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Optimizing Resource Usage in an Unobtrusive Way Through Smart Aggregation: The Case of Electric Vehicle Charging in Amsterdam

  • Kees van MontfortEmail author
  • Halldora Thorsdottir
  • René Bohnsack
Chapter
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Abstract

The increasing popularity of electric vehicles (EVs) is known to amplify the already present peaks in electricity demand. The possibility to remotely control and influence the charging of many EVs using the Internet of Things (IoT) via an aggregator has been proposed to optimize resource usage, to alleviate peak problems, and to exploit revenues that may be harnessed from fluctuating electricity prices. However, so far, the potential hinged on the acceptance of users, particularly the willingness to change their charging behavior. In this study, we develop an unobtrusive and easily implementable optimization method. Its effectiveness is tested on 360,000 charging sessions at public charging points in Amsterdam during the year 2015, providing a realistic assessment of the effects of optimization in terms of reduced costs, change in peak demand, and long occupancy of charging points. Based on the model, an average reduction of electricity costs between 20% and 30% can be achieved, depending on the day of the week. We also show that changing EV owner’s charging preferences such as starting earlier or later can benefit certain groups of EV drivers substantially and reduce electricity charging costs up to 35%.

Keywords

Electric vehicles Smart charging Optimization 

References

  1. Ahn C, Li C-T, Peng H (2011) Optimal decentralized charging control algorithm for electrified vehicles connected to SMART grid. J Power Sources 196(23):10369–10379CrossRefGoogle Scholar
  2. Amin MS, Wollenberg BF (2015) Towards a smart grid: power delivery for the 21st century. IEEE Power Energy Mag 3(5):34–41CrossRefGoogle Scholar
  3. Anderson R, Boulanger A, Powell W, Scott W (2011) Adaptive stochastic control for the smart grid. Proc IEEE 99(6):1098–1115CrossRefGoogle Scholar
  4. Bancz-Chicharro F, Latorre J, Ramos A (2014) Smart charging profiles for electric vehicles. Comput Manag Sci 11(1):87–110CrossRefGoogle Scholar
  5. Bohnsack R (2018) Local niches and firm responses in sustainability transitions: the case of low-emission vehicles in China. Technovation 70–71:20–32CrossRefGoogle Scholar
  6. Bohnsack R, Pinkse J (2017) Value propositions for disruptive technologies: reconfiguration tactics in the case of electric vehicles. Calif Manag Rev 59(4):79–96CrossRefGoogle Scholar
  7. Bohnsack R, Pinkse J, Kolk A (2014) Business models for sustainable technologies: exploring business model evolution in the case of electric vehicles. Res Policy 43(2):284–300CrossRefGoogle Scholar
  8. Brons M, Nijkamp P, Pels E, Rietveld P (2008) A meta-analysis of the price elasticity of gasoline demand. A SUR approach. Energy Econ 30(5):2105–2122CrossRefGoogle Scholar
  9. De Creamer K, Vandaest S, Claessens B, Deconinck G (2014) An event-driven dual coordination mechanism for demand side management of phevs. Trans Smart Grid 5(2):751–760CrossRefGoogle Scholar
  10. De Creamer K, Vandaest S, Claessens B, Deconinck G (2015) Integration of distribution grid constraints in an event-driven control strategy for plug-in electric vehicles in a multi-aggregator setting. In: Plug in electric vehicles in smart grids. Springer, Singapore, pp 129–171Google Scholar
  11. Dias MB, Zlot R, Kalra N, Stentz A (2011) Market-based multi-robot coordination: a survey and analysis. Proc IEEE 94(7):1257–1270. (2006)CrossRefGoogle Scholar
  12. Eberle U, Helmolt R (2010) Sustainable transportation based on electric vehicle concepts: a brief overview. Energy Environ Sci 3(6):689–699CrossRefGoogle Scholar
  13. Espey M (1998) Gasoline demand revisited: an international meta-analysis of elasticities. Energy Econ 20(3):273–295CrossRefGoogle Scholar
  14. Gan L, Topcu U, Low S (2013) Optimal decentralized protocols for electric vehicle charging. IEEE Trans Power Syst 28(2):940–951CrossRefGoogle Scholar
  15. Girotra K, Netessine S (2013) Business model innovation for sustainability. Manuf Serv Oper Manag 15(4):537–544CrossRefGoogle Scholar
