A common approach to intelligent energy and mobility services in a smart city environment

  • Marco Lützenberger
  • Nils Masuch
  • Tobias Küster
  • Daniel Freund
  • Marcus Voß
  • Christopher-Eyk Hrabia
  • Denis Pozo
  • Johannes Fähndrich
  • Frank Trollmann
  • Jan Keiser
  • Sahin Albayrak
Original Research

Abstract

Due to the fact that electric vehicles have not broadly entered the vehicle market there are many attempts to convince producers to integrate technologies that utilise embedded batteries for purposes different from driving. The vehicle-to-grid technology, for instance, literally turns electric vehicles into a mobile battery, enabling new areas of applications (e.g., to provide regulatory energy, to do grid-load balancing, or to buffer surpluses of energy) and business perspectives. Utilising a vehicle’s battery, however is not without a price—in this case: the driver’s mobility. Given this dependency, it is interesting that most available works consider the application of electric vehicles for energy and grid-related problems in isolation, that is, detached from mobility-related issues. The distributed artificial intelligence laboratory, or DAI-Lab, is a third-party funded research lab at Technische Universität Berlin and integrates the chair for agent technologies in business applications and telecommunication. The DAI-Lab has engaged in a large number of both, past and upcoming projects concerned with two aspects of managing electric vehicles, namely: energy and mobility. This article aims to summarise experiences that were collected during the last years and to present developed solutions which consider energy and mobility-related problems jointly.

