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AuthorMhaisen, N.
AuthorFetais, N.
AuthorMassoud, Ahmed
Available date2022-03-23T06:57:31Z
Publication Date2020
Publication Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089471
URIhttp://hdl.handle.net/10576/28742
AbstractWith the increase in Electric Vehicles (EVs) penetration, their charging needs form an additional burden on the grid. Thus, charging coordination is necessary for safe and efficient EV use. The scheduling of EVs is especially essential in Vehicle-to-Grid schemes, where EVs energy cost can be reduced by intelligently managing the charging and discharging according to real-time prices and owners' needs. Further, utilities can perform load shifting by price-based demand-response programs. However, the scheduling problem is challenging in the presence of multiple unknown variables, such as real-time prices, commuting behavior, and energy needs. Most of the scheduling techniