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Towards an Agent-Based Negotiation Scheme for Scheduling Electric Vehicles Charging

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9571)

Abstract

We consider the problem of scheduling Electric Vehicle (EV) charging within a single charging station aiming to maximize the number of charged EVs, as well as the amount of charged energy. In so doing, we propose one offline optimal solution using Mixed Integer Programming (MIP) techniques, and two online solutions which incrementally execute the MIP algorithm each time an EV arrives at the charging station. Moreover, we apply agent based negotiation techniques between the station and the EVs in order to service EVs when the MIP problem is initially unsolvable due to insufficient resources (i.e., requested energy, charging time window). We evaluate our solutions in a setting partially using real data, and we show that when applying negotiation techniques, the number of EVs charged increases on average by \(7\,\%\), energy utilization by \(6.5\,\%\), while there is only a small deficit (about \(10\,\%\)) on average agent utility which is unavoidable due to the fact that the initial incremental demand-response problem is unsolvable.

Keywords

  • Execution Time
  • Charge Station
  • Schedule Algorithm
  • Electric Vehicle
  • Smart Grid

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Emmanouil S. Rigas .

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Seitaridis, A., Rigas, E.S., Bassiliades, N., Ramchurn, S.D. (2016). Towards an Agent-Based Negotiation Scheme for Scheduling Electric Vehicles Charging. In: Rovatsos, M., Vouros, G., Julian, V. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2015 2015. Lecture Notes in Computer Science(), vol 9571. Springer, Cham. https://doi.org/10.1007/978-3-319-33509-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-33509-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33508-7

  • Online ISBN: 978-3-319-33509-4

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