Online Battery Protective Energy Management for Energy-Transportation Nexus

نویسندگان

چکیده

Grid-connected electric vehicles (GEVs) and energy-transportation nexus bring a bright prospect to improve the penetration of renewable energy economy microgrids (MGs). However, it is challenging determine optimal vehicle-to-grid (V2G) strategies due complex battery aging mechanism volatile MG states. This article develops novel online anti-aging management method for by using deep reinforcement learning (DRL) framework. Based on characteristic analysis rain-flow cycle counting technology, quantification cost in V2G realized modeling impact number cycles, depth discharge, charge discharge rate. The established life loss model used evaluate effectiveness agent actions. coordination GEVs charging modeled as multiobjective DRL algorithm. training objective maximize while reducing power fluctuations vehicle costs. developed verified be effective balancing control U.K. provides an efficient economical tool optimally coordinating energy, thus helping promote low-cost decarbonization transition.

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ژورنال

عنوان ژورنال: IEEE Transactions on Industrial Informatics

سال: 2022

ISSN: ['1551-3203', '1941-0050']

DOI: https://doi.org/10.1109/tii.2022.3163778