj,t是节点j的风机t时刻的有功出力。
所述步骤三中,***爬坡降幅模型使得***热备用可优化,具体如下:
①微型燃气轮机爬坡降幅:
Figure BDA0003214240300000061
微型燃气轮机有功出力是根据爬坡降幅最小原则设定,当其出力在有功出力上下限附近,微型燃气轮机的有功出力调整范围不能按照最大爬坡率进行调整时,就会影响***的抗干扰能力,微型燃气轮机爬坡降幅越小,***热备用容量就越高。CEXTRA,MT是微型燃气轮机爬坡降幅,Nn为主动配电网节点个数,T是调度周期,CEXTRA1j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡上限减小的爬坡降幅,CEXTRA2j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡下限减小的爬坡降幅。
②风机爬坡降幅:
Figure BDA0003214240300000062
CEXTRA,WT是风机爬坡降幅,其具体含义见步骤二微型燃气轮机和风机爬坡降幅约束;Nn为主动配电网节点个数,T是调度周期,CEXTRA3j,t是节点j的风机在t时刻爬坡降幅。
③储能爬坡降幅:
Figure BDA0003214240300000063
Figure BDA0003214240300000064
CEXTRA,ESS=CEXTRA,ESSu+CEXTRA,ESSl (32)
CEXTRA,ESS是储能爬坡降幅;CEXTRA,ESSu是代表储能增加放电或减小充电能力的爬坡降幅,其值越小,增加放电或减小充电能力越大;CEXTRA,ESSl是代表储能减小放电或增加充电能力的爬坡降幅,其值越小,减小放电或增加充电能力越大;PESSCj,max、PESSDj,max是节点j处储能的充、放电功率上限,PESSl,t是t时刻储能的负旋转备用容量。
④需求响应负荷和与上级电网交互功率爬坡降幅:
需求响应负荷和与上级电网交互功率的正爬坡降幅为各时段Pxyl,t之和,负爬坡降幅为各时段Pxyu,t之和,正、负爬坡降幅之和为常数,因此其不计入***爬坡降幅目标函数。
所述步骤四中,基于***爬坡降幅、调度成本的多目标优化函数,其具体模型为下式(33)、式(34)、式(35)所示:
min C=σ1C12CEXTRA (33)
式(33)中:C为优化策略目标函数,σ1为调度成本权重系数,σ2为***爬坡降幅权重系数,其值在区间[0,1]内,且σ12=1。C1是所述步骤一中,所搭建的IEEE-33总线配电网测试***调度成本:
C1=CMT+CWT+CESS+Closs+CP0+CDSR (34)
式(34)中,C1为优化策略成本,CMT为微型燃气轮机发电的成本,CWT为风机发电成本,CESS为储能损耗成本,Closs为网络损耗成本,CP0为与上级电网交互成本,CDSR为需求响应的成本。CEXTRA为***爬坡降幅函数,表达式为:
CEXTRA=ρ1CEXTRA,MT2CEXTRA,WT3CEXTRA,ESS (35)
式(35)中,ρ1、ρ2、ρ3分别为微型燃气轮机、风机、储能三种爬坡降幅的优先系数或权重系数,其值在区间[0,1]内,且ρ123=1,储能主要职能为削峰填谷,次要职能为提高***备用容量,其权重较低,而风机属于清洁能源,一般情况下不弃风发电,其权重最低,因此,确定权重时要求ρ132,CEXTRA,MT、CEXTRA,WT、CEXTRA,ESS分别为微型燃气轮机、风机、储能爬坡降幅。
通过调整各权重系数,即可实现成本和***爬坡降幅的配合,将***爬坡降幅引入主动配电网中,即可完成主动配电网的热备用容量优化。
热备用包括基本备用容量、以及通过***爬坡降幅优化后增加的热备用。
本发明一种基于***热备用的主动配电网优化调度方法,技术效果如下:
1)本发明调度方法步骤二中,基础热备用容量的优势体现于:基础热备用容量相对于传统热备用加入了主动配电网的储能、新能源发电、与上级电网交互、负荷削减四种元素,有更多的调度资源可为热备用容量。
2)本发明调度方法步骤三中,相对于基础热备用容量,***爬坡降幅为主动配电网需要考虑的目标之一,和调度成本一样,是调度方案的结果。通过调节权重因子权衡调度成本和***爬坡降幅,使其具有能够对多目标有效优化调度。
3)本发明调度方法建立新能源、储能、与上级电网交互、负荷削减、微型燃气轮机的爬坡降幅模型,通过权重系数确定五种调度资源的重要程度。
4)本发明调度方法能够进一步提高主动配电网中的***热备用,保障配电网可靠运行。
5)该方法提供了针对五种***爬坡降幅的控制策略,使其可以根据各调度资源的重要程度确定权重因子,完成多目标优化。
