To charge or not to charge?
Decentralized charging decisions for the smart grid
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2010
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Other Publication
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Abstract
The rise of electric vehicles brings a number of challenges for our infrastructure and energy security. Waraich (Waraich et al., 2009) demonstrates that bad choices of charging times made by the owners of electric vehicles (EVs) or plug‐in hybrid electric vehicles (PHEVs) can lead to severe problems for the electric grid due to peak loads. Coordinated charging decisions controlled by a centralized smart charging system as simulated by Clement‐Nyns et al. (Clement‐Nyns, Haesen, & Driesen, 2010), Kang (Kang & Recker, 2009) or Waraich (Waraich et al., 2009) could mitigate such bottlenecks but would incur significant infrastructural investments.
This research aims to develop decentralized smart charging strategies to reduce the dependence on communication systems and infrastructures and to facilitate individual onboard processing of the optimal charging times to reach a system optimum.
The report presents possible charging schemes for decentralized smart charging and vehicle to grid (V2G) systems and documents the implementation of one smart charging algorithm in MATSim. The chosen approach uses linear programming to first optimize the duration of charging events within the parking periods by minimizing the charging times in peak load hours. In a second step, probability density functions indicating the distribution of charging slots over the simulated day, guide the exact time choices.
The developed algorithm successfully shifts charging times to off‐peak periods and the results clearly indicate the benefits of investments into high speed charging infrastructure. Finally, an outlook on future extensions and refinements of the charging algorithm is given.
This research aims to develop decentralized smart charging strategies to reduce the dependence on communication systems and infrastructures and to facilitate individual onboard processing of the optimal charging times to reach a system optimum.
The report presents possible charging schemes for decentralized smart charging and vehicle to grid (V2G) systems and documents the implementation of one smart charging algorithm in MATSim. The chosen approach uses linear programming to first optimize the duration of charging events within the parking periods by minimizing the charging times in peak load hours. In a second step, probability density functions indicating the distribution of charging slots over the simulated day, guide the exact time choices.
The developed algorithm successfully shifts charging times to off‐peak periods and the results clearly indicate the benefits of investments into high speed charging infrastructure. Finally, an outlook on future extensions and refinements of the charging algorithm is given.
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published
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ETH Zürich, Institut für Verkehrsplanung, Transporttechnik, Strassen- und Eisenbahnbau
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03521 - Axhausen, Kay W. (emeritus) / Axhausen, Kay W. (emeritus)
02226 - NSL - Netzwerk Stadt und Landschaft / NSL - Network City and Landscape
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
Notes
Semesterarbeit Herbst 2010.