CN113570282A - 多主体微电网群联合储能系统容量配置与成本分摊方法 - Google Patents

多主体微电网群联合储能系统容量配置与成本分摊方法 Download PDF

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CN113570282A
CN113570282A CN202111110147.2A CN202111110147A CN113570282A CN 113570282 A CN113570282 A CN 113570282A CN 202111110147 A CN202111110147 A CN 202111110147A CN 113570282 A CN113570282 A CN 113570282A
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汪致洵
方仍存
詹智红
杨东俊
刘畅
侯婷婷
贺兰菲
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Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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Abstract

一种多主体微电网群联合储能系统容量配置与成本分摊方法,包括以下步骤:以微电网群为整体,建立储能容量优化配置模型求解储能的最优配置容量、微电网群年综合运营成本、四季典型日中考虑储能参与运行后的微电网群总净功率曲线;以各微电网独立运营为对象,建模求解各微电网独立建设储能运营的年综合运营成本,由此计算微电网群联合建设储能系统获取的额外收益;从能量贡献度、净功率波形相似度两个维度分别评估各微电网对于额外收益的贡献程度,由此得到各微电网的成本分摊因子,从而计算各微电网联合运营后需要承担的年运营成本。本设计不仅能有效量化多主体微电网对于联合储能系统的容量需求程度,而且储能系统成本合理分摊。

Description

多主体微电网群联合储能系统容量配置与成本分摊方法
技术领域
本发明涉及电力系统微电网优化配置领域,尤其涉及一种多主体微电网群联合储能系统容量配置与成本分摊方法。
背景技术
为实现“30·60”的双碳长期目标,需要构建清洁、低碳、安全、高效的能源体系,深化电力体制改革,构建以新能源为主体的新型电力系统。随着分布式清洁能源、电动汽车以及用户侧储能的快速发展,新型电力系统承担着接入多种能源和供给用户能源消费等核心功能,而支持多元用户互动和分布式可再生能源接入的微电网将成为新型电力系统末端的重要组成部分。
并网型微电网可通过与大电网并网实现稳定运行,但仍需要其具备独立运行能力,国家发改委印发的《推进并网型微电网建设试行办法》指出“微电网独立运行时能保障重要负荷连续供电(不低于 2 小时)”。通过源荷的合理规划配置,微电网可在一定时间段内实现电力电量的平衡,但考虑到新能源出力的随机性和波动性,储能系统仍是微电网不可或缺的重要单元,其不仅可以提升微电网内部自平衡与独立运行能力,还可提高微电网电能质量与用电可靠性。此外,微电网作为电力系统源荷双重用户,在市场化电价机制中,其在上网电价与销售电价中均需要分摊并网接入工程成本与输配电成本。若微电网本身没有储能系统,其购售电量完全由微电网内部发电与负荷的实时差值决定,与大电网频繁、不受自主调度的能量交换将导致运行成本攀升。因此,配置储能系统可以优化与大电网的能量交换过程,充分利用分时电价信号提升微电网群售电收益,减少总体运营成本。
在微电网储能研究方面,一文献(“自治型微电网群多元复合储能系统容量配置方法”,田培根等,电力系统自动化,第37卷,第1期,第168-173页,2013年1月10日)针对自治型微电网群的稳定离网运行需求,提出了功率型储能与能量型储能的混合优化配置方法;中国专利,申请公布号为CN111200281A,申请公布日为2020年5月26日的发明公开了一种互联微网储能配置扩容优化方法;中国专利,申请公布号为CN111882105A,申请公布日为2020年11月3日的发明公开了一种含共享储能系统的微电网群及其日前经济优化调度方法;上述研究是从单一主体角度考虑系统的整体成本和利益最优,或是从共享储能本身投资回报率角度进行优化配置。
在电力市场化改革背景下,未来微电网可能由多个独立运营商管理,考虑到储能系统的规模效应(随储能系统容量规模增长,其单位投资成本将有所降低),多个内部互联的微电网可以联合出资配置集中型储能系统。考虑到微电网间的出力特性在时间序列上的差异,联合储能系统在运行调度时统一管理,实现储能资源在多微电网间的共享复用,可以有效提高设备的资源利用率,降低项目整体的投资成本,使得共同利益最大化。对于这种由多主体出资的储能系统配置问题,缺乏一种能有效量化分析各独立微电网对于储能系统容量需求程度的方法,导致储能系统成本如何合理分摊这一问题仍有待解决。
发明内容
本发明的目的是克服现有技术中存在的储能配置方法未计及多主体微电网对于联合储能系统的容量需求差异,导致储能系统成本难以合理分摊的缺陷与问题,提供一种能有效量化多主体微电网对于联合储能系统的容量需求程度且储能系统成本合理分摊的多主体微电网群联合储能系统容量配置与成本分摊方法。
为实现以上目的,本发明的技术解决方案是:一种多主体微电网群联合储能系统容量配置与成本分摊方法,包括以下步骤:
S1、获取微电网群中每个微电网各季度典型日的历史风光数据、负荷预测数据、新建风光发电设备容量;
S2、通过风机与光伏出力模型计算各典型日下每个微电网的发电出力曲线,并与各微电网的负荷曲线相减计算得到各微电网在各典型日下的净功率曲线;
S3、将各微电网在各典型日下的净功率曲线进行叠加,得到微电网群在各典型日下的净功率曲线;
S4、以微电网群为整体,建立考虑储能参与优化运行的微电网群储能容量优化配置模型;
S5、对微电网群储能容量优化配置模型进行求解,得到储能的最优配置容量、微电网群年综合运营成本、四季典型日中考虑储能参与运行后的微电网群总净功率曲线;
S6、分别以各微电网独立运营为对象,重复步骤S4、步骤S5的建模求解过程,求解得到各微电网独立建设储能运营的年综合运营成本;
S7、若各微电网独立建设储能运营的年综合运营成本之和高于微电网群年综合运营成本,则进入步骤S8;
S8、计算微电网群联合建设储能系统获取的额外收益;
S9、从能量贡献度、净功率波形相似度两个维度分别评估各微电网对于额外收益的贡献程度;
S10、将能量贡献度和净功率波形相似度进行融合,得到各微电网的成本分摊因子;
S11、根据成本分摊因子和额外收益,计算各微电网联合运营后,各微电网需要承担的年运营成本。
步骤S2中,各微电网在各典型日下的净功率曲线为:
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上式中,
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为典型日
Figure 312032DEST_PATH_IMAGE003
Figure 238400DEST_PATH_IMAGE004
时刻微电网
Figure 82466DEST_PATH_IMAGE005
的净功率曲线,
Figure 436087DEST_PATH_IMAGE006
为典型日
Figure 234278DEST_PATH_IMAGE003
Figure 964337DEST_PATH_IMAGE004
时刻微电网
Figure 429953DEST_PATH_IMAGE005
的风机出力,
Figure 954476DEST_PATH_IMAGE007
为典型日
Figure 239963DEST_PATH_IMAGE003
Figure 508134DEST_PATH_IMAGE004
时刻微电网
Figure 64142DEST_PATH_IMAGE005
的光伏出力,
Figure 759566DEST_PATH_IMAGE008
为典型 日
Figure 532350DEST_PATH_IMAGE003
Figure 338632DEST_PATH_IMAGE004
时刻微电网
Figure 247682DEST_PATH_IMAGE005
的负荷需求。
步骤S3中,微电网群在各典型日下的净功率曲线为:
Figure 114007DEST_PATH_IMAGE009
上式中,
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为典型日
Figure 718480DEST_PATH_IMAGE003
Figure 13195DEST_PATH_IMAGE004
时刻微电网群的净功率曲线。
步骤S4具体包括以下步骤:
S41、设置微电网群储能容量优化配置模型的目标函数为:
Figure 74256DEST_PATH_IMAGE011
上式中,
Figure 556053DEST_PATH_IMAGE012
为微电网群年运营成本,
Figure 235296DEST_PATH_IMAGE013
为储能系统的年投资及运行维护成本,
Figure 384517DEST_PATH_IMAGE014
为微电网群接入大电网的年输配电成本,
Figure 858224DEST_PATH_IMAGE015
为微电网群向大电网购电的成本,
Figure 827317DEST_PATH_IMAGE016
为微电网群向大电网售电的收入;
储能系统的年投资及运行维护成本为:
Figure 280557DEST_PATH_IMAGE017
上式中,
Figure 18706DEST_PATH_IMAGE018
为储能配置容量,
Figure 928893DEST_PATH_IMAGE019
为储能单位容量投资成本,
