AI Technologies and Their Applications in Small-Scale Electric Power Systems

被引：0
|
作者：
Shahid, Arqum [1 ]
Plaum, Freddy [1 ]
Korotko, Tarmo [1 ]
Rosin, Argo [1 ,2 ]
机构：
[1] Tallinn Univ Technol, Dept Elect Power Engn & Mechatron, EE-12616 Tallinn, Estonia
[2] Tallinn Univ Technol, FinEst Ctr Smart Cities, EE-12616 Tallinn, Estonia
来源：
IEEE ACCESS | 2024年 / 12卷
关键词：
electric power systems; performance indicators; Artificial intelligence; ENERGY-STORAGE SYSTEMS; OPTIMIZATION;
D O I：
10.1109/ACCESS.2024.3440067
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
As the landscape of electric power systems is transforming towards decentralization, small-scale electric power systems have garnered increased attention. Meanwhile, the proliferation of artificial intelligence (AI) technologies has provided new opportunities for power system management. Thus, this review paper examines AI technology applications and their range of uses in small-scale electrical power systems. First, a brief overview of the evolution of small-scale electric power systems and the importance of AI integration is given. The background section explains the principles of small-scale electric power systems, including stand-alone systems, grid-interactive systems, microgrids, hybrid systems, and virtual power plants. A thorough analysis is conducted on the effects of AI technologies on power system aspects such as energy consumption, demand response, grid management, operation, energy generation, and storage. Based on this foundation, AI Acceleration Performance Indicators (AAPIs) for small-scale electric power systems are developed to establish a standardized framework for evaluating and comparing different studies. AAPI framework considers a binary scoring for five quantitative Key Performance Indicators (KPIs) and five qualitative KPIs examined through a three-tiered scale - established, evolved, and emerging.
引用
收藏
页码：109984 / 110001
页数：18
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