期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 19, 期 9, 页码 3046-3058出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2018.2841965
关键词
Electric vehicle; charging scheme; queuing model; contract theory; admission control
资金
- NSFC [61374189, 61422201, 61370159, 61372077, U1301255, U1501251]
- Science and Technology Program of Guangdong Province [2015B010129001, 2016B090918080]
- Special-Support Project of Guangdong Province [2014TQ01X100]
- High Education Excellent Young Teacher Program of Guangdong Province [YQ2013057]
- Science and Technology Program of Guangzhou [2014J2200097, 201707010490]
- Research Council of Norway [240079/F20]
- Fundamental Research Funds for the Central Universities, China [2672018ZYGX2018J001]
Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid.
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