Figure 385283DEST_PATH_IMAGE020
为设计使用年 限,
Figure 140749DEST_PATH_IMAGE021
为折现率,
Figure 264563DEST_PATH_IMAGE022
为储能系统年运行维护系数;
S42、设置微电网群储能容量优化配置模型的约束条件;
(1)储能系统参与运行后,微电网群的功率平衡约束为:
Figure 80072DEST_PATH_IMAGE023
上式中,
Figure 522292DEST_PATH_IMAGE024
为储能系统在典型日
Figure 81450DEST_PATH_IMAGE003
Figure 794191DEST_PATH_IMAGE004
时刻的充电功率,
Figure 515022DEST_PATH_IMAGE025
为储能系统在典 型日
Figure 946003DEST_PATH_IMAGE003
Figure 43272DEST_PATH_IMAGE004
时刻的放电功率,
Figure 876099DEST_PATH_IMAGE026
为微电网群在典型日
Figure 767832DEST_PATH_IMAGE003
Figure 718733DEST_PATH_IMAGE004
时刻向大电网的购电功率,
Figure 619693DEST_PATH_IMAGE027
为微电网群在典型日
Figure 307026DEST_PATH_IMAGE003
Figure 635239DEST_PATH_IMAGE004
时刻向大电网的售电功率;
(2)微电网群的购售电功率需满足:
Figure 775233DEST_PATH_IMAGE028
上式中,
Figure 479884DEST_PATH_IMAGE029
为微电网群购售电状态,1表示购电,0表示售电,
Figure 21724DEST_PATH_IMAGE030
为一个无穷大正数;
(3)微电网群接入大电网的年输配电成本为:
Figure 25233DEST_PATH_IMAGE031
上式中,
Figure 652523DEST_PATH_IMAGE032
为微电网群与大电网的峰值交互功率,
Figure 895286DEST_PATH_IMAGE033
为接入工程的容量电 价,
Figure 291632DEST_PATH_IMAGE034
为接入工程的单位电量电价;
(4)微电网群与大电网的峰值交互功率为:
Figure 961648DEST_PATH_IMAGE035
上式用辅助形式线性化处理为:
Figure 76234DEST_PATH_IMAGE036
(5)微电网群向大电网购电的成本为:
Figure 857109DEST_PATH_IMAGE037
Figure 373541DEST_PATH_IMAGE038
上式中,
Figure 715922DEST_PATH_IMAGE039
为微电网群在典型日
Figure 317805DEST_PATH_IMAGE003
Figure 902370DEST_PATH_IMAGE004
时刻向大电网购电的成本,
Figure 273309DEST_PATH_IMAGE040
为峰 时段的购电电价,
Figure 285127DEST_PATH_IMAGE041
为谷时段的购电电价,
Figure 108727DEST_PATH_IMAGE042
为平时段的购电电价,
Figure 496983DEST_PATH_IMAGE043
为峰时 段的时间集合,
Figure 722428DEST_PATH_IMAGE044
为谷时段的时间集合,
Figure 403682DEST_PATH_IMAGE045
为平时段的时间集合;
上述分段表达式线性化处理为:
Figure 980157DEST_PATH_IMAGE046
(6)微电网群向大电网售电的收入为:
Figure 906525DEST_PATH_IMAGE047
上式中,
Figure 986476DEST_PATH_IMAGE048
为上网电价;
(7)储能系统约束为:
Figure 605677DEST_PATH_IMAGE049
上式中,
Figure 669447DEST_PATH_IMAGE050
为储能系统的能量水平,
Figure 399506DEST_PATH_IMAGE051
为储能系统在典型日
Figure 835429DEST_PATH_IMAGE003
Figure 625530DEST_PATH_IMAGE004
时刻的能 量水平,
Figure 911018DEST_PATH_IMAGE052
为储能系统在典型日
Figure 179188DEST_PATH_IMAGE003
Figure 233732DEST_PATH_IMAGE053
时刻的能量水平,
Figure 194735DEST_PATH_IMAGE054
为储能系统在典型日
Figure 701940DEST_PATH_IMAGE003
Figure 39380DEST_PATH_IMAGE004
时刻的充电功率,
Figure 730123DEST_PATH_IMAGE055
为储能系统在典型日
Figure 862027DEST_PATH_IMAGE003
Figure 731894DEST_PATH_IMAGE004
时刻的放电功率,
Figure 341867DEST_PATH_IMAGE056
为储能充放 电效率,
Figure 135117DEST_PATH_IMAGE057
为时间步长;
Figure 172343DEST_PATH_IMAGE058
为储能的充放电状态,1表示充电,0表示放电;
Figure 185299DEST_PATH_IMAGE059
为储 能的最大充放电功率,
Figure 598962DEST_PATH_IMAGE060
为重要负荷的最大功率需求。
步骤S5中,采用Cplex求解软件对微电网群储能容量优化配置模型进行求解。
步骤S7中,判断各微电网独立建设储能运营的年综合运营成本之和
Figure 748184DEST_PATH_IMAGE061
是 否高于微电网群年综合运营成本
Figure 221891DEST_PATH_IMAGE062
Figure 692448DEST_PATH_IMAGE063
,则进入步骤S8;
Figure 644224DEST_PATH_IMAGE064
,则各微电网仍采用各自独立运营模式。
步骤S8中,微电网群联合建设储能系统获取的额外收益
Figure 913531DEST_PATH_IMAGE065
为:
Figure 292560DEST_PATH_IMAGE066
步骤S9具体包括以下步骤:
S91、计算微电网
Figure 748949DEST_PATH_IMAGE005
在各季节典型日内的净发电量;
S92、采用Sigmoid函数将微电网
Figure 769995DEST_PATH_IMAGE005
的净发电量映射到
Figure 628229DEST_PATH_IMAGE067
区间,由此得到微电网
Figure 948133DEST_PATH_IMAGE005
的能量贡献度:
Figure 157398DEST_PATH_IMAGE068
Figure 450976DEST_PATH_IMAGE069
上式中,
Figure 429296DEST_PATH_IMAGE070
为微电网
Figure 681286DEST_PATH_IMAGE005
在典型日
Figure 377846DEST_PATH_IMAGE003
中的净发电量,
Figure 242159DEST_PATH_IMAGE071
为进行归一化后的能量贡 献度;
S93、先分别消除微电网
Figure 809407DEST_PATH_IMAGE005
和微电网群净功率曲线的振幅偏移,再计算微电网
Figure 232298DEST_PATH_IMAGE005
在典 型日
Figure 150575DEST_PATH_IMAGE003
中净功率曲线的平均值
Figure 785956DEST_PATH_IMAGE072
,以及微电网群在典型日
Figure 738869DEST_PATH_IMAGE003
Figure 67082DEST_PATH_IMAGE004
时刻总净功率曲线的平 均值
Figure 705611DEST_PATH_IMAGE073
,然后得到修正后的净功率曲线:
Figure 675841DEST_PATH_IMAGE074
Figure 217681DEST_PATH_IMAGE075
上式中,
Figure 716796DEST_PATH_IMAGE076
为修正后的微电网
Figure 609665DEST_PATH_IMAGE005
在典型日
Figure 586848DEST_PATH_IMAGE003
Figure 15818DEST_PATH_IMAGE004
时刻的净功率曲线,
Figure 685834DEST_PATH_IMAGE077
为修 正后的微电网群在典型日
Figure 800420DEST_PATH_IMAGE003
Figure 846874DEST_PATH_IMAGE004
时刻的总净功率曲线,
Figure 363306DEST_PATH_IMAGE078
为微电网群在典型日
Figure 204223DEST_PATH_IMAGE003
Figure 71685DEST_PATH_IMAGE004
时刻 的总净功率曲线;
S94、采用余弦相似度法计算各典型日内微电网
Figure 148926DEST_PATH_IMAGE005
净功率曲线与微电网群净功率曲 线的余弦相似度:
Figure 785443DEST_PATH_IMAGE079
上式中,
Figure 62841DEST_PATH_IMAGE080
为典型日
Figure 886440DEST_PATH_IMAGE003
内微电网
Figure 540276DEST_PATH_IMAGE005
净功率曲线与微电网群净功率曲线的余 弦相似度;
将余弦相似度归一化到
Figure 765721DEST_PATH_IMAGE067
区间:
Figure 715484DEST_PATH_IMAGE081
上式中,
Figure 291959DEST_PATH_IMAGE082
为微电网
Figure 218327DEST_PATH_IMAGE005
的净功率波形相似度。
步骤S10中,采用固定加权参数将能量贡献度和净功率波形相似度进行融合,得到各微电网的成本分摊因子:
Figure 563857DEST_PATH_IMAGE083
上式中,
Figure 183058DEST_PATH_IMAGE084
为微电网
Figure 246829DEST_PATH_IMAGE005
的成本分摊因子,
Figure 711308DEST_PATH_IMAGE085
Figure 675460DEST_PATH_IMAGE086
为权重系数。
步骤S11中,各微电网联合运营后,微电网
Figure 465561DEST_PATH_IMAGE005
需要承担的年运营成本
Figure 485470DEST_PATH_IMAGE087
为:
Figure 284798DEST_PATH_IMAGE088
上式中,
Figure 339342DEST_PATH_IMAGE089
为微电网
Figure 34766DEST_PATH_IMAGE005
独立建设储能运营的年综合运营成本。
与现有技术相比,本发明的有益效果为:
本发明一种多主体微电网群联合储能系统容量配置与成本分摊方法中,首先,以微电网群整体运营成本最低为目标,建立优化模型求解储能系统的最优容量配置和运行调度方案;其次,以各微电网为单一主体,建立优化模型求解各微电网独立运营时各自的运营成本,由此计算联合运营给微电网群带来的额外运营效益;最后,分别从能量贡献度、净功率波形相似度两个维度量化分析各微电网对于额外运营效益的贡献程度,由此确定额外收益在各微电网间的分摊比例,从而计算出各微电网联合运营后实际需支付的运营成本。因此,本发明不仅能有效量化多主体微电网对于联合储能系统的容量需求程度,而且储能系统成本合理分摊。
附图说明
图1是本发明中多主体微电网群系统的结构示意图。
图2是本发明多主体微电网群联合储能系统容量配置与成本分摊方法的流程图。
图3是本发明中能量贡献度和净功率波形相似度的计算流程图。
具体实施方式
以下结合附图说明和具体实施方式对本发明作进一步详细的说明。
参见图1至图3,一种多主体微电网群联合储能系统容量配置与成本分摊方法,包括以下步骤:
S1、获取微电网群中每个微电网各季度典型日的历史风光数据、负荷预测数据、新建风光发电设备容量;
S2、通过风机与光伏出力模型计算各典型日下每个微电网的发电出力曲线,并与各微电网的负荷曲线相减计算得到各微电网在各典型日下的净功率曲线;
S3、将各微电网在各典型日下的净功率曲线进行叠加,得到微电网群在各典型日下的净功率曲线;
S4、以微电网群为整体,建立考虑储能参与优化运行的微电网群储能容量优化配置模型;
S5、对微电网群储能容量优化配置模型进行求解,得到储能的最优配置容量、微电网群年综合运营成本、四季典型日中考虑储能参与运行后的微电网群总净功率曲线;
S6、分别以各微电网独立运营为对象,重复步骤S4、步骤S5的建模求解过程,求解得到各微电网独立建设储能运营的年综合运营成本;
S7、若各微电网独立建设储能运营的年综合运营成本之和高于微电网群年综合运营成本,则进入步骤S8;
S8、计算微电网群联合建设储能系统获取的额外收益;
S9、从能量贡献度、净功率波形相似度两个维度分别评估各微电网对于额外收益的贡献程度;
S10、将能量贡献度和净功率波形相似度进行融合,得到各微电网的成本分摊因子;
S11、根据成本分摊因子和额外收益,计算各微电网联合运营后,各微电网需要承担的年运营成本。
步骤S2中,各微电网在各典型日下的净功率曲线为:
Figure 807550DEST_PATH_IMAGE001
上式中,
Figure 380876DEST_PATH_IMAGE002
为典型日
Figure 289926DEST_PATH_IMAGE003
Figure 156251DEST_PATH_IMAGE004
时刻微电网
Figure 150751DEST_PATH_IMAGE005
的净功率曲线,
Figure 26304DEST_PATH_IMAGE006
为典型日
Figure 55439DEST_PATH_IMAGE003
Figure 827086DEST_PATH_IMAGE004
时刻微电网
Figure 574462DEST_PATH_IMAGE005
的风机出力,
Figure 722547DEST_PATH_IMAGE007
为典型日
Figure 606189DEST_PATH_IMAGE003
Figure 318711DEST_PATH_IMAGE004
时刻微电网
Figure 287804DEST_PATH_IMAGE005
的光伏出力,
Figure 239580DEST_PATH_IMAGE008
为典型 日
Figure 977729DEST_PATH_IMAGE003
Figure 622337DEST_PATH_IMAGE004
时刻微电网
Figure 813147DEST_PATH_IMAGE005
的负荷需求。
步骤S3中,微电网群在各典型日下的净功率曲线为:
Figure 568613DEST_PATH_IMAGE009
上式中,
Figure 426848DEST_PATH_IMAGE010
为典型日
Figure 976778DEST_PATH_IMAGE003
Figure 156349DEST_PATH_IMAGE004
时刻微电网群的净功率曲线。
步骤S4具体包括以下步骤:
S41、设置微电网群储能容量优化配置模型的目标函数为:
Figure 715506DEST_PATH_IMAGE011
上式中,
Figure 162668DEST_PATH_IMAGE012
为微电网群年运营成本,
Figure 149078DEST_PATH_IMAGE013
为储能系统的年投资及运行维护成本,
Figure 580060DEST_PATH_IMAGE014
为微电网群接入大电网的年输配电成本,
Figure 411749DEST_PATH_IMAGE015
为微电网群向大电网购电的成本,
Figure 978997DEST_PATH_IMAGE016
为微电网群向大电网售电的收入;
储能系统的年投资及运行维护成本为:
Figure 870730DEST_PATH_IMAGE017
上式中,
Figure 523428DEST_PATH_IMAGE018
为储能配置容量,
Figure 424388DEST_PATH_IMAGE019
为储能单位容量投资成本,
Figure 344677DEST_PATH_IMAGE020
为设计使用年 限,
Figure 204049DEST_PATH_IMAGE021
为折现率,
Figure 344043DEST_PATH_IMAGE022
为储能系统年运行维护系数;
S42、设置微电网群储能容量优化配置模型的约束条件;
(1)储能系统参与运行后,微电网群的功率平衡约束为:
Figure 783114DEST_PATH_IMAGE023
上式中,
Figure 324954DEST_PATH_IMAGE024
为储能系统在典型日
Figure 824069DEST_PATH_IMAGE003
Figure 451359DEST_PATH_IMAGE004
时刻的充电功率,
Figure 195586DEST_PATH_IMAGE025
为储能系统在典 型日
Figure 857512DEST_PATH_IMAGE003
Figure 527528DEST_PATH_IMAGE004
时刻的放电功率,
Figure 376535DEST_PATH_IMAGE026
为微电网群在典型日
Figure 157409DEST_PATH_IMAGE003
Figure 673841DEST_PATH_IMAGE004
时刻向大电网的购电功率,
Figure 249179DEST_PATH_IMAGE027
为微电网群在典型日
Figure 585482DEST_PATH_IMAGE003
Figure 170048DEST_PATH_IMAGE004
时刻向大电网的售电功率;
(2)微电网群的购售电功率需满足:
Figure 275407DEST_PATH_IMAGE028
上式中,
Figure 803338DEST_PATH_IMAGE029
为微电网群购售电状态,1表示购电,0表示售电,
Figure 892517DEST_PATH_IMAGE030
为一个无穷大正数;
(3)微电网群接入大电网的年输配电成本为:
Figure 15194DEST_PATH_IMAGE031
上式中,
Figure 506218DEST_PATH_IMAGE032
为微电网群与大电网的峰值交互功率,
Figure 688938DEST_PATH_IMAGE033
为接入工程的容量电 价,
Figure 734254DEST_PATH_IMAGE034
为接入工程的单位电量电价;
(4)微电网群与大电网的峰值交互功率为:
Figure 660622DEST_PATH_IMAGE035
上式用辅助形式线性化处理为:
Figure 6153DEST_PATH_IMAGE036
(5)微电网群向大电网购电的成本为:
Figure 861238DEST_PATH_IMAGE037
