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Electric Vehicle Charging Rescheduling to Mitigate Local Congestions in the Distribution System
handle: 11573/1567063
The rapid increase of electric vehicles (EVs) penetration represents an important challenge for the distribution system operator (DSO). The main issues arise not only from an optimal grid expansion planning point of view but also for managing the system in the near future. The paper focuses on a short-term period EV penetration scenario and proposes a mechanism of EV charging sessions rescheduling in order to mitigate grid congestions. The collaboration between three actors, namely the DSO, the fleet manager and the end user, allows the DSO to identify rescheduling programs in order to face overload events, that represent the most important issue for the grid caused by the widespread penetration of EVs. The IEEE 33-bus test system is used to check the potential of the proposed mechanism. Load flow simulations show that more than 50% of congestions are avoided by means of this approach. The promising results constitute a base case for a future work in which the optimal rescheduling program would be investigated.
- Sapienza University of Rome Italy
- ASM International United States
- Roma Tre University Italy
- ASM International United States
distribution system; electric vehicle; overload; rescheduling; test system
distribution system; electric vehicle; overload; rescheduling; test system
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).8 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