附图说明
图1为包含储能、风能、微型燃气轮机、与上级电网交互、负荷削减的主动配电网结构示意图。
图2为总负载曲线图。
图3为主动配电网有/无***爬坡降幅的***爬坡上限对比图。
图4为主动配电网有/无***爬坡降幅的***爬坡下限对比图。
具体实施方式
基于***热备用的主动配电网优化调度方法,包括以下步骤:
步骤一:搭建包含储能、风能、微型燃气轮机、与上级电网交互、负荷削减的主动配电网,作为***热备用容量的测试***。所搭建的测试***是一个IEEE-33总线配电网,微型燃气轮机参数见表1,储能参数见表2,其它成本参数见表3,总负荷变化曲线见图2,两个风机有功出力上限根据历史数据采用神经网络算法进行预测,此算例采用相同的预测出力,其预测有功出力见表4。电压等级为12.66kV,节点电压上下限分别为电压等级的1.05倍和0.95倍,节点7、20、33连接的负荷可削减,可削减上限均为200kW.h,节点1与上级电网交互有功功率上限400kW.h,与上级电网交互无功功率上限为400kVar,负荷、风电预测误差率L、F均为0.05,调度成本权重系数为0.6,爬坡降幅权重系数为0.4,微型燃气轮机、风机、储能爬坡降幅在***爬坡降幅中的权重系数分别为0.6、0.1、0.3。
表1微型燃气轮机模型参数
Figure BDA0003214240300000081
Figure BDA0003214240300000091
表2储能模型参数
Figure BDA0003214240300000092
表3其它成本参数
Figure BDA0003214240300000093
表4风机预测出力
Figure BDA0003214240300000094
主动配电网的调度成本:
C1=CMT+CWT+CESS+Closs+CP0+CDSR (1)
式中,C1为优化策略成本,CMT为微型燃气轮机发电的成本,CWT为风机发电成本,CESS为储能损耗成本,Closs为网络损耗成本,CP0为与上级电网交互成本,CDSR为需求响应的成本,其表达式为:
Figure BDA0003214240300000101
Figure BDA0003214240300000102
Figure BDA0003214240300000103
Figure BDA0003214240300000104
Figure BDA0003214240300000105
Figure BDA0003214240300000106
式中:
Figure BDA0003214240300000107
是节点j的微型燃气轮机在t时刻的有功出力,aj、bj、cj为节点j的微型燃气轮机燃料成本系数,Nn为主动配电网网络节点个数;PWTj,t,max是节点j风机t时刻最大有功出力预测值,PWTj,t是节点j的风机t时刻的有功出力,km,j为节点j风机发电弃电单位惩罚成本;PESSC,j,t、PESSD,j,t分别是节点j的储能在t时刻的充电、放电功率,kc,j、kd,j分别是j节点储能的单位充、放电的折算成本费用;lij,t是支路ij在t时刻电流的平方,rij是支路ij的电阻,ω是售电价格,E是主动配电网的线路集合;P0j,t是t时刻节点j与上级电网交互的功率,λP0是主动配电网与上级电网交互单位功率的价格;PCUTj,t是j节点t时刻负荷削减量,λCUT是削减负荷的价格。
步骤二:搭建***热备用容量数学模型:
首先建立储能、负荷需求响应、与上级电网交互的备用容量模型,再通过改进的微型燃气轮机、新能源出力模型建立的爬坡降幅约束,最后,建立***热备用容量模型。
1.储能备用容量:
Figure BDA0003214240300000108
Figure BDA0003214240300000109
其中:PESSu,t是t时刻储能的正旋转备用容量,PESSl,t是t时刻储能的负旋转备用容量,ηc,j、ηd,j分别是节点j的储能的充、放电效率,PESSCj,max、PESSDj,max分别是节点j处储能的充、放电功率上限,SESSj,t是节点j的储能在t时刻的电量,SESSj,max是节点j的储能电量的上限。
2.负荷需求响应和与上级电网交互功率的备用容量:
Figure BDA0003214240300000111
Figure BDA0003214240300000112
其中:Pxyu,t、Pxyl,t分别为t时刻需求响应负荷和与上级电网交互功率的正、负旋转备用容量,PCUTj,max是节点j的可控负荷的最大削减量,P0j,max是主动配电网节点j与上级电网交互的有功功率的上限。
3.***的正、负旋转备用容量约束