Figure 393851DEST_PATH_IMAGE038
上式中,
Figure 123909DEST_PATH_IMAGE039
为微电网群在典型日
Figure 323947DEST_PATH_IMAGE003
Figure 848469DEST_PATH_IMAGE004
时刻向大电网购电的成本,
Figure 133957DEST_PATH_IMAGE040
为峰 时段的购电电价,
Figure 402127DEST_PATH_IMAGE041
为谷时段的购电电价,
Figure 456671DEST_PATH_IMAGE042
为平时段的购电电价,
Figure 152094DEST_PATH_IMAGE043
为峰时 段的时间集合,
Figure 423413DEST_PATH_IMAGE044
为谷时段的时间集合,
Figure 557591DEST_PATH_IMAGE045
为平时段的时间集合;
上述分段表达式线性化处理为:
Figure 466642DEST_PATH_IMAGE046
(6)微电网群向大电网售电的收入为:
Figure 598546DEST_PATH_IMAGE047
上式中,
Figure 593046DEST_PATH_IMAGE048
为上网电价;
(7)储能系统约束为:
Figure 203019DEST_PATH_IMAGE049
上式中,
Figure 966576DEST_PATH_IMAGE050
为储能系统的能量水平,
Figure 770846DEST_PATH_IMAGE051
为储能系统在典型日
Figure 987064DEST_PATH_IMAGE003
Figure 400728DEST_PATH_IMAGE004
时刻的能 量水平,
Figure 284370DEST_PATH_IMAGE052
为储能系统在典型日
Figure 758077DEST_PATH_IMAGE003
Figure 461591DEST_PATH_IMAGE053
时刻的能量水平,
Figure 678945DEST_PATH_IMAGE054
为储能系统在典型日
Figure 417094DEST_PATH_IMAGE003
Figure 796123DEST_PATH_IMAGE004
时刻的充电功率,
Figure 756907DEST_PATH_IMAGE055
为储能系统在典型日
Figure 512373DEST_PATH_IMAGE003
Figure 370608DEST_PATH_IMAGE004
时刻的放电功率,
Figure 654958DEST_PATH_IMAGE056
为储能充放 电效率,
Figure 598644DEST_PATH_IMAGE057
为时间步长;
Figure 157801DEST_PATH_IMAGE058
为储能的充放电状态,1表示充电,0表示放电;
Figure 604963DEST_PATH_IMAGE059
为储 能的最大充放电功率,
Figure 591373DEST_PATH_IMAGE060
为重要负荷的最大功率需求。
步骤S5中,采用Cplex求解软件对微电网群储能容量优化配置模型进行求解。
步骤S7中,判断各微电网独立建设储能运营的年综合运营成本之和
Figure 756775DEST_PATH_IMAGE061
是 否高于微电网群年综合运营成本
Figure 355509DEST_PATH_IMAGE062
Figure 922757DEST_PATH_IMAGE063
,则进入步骤S8;
Figure 80069DEST_PATH_IMAGE064
,则各微电网仍采用各自独立运营模式。
步骤S8中,微电网群联合建设储能系统获取的额外收益
Figure 467188DEST_PATH_IMAGE065
为:
Figure 368148DEST_PATH_IMAGE066
步骤S9具体包括以下步骤:
S91、计算微电网
Figure 55481DEST_PATH_IMAGE005
在各季节典型日内的净发电量;
S92、采用Sigmoid函数将微电网
Figure 383694DEST_PATH_IMAGE005
的净发电量映射到
Figure 258109DEST_PATH_IMAGE067
区间,由此得到微电网
Figure 697181DEST_PATH_IMAGE005
的能量贡献度:
Figure 239021DEST_PATH_IMAGE068
Figure 236670DEST_PATH_IMAGE069
上式中,
Figure 598381DEST_PATH_IMAGE070
为微电网
Figure 841144DEST_PATH_IMAGE005
在典型日
Figure 237490DEST_PATH_IMAGE003
中的净发电量,
Figure 641927DEST_PATH_IMAGE071
为进行归一化后的能量贡 献度;
S93、先分别消除微电网
Figure 756513DEST_PATH_IMAGE005
和微电网群净功率曲线的振幅偏移,再计算微电网
Figure 802967DEST_PATH_IMAGE005
在典 型日
Figure 788240DEST_PATH_IMAGE003
中净功率曲线的平均值
Figure 629157DEST_PATH_IMAGE072
,以及微电网群在典型日
Figure 732505DEST_PATH_IMAGE003
Figure 51491DEST_PATH_IMAGE004
时刻总净功率曲线的平 均值
Figure 422429DEST_PATH_IMAGE073
,然后得到修正后的净功率曲线:
Figure 168668DEST_PATH_IMAGE074
Figure 257847DEST_PATH_IMAGE075
上式中,
Figure 380524DEST_PATH_IMAGE076
为修正后的微电网
Figure 340389DEST_PATH_IMAGE005
在典型日
Figure 788688DEST_PATH_IMAGE003
Figure 99584DEST_PATH_IMAGE004
时刻的净功率曲线,
Figure 25952DEST_PATH_IMAGE077
为修 正后的微电网群在典型日
Figure 598579DEST_PATH_IMAGE003
Figure 952200DEST_PATH_IMAGE004
时刻的总净功率曲线,
Figure 750392DEST_PATH_IMAGE078
为微电网群在典型日
Figure 214871DEST_PATH_IMAGE003
Figure 414908DEST_PATH_IMAGE004
时刻 的总净功率曲线;
S94、采用余弦相似度法计算各典型日内微电网
Figure 939430DEST_PATH_IMAGE005
净功率曲线与微电网群净功率曲 线的余弦相似度:
Figure 959339DEST_PATH_IMAGE079
上式中,
Figure 227509DEST_PATH_IMAGE080
为典型日
Figure 282053DEST_PATH_IMAGE003
内微电网
Figure 744521DEST_PATH_IMAGE005
净功率曲线与微电网群净功率曲线的余 弦相似度;
将余弦相似度归一化到
Figure 251725DEST_PATH_IMAGE067
区间:
Figure 58007DEST_PATH_IMAGE081
上式中,
Figure 232637DEST_PATH_IMAGE082
为微电网
Figure 833382DEST_PATH_IMAGE005
的净功率波形相似度。
步骤S10中,采用固定加权参数将能量贡献度和净功率波形相似度进行融合,得到各微电网的成本分摊因子:
Figure 93462DEST_PATH_IMAGE083
上式中,
Figure 703435DEST_PATH_IMAGE084
为微电网
Figure 466992DEST_PATH_IMAGE005
的成本分摊因子,
Figure 769797DEST_PATH_IMAGE085
Figure 251594DEST_PATH_IMAGE086
为权重系数。
步骤S11中,各微电网联合运营后,微电网
Figure 898214DEST_PATH_IMAGE005
需要承担的年运营成本
Figure 781856DEST_PATH_IMAGE087
为:
Figure 255563DEST_PATH_IMAGE088
上式中,
Figure 959077DEST_PATH_IMAGE089
为微电网
Figure 910852DEST_PATH_IMAGE005
独立建设储能运营的年综合运营成本。
本发明的原理说明如下:
本设计用于解决现有储能配置方法未计及多主体微电网对于联合储能系统的容量需求差异,导致储能系统成本难以合理分摊的缺陷。本设计能够在微电网隶属于不同利益主体时,通过联合建设储能,平滑功率波动,降低向大电网支付的辅助服务及输配电费用,获取额外利润,并通过利润的合理分摊使得各微电网主体均可降低自身的运营成本,最终实现多方共赢。
本设计的相似度计算方式还可以为标准欧式距离算法、明氏距离算法、皮尔森相关系数等时间序列相似性度量方法。
本设计的能量贡献度计算方式还可以任选以净发电量为基准的归一化方式,例如min-max标准化、log函数转换以及不同系数的Sigmoid函数。
微电网累计净发电量越多,则能量贡献度越高,对于储能系统的需求程度越低,从而应分摊的收益越高;微电网净功率波形与联合运行后微电网群的净功率波形相似度越高,则微电网需要储能系统参与微电网内部削峰填谷的需求度也越低,从而应分摊的额外收益也越高。
实施例:
