期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 12, 页码 8112-8121出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3059288
关键词
Manganese; Stochastic processes; Resource management; Charging stations; Optimization; Roads; Informatics; Charger allocation; convex optimization; electric vehicle (EV); traffic flow distribution; two-stage stochastic programming
类别
资金
- Research Grants Council of Hong Kong Special Administrative Region [CityU-11 206 320, TII-20-4783]
The two-stage stochastic programming model proposed in the study can find high-quality charger allocation and optimal flow distribution policies under different traffic conditions, preventing over-investment on charging resources.
A two-stage stochastic programming model is established to minimize EV's expected total journey time under stochastic traffic conditions, by jointly optimizing the allocation of chargers and the distribution of EV flows. Based on sample average approximation, a feasible deterministic equivalent of the original stochastic problem is obtained. Then, a hybrid solution method, composing of a Tabu-based search and sequential quadratic programming (SQP), is proposed. The Tabu heuristic manages the charger allocation problem, where each solution candidate undergoes a second-stage EV flow optimization. SQP is applied to optimally distribute the EV flows, which is proved to be a convex problem. Extensive simulations are carried out using the eastern Massachusetts highway network. Results show that the proposed algorithm outperforms existing approaches by finding high-quality charger allocation and optimal flow distribution policies under different traffic conditions. Additionally, the two-stage model designates charging resource sufficiency by statistically estimating a lower bound for the number of chargers to allocate, which in practice helps to prevent over-investment on charging resources.
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