Energy Storage Science and Technology ›› 2023, Vol. 12 ›› Issue (6): 2011-2021.doi: 10.19799/j.cnki.2095-4239.2023.0068

• Technical Economic Analysis of Energy Storage • Previous Articles     Next Articles

Active identification method for target users of an integrated energy service provider's energy storage business

Yawei GUO(), Xianyong XIAO, Zixuan ZHENG, Yunzhu CHEN(), Xulin CHEN   

  1. College of Electrical Engineering, Sichuan University, Chengdu 610065, Sichuan, China
  • Received:2023-02-12 Revised:2023-03-11 Online:2023-06-05 Published:2023-06-21
  • Contact: Yunzhu CHEN E-mail:1131732612@qq.com;1159228002@qq.com

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

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