Open AccessA user selection algorithm for aggregating electric vehicle demands based on a multi‐armed bandit approach
Author(s) -
Hu Qinran,
Zhang Nianchu,
Quan Xiangjun,
Bai Linquan,
Wang Qi,
Chen Xinyi
Publication year - 2021
Publication title -
iet energy systems integration
Language(s) - English
Resource type - Journals
ISSN - 2516-8401
DOI - 10.1049/esi2.12027
Subject(s) - electric vehicle , computer science , randomness , sorting , mathematical optimization , aggregate (composite) , selection (genetic algorithm) , set (abstract data type) , operations research , algorithm , engineering , artificial intelligence , power (physics) , physics , mathematics , quantum mechanics , statistics , materials science , composite material , programming language
Abstract In systems with high penetration of renewables, demand side resources have been aggregated to facilitate system operation. However, the natural uncertainty and randomness of users' behaviour may deteriorate the demand aggregation performance, including a large mismatch from the expected aggregation target and unnecessary cost while executing aggregation. Here, the most fast‐growing demand side resource, electric vehicle is targeted, and an algorithm based on a multi‐armed bandit approach is proposed to aggregate those electric vehicle demands. In the proposed multi‐armed bandit model, each electric vehicle user's behaviour is viewed as two arms. Then, a combinatorial upper confidence bound mixed sorting algorithm, which selects the optimal set of users participating in demand aggregation, is developed. The case studies show that the proposed method can reduce the demand aggregation mismatch and eliminate the unnecessary cost. Additionally, it can be observed that the user experience is also improved.