①微型燃气轮机出力约束
PMTj,t≤PMTj,max (12)
αj,t(PMTj,max-Kj,max)+(1-αj,t)PMTj,min≤PMTj,t (13)
PMTj,t≤(1-αj,t)(PMTj,max-Kj,max)+αj,tPMTj,max (14)
PMTj,t≤βj,t(PMTj,min-Kj,min)+(1-βj,t)PMTj,max (15)
(1-βj,t)(PMTj,min-Kj,min)+βj,tPMTj,min≤PMTj,t (16)
αj,tj,t≤1 (17)
αj,tj,t∈{0,1} (18)
Kj,min≤PMTj,t+1-PMTj,t≤Kj,max (19)
Figure BDA0003214240300000113
其中:PMTj,min、PMTj,max分别为节点j的微型燃气轮机有功出力的下限、上限;Kj,min、Kj,max分别为节点j的微型燃气轮机有功出力的爬坡率下限(值大小为负)、上限(值大小为正);αj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,max-Kj,max至PMTj,max的区间之内,βj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,min至PMTj,min-Kj,min的区间之内,是时值为1,否时值为0;QMTj,t是t时刻j节点的微型燃气轮机无功出力,SMTj是节点j的微型燃气轮机的装机容量。
②风电厂出力约束
PWTj,t≤δj,tPWTj,t,max+(1-δj,t)(1-F)PWTj,t,max (21)
PWTj,t≥δj,t(1-F)PWTj,t,max (22)
0≤QWTj,t≤QWTj,t,max (23)
δj,t∈{0,1} (24)
其中:QWTj,t,max、QWTj,t分别是t时刻节点j的风机无功出力最大出力值、风机无功出力;F是风电预测误差率;δj,t是t时刻节点j的风机有功出力PWTj,t是/否在(1-F)PWTj,t,max至PWTj,t,max的区间之内,是时值为1,否时值为0。
③微型燃气轮机和风机爬坡降幅约束
微型燃气轮机爬坡降幅约束:
PMTj,t-PMTj,max+Kj,max≤CEXTRA1j,t+M(1-αj,t) (25)
0≤CEXTRA1j,t+Mαj,t (26)
PMTj,min-Kj,min-PMTj,t≤CEXTRA2j,t+M(1-βj,t) (27)
0≤CEXTRA2j,t+Mβj,t (28)
风机爬坡降幅约束:
PWTj,t≤CEXTRA3j,t+Mδj,t (29)
(1-F)PWTj,t,max≤CEXTRA3j,t+M(1-δj,t) (30)
其中:CEXTRA1j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡上限减小的爬坡降幅,CEXTRA2j,t是节点j的微型燃气轮机t时刻有功出力使得爬坡下限减小的爬坡降幅,CEXTRA3j,t是节点j的风机在t时刻的爬坡降幅,M是无穷大的实数。由于风机出力在(1-F)PWTj,t,max至PWTj,t,max范围内的不稳定性,风机的最大正旋转备用容量为(1-F)PWTj,t,max,当风机出力在(1-F)PWTj,t,max以下时,可以增发功率作为***的正旋转备用容量,而风机弃风成本较大,因此其不作为***的负旋转备用容量;
④***的正、负旋转备用容量约束:
Figure BDA0003214240300000131
Figure BDA0003214240300000132
式中:Pctu,t是t时刻需要正旋转备用容量的***预测误差,Pctl,t是t时刻需要负旋转备用容量的***预测误差,其计算公式:
Figure BDA0003214240300000133
Figure BDA0003214240300000134
式中:PLj,t是t时刻节点j的负荷的有功功率,L是负荷预测误差率。当风机有功出力预测上限向上波动时,此时不影响正、负旋转备用容量;当风机出力在(1-F)PWTj,t,max至PWTj,t,max的范围内时,PWTj,t-PWTj,t,max(1-F)的风电有功出力是不稳定的,需要***有增发功率来应对风电出力减少的能力。
步骤三:搭建***爬坡降幅数学模型:
1.微型燃气轮机爬坡降幅
Figure BDA0003214240300000135
微型燃气轮机有功出力是根据爬坡降幅最小原则设定,当其出力在有功出力上下限附近,微型燃气轮机的有功出力调整范围不能按照最大爬坡率进行调整时,就会影响***的抗干扰能力,微型燃气轮机爬坡降幅越小,***热备用容量就越高。CEXTRA,MT是微型燃气轮机爬坡降幅。
