Intelligent charge scheduling of electric vehicles in residential areas
| Autor: | Guangming Luo, Pengguang Li, Pengxu Wang, Haoyi Zhang, Yan Mo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
monte carlo methods
power transformers demand side management scheduling electric vehicles distribution networks battery powered vehicles residential area intelligent charge scheduling strategy real-time guide tariffs load information mathematical model charging task distributed computing optimised charging plan intelligent charge scheduling system monte–carlo simulation method actual customer charging behaviour distribution transformer load curve average charge uncoordinated coordinated charging conditions required charge smoother load curve Engineering (General). Civil engineering (General) TA1-2040 |
| Zdroj: | The Journal of Engineering (2019) |
| Druh dokumentu: | article |
| ISSN: | 2051-3305 |
| DOI: | 10.1049/joe.2018.9166 |
| Popis: | This article proposes an intelligent charge scheduling strategy for electric vehicles (EVs) in residential areas based on real-time guide (RTG) tariffs which are set dynamically according to the load information. The architecture of the system realises the interconnection of information on vehicles and distribution networks. The authors first establish a mathematical model, the objective of which is to accomplish the charging task in an economical way within a given deadline. The authors then introduce dual theory for a distributed computing to the model. EVs obtain an optimised charging plan using the intelligent charge scheduling system, which ensures the safe operation of the distribution transformer. Finally, taking the load curve of a residential area as an example, the Monte–Carlo simulation method is utilised to calculate the charging needs of customers based on actual customer charging behaviour. The distribution transformer load curve and average charge per vehicle are simulated under both uncoordinated and coordinated charging conditions, and the simulation results indicate that the scheduling strategy can reduce the required charge and achieve a smoother load curve, and is suitable for vehicle online scheduling. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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