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Multi-agent Simulation of Intelligent Energy Regulation in Vehicle-to-Grid

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Multi-Agent-Based Simulation XXIV (MABS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14558))

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Abstract

The vehicle-to-grid feature of today’s electric vehicles suggests using them as batteries for stabilizing the power grid besides using them to fulfill mobility needs. In the context of car-sharing, the car-sharing provider may thus try to foster two goals: they may be interested in stabilizing the grid and ensuring the usage of as much green energy as possible. At the same time, they try to maximize satisfaction of the customer’s requests. As such, each car-sharing provider has to implement a policy on how to react to booking requests. On the other hand, customers may react to how mobility needs are fulfilled and adapt their booking strategy. In this paper, we study the problem of how to model elements of car-sharing providers as well as those of customers in a multi-agent simulation. We identify the principal elements and targets while leaving concrete simulations as future work.

This project was funded by the state of Schleswig-Holstein, Germany.

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References

  1. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)

    Article  Google Scholar 

  2. Ali, A.T., Leucker, M., Schuldei, A., Stellbrink, L., Sachenbacher, M.: A comparative analysis of multi-agent simulation platforms for energy and mobility management. In: Malvone, V., Murano, A. (eds.) Multi-Agent Systems, EUMAS 2023. LNCS, vol. 14282, pp. 295–311. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43264-4_19

  3. Amouroux, E., Chu, T.-Q., Boucher, A., Drogoul, A.: GAMA: an environment for implementing and running spatially explicit multi-agent simulations. In: Ghose, A., Governatori, G., Sadananda, R. (eds.) PRIMA 2007. LNCS (LNAI), vol. 5044, pp. 359–371. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01639-4_32

    Chapter  Google Scholar 

  4. Anebagilu, P.K., Dietrich, J., Prado-Stuardo, L., Morales, B., Winter, E., Arumi, J.L.: Application of the theory of planned behavior with agent-based modeling for sustainable management of vegetative filter strips. J. Environ. Manage. 284, 112014 (2021)

    Article  Google Scholar 

  5. Beltaief, O., El, Hadouaj, S., Ghedira, K.: Multi-agent simulation model of pedestrians crowd based on psychological theories. In: 2011 4th International Conference on Logistics, pp. 150–156. IEEE (2011)

    Google Scholar 

  6. Berger, G., Feindt, P.H., Holden, E., Rubik, F.: Sustainable mobility-challenges for a complex transition (2014)

    Google Scholar 

  7. Burger, B.: Energy charts. https://energy-charts.info/charts/price_spot_market/chart.htm?l=de&c=DE&year=2022&interval=year

  8. Caillou, P., Gaudou, B., Grignard, A., Truong, C.Q., Taillandier, P.: A simple-to-use BDI architecture for agent-based modeling and simulation. In: Jager, W., Verbrugge, R., Flache, A., de Roo, G., Hoogduin, L., Hemelrijk, C. (eds.) Advances in Social Simulation 2015. AISC, vol. 528, pp. 15–28. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47253-9_2

    Chapter  Google Scholar 

  9. Carver, C.S., Scheier, M.F.: Control theory: a useful conceptual framework for personality-social, clinical, and health psychology. Psychol. Bull. 92(1), 111 (1982)

    Article  Google Scholar 

  10. Datseris, G., Vahdati, A.R., DuBois, T.C.: Agents.jl: a performant and feature-full agent based modelling software of minimal code complexity (2021)

    Google Scholar 

  11. Edmonds, B., Bryson, J.J.: The insufficiency of formal design methods-the necessity of an experimental approach for the understanding and control of complex MAS. In: 2004 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004, vol. 1, pp. 938–945. IEEE Computer Society (2004)

    Google Scholar 

  12. Fang, X., Misra, S., Xue, G., Yang, D.: Smart grid-the new and improved power grid: a survey. IEEE Commun. Surv. Tut. 14(4), 944–980 (2011)

    Article  Google Scholar 

  13. Franke, T., Krems, J.F.: Understanding charging behaviour of electric vehicle users. Transport. Res. F: Traffic Psychol. Behav. 21, 75–89 (2013)

    Article  Google Scholar 

  14. Guastello, S.: Progress in applied nonlinear dynamics: welcome to NDPLS volume 8. Nonlin. Dyn. Psychol. Life Sci. 8, 1–15 (2004)

    Google Scholar 

  15. Güney, T.: Renewable energy, non-renewable energy and sustainable development. Int. J. Sustain. Dev. World Ecol. 26(5), 389–397 (2019)

    Article  Google Scholar 

  16. Van der Hoek, W., Wooldridge, M.: Multi-agent systems. Found. Artif. Intell. 3, 887–928 (2008)

    Article  Google Scholar 

  17. International Organisation for Standardizsation: ISO 15118-1:2019 Road vehicles – Vehicle to grid communication interface – Part 1: General information and use-case definition. Standard, International Organization for Standardization, April 2019

    Google Scholar 

  18. Inturri, G., et al.: Multi-agent simulation for planning and designing new shared mobility services. Res. Transp. Econ. 73, 34–44 (2019)

    Article  Google Scholar 

  19. Izquierdo, L.R., Izquierdo, S.S., Gotts, N.M., Polhill, J.G.: Transient and asymptotic dynamics of reinforcement learning in games. Games Econom. Behav. 61(2), 259–276 (2007)

