Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/3C002D2C-85C9-4B82-A176-089C5DC26C20 | ||
| Year | 2021 | ||
| Type of Item | Master Thesis | ||
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| Bibliographic Citation | Georgios Konstantinidis, "Optimal operation scheduling of large charging stations of electric vehicles", Master Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2021 https://doi.org/10.26233/heallink.tuc.90233 | ||
| Appears in Collections | |||
| Relations with other Items | Is Next in Sequence from the Item: |
The increase of the number of electric vehicles will lead to a large increase in electricity demand. Smart charging of electric vehicles (EVs) has many benefits such as reduction of charging costs, limitation of large investments in the distribution network, etc. The combination of smart charging of EVs with ancillary services such as Vehicle-to-Grid (V2G) (energy transfer from EVs to the distribution network) can provide more benefits to the electricity network as well as to the owner of the EV. In this work, a method for the efficient charging of electric vehicles (EVs) at the parking lot (PL) level, including V2G operation and taking into account lifetime of EV batteries, distribution network and local transformer loading, is proposed. The main goal of the proposed charging method is to reduce the cost of charging at the levels of PL and EV. The Particle Swarm Optimization (PSO) algorithm was used to derive the charging schedule of the EVs. The proposed method is compared with two conventional charging strategies: 1) the EVs charge with the maximum power allowed by the charging station and the on-board power converter of each EV and 2) the EVs charge with the constant power required to achieve the desired level of charge at the end of the charging period. In addition, the results of the proposed method were compared with an equivalent aggregation model of the EVs parking lot where all EVs’ batteries were aggregated to one battery. Real-world data were used for the price of electricity, the load of the distribution network and the activity of the PL (arrivals / departures of the hosted EVs). The effectiveness of the proposed method is demonstrated by the results obtained by the indicative operation scenarios. Finally, it is noted that the application of the proposed charging method does not require advanced calculation, measurement and communication systems.
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