  16. Gottwalt S, Ketter W, Block C, Collins J, Weinhardt C (2011) Demand side management – a simulation of household behaviour under variable prices. Energy Policy 39:8163–8174CrossRefGoogle Scholar
  17. Hahn T, Schönfelder M, Jochem P, Heuveline V, Fichtner W (2013) Smart grid renew. Energy 4(5):398–408Google Scholar
  18. Helms T, Loock M, Bohnsack R (2016) Timing-based business models for flexibility creation in the electric power sector. Energy Policy 92:348–358CrossRefGoogle Scholar
  19. Hidrue M, Parsons G, Kempton W, Gardner M (2011) Willingness to pay for electric vehicles and their attributes. Resour Energy Econ 33(3):686–705CrossRefGoogle Scholar
  20. Mak H-Y, Rong Y, Shen Z-J (2013) Infrastructure planning for electric vehicles with battery swapping. Manag Sci 59(7):1557–1575CrossRefGoogle Scholar
  21. Mohsenian-Rad A, Wong V, Jatskevich J, Schober R, Leon-Garcia A (2010) Autonomous demand-side management based on game theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid 1(3):320–331CrossRefGoogle Scholar
  22. Molderink A, Bakker V, Bosman M, Hurink J, Smit G (2010) Management and control of domestic smart grid technology. IEEE Trans Smart Grid 1(2):109–119CrossRefGoogle Scholar
  23. Rijksdienst voor Ondernemend Nederland (2019). www.rvo.nl
  24. Schäuble J, Kaschub T, Ensslen A, Jochem P, Fichtner W (2017) Generating electric vehicle load profiles from empirical data of three EV fleets in Southwest Germany. J Clean Prod 150:253–266CrossRefGoogle Scholar
  25. Sioshansi R (2012) Modeling the impacts of electricity tariffs on plug-in hybrid electric vehicle charging, costs, and emissions. Oper Res 60(2):1–11Google Scholar
  26. Skerlos S, Winebrake J (2010) Targeting plug-in-hybrid electric vehicle policies to increase social benefits. Energy Policy 38:705–708CrossRefGoogle Scholar
  27. Valogianni K (2016) Sustainable electric vehicle management using coordinated machine learning. PhD thesis, Erasmus University Rotterdam, Rotterdam, The NetherlandsGoogle Scholar
  28. Van Montfort K, Van der Poel G, Visser J, Van den Hoed R (2016) Prediction of necessary public charging infrastructure of electric vehicles. Proceedings of the HEVC conference 2016, London, November 2–3Google Scholar
  29. Vandael S, Claessens B, Hommelberg M, Holvoet T, Deconinck G (2013) A scalable three-step approach for demand side management of plug-in hybrid vehicles. IEEE Trans Smart Grid 4(2):720–728CrossRefGoogle Scholar
  30. Vandael S, Claessens B, Ernst D, Holvoet T, Deconinck G (2015) Charging in a day-ahead electricity market. IEEE Trans Smart Grid 6(4):1795–1805CrossRefGoogle Scholar
  31. Verzijlbergh RA, Grond MO, Lukszo Z, Slootweg JG, Ilic MD (2012) Network impacts and costs savings of controlled EV charging. IEEE Trans Smart Grid 3(3):1203–1212CrossRefGoogle Scholar
  32. Wang B, Hu B, Qiu C, Chu P, Gadh R (2015) EV charging algorithm implementation with user price preference. IEEE Trans Power Syst 28(2):940–951Google Scholar
  33. Wolbertus R, Van Den Hoed R, Maase S (2016) Benchmarking charging infrastructure utilization, Proceedings of the EVS29 symposium, Montréal, Québec, Canada, June 19–22Google Scholar
  34. Wu D, Aliprantis D, Ying L (2012) Load scheduling and dispatch for aggregators of plug-in electric vehicles. IEEE Trans Smart Grid 3(1):368–376CrossRefGoogle Scholar
  35. Xu J, Wong V (2011) An approximate dynamic programming approach for coordinated charging control at vehicle-to-grid aggregator. Proceedings of the IEEE conference on Smart Grid Communications, pp 279–284Google Scholar
  36. Yong JY, Ramachandaramurthy VK, Tan KM, Mithulananthan N (2015) A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects. Renew Sust Energ Rev 49:365–385CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kees van Montfort
    • 1
    • 2
    Email author
  • Halldora Thorsdottir
    • 1
  • René Bohnsack
    • 3
  1. 1.Urban Technology, University of Applied Science AmsterdamAmsterdamThe Netherlands
  2. 2.Nyenrode Business UniversiteitBreukelenThe Netherlands
  3. 3.School of Business and Economics, Universidade Católica PortuguesaLisbonPortugal

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