Abstract
Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.
Original language | English (US) |
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Article number | 7330009 |
Pages (from-to) | 659-669 |
Number of pages | 11 |
Journal | IEEE Transactions on Intelligent Transportation Systems |
Volume | 17 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2016 |
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications
Keywords
- Battery swap
- centralized charging
- electric vehicle
- genetic algorithm
- particle swarm optimization