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 language | English |
|---|---|
| Article number | 9745763 |
| Pages (from-to) | 8203-8212 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 18 |
| Issue number | 11 |
| Early online date | 31 Mar 2022 |
| DOIs | |
| Publication status | Published - 30 Nov 2022 |
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