综合能源服务商储能业务目标用户主动识别方法

doi:10.19799/j.cnki.2095-4239.2023.0068

摘要/Abstract

摘要：

能源转型背景下，储能要实现市场化、规模化的转变，但由于当前用户对自身需求认知不准确、且供应商多采用人工调研筛选的方式，导致用户筛选不准、主观性强、效率低，储能业务开展困难。为解决上述问题，本工作提出一种综合能源服务商(integrated energy service provider，IESP)用户侧储能业务目标用户的主动识别方法。首先，基于多源数据，构建了兼顾用户多元电力需求与服务价值特征的用户侧储能目标用户主动识别特征库，该特征既能反映用户的节能增效需求和电能质量需求等需求信息，又能反映用户是否值得供应商服务等供应信息；其次，针对传统量化方法劣化指标影响易被优势指标弥补，造成识别结果不准确，本工作建立了改进的GRA-TOPSIS用户特征量化模型；再次，根据量化结果，建立目标用户主动识别坐标系，可视化目标用户主动识别结果，为储能服务开展提供支撑，帮助IESP直观锁定目标用户；最后，通过实例分析验证了所提方法的可行性与有效性。

关键词: 多源数据, 用户侧储能, 主动识别, 逼近理想解排序, 灰色关联分析

Abstract:

In energy transformation, national policies require energy storage to achieve marketization and scalable transformation. However, due to inaccurate perceptions of users' needs, manual research and selection by suppliers; user selection is inaccurate, subjective, and inefficient, making the development of energy storage business challenging. To solve these issues, this paper proposes an active identification method for target users of the integrated energy service provider (IESP) user-side energy storage business. First, based on multi-source data, a user-side energy storage target user active identification feature library is constructed, considering users' multiple power demands and service value characteristics. This feature can reflect users' information demand, such as energy-saving, efficiency-enhancing demands and power quality demand, as well as supply information such as whether users are worth serving by the supplier. Secondly, we developed an improved GRA-TOPSIS user feature quantification model to address the limitation of traditional quantification methods, where the influence of the deterioration index is easily compensated by the advantage index, resulting in inaccurate recognition results. Thirdly, according to the quantitative results, we establish a target user activity identification coordinate system to visualize a target user activity identification results, which provides support for implementing energy storage services, and help IESP in intuitively identifying target user. Finally, the feasibility and effectiveness of the proposed method are verified through a case study.

Key words: multi-source data, user-side energy storage, active identification, technique for order preference by similarity to an ideal solution, gray relational analysis

中图分类号:

TM 732

郭亚威, 肖先勇, 郑子萱, 陈韵竹, 陈旭林. 综合能源服务商储能业务目标用户主动识别方法[J]. 储能科学与技术, 2023, 12(6): 2011-2021.

Yawei GUO, Xianyong XIAO, Zixuan ZHENG, Yunzhu CHEN, Xulin CHEN. Active identification method for target users of an integrated energy service provider's energy storage business[J]. Energy Storage Science and Technology, 2023, 12(6): 2011-2021.

图/表 11

图1

图2

图3

表1

表2

用户特征量化结果"

项目	用电需求特征 $ξ -$	服务价值特征 $ξ +$
用户A	0.4078	0.4778
用户B	0.5340	0.5445
用户C	0.4437	0.4587
用户D	0.5431	0.3932
用户E	0.4393	0.3942
Ⅰ、Ⅱ界限	0.4234	0.4469
Ⅱ、Ⅲ界限	0.4739	0.4267

表2

图4

表3

表A1

表A2

表B1

表B2

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用户	重要用电环节
制药企业(A)	通风机、精密温控系统
电子材料厂(B)	电子玻璃纤维加热炉
精密制造厂(C)	精密机床
微电子企业(D)	多条硅蚀刻生产线
化工厂(E)	化学反应堆水泵

用户	用户储能服务降费收益/万元	用户储能服务净收益/万元	用户服务价值量化值	IESP储能服务收益/万元
A	1672.80	1063.80	0.4778	781.54
B	3013.79	2404.79	0.5445	954.08

用户	A₁₁	A₁₂	A₁₃	A₁₄	A₁₅	A₁₆	A₂₁	A₂₂	A₂₃	B₁	B₂	B₃	B₄
A	1212	542	1.4	67	0.95	24	2.61	3255	0.056	2588	0.82	0.75	0.15
B	4652	2066	6.7	145	0.65	95	5.86	14042	0.151	3566	0.95	0.86	0.22
C	1855	1121	3.5	97	0.87	56	3.55	5366	0.106	1852	0.86	0.72	0.11
D	7700	3113	7.5	104	0.56	89	7.56	16055	0.193	1024	0.66	0.43	0.05
E	1464	1065	2.9	123	0.75	31	3.02	4799	0.092	923	0.71	0.55	0.03

类型		A₁₁	A₁₂	A₁₃	A₁₄	A₁₅	A₁₆	A₂₁	A₂₂	A₂₃	B₁	B₂	B₃	B₄
用户	A	0.0563	0.0693	0.0822	0.0843	0.0086	0.0787	0.0201	0.1486	0.1301	0.0618	0.0492	0.0342	0.0422
	B	0.0265	0.0537	0.0315	0.0561	0.0733	0.0052	0.1708	0.0098	0.0447	0.0190	0	0.0154	0
	C	0.0507	0.0537	0.0621	0.0734	0.0259	0.0456	0.0603	0.0860	0.0447	0.0940	0.0341	0.0393	0.0663
	D	0	0	0.0239	0.0709	0.0927	0.0114	0.0603	0.0860	0.1054	0.1303	0.1098	0.0888	0.1025
	E	0.0541	0.0552	0.0678	0.0640	0.0517	0.0715	0.2160	0.0215	0	0.1347	0.0909	0.0683	0.1146
等级划分	I、II界限	0.0495	0.0569	0.0764	0.0724	0.0194	0.0725	0.1205	0.1349	0.1193	0.1225	0.0379	0.0342	0.0724
等级划分	II、III界限	0.0321	0.0300	0.0573	0.0362	0.0517	0.0414	0.0452	0.1369	0.1193	0.0700	0.0946	0.0683	0.1025
最优值		0.0582	0.0704	0.0860	0	0	0.0932	0	0.1760	0.1461	0.1532	0.1704	0.1281	0.1327
最劣值		0.0234	0.0030	0	0.1085	0.0840	0	0.1959	0	0.0147	0	0	0	0.0121

项目	7:00—9:00	9:00—15:00	15:00—20:00	20:00—22:00	22:00—23:00	23:00—7:00
电价/元	0.7176	1.0471	0.7176	1.2555	0.7176	0.3680
运行状态	待机	放电	充电	放电	待机	充电