References

  1. Aabrandt A, Andersen P, Pedersen A, You S, Poulsen B, O’Connell N, Ostergaard J (2012) Prediction and optimization methods for electric vehicle charging schedules in the edison project. In: Innovative smart grid technologies (ISGT), 2012 IEEE PES, pp 1–7. doi:10.1109/ISGT.2012.6175718
  2. Acar E, Lützenberger M, Schulz M (2015) Intermodal mobility assistance for megacities. In: Hopfgartner F (ed) Smart information systems. Advances in computer vision and pattern recognition, Springer, Berlin, pp 345–368Google Scholar
  3. Beucker S, Bergset L, Beeck H, Bogdanova T, Bormann F, Riedel M, Bierter W (2012) Geschäftsmodelle für den zukunftsmarkt des dezentralen energiemanagements in privathaushalten. Ergebnisse aus dem forschungsprojekt connected energy–SHAPE, http://www.connected-living.org/projekte/shape/projektbeschreibung/
  4. Fähndrich J, Ahrndt S, Albayrak S (2013) Self-explaining agents. J Teknol (Sci and Eng) 3(63):53–64Google Scholar
  5. Formato G, Troiano L, Vaccaro A (2014) Achieving consensus in self-organizing multi agent systems for smart microgrids computing in the presence of interval uncertainty. J Ambient Intell Hum Comput 5(6):821–828CrossRefGoogle Scholar
  6. Freund D, Raab AF, Küster T, Albayrak S, Strunz K (2012) Agent-based integration of an electric car sharing fleet into a smart distribution feeder. In: 3rd IEEE PES international conference and exhibition on innovative smart grid technologies (ISGT Europe). IEEE, Berlin, Germany, pp 1–8Google Scholar
  7. Gray T, Francfort J (2012) Bi-directional fast charging study report. In: Technical report, U.S Department of EnergyGoogle Scholar
  8. Kamboj S, Pearre N, Kempton W, Decker K, Trnka K, Kern C (2010) Exploring the formation of electric vehicle coalitions for vehicle-to-grid power regulation. In: Proceedings of the 1st international workshop on agent technologies for energy systems (ATES 2010). Canada, Toronto, pp 1–8Google Scholar
  9. Keiser J, Lützenberger M, Masuch N (2012) Agents cut emissions–on how a multi-agent system contributes to a more sustainable energy consumption. Proced Comput Sci 10:866–873CrossRefGoogle Scholar
  10. Keiser J, Glass J, Masuch N, Lützenberger M, Albayrak S (2011) A distributed multi-operator W2V2G management approach. In: Proceedings of the 2nd IEEE International conference on smart grid communications, Brussels, Belgium, IEEE, pp 291–296. doi:10.1109/SmartGridComm.6102332
  11. Keiser J, Masuch N, Lützenberger M, Grunewald D, Kern M, Trollmann F, Acar E, Salma ÇA, Dang XT (2014) IMA–an adaptable and dynamic service platform for intermodal mobility assistance. In: Proceedings of the 17th international IEEE conference on intelligent transportation systems (ITSC 2014), Qingdao, ChinaGoogle Scholar
  12. Konnerth T, Chinnow J, Kaiser S, Grunewald D, Bsufka K, Albayrak S (2012) Integration of simulations and MAS for smart grid management systems. In: Proceedings of the 3rd international workshop on agent technologies for energy systems (ATES 2012). Valencia, Spain, pp 51–58Google Scholar
  13. Krems JF, Weinmann O, Weber J, Westermann D, Albayrak S (eds) (2013) Elektromobilität in Metropolregionen: Die Feldstudie MINI E Berlin powered by Vattenfall. No. 766 in Reihe 12: Verkehrstechnik/Fahrzeugtechnik. VDI Verlag GmbHGoogle Scholar
  14. Küster T, Lützenberger M, Freund D, Albayrak S (2013) Distributed evolutionary optimisation for electricity price responsive manufacturing using multi-agent system technology. Int J Adv Intell Syst 7(1 and 2):27–40Google Scholar
  15. Küster T, Lützenberger M, Voß M, Freund D, Albayrak S (2014) Applying heuristics and stochastic optimization for load-responsive charging in a smart grid architecture. In: Proc. of 5th IEEE PES innovative smart grid technologies (ISGT) European 2014 conferenceGoogle Scholar
  16. Lützenberger M, Masuch N, Küster T, Keiser J, Freund D, Voß M, Hrabia CE, Pozo D, Fähndrich J, Trollmann F, Albayrak S (2014) Towards a holistic approach for problems in the energy and mobility domain. Proced Comput Sci 32:780–787. doi:10.1016/j.procs.2014.05.491 CrossRefGoogle Scholar
  17. Lützenberger M, Keiser J, Masuch N, Albayrak S (2012) Agent based assistance for electric vehicles–an evaluation. In: Huang R, Ghorbani AA, Pasi G, Yamaguchi T, Yen NY, Jin B (eds) Active media technology, 8th international conference, AMT 2012, Macau, China, December 2012, Proceedings, vol 7669., Lecture notes in computer scienceSpringer, Berlin, pp 145–154Google Scholar
  18. Lützenberger M, Küster T, Konnerth T, Thiele A, Masuch N, Heßler A, Keiser J, Burkhardt M, Kaiser S, Tonn J, Kaisers M, Albayrak S (2013) A multi-agent approach to professional software engineering. In: Cossentino M, Seghrouchni AEF, Winikoff M (eds) Engineering multi-agent systems–first international workshop, EMAS 2013, St. Paul, MN, USA, May 6–7, 2013, Revised selected papers, Lecture notes in artificial intelligence, vol 8245, Springer, pp 158–177Google Scholar
  19. Markel T, Bennion K, Kramer W, Bryan J, Giedd J (2009) Field testing plug-in hybrid electric vehicles with charge control technology in the xcel energy territory. In: Technical report, National Renewable Energy LaboratoryGoogle Scholar
  20. Masuch N, Lützenberger M, Keiser J (2013) An open extensible platform for intermodal mobility assistance. Proced Comput Sci 19:396–403. doi:10.1016/j.procs.2013.06.054. The 4th International Conference on Ambient Systems, Networks and Technologies (ANT, the 3rd International Conference on Sustainable Energy Information Technology (SEIT-2013)CrossRefGoogle Scholar
  21. Masuch N, Hirsch B, Burkhardt M, Heßler A, Albayrak S (2012a) SeMa2: a hybrid semantic service matching approach. In: Blake MB, Cabral L, König-Ries B, Küster U, Martin D (eds) Semantic web services. Springer, Berlin, pp 35–47Google Scholar
  22. Masuch N, Keiser J, Lützenberger M, Albayrak S (2012b) Wind power-aware vehicle-to-grid algorithms for sustainable ev energy management systems. In: Proceedings of the IEEE international electric vehicle conference. IEEE, Greenville, SC, USA, pp 1–7Google Scholar
  23. Masuch N, Lützenberger M, Ahrndt S, Heßler A, Albayrak S (2011) A context-aware mobile accessible electric vehicle management system. In: Proceedings of the federated conference on computer science and information systems. Szczecin, Poland, pp 305–312Google Scholar
  24. Rechenberg I (1973) Evolutionsstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution. No. 15 in Problemata, Frommann-Holzboog, Stuttgart-Bad CannstattGoogle Scholar
  25. Ruelens F, Vandael S, Leterme W, Claessens B, Hommelberg M, Holvoet T, Belmans R (2012) Demand side management of electric vehicles with uncertainty on arrival and departure times. In: 3rd IEEE PES international conference and exhibition on innovative smart grid technologies (ISGT Europe), pp 1–8Google Scholar
  26. Spiegel S, Albayrak S (2014) Energy disaggregation meets heating control. In: Proceedings of 29th symposium on applied computing (SAC), ACMGoogle Scholar
  27. Sundström O, Binding C (2010) Optimization methods to plan the charging of electric vehicle fleets. In: Proceedings of the international conference on control, communication and power engineering (CCPE 2012). Chennai, India, pp 232–328Google Scholar
  28. Tomić J, Kempton W (2007) Using fleets of electric-drive vehicles for grid support. J Power Sour 168(2):459–468CrossRefGoogle Scholar
  29. Vastardis N, Yang K (2014) An enhanced community-based mobility model for distributed mobile social networks. J Ambient Intell Hum Comput 5(1):65–75CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Marco Lützenberger
    • 1
  • Nils Masuch
    • 1
  • Tobias Küster
    • 1
  • Daniel Freund
    • 1
  • Marcus Voß
    • 1
  • Christopher-Eyk Hrabia
    • 1
  • Denis Pozo
    • 1
  • Johannes Fähndrich
    • 1
  • Frank Trollmann
    • 1
  • Jan Keiser
    • 1
  • Sahin Albayrak
    • 1
  1. 1.DAI-Labor, Technische Universität BerlinBerlinGermany

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