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Online Spatial-Temporal EV Charging Scheduling with Incentive Promotion

Published: 05 November 2024 Publication History

Abstract

The growing adoption of electric vehicles (EVs) has resulted in an increased demand for public EV charging infrastructure. Currently, the collaboration between these stations has become vital for efficient charging scheduling and cost reduction. However, most existing scheduling methods primarily focus on recommending charging stations without considering users’ charging preferences. Adopting these strategies may require considerable modifications to how people charge their EVs, which could lead to a reluctance to follow the scheduling plan from charging services in real-world situations. To address these challenges, we propose the POSKID framework in this article. It focuses on spatial-temporal charging scheduling, aiming to recommend a feasible charging arrangement, including a charging station and a charging time slot, to each EV user while minimizing overall operating costs and ensuring users’ charging satisfaction. The framework adopts an online charging mechanism that provides recommendations without prior knowledge of future electricity information or charging requests. To enhance users’ willingness to accept the recommendations, POSKID incorporates an incentive strategy and a novel embedding method combined with Bayesian personalized analysis. These techniques reveal users’ implicit charging preferences, enhancing the success probability of the charging scheduling task. Furthermore, POSKID integrates an online candidate arrangement selection and an explore-exploit strategy to improve the charging arrangement recommendations based on users’ feedback. Experimental results using real-world datasets validate the effectiveness of POSKID in optimizing charging management, surpassing other strategies. The results demonstrate that POSKID benefits each charging station while ensuring user charging satisfaction.

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Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 5
October 2024
719 pages
EISSN:2157-6912
DOI:10.1145/3613688
  • Editor:
  • Huan Liu
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2024
Online AM: 17 July 2024
Accepted: 14 June 2024
Revised: 20 March 2024
Received: 21 August 2023
Published in TIST Volume 15, Issue 5

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Author Tags

  1. Online Charging Management
  2. Spatial-Temporal Scheduling
  3. Online Knapsack Problem
  4. Personalized Inference
  5. Knowledge Graph Embedding
  6. Reinforcement Learning

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  • National Science and Technology Council (NSTC), R.O.C.

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