Online Battery Protective Energy Management for Energy-Transportation Nexus

Shuangqi Li, Pengfei Zhao, Chenghong Gu, Jianwei Li, Shuang Cheng, Minghao Xu

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Abstract

Grid-connected electric vehicles (GEVs) and Energy-Transportation Nexus bring a bright prospect to improve the economy of microgrids. However, it is challenging to determine optimal vehicle-to-grid (V2G) strategies due to the complex battery aging mechanism and volatile microgrid states. This paper develops a novel online battery anti-aging energy management method for Energy-Transportation Nexus. Based on battery aging characteristic analysis and rain-flow cycle counting technology, the quantification of aging cost in V2G strategies is realized by modelling the impact of number of cycles, depth of discharge, and charge and discharge rate. The coordination of GEVs charging is modelled as multi-objective learning by using a deep reinforcement learning algorithm. The training objective is to maximize renewable penetration while reducing microgrid power fluctuations and vehicle battery aging. This research provides an efficient and economical tool for microgrid power balancing by optimally coordinating GEVs charging and renewable energy, thus helping promote a low-cost decarbonization transition.
Original languageEnglish
Article number9745763
Pages (from-to)8203-8212
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume18
Issue number11
Early online date31 Mar 2022
DOIs
Publication statusPublished - 30 Nov 2022

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