2.风机爬坡降幅
Figure BDA0003214240300000136
CEXTRA,WT是风机爬坡降幅;其具体含义见步骤二微型燃气轮机和风机爬坡降幅约束。
3.储能爬坡降幅
Figure BDA0003214240300000141
Figure BDA0003214240300000142
CEXTRA,ESS=CEXTRA,ESSu+CEXTRA,ESSl (39)
其中:CEXTRA,ESS是储能爬坡降幅;CEXTRA,ESSu是代表储能增加放电或减小充电能力的爬坡降幅,其值越小,增加放电或减小充电能力越大;CEXTRA,ESSl是代表储能减小放电或增加充电能力的爬坡降幅,其值越小,减小放电或增加充电能力越大。
4.需求响应负荷和与上级电网交互功率爬坡降幅:
需求响应负荷和与上级电网交互功率的正爬坡降幅为各时段Pxyl,t之和,负爬坡降幅为各时段Pxyu,t之和,正、负爬坡降幅之和为常数,因此其不计入***爬坡降幅目标函数。步骤四:构建基于***爬坡降幅、调度成本的多目标优化函数。
min C=σ1C12CEXTRA (40)
式中:C为优化策略目标函数,σ1为调度成本权重系数,σ2为***爬坡降幅权重系数,其值在区间[0,1]内,且σ12=1。式中,CEXTRA为***爬坡降幅函数,表达式为:
CEXTRA=ρ1CEXTRA,MT2CEXTRA,WT3CEXTRA,ESS (41)
其中:ρ1、ρ2、ρ3分别为微型燃气轮机、风机、储能三种爬坡降幅的优先系数或权重系数,其值在区间[0,1]内,且ρ123=1,储能主要职能为削峰填谷,次要职能为提高***备用容量,其权重较低,而风机属于清洁能源,一般情况下不弃风发电,其权重最低,因此,确定权重时要求ρ132
通过调整各权重系数,即可实现成本和***爬坡降幅的配合,将***爬坡降幅引入主动配电网中,即可完成主动配电网的热备用容量优化。
将***爬坡降幅分为***正爬坡降幅、***负爬坡降幅,图2为主动配电网有/无***爬坡降幅的***正爬坡降幅对比图,图3为主动配电网有/无***爬坡降幅的***负爬坡降幅对比图。***正爬坡降幅在8时、9时、16时、18-20时明显减小,共1712.46kW.h;***负爬坡降幅在5-8时、13时、16-19时减小,共1687.72kW.h。因此,计及***热备用的主动配电网优化调度方案有效提高了***的热备用容量,从而提高了***的稳定性。
***热备用包括爬坡上限以及爬坡下限。本发明对电力***中出现的负荷、新能源出力扰动,预留基本备用容量,保障电力***的正常运行。同时,本发明提出了一种能优化***热备用的***爬坡降幅。该***爬坡降幅综合了储能、新能源、微型燃气轮机、负荷削减、与上级电网交互共五种备用容量,实现基于主动配电网调度资源的热备用优化。本发明方法步骤简单,结果良好,可以提高配电网的可靠性。

Claims (4)

1.基于***热备用主动配电网优化调度方法,其特征在于包括以下步骤:
步骤一:搭建含储能、新能源发电、微型燃气轮机、与上级电网交互、负荷削减的主动配电网,作为***热备用容量的测试***;
步骤二:搭建主动配电网的***热备用容量模型,作为***的基本热备用容量;
步骤三:搭建主动配电网的***爬坡降幅模型,提高***热备用容量;
步骤四:构建基于***爬坡降幅、调度成本的多目标优化函数,通过权重因子确定储能、微型燃气轮机、新能源的爬坡降幅以及调度成本重要程度。
2.根据权利要求1所述基于***热备用主动配电网优化调度方法,其特征在于:所述步骤二中,首先建立储能、负荷需求响应、与上级电网交互的备用容量模型,再通过改进的微型燃气轮机、新能源出力模型建立爬坡降幅约束,最后,建立***热备用容量模型:
(1)储能备用容量:
Figure FDA0003214240290000011
Figure FDA0003214240290000012
其中:Nn为主动配电网节点个数;PESSu,t是t时刻储能的正旋转备用容量,PESSl,t是t时刻储能的负旋转备用容量;ηc,j、ηd,j分别是节点j的储能的充、放电效率,PESSCj,max、PESSDj,max分别是节点j处储能的充、放电功率上限,SESSj,t是节点j的储能在t时刻的电量,SESSj,max是节点j的储能电量的上限,PESSC,j,t、PESSD,j,t分别是j节点储能在t时刻的充电、放电功率;
(2)负荷需求响应和其与上级电网交互功率的备用容量:
Figure FDA0003214240290000013