参见图1,微电网群由不同的微电网运营商1~3组成,其中,微电网运营商1通过AC/DC变换器4连入电力公共母线7;微电网运营商2和微电网运营商3分别通过AC/AC变换器5、AC/AC变换器6连入电力公共母线7;所建设的联合储能系统9通过DC/DC变换器10连入电力公共母线7;电力公共母线7通过PCC节点11与外部电网13互联;微电网运营商1~3、联合储能系统9与微电网群中央控制系统8通过通信母线12进行通信,由微电网群中央控制系统8负责统一运行调度。
参见图2,一种多主体微电网群联合储能系统容量配置与成本分摊方法,包括以下步骤:
S1、获取微电网群中每个微电网各季度典型日的历史风光数据、负荷预测数据、新建风光发电设备容量;其中,风光发电设备容量和未来负荷水平在微电网筹建时已根据用地面积、各区域功能属性以及整体投资经济性提前规划确定;
S2、通过风机与光伏出力模型计算各典型日下每个微电网的发电出力曲线,并与各微电网的负荷曲线相减计算得到各微电网在各典型日下的净功率曲线:
Figure 914580DEST_PATH_IMAGE001
上式中,
Figure 293609DEST_PATH_IMAGE002
为典型日
Figure 749998DEST_PATH_IMAGE003
Figure 505465DEST_PATH_IMAGE004
时刻微电网
Figure 832541DEST_PATH_IMAGE005
的净功率曲线,
Figure 680673DEST_PATH_IMAGE006
为典型日
Figure 624358DEST_PATH_IMAGE003
Figure 183516DEST_PATH_IMAGE004
时刻微电网
Figure 630678DEST_PATH_IMAGE005
的风机出力,
Figure 351509DEST_PATH_IMAGE007
为典型日
Figure 782490DEST_PATH_IMAGE003
Figure 614180DEST_PATH_IMAGE004
时刻微电网
Figure 447007DEST_PATH_IMAGE005
的光伏出力,
Figure 843134DEST_PATH_IMAGE008
为典型 日
Figure 495832DEST_PATH_IMAGE003
Figure 396792DEST_PATH_IMAGE004
时刻微电网
Figure 818546DEST_PATH_IMAGE005
的负荷需求;
S3、将各微电网在各典型日下的净功率曲线进行叠加,得到微电网群在各典型日下的净功率曲线:
Figure 881180DEST_PATH_IMAGE009
上式中,
Figure 21174DEST_PATH_IMAGE010
为典型日
Figure 725825DEST_PATH_IMAGE003
Figure 267665DEST_PATH_IMAGE004
时刻微电网群的净功率曲线;
S4、以微电网群为整体,建立考虑储能参与优化运行的微电网群储能容量优化配置模型,在各季节典型日场景中分别进行优化,各季节典型日权重均等,优化变量为微电网中储能系统功率及容量;具体包括以下步骤:
S41、设置微电网群储能容量优化配置模型的目标函数为:
Figure 501200DEST_PATH_IMAGE011
上式中,
Figure 629956DEST_PATH_IMAGE012
为微电网群年运营成本,
Figure 872718DEST_PATH_IMAGE013
为储能系统的年投资及运行维护成本,
Figure 3485DEST_PATH_IMAGE014
为微电网群接入大电网的年输配电成本,
Figure 673501DEST_PATH_IMAGE015
为微电网群向大电网购电的成本,
Figure 788087DEST_PATH_IMAGE016
为微电网群向大电网售电的收入;
储能系统的年投资及运行维护成本为:
Figure 303382DEST_PATH_IMAGE017
上式中,
Figure 819814DEST_PATH_IMAGE018
为储能配置容量,
Figure 660731DEST_PATH_IMAGE019
为储能单位容量投资成本,
Figure 997035DEST_PATH_IMAGE020
为设计使用年 限,
Figure 581600DEST_PATH_IMAGE021
为折现率,
Figure 451074DEST_PATH_IMAGE022
为储能系统年运行维护系数;
S42、设置微电网群储能容量优化配置模型的约束条件;
(1)储能系统参与运行后,微电网群的功率平衡约束为:
Figure 197313DEST_PATH_IMAGE023
上式中,
Figure 20912DEST_PATH_IMAGE024
为储能系统在典型日
Figure 409168DEST_PATH_IMAGE003
Figure 369034DEST_PATH_IMAGE004
时刻的充电功率,
Figure 551754DEST_PATH_IMAGE025
为储能系统在典 型日
Figure 128228DEST_PATH_IMAGE003
Figure 789017DEST_PATH_IMAGE004
时刻的放电功率,
Figure 868968DEST_PATH_IMAGE026
为微电网群在典型日
Figure 989633DEST_PATH_IMAGE003
Figure 522246DEST_PATH_IMAGE004
时刻向大电网的购电功率,
Figure 252305DEST_PATH_IMAGE027
为微电网群在典型日
Figure 186763DEST_PATH_IMAGE003
Figure 976864DEST_PATH_IMAGE004
时刻向大电网的售电功率;
(2)微电网群在任一时刻仅能向大电网购电或售电,二者不可同时进行,因此微电网群的购售电功率需满足:
Figure 996773DEST_PATH_IMAGE028
上式中,
Figure 264943DEST_PATH_IMAGE029
为微电网群购售电状态,1表示购电,0表示售电,
Figure 53907DEST_PATH_IMAGE030
为一个无穷大正数, 本设计取为
Figure 14910DEST_PATH_IMAGE090
(3)微电网群接入大电网的年输配电成本为:
Figure 522115DEST_PATH_IMAGE031
上式中,
Figure 922589DEST_PATH_IMAGE032
为微电网群与大电网的峰值交互功率,
Figure 831639DEST_PATH_IMAGE033
为接入工程的容量电 价,
Figure 697964DEST_PATH_IMAGE034
为接入工程的单位电量电价;
(4)微电网群与大电网的峰值交互功率为:
Figure 958044DEST_PATH_IMAGE035
上式用辅助形式线性化处理为:
Figure 568017DEST_PATH_IMAGE036
(5)微电网群向大电网购电的成本按照现行的省级电网分时销售电价执行,分为峰谷平三个时段进行结算,计算公式为:
Figure 331574DEST_PATH_IMAGE037
Figure 368800DEST_PATH_IMAGE038
上式中,
Figure 116176DEST_PATH_IMAGE039
为微电网群在典型日
Figure 529840DEST_PATH_IMAGE003
Figure 649368DEST_PATH_IMAGE004
时刻向大电网购电的成本,
Figure 123075DEST_PATH_IMAGE040
为峰 时段的购电电价,
Figure 92168DEST_PATH_IMAGE041
为谷时段的购电电价,
Figure 778364DEST_PATH_IMAGE042
为平时段的购电电价,
Figure 782092DEST_PATH_IMAGE043
为峰时 段的时间集合,
Figure 161121DEST_PATH_IMAGE044
为谷时段的时间集合,
Figure 351931DEST_PATH_IMAGE045
为平时段的时间集合;
上述分段表达式线性化处理为:
Figure 107397DEST_PATH_IMAGE046
(6)微电网群向大电网售电的收入按照无补贴平价上网政策执行,由电网企业统一收购,计算公式为:
Figure 965632DEST_PATH_IMAGE047
上式中,
Figure 515562DEST_PATH_IMAGE048
为上网电价,电价标准去当地燃煤标杆环保电价;
(7)假定储能充放电深度为60%,除满足功率平衡与容量约束外,储能系统最小容量需满足重要负荷2小时的用电需求,储能系统约束为:
Figure 957782DEST_PATH_IMAGE049
上式中,
Figure 251360DEST_PATH_IMAGE050
为储能系统的能量水平,
Figure 964101DEST_PATH_IMAGE051
为储能系统在典型日
Figure 684933DEST_PATH_IMAGE003
Figure 115914DEST_PATH_IMAGE004
时刻的能 量水平,
Figure 213183DEST_PATH_IMAGE052
为储能系统在典型日
Figure 780430DEST_PATH_IMAGE003
Figure 937742DEST_PATH_IMAGE053
时刻的能量水平,
Figure 357485DEST_PATH_IMAGE054
为储能系统在典型日
Figure 727286DEST_PATH_IMAGE003
Figure 414619DEST_PATH_IMAGE004
时刻的充电功率,
Figure 742833DEST_PATH_IMAGE055
为储能系统在典型日
Figure 882827DEST_PATH_IMAGE003
Figure 321898DEST_PATH_IMAGE004
时刻的放电功率,
Figure 863738DEST_PATH_IMAGE056
为储能充放 电效率,
Figure 362853DEST_PATH_IMAGE057
为时间步长;
Figure 724564DEST_PATH_IMAGE058
为储能的充放电状态,1表示充电,0表示放电;
Figure 701747DEST_PATH_IMAGE059
为储 能的最大充放电功率,
Figure 868067DEST_PATH_IMAGE060
为重要负荷的最大功率需求;
S5、采用Cplex求解软件对微电网群储能容量优化配置模型(优化配置模型为混合整数线性规划模型)进行求解,得到储能的最优配置容量、微电网群年综合运营成本、四季典型日中考虑储能参与运行后的微电网群总净功率曲线;
S6、分别以各微电网独立运营为对象,重复步骤S4、步骤S5的建模求解过程,求解 得到各微电网独立建设储能运营的年综合运营成本
Figure 272504DEST_PATH_IMAGE089
S7、判断各微电网独立建设储能运营的年综合运营成本之和
Figure 387090DEST_PATH_IMAGE061
是否高于 微电网群年综合运营成本
Figure 167964DEST_PATH_IMAGE062
Figure 418817DEST_PATH_IMAGE063
,证明联合建设储能系统能获取额外收益,则进入步骤 S8;
Figure 259734DEST_PATH_IMAGE064
,则各微电网仍采用各自独立运营模式;
S8、计算微电网群联合建设储能系统获取的额外收益
Figure 861617DEST_PATH_IMAGE065
Figure 446182DEST_PATH_IMAGE066
S9、从能量贡献度、净功率波形相似度两个维度分别评估各微电网对于额外收益的贡献程度;具体包括以下步骤:
S91、计算微电网
Figure 551541DEST_PATH_IMAGE005
在各季节典型日内的净发电量,当微电网
Figure 64824DEST_PATH_IMAGE005
一天内累计发电电量 大于累计负荷需求(即一天内净功率累计值为正)时,表示微电网
Figure 622845DEST_PATH_IMAGE005
向微电网群输出电能,能 量贡献度为正值;
S92、采用Sigmoid函数将微电网
Figure 11101DEST_PATH_IMAGE005
的净发电量映射到
Figure 236546DEST_PATH_IMAGE067
区间,由此得到微电网
Figure 419265DEST_PATH_IMAGE005
的能量贡献度:
Figure 730161DEST_PATH_IMAGE068
Figure 656529DEST_PATH_IMAGE069
上式中,
Figure 736480DEST_PATH_IMAGE070
为微电网
Figure 90101DEST_PATH_IMAGE005
在典型日
Figure 888293DEST_PATH_IMAGE003
中的净发电量,
Figure 851307DEST_PATH_IMAGE071
为进行归一化后的能量贡 献度;
S93、先分别消除微电网
Figure 785765DEST_PATH_IMAGE005
和微电网群净功率曲线的振幅偏移,再计算微电网
Figure 575867DEST_PATH_IMAGE005
在典 型日
Figure 595775DEST_PATH_IMAGE003
中净功率曲线的平均值
Figure 863946DEST_PATH_IMAGE072
,以及微电网群在典型日
Figure 918489DEST_PATH_IMAGE003
Figure 348334DEST_PATH_IMAGE004
时刻总净功率曲线的平 均值
Figure 121118DEST_PATH_IMAGE073
,然后得到修正后的净功率曲线:
Figure 192979DEST_PATH_IMAGE074
Figure 836450DEST_PATH_IMAGE075
上式中,
Figure 469819DEST_PATH_IMAGE076
为修正后的微电网
Figure 464320DEST_PATH_IMAGE005
在典型日
Figure 74292DEST_PATH_IMAGE003
Figure 103428DEST_PATH_IMAGE004
时刻的净功率曲线,
Figure 140654DEST_PATH_IMAGE077
为修 正后的微电网群在典型日
Figure 888031DEST_PATH_IMAGE003
Figure 36115DEST_PATH_IMAGE004
时刻的总净功率曲线,
Figure 919758DEST_PATH_IMAGE078
为微电网群在典型日
Figure 862306DEST_PATH_IMAGE003
Figure 324075DEST_PATH_IMAGE004
时刻 的总净功率曲线;
S94、采用余弦相似度法计算各典型日内微电网
Figure 541429DEST_PATH_IMAGE005
净功率曲线与微电网群净功率曲 线的余弦相似度,该方法用向量空间中两个向量夹角的余弦值衡量个体间差异,计算公式 为:
Figure 279578DEST_PATH_IMAGE079
上式中,
Figure 658607DEST_PATH_IMAGE080
为典型日
Figure 114996DEST_PATH_IMAGE003
内微电网
Figure 870462DEST_PATH_IMAGE005
净功率曲线与微电网群净功率曲线的余 弦相似度,该相似度取值范围为
Figure 728697DEST_PATH_IMAGE091
将余弦相似度归一化到
Figure 544206DEST_PATH_IMAGE067
区间:
Figure 956733DEST_PATH_IMAGE081
上式中,
Figure 17355DEST_PATH_IMAGE082
为微电网
Figure 730096DEST_PATH_IMAGE005
的净功率波形相似度;
S10、采用固定加权参数将能量贡献度和净功率波形相似度进行融合,得到各微电网的成本分摊因子:
Figure 450928DEST_PATH_IMAGE083
上式中,
Figure 881909DEST_PATH_IMAGE084
为微电网
Figure 979178DEST_PATH_IMAGE005
的成本分摊因子,
Figure 280846DEST_PATH_IMAGE085
Figure 438158DEST_PATH_IMAGE086
为权重系数,根据实际项目情况进 行选择;
S11、根据成本分摊因子和额外收益,可按照各微电网的成本分摊因子对于额外收 益进行分摊,在微电网原有独立运营时需承担的运营成本中减掉这部分收益,假设各微电 网联合运营后,微电网
Figure 90856DEST_PATH_IMAGE005
需要承担的年运营成本
Figure 726237DEST_PATH_IMAGE087
为:
Figure 413570DEST_PATH_IMAGE088
上式中,
Figure 240319DEST_PATH_IMAGE089
为微电网
Figure 380313DEST_PATH_IMAGE005
独立建设储能运营的年综合运营成本。
在多主体微电网联合后,可以看出微电网
Figure 553805DEST_PATH_IMAGE005
需要承担的年运营成本
Figure 95645DEST_PATH_IMAGE087
将低于 微电网
Figure 329180DEST_PATH_IMAGE005
独立建设储能运营的年综合运营成本
Figure 956471DEST_PATH_IMAGE089
,满足激励相容原理,从而实现各主 体的多赢。

Claims (10)

1.一种多主体微电网群联合储能系统容量配置与成本分摊方法,其特征在于,包括以下步骤:
S1、获取微电网群中每个微电网各季度典型日的历史风光数据、负荷预测数据、新建风光发电设备容量;
S2、通过风机与光伏出力模型计算各典型日下每个微电网的发电出力曲线,并与各微电网的负荷曲线相减计算得到各微电网在各典型日下的净功率曲线;
S3、将各微电网在各典型日下的净功率曲线进行叠加,得到微电网群在各典型日下的净功率曲线;
S4、以微电网群为整体,建立考虑储能参与优化运行的微电网群储能容量优化配置模型;
S5、对微电网群储能容量优化配置模型进行求解,得到储能的最优配置容量、微电网群年综合运营成本、四季典型日中考虑储能参与运行后的微电网群总净功率曲线;
S6、分别以各微电网独立运营为对象,重复步骤S4、步骤S5的建模求解过程,求解得到各微电网独立建设储能运营的年综合运营成本;
S7、若各微电网独立建设储能运营的年综合运营成本之和高于微电网群年综合运营成本,则进入步骤S8;
S8、计算微电网群联合建设储能系统获取的额外收益;
S9、从能量贡献度、净功率波形相似度两个维度分别评估各微电网对于额外收益的贡献程度;
S10、将能量贡献度和净功率波形相似度进行融合,得到各微电网的成本分摊因子;
S11、根据成本分摊因子和额外收益,计算各微电网联合运营后,各微电网需要承担的年运营成本。
2.根据权利要求1所述的一种多主体微电网群联合储能系统容量配置与成本分摊方法,其特征在于:步骤S2中,各微电网在各典型日下的净功率曲线为:
Figure 994354DEST_PATH_IMAGE001
上式中,
Figure 921858DEST_PATH_IMAGE002
为典型日
Figure 90409DEST_PATH_IMAGE003
Figure 939416DEST_PATH_IMAGE004
时刻微电网
Figure DEST_PATH_IMAGE005