    Article  MathSciNet  Google Scholar 

  20. Jiang, H., Vidal, J.M., Huhns, M.N.: EBDI: an architecture for emotional agents. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1–3 (2007)

    Google Scholar 

  21. Mwasilu, F., Justo, J.J., Kim, E.K., Do, T.D., Jung, J.W.: Electric vehicles and smart grid interaction: a review on vehicle to grid and renewable energy sources integration. Renew. Sustain. Energy Rev. 34, 501–516 (2014)

    Article  Google Scholar 

  22. Nansubuga, B., Kowalkowski, C.: Carsharing: a systematic literature review and research agenda. J. Serv. Manage. 32(6), 55–91 (2021). https://doi.org/10.1108/JOSM-10-2020-0344. https://www.emerald.com/insight/content/doi/10.1108/JOSM-10-2020-0344/full/html

  23. Paska, J., Biczel, P., Kłos, M.: Hybrid power systems-an effective way of utilising primary energy sources. Renew. Energy 34(11), 2414–2421 (2009)

    Article  Google Scholar 

  24. Qazi, A., et al.: Towards sustainable energy: a systematic review of renewable energy sources, technologies, and public opinions. IEEE Access 7, 63837–63851 (2019)

    Article  Google Scholar 

  25. Rao, A.S., Georgeff, M.P.: Modeling rational agents within a BDI-architecture. In: Readings in Agents, pp. 317–328 (1997)

    Google Scholar 

  26. Rizvi, S.A.A., Xin, A., Masood, A., Iqbal, S., Jan, M.U., Rehman, H.: Electric vehicles and their impacts on integration into power grid: a review. In: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–6. IEEE (2018)

    Google Scholar 

  27. Scalco, A., Ceschi, A., Sartori, R.: Application of psychological theories in agent-based modeling: the case of the theory of planned behavior. Nonlinear Dyn. Psychol. Life Sci. 22, 15–33 (2018)

    Google Scholar 

  28. Schrills, T., Rosenbusch, L., Zoubir, M., Stahl, J., Franke, T.: Supporting interaction with CO\(_2\) as a resource with individual carbon footprint trackers as everyday assistants. In: Black, N.L., Neumann, W.P., Noy, I. (eds.) IEA 2021. LNNS, vol. 220, pp. 573–581. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-74605-6_73

    Chapter  Google Scholar 

  29. Shaheen, S., Cohen, A., Farrar, E.: Carsharing’s impact and future. In: Advances in Transport Policy and Planning, vol. 4, pp. 87–120. Elsevier (2019). https://doi.org/10.1016/bs.atpp.2019.09.002. https://linkinghub.elsevier.com/retrieve/pii/S2543000919300356

  30. Taatgen, N.A., Lebiere, C., Anderson, J.R.: Modeling paradigms in ACT-R. In: Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation, pp. 29–52 (2006)

    Google Scholar 

  31. Tisue, S., Wilensky, U.: NetLogo: a simple environment for modeling complexity. In: International Conference on Complex Systems, vol. 21, pp. 16–21. Citeseer (2004)

    Google Scholar 

  32. Tol, R.S.J.: The economic effects of climate change. J. Econ. Perspect. 23(2), 29–51 (2009)

    Article  Google Scholar 

  33. Van Kriekinge, G., De Cauwer, C., Sapountzoglou, N., Coosemans, T., Messagie, M.: Peak shaving and cost minimization using model predictive control for uni-and bi-directional charging of electric vehicles. Energy Rep. 7, 8760–8771 (2021)

    Article  Google Scholar 

  34. W Axhausen, K., Horni, A., Nagel, K.: The Multi-Agent Transport Simulation MATsim. Ubiquity Press (2016)

    Google Scholar 

  35. Wang, W., Yuan, B., Sun, Q., Wennersten, R.: Application of energy storage in integrated energy systems - a solution to fluctuation and uncertainty of renewable energy. J. Energy Storage 52, 104812 (2022)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute for Software Engineering and Programming Languages, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany

    Aliyu Tanko Ali, Andreas Schuldei & Martin Leucker

  2. Institute for Multimedia and Interactive Systems, University of Lübeck, Ratzeburger Allee 160, Lübeck, Germany

    Tim Schrills, Leonard Stellbrink, André Calero Valdez & Thomas Franke

Authors
  1. Aliyu Tanko Ali
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  2. Tim Schrills
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  3. Andreas Schuldei
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  4. Leonard Stellbrink
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  5. André Calero Valdez
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  6. Martin Leucker
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  7. Thomas Franke
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Correspondence to Aliyu Tanko Ali .

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Editors and Affiliations

  1. Clermont Auvergne University, Saint‐Étienne, France

    Luis G. Nardin

  2. Grantham Research Institute on Climate Change and the Environment, London, UK

    Sara Mehryar

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Ali, A.T. et al. (2024). Multi-agent Simulation of Intelligent Energy Regulation in Vehicle-to-Grid. In: Nardin, L.G., Mehryar, S. (eds) Multi-Agent-Based Simulation XXIV. MABS 2023. Lecture Notes in Computer Science(), vol 14558. Springer, Cham. https://doi.org/10.1007/978-3-031-61034-9_11

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  • DOI: https://doi.org/10.1007/978-3-031-61034-9_11

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  • Online ISBN: 978-3-031-61034-9

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