Abstract
The allocation of fast charging stations is a severe investment for the future mobility system with electric vehicles. The allocation of the first charging stations influences the profitability of all other fast charging stations and should therefore be perfectly arranged. Hence, we applied and extended the flow-refueling location model (FRLM) developed by Capar et al. (Eur J Oper Res 227(1):142–151, 2013) to the German autobahn with a focus on the states Baden-Württemberg and Bavaria with 595 nodes and 3569 highway km. Our model extension comprehends mainly the inclusion of the access distance for traffic participants to their closest network node. In order to analyze the impact of different vehicle ranges and the desired coverage of flows we defined four scenarios. The results indicate the significance of vehicle range and the desired coverage value. 20 optimally allocated fast charging stations along the highways lead already to a coverage of about 62 % (100 km vehicle range) or even 83 % (150 km vehicle range) of all trips. A complete coverage of trips requires at least 50 (150 km vehicle range), 77 (100 km vehicle range) or even 84 (70 km vehicle range) fast charging stations. The last 30 % coverage leads to a tripling of charging stations. Furthermore, a first estimation of the corresponding surcharge for fixed costs per charging process amounts to about 20 % of the total costs for a charging process.
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Notes
For the maps the website http://umap.openstreetmap.fr/ is used.
We applied a sensitivity analysis with alternative values for EV market penetration (e.g. 400,000) and fixed costs per charging station (e.g. 100,000 euros) and derived average costs within the same range (e.g. 0.99 euros).
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Acknowledgments
The authors would like to thank all participants of the workshop “Sustainability and Decision Making” at the RWTH Aachen in February 2015, especially Grit Walther, for fruitful discussions and helpful comments.
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Appendix
Appendix
Assumptions for the costs calculation in Sect. 4.4.
Explanation | Sources and calculations | Value | Unit | Uncertainty level | |
|---|---|---|---|---|---|
1. | Total number of electric vehicles (EV) in 2020 | Plötz (2014c) | 500,000 | No. of EV | Very high |
2. | Total number of passenger cars in Germany 2014 | Kraftfahrtbundesamt | 44,000,000 | No. of passenger cars | Almost sure |
3. | Total vehicle trips per day with a distance >80 km in Baden-Württemberg and Bavaria (BWB) | OD-Data with the assumption that the total amount of cars in 2020 will be the same as in 2014 | 767,123 | No. of vehicle trips per day | Moderate |
4. | EV trips per day with a distance >80 km in BWB in 2020 | L1/L2*L3 | 8717 | No. of EV trips per day | Moderate (result) |
5. | Assumption on the share of main fast charging technology (e.g., CCS) used by EV in 2020 | Assumption | 85 % | percentage | Moderate |
6. | EV trips per day with a distance >80 km in BWB with CCS in 2020 | L4*L5 | 7410 | No. EV trips/day | Moderate (result) |
7. | Average distance of roundtrips >80 km in BWB | OD-Data | 300 | km | Moderate |
8. | Average number of charges needed for an average 300 km trip | Assumption | 2.5 | No. of charges/trip | Moderate |
9. | Average number of charges per day in BWB | L6*L8 | 18,524 | No. of charges/day | Moderate (result) |
10. | Percentage of flows covered | Assumption from paper | 80 % | percantage | Moderate |
11. | Average number of charges per day in BWB with a coverage of 80 % (demand) | L9*L10 | 14,819 | No. of charges/day | Moderate (result) |
12. | Required No. of charging stations to cover 80 % of all EV flows in BWB | Result from model | 34 | No. of charging stations | High (result) |
13. | Average number of charges per charging station per day in BWB (demand) | L11/L12 | 436 | No. of charges/charging station | Moderate (result) |
14. | Maximum possible charges per charging point per day | (60 min/h)/(20 min/charge)*24 h/day | 72 | No. of charges/charging point | High |
15. | Optimistic charges per charging point per day (25 % workload) | Assumption | 18 | No. of charges/charging point | High |
16. | Required number of charging points per charging station | L13/L15 | 24 | No. of charging points/charging station | High (result) |
17. | Average cost for one charging point | ABB/Elektromobilität verbindet | 30,000 | € per charging point | Moderate |
18. | Fix cost for one charging station | Elektromobilität verbindet | 30,000 | € per charging station | Moderate |
19. | Cost estimation for 80 % coverage | (L16*L17 + L18)*L12 | 25,719,045 | € | High (result) |
20. | Linear depreciation for 6 years for one station | L19/(L12*6) | 126,074 | € per year and charging station | High (result) |
21. | Maintenance costs per month | Assumption | 1000 | € per month and charging station | Moderate |
22. | Cost per day | (L20/365) + (L21/30) | 379 | € per day and charging station | High (result) |
23. | Fix cost percentage per charging process | L22/L13 | 0.87 | € per charging process | High (result) |
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Jochem, P., Brendel, C., Reuter-Oppermann, M. et al. Optimizing the allocation of fast charging infrastructure along the German autobahn. J Bus Econ 86, 513–535 (2016). https://doi.org/10.1007/s11573-015-0781-5
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DOI: https://doi.org/10.1007/s11573-015-0781-5