的净功率曲线,
Figure 251449DEST_PATH_IMAGE006
为典型日
Figure 767881DEST_PATH_IMAGE003
Figure 608798DEST_PATH_IMAGE004
时 刻微电网
Figure 945102DEST_PATH_IMAGE005
的风机出力,
Figure 529667DEST_PATH_IMAGE007
为典型日
Figure 635026DEST_PATH_IMAGE003
Figure 148309DEST_PATH_IMAGE004
时刻微电网
Figure 237488DEST_PATH_IMAGE005
的光伏出力,
Figure 360165DEST_PATH_IMAGE008
为典型日
Figure 851189DEST_PATH_IMAGE003
Figure 768329DEST_PATH_IMAGE004
时刻微电网
Figure 79225DEST_PATH_IMAGE005
的负荷需求。
3.根据权利要求2所述的一种多主体微电网群联合储能系统容量配置与成本分摊方法,其特征在于:步骤S3中,微电网群在各典型日下的净功率曲线为:
Figure 5593DEST_PATH_IMAGE009
上式中,
Figure 85544DEST_PATH_IMAGE010
为典型日
Figure 439165DEST_PATH_IMAGE003
Figure 741751DEST_PATH_IMAGE004
时刻微电网群的净功率曲线。
4.根据权利要求3所述的一种多主体微电网群联合储能系统容量配置与成本分摊方法,其特征在于:步骤S4具体包括以下步骤:
S41、设置微电网群储能容量优化配置模型的目标函数为:
Figure 471810DEST_PATH_IMAGE011
上式中,
Figure 406268DEST_PATH_IMAGE012
为微电网群年运营成本,
Figure 930790DEST_PATH_IMAGE013
为储能系统的年投资及运行维护成本,
Figure 216278DEST_PATH_IMAGE014
为微电网群接入大电网的年输配电成本,
Figure 750027DEST_PATH_IMAGE015
为微电网群向大电网购电的成本,
Figure 804571DEST_PATH_IMAGE016
为 微电网群向大电网售电的收入;
储能系统的年投资及运行维护成本为:
Figure 234415DEST_PATH_IMAGE017
上式中,
Figure 7199DEST_PATH_IMAGE018
为储能配置容量,
Figure 580525DEST_PATH_IMAGE019
为储能单位容量投资成本,
Figure 223996DEST_PATH_IMAGE020
为设计使用年限,
Figure 355900DEST_PATH_IMAGE021
为折现率,
Figure 350401DEST_PATH_IMAGE022
为储能系统年运行维护系数;
S42、设置微电网群储能容量优化配置模型的约束条件;
(1)储能系统参与运行后,微电网群的功率平衡约束为:
Figure 960374DEST_PATH_IMAGE023
上式中,
Figure 723931DEST_PATH_IMAGE024
为储能系统在典型日
Figure 26736DEST_PATH_IMAGE003
Figure 242954DEST_PATH_IMAGE004
时刻的充电功率,
Figure 656618DEST_PATH_IMAGE025
为储能系统在典型日
Figure 274681DEST_PATH_IMAGE003
Figure 246923DEST_PATH_IMAGE004
时刻的放电功率,
Figure 216016DEST_PATH_IMAGE026
为微电网群在典型日
Figure 433370DEST_PATH_IMAGE003
Figure 905940DEST_PATH_IMAGE004
时刻向大电网的购电功率,
Figure 550548DEST_PATH_IMAGE027
为 微电网群在典型日
Figure 741358DEST_PATH_IMAGE003
Figure 496824DEST_PATH_IMAGE004
时刻向大电网的售电功率;
(2)微电网群的购售电功率需满足:
Figure 355059DEST_PATH_IMAGE028
上式中,
Figure 904989DEST_PATH_IMAGE029
为微电网群购售电状态,1表示购电,0表示售电,
Figure 84560DEST_PATH_IMAGE030
为一个无穷大正数;
(3)微电网群接入大电网的年输配电成本为:
Figure 643717DEST_PATH_IMAGE031
上式中,
Figure 356458DEST_PATH_IMAGE032
为微电网群与大电网的峰值交互功率,
Figure 77289DEST_PATH_IMAGE033
为接入工程的容量电价,
Figure 242692DEST_PATH_IMAGE034
为接入工程的单位电量电价;
(4)微电网群与大电网的峰值交互功率为:
Figure 339961DEST_PATH_IMAGE035
上式用辅助形式线性化处理为:
Figure 907208DEST_PATH_IMAGE036
(5)微电网群向大电网购电的成本为:
Figure 64520DEST_PATH_IMAGE037
Figure 717218DEST_PATH_IMAGE038
上式中,
Figure 352599DEST_PATH_IMAGE039
为微电网群在典型日
Figure 556045DEST_PATH_IMAGE003
Figure 884259DEST_PATH_IMAGE004
时刻向大电网购电的成本,
Figure 24253DEST_PATH_IMAGE040
为峰时段的 购电电价,
Figure 197745DEST_PATH_IMAGE041
为谷时段的购电电价,
Figure 739585DEST_PATH_IMAGE042
为平时段的购电电价,
Figure 973120DEST_PATH_IMAGE043
为峰时段的时 间集合,
Figure 600411DEST_PATH_IMAGE044
为谷时段的时间集合,
Figure 843173DEST_PATH_IMAGE045
为平时段的时间集合;
上述分段表达式线性化处理为:
Figure 239520DEST_PATH_IMAGE046
(6)微电网群向大电网售电的收入为:
Figure 145421DEST_PATH_IMAGE047
上式中,
Figure 260007DEST_PATH_IMAGE048
为上网电价;
(7)储能系统约束为:
Figure 40882DEST_PATH_IMAGE049
上式中,
Figure 291734DEST_PATH_IMAGE050
为储能系统的能量水平,
Figure 132651DEST_PATH_IMAGE051
为储能系统在典型日
Figure 734534DEST_PATH_IMAGE003
Figure 53520DEST_PATH_IMAGE004
时刻的能量水 平,
Figure 424458DEST_PATH_IMAGE052
为储能系统在典型日
Figure 436277DEST_PATH_IMAGE003
Figure 259876DEST_PATH_IMAGE053
时刻的能量水平,
Figure 881088DEST_PATH_IMAGE054
为储能系统在典型日
Figure 106533DEST_PATH_IMAGE003
Figure 289253DEST_PATH_IMAGE004
时刻的充电功率,
Figure 600148DEST_PATH_IMAGE055
为储能系统在典型日
Figure 526516DEST_PATH_IMAGE003
Figure 606468DEST_PATH_IMAGE004
时刻的放电功率,
Figure 960089DEST_PATH_IMAGE056
为储能充放电效 率,
Figure 758280DEST_PATH_IMAGE057
为时间步长;
Figure 222760DEST_PATH_IMAGE058
为储能的充放电状态,1表示充电,0表示放电;
Figure 924262DEST_PATH_IMAGE059
为储能的 最大充放电功率,
Figure 448784DEST_PATH_IMAGE060
为重要负荷的最大功率需求。
5.根据权利要求4所述的一种多主体微电网群联合储能系统容量配置与成本分摊方法,其特征在于:步骤S5中,采用Cplex求解软件对微电网群储能容量优化配置模型进行求解。
6.根据权利要求5所述的一种多主体微电网群联合储能系统容量配置与成本分摊方法,其特征在于:
步骤S7中,判断各微电网独立建设储能运营的年综合运营成本之和
Figure 468693DEST_PATH_IMAGE061
是否高 于微电网群年综合运营成本
Figure 736863DEST_PATH_IMAGE062
Figure 791407DEST_PATH_IMAGE063
,则进入步骤S8;
Figure 752409DEST_PATH_IMAGE064
,则各微电网仍采用各自独立运营模式。
7.根据权利要求6所述的一种多主体微电网群联合储能系统容量配置与成本分摊方 法,其特征在于:步骤S8中,微电网群联合建设储能系统获取的额外收益
Figure 259614DEST_PATH_IMAGE065
为:
Figure 65896DEST_PATH_IMAGE066
8.根据权利要求7所述的一种多主体微电网群联合储能系统容量配置与成本分摊方法,其特征在于:步骤S9具体包括以下步骤:
S91、计算微电网
Figure 240525DEST_PATH_IMAGE005
在各季节典型日内的净发电量;
S92、采用Sigmoid函数将微电网
Figure 841271DEST_PATH_IMAGE005
的净发电量映射到
Figure 605746DEST_PATH_IMAGE067
区间,由此得到微电网
Figure 215719DEST_PATH_IMAGE005
的能 量贡献度:
Figure 244854DEST_PATH_IMAGE068
Figure 282081DEST_PATH_IMAGE069
上式中,
Figure 763877DEST_PATH_IMAGE070
为微电网
Figure 911962DEST_PATH_IMAGE005
在典型日
Figure 795604DEST_PATH_IMAGE003
中的净发电量,
Figure 269311DEST_PATH_IMAGE071
为进行归一化后的能量贡献度;
S93、先分别消除微电网
Figure 238404DEST_PATH_IMAGE005
和微电网群净功率曲线的振幅偏移,再计算微电网
Figure 426065DEST_PATH_IMAGE005
在典型日
Figure 429793DEST_PATH_IMAGE003
中净功率曲线的平均值
Figure 74401DEST_PATH_IMAGE072
,以及微电网群在典型日
Figure 530790DEST_PATH_IMAGE003
Figure 20678DEST_PATH_IMAGE004
时刻总净功率曲线的平均值
Figure 613333DEST_PATH_IMAGE073
,然后得到修正后的净功率曲线:
Figure 428842DEST_PATH_IMAGE074
Figure 106948DEST_PATH_IMAGE075
上式中,
Figure 666106DEST_PATH_IMAGE076
为修正后的微电网
Figure 877382DEST_PATH_IMAGE005
在典型日
Figure 332634DEST_PATH_IMAGE003
Figure 763615DEST_PATH_IMAGE004
时刻的净功率曲线,
Figure 860884DEST_PATH_IMAGE077
为修正后 的微电网群在典型日
Figure 428132DEST_PATH_IMAGE003
Figure 319864DEST_PATH_IMAGE004
时刻的总净功率曲线,
Figure 238142DEST_PATH_IMAGE078
为微电网群在典型日
Figure 139102DEST_PATH_IMAGE003
Figure 295277DEST_PATH_IMAGE004
时刻的总 净功率曲线;
S94、采用余弦相似度法计算各典型日内微电网
Figure 623490DEST_PATH_IMAGE005
净功率曲线与微电网群净功率曲线的 余弦相似度:
Figure 999370DEST_PATH_IMAGE079
上式中,
Figure 704021DEST_PATH_IMAGE080
为典型日
Figure 777019DEST_PATH_IMAGE003
内微电网
Figure 276133DEST_PATH_IMAGE005
净功率曲线与微电网群净功率曲线的余弦相 似度;
将余弦相似度归一化到
Figure 903424DEST_PATH_IMAGE067
区间:
Figure 146186DEST_PATH_IMAGE081
上式中,
Figure 276953DEST_PATH_IMAGE082
为微电网
Figure 946969DEST_PATH_IMAGE005
的净功率波形相似度。
9.根据权利要求8所述的一种多主体微电网群联合储能系统容量配置与成本分摊方法,其特征在于:步骤S10中,采用固定加权参数将能量贡献度和净功率波形相似度进行融合,得到各微电网的成本分摊因子:
Figure 554231DEST_PATH_IMAGE083
上式中,
Figure 69526DEST_PATH_IMAGE084
为微电网
Figure 585958DEST_PATH_IMAGE005
的成本分摊因子,
Figure 426875DEST_PATH_IMAGE085
Figure 763179DEST_PATH_IMAGE086
为权重系数。
10.根据权利要求9所述的一种多主体微电网群联合储能系统容量配置与成本分摊方 法,其特征在于:步骤S11中,各微电网联合运营后,微电网
Figure 347744DEST_PATH_IMAGE005
需要承担的年运营成本
Figure 718682DEST_PATH_IMAGE087
为:
Figure 730501DEST_PATH_IMAGE088
上式中,
Figure 288521DEST_PATH_IMAGE089
为微电网
Figure 178242DEST_PATH_IMAGE005
独立建设储能运营的年综合运营成本。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114530872A (zh) * 2021-11-16 2022-05-24 国网浙江省电力有限公司乐清市供电公司 一种多边共享的储能优化配置及其成本分摊方法

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106786523A (zh) * 2016-12-19 2017-05-31 华北电力大学 考虑新电改政策影响的分布式电源和微电网运营互动方法
WO2018131174A1 (en) * 2017-01-13 2018-07-19 Nec Corporation Microgrid power management system and method of managing
CN109103914A (zh) * 2018-10-17 2018-12-28 上海电力设计院有限公司 考虑源荷储协同运行的微电网储能优化配置方法
US20190140477A1 (en) * 2017-11-09 2019-05-09 Wisys Technology Foundation, Inc. Micro-Grid Energy Management System
CN112491087A (zh) * 2020-11-20 2021-03-12 西安热工研究院有限公司 一种基于需求侧响应的风光储独立微电网经济优化方法
CN113361875A (zh) * 2021-05-24 2021-09-07 三峡大学 计及需求侧响应和共享储能的多微电网综合能源系统优化调度方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106786523A (zh) * 2016-12-19 2017-05-31 华北电力大学 考虑新电改政策影响的分布式电源和微电网运营互动方法
WO2018131174A1 (en) * 2017-01-13 2018-07-19 Nec Corporation Microgrid power management system and method of managing
US20190140477A1 (en) * 2017-11-09 2019-05-09 Wisys Technology Foundation, Inc. Micro-Grid Energy Management System
CN109103914A (zh) * 2018-10-17 2018-12-28 上海电力设计院有限公司 考虑源荷储协同运行的微电网储能优化配置方法
CN112491087A (zh) * 2020-11-20 2021-03-12 西安热工研究院有限公司 一种基于需求侧响应的风光储独立微电网经济优化方法
CN113361875A (zh) * 2021-05-24 2021-09-07 三峡大学 计及需求侧响应和共享储能的多微电网综合能源系统优化调度方法

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
HASAN SAEED QAZI, NIAN LIU, TONG WANG: "Coordinated Energy and Reserve Sharing of Isolated Microgrid Cluster using Deep Reinforcement Learning", 《2020 5TH ASIA CONFERENCE ON POWER AND ELECTRICAL ENGINEERING (ACPEE)》 *
ZHONGNANFENG,等: "Design and dispatching of all-clean energy producing-consuming system with six-energy coupling", 《ELECTRICAL POWER AND ENERGY SYSTEMS》 *
胡仕灿: "微电网复合储能容量配置的多目标优化", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *
胡蓉,等: "基于交替乘子法与Shapley分配法的多微网联合经济调度", 《电力建设》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114530872A (zh) * 2021-11-16 2022-05-24 国网浙江省电力有限公司乐清市供电公司 一种多边共享的储能优化配置及其成本分摊方法

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