Figure FDA0003214240290000014
其中:Pxyu,t、Pxyl,t分别为t时刻需求响应负荷和与上级电网交互功率的正、负旋转备用容量,PCUTj,max是节点j的可控负荷的最大削减量,P0j,max是主动配电网节点j与上级电网交互的有功功率的上限,PCUTj,t是j节点t时刻负荷削减量,P0j,t是t时刻节点j与上级电网交互的功率;
(3)***的正、负旋转备用容量约束:
①、微型燃气轮机出力约束:
PMTj,t≤PMTj,max (5)
αj,t(PMTj,max-Kj,max)+(1-αj,t)PMTj,min≤PMTj,t (6)
PMTj,t≤(1-αj,t)(PMTj,max-Kj,max)+αj,tPMTj,max (7)
PMTj,t≤βj,t(PMTj,min-Kj,min)+(1-βj,t)PMTj,max (8)
(1-βj,t)(PMTj,min-Kj,min)+βj,tPMTj,min≤PMTj,t (9)
αj,tj,t≤1 (10)
αj,tj,t∈{0,1} (11)
Kj,min≤PMTj,t+1-PMTj,t≤Kj,max (12)
Figure FDA0003214240290000021
其中:PMTj,min、PMTj,max分别为节点j的微型燃气轮机有功出力的下限、上限;
Kj,min、Kj,max分别为节点j的微型燃气轮机有功出力的爬坡率下限、上限;
αj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,max-Kj,max至PMTj,max的区间之内;
βj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,min至PMTj,min-Kj,min的区间之内,是时值为1,否时值为0;
QMTj,t是t时刻j节点的微型燃气轮机无功出力;SMTj是节点j的微型燃气轮机的装机容量;
PMTj,t是j节点微型燃气轮机在t时刻的有功;
②、风电厂出力约束:
PWTj,t≤δj,tPWTj,t,max+(1-δj,t)(1-F)PWTj,t,max (14)
PWTj,t≥δj,t(1-F)PWTj,t,max (15)
0≤QWTj,t≤QWTj,t,max (16)
δj,t∈{0,1} (17)
其中:PWTj,t是节点j的风机t时刻的有功出力,PWTj,t,max是节点j风机t时刻最大有功出力预测值;
QWTj,t,max、QWTj,t分别是t时刻节点j的风机无功出力最大出力值、风机无功出力;
F是风电预测误差率;
δj,t是t时刻节点j的风机有功出力PWTj,t是/否在(1-F)PWTj,t,max至PWTj,t,max的区间之内,是时值为1,否时值为0;
③、微型燃气轮机和风机爬坡降幅约束:
微型燃气轮机爬坡降幅约束:
PMTj,t-PMTj,max+Kj,max≤CEXTRA1j,t+M(1-αj,t) (18)
0≤CEXTRA1j,t+Mαj,t (19)
PMTj,min-Kj,min-PMTj,t≤CEXTRA2j,t+M(1-βj,t) (20)
0≤CEXTRA2j,t+Mβj,t (21)
风机爬坡降幅约束:
PWTj,t≤CEXTRA3j,t+Mδj,t (22)
(1-F)PWTj,t,max≤CEXTRA3j,t+M(1-δj,t) (23)
其中:PWTj,t是节点j的风机t时刻的有功出力,PWTj,t,max是节点j风机t时刻最大有功出力预测值;Kj,min、Kj,max分别为j节点的微型燃气轮机有功出力的爬坡率下限、上限;
αj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,max-Kj,max至PMTj,max的区间之内,βj,t是t时刻节点j的微型燃气轮机有功出力PMTj,t是/否在PMTj,min至PMTj,min-Kj,min的区间之内,是时值为1,否时值为0;PMTj,min、PMTj,max分别为j节点微型燃气轮机有功出力的下限、上限;δj,t是t时刻节点j的风机有功出力PWTj,t是/否在(1-F)PWTj,t,max至PWTj,t,max区间之内,是时值为1,否时值为0;
CEXTRA1j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡上限减小的爬坡降幅;CEXTRA2j,t是节点j的微型燃气轮机t时刻有功出力使得爬坡下限减小的爬坡降幅;
CEXTRA3j,t是节点j的风机在t时刻爬坡降幅,M是无穷大的实数;
由于风机出力在(1-F)PWTj,t,max至PWTj,t,max范围内的不稳定性,风机的最大正旋转备用容量为(1-F)PWTj,t,max,当风机出力在(1-F)PWTj,t,max以下时,可以增发功率作为***的正旋转备用容量,而风机弃风成本较大,因此其不作为***的负旋转备用容量;
④、***的正、负旋转备用容量约束:
Figure FDA0003214240290000041
Figure FDA0003214240290000042
式中:Pctu,t是t时刻需要正旋转备用容量的***预测误差,Nn为主动配电网节点个数,Kj,min、Kj,max分别为j节点的微型燃气轮机有功出力的爬坡率下限、上限;CEXTRA1j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡上限减小的爬坡降幅,CEXTRA2j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡下限减小的爬坡降幅,CEXTRA3j,t是节点j的风机在t时刻爬坡降幅;PESSu,t是t时刻储能的正旋转备用容量,PESSl,t是t时刻储能的负旋转备用容量;Pxyu,t、Pxyl,t分别为t时刻符合削减和与上级电网交互功率的正、负旋转备用容量;Pctl,t是t时刻需要负旋转备用容量的***预测误差;PWTj,t,max是节点j风机t时刻最大有功出力预测值;
Pctl,t是t时刻需要负旋转备用容量的***预测误差,其计算公式:
Figure FDA0003214240290000043
Figure FDA0003214240290000044
式中:PLj,t是t时刻节点j的负荷的有功功率,L是负荷预测误差率;当风机有功出力预测上限向上波动时,此时不影响正、负旋转备用容量;当风机出力在(1-F)PWTj,t,max至PWTj,t,max的范围内时,PWTj,t-PWTj,t,max(1-F)的风电有功出力是不稳定的,需要***有增发功率来应对风电出力减少的能力;PWTj,t,max是节点j风机t时刻最大有功出力预测值,PWTj,t是节点j的风机t时刻的有功出力。
3.根据权利要求1所述基于***热备用主动配电网优化调度方法,其特征在于:所述步骤三中,***爬坡降幅模型使得***热备用可优化,具体如下:
①微型燃气轮机爬坡降幅:
Figure FDA0003214240290000051
微型燃气轮机有功出力是根据爬坡降幅最小原则设定,当其出力在有功出力上下限附近,微型燃气轮机的有功出力调整范围不能按照最大爬坡率进行调整时,就会影响***的抗干扰能力,微型燃气轮机爬坡降幅越小,***热备用容量就越高;CEXTRA,MT是微型燃气轮机爬坡降幅,Nn为主动配电网节点个数,T是调度周期,CEXTRA1j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡上限减小的爬坡降幅,CEXTRA2j,t是节点j的微型燃气轮机在t时刻有功出力使得爬坡下限减小的爬坡降幅;
②风机爬坡降幅:
Figure FDA0003214240290000052
CEXTRA,WT是风机爬坡降幅,其具体含义见步骤二微型燃气轮机和风机爬坡降幅约束;Nn为主动配电网节点个数,T是调度周期,CEXTRA3j,t是节点j的风机在t时刻爬坡降幅;
③储能爬坡降幅:
Figure FDA0003214240290000053
Figure FDA0003214240290000054
CEXTRA,ESS=CEXTRA,ESSu+CEXTRA,ESSl (32)
CEXTRA,ESS是储能爬坡降幅;CEXTRA,ESSu是代表储能增加放电或减小充电能力的爬坡降幅,其值越小,增加放电或减小充电能力越大;CEXTRA,ESSl是代表储能减小放电或增加充电能力的爬坡降幅,其值越小,减小放电或增加充电能力越大;PESSCj,max、PESSDj,max是节点j处储能的充、放电功率上限,PESSl,t是t时刻储能的负旋转备用容量;
④需求响应负荷和与上级电网交互功率爬坡降幅:
需求响应负荷和与上级电网交互功率的正爬坡降幅为各时段Pxyl,t之和,负爬坡降幅为各时段Pxyu,t之和,正、负爬坡降幅之和为常数,因此其不计入***爬坡降幅目标函数。
4.根据权利要求1所述基于***热备用主动配电网优化调度方法,其特征在于:所述步骤四中,基于***爬坡降幅、调度成本的多目标优化函数,其具体模型为下式(33)、式(34)、式(35)所示:
minC=σ1C12CEXTRA (33)
式(33)中:C为优化策略目标函数,σ1为调度成本权重系数,σ2为***爬坡降幅权重系数,其值在区间[0,1]内,且σ12=1;C1是所述步骤一中,所搭建的IEEE-33总线配电网测试***调度成本:
C1=CMT+CWT+CESS+Closs+CP0+CDSR (34)
式(34)中,C1为优化策略成本,CMT为微型燃气轮机发电的成本,CWT为风机发电成本,CESS为储能损耗成本,Closs为网络损耗成本,CP0为与上级电网交互成本,CDSR为需求响应的成本;CEXTRA为***爬坡降幅函数,表达式为:
CEXTRA=ρ1CEXTRA,MT2CEXTRA,WT3CEXTRA,ESS (35)
式(35)中,ρ1、ρ2、ρ3分别为微型燃气轮机、风机、储能三种爬坡降幅的优先系数或权重系数,其值在区间[0,1]内,且ρ123=1,储能主要职能为削峰填谷,次要职能为提高***备用容量,其权重较低,而风机其权重最低,因此,确定权重时要求ρ132,CEXTRA,MT、CEXTRA,WT、CEXTRA,ESS分别为微型燃气轮机、风机、储能爬坡降幅。
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102983573A (zh) * 2012-11-09 2013-03-20 天津大学 基于安全域的安全约束经济调度方法
US20150039145A1 (en) * 2013-07-31 2015-02-05 Abb Technology Ag Microgrid Energy Management System and Method for Controlling Operation of a Microgrid
DE102014001535A1 (de) * 2013-08-07 2015-02-12 Abb Ag Optimiertes Gebäudetechnik-Energiemanagementsystem
US20160099567A1 (en) * 2014-10-02 2016-04-07 Mitsubishi Electric Research Laboratories, Inc. Dynamic and Adaptive Configurable Power Distribution System
CN110417006A (zh) * 2019-07-24 2019-11-05 三峡大学 考虑多能协同优化的综合能源***多时间尺度能量调度方法
CN113222252A (zh) * 2021-05-13 2021-08-06 国网辽宁省电力有限公司经济技术研究院 一种计及储热备用效益的电热综合能源***优化调度方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102983573A (zh) * 2012-11-09 2013-03-20 天津大学 基于安全域的安全约束经济调度方法
US20150039145A1 (en) * 2013-07-31 2015-02-05 Abb Technology Ag Microgrid Energy Management System and Method for Controlling Operation of a Microgrid
DE102014001535A1 (de) * 2013-08-07 2015-02-12 Abb Ag Optimiertes Gebäudetechnik-Energiemanagementsystem
US20160099567A1 (en) * 2014-10-02 2016-04-07 Mitsubishi Electric Research Laboratories, Inc. Dynamic and Adaptive Configurable Power Distribution System
CN110417006A (zh) * 2019-07-24 2019-11-05 三峡大学 考虑多能协同优化的综合能源***多时间尺度能量调度方法
CN113222252A (zh) * 2021-05-13 2021-08-06 国网辽宁省电力有限公司经济技术研究院 一种计及储热备用效益的电热综合能源***优化调度方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
李珍珍;周红艳;周冬;: "基于多目标决策分析的智能微电网日前调度模型", 电力学报, vol. 30, no. 01, pages 76 - 90 *

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