A data-driven approach for collaborative optimization of large-scale electric vehicles considering energy consumption uncertainty

Xingxing Cheng, Rongquan Zhang, Siqi Bu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

With the explosive growth of electric vehicles (EVs), it is an urgent task to incorporate low-carbon EVs with advanced optimization strategies to achieve orderly charging–discharging and economical operations for EVs. Nevertheless, current charging–discharging optimization strategies for EVs may be impractical since their energy consumption uncertainty and the driving trip cost are generally not considered. Therefore, a collaborative optimization model for large-scale EV charging–discharging with energy consumption uncertainty in this paper is proposed to simultaneously maximize passenger revenue and reduce the costs of the driving, charging–discharging, and battery depletion. Subsequently, a data-driven approach is developed to tackle the model. In this approach, an uncertainty predictor based on wavelet transform, deep deterministic policy gradient, and quantile regression is first applied to estimate the energy consumption uncertainty. Then, an adaptive learning rate firefly algorithm is presented to identify the most satisfactory solution for the optimization model. Finally, taking the actual data of 300 EVs in a city in China as case studies, the simulation results reveal that the proposed method is effective and has high application significance.

Original languageEnglish
Article number109461
JournalElectric Power Systems Research
Volume221
DOIs
Publication statusPublished - Aug 2023

Keywords

  • Adaptive learning rate firefly algorithm
  • Collaborative optimization
  • Data-driven
  • Deep reinforcement learning
  • Electric vehicles
  • Energy consumption prediction

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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