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Article

Real-Time Control of Plug-in Electric Vehicles for Congestion Management of Radial LV Networks: A Comparison of Implementations

1
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 11428 Stockholm, Sweden
2
Barcelona School of Industrial Engineering, Technical University of Catalonia, 08028 Barcelona, Spain
3
Institute of Energy Systems, Energy Efficiency and Energy Economics (ie3), TU Dortmund University, 44227 Dortmund, Germany
4
Institute for Integrated Energy Systems, University of Victoria, Victoria, BC V8W 2Y2, Canada
*
Author to whom correspondence should be addressed.
Energies 2020, 13(16), 4227; https://doi.org/10.3390/en13164227
Received: 5 May 2020 / Revised: 8 August 2020 / Accepted: 13 August 2020 / Published: 15 August 2020
(This article belongs to the Special Issue Flexibility in Distribution Systems from EVs and Batteries)
The global proliferation of plug-in electric vehicles (PEVs) poses a major challenge for current and future distribution systems. If uncoordinated, their charging process may cause congestion on both network transformers and feeders, resulting in overheating, deterioration, protection triggering and eventual risk of failure, seriously compromising the stability and reliability of the grid. To mitigate such impacts and increase their hosting capacity in radial distribution systems, the present study compares the levels of effectiveness and performances of three alternative centralized thermal management formulations for a real-time agent-based charge control algorithm that aims to minimize the total impact upon car owners. A linear formulation and a convex formulation of the optimization problem are presented and solved respectively by means of integer linear programming and a genetic algorithm. The obtained results are then compared, in terms of their total impact on the end-users and overall performance, with those of the current heuristic implementation of the algorithm. All implementations were tested using a simulation environment considering multiple vehicle penetration and base load levels, and equipment modeled after commercially available charging stations and vehicles. Results show how faster resolution times are achieved by the heuristic implementation, but no significant differences between formulations exist in terms of network management and end-user impact. Every vehicle reached its maximum charge level while all thermal impacts were mitigated for all considered scenarios. The most demanding scenario showcased over a 30% reduction in the peak load for all thermal variants. View Full-Text
Keywords: plug-in electric vehicles; radial low voltage networks; real-time control; centralized thermal management; active distribution networks; user impact minimization plug-in electric vehicles; radial low voltage networks; real-time control; centralized thermal management; active distribution networks; user impact minimization
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MDPI and ACS Style

García Veloso, C.; Rauma, K.; Fernández, J.; Rehtanz, C. Real-Time Control of Plug-in Electric Vehicles for Congestion Management of Radial LV Networks: A Comparison of Implementations. Energies 2020, 13, 4227. https://doi.org/10.3390/en13164227

AMA Style

García Veloso C, Rauma K, Fernández J, Rehtanz C. Real-Time Control of Plug-in Electric Vehicles for Congestion Management of Radial LV Networks: A Comparison of Implementations. Energies. 2020; 13(16):4227. https://doi.org/10.3390/en13164227

Chicago/Turabian Style

García Veloso, César, Kalle Rauma, Julián Fernández, and Christian Rehtanz. 2020. "Real-Time Control of Plug-in Electric Vehicles for Congestion Management of Radial LV Networks: A Comparison of Implementations" Energies 13, no. 16: 4227. https://doi.org/10.3390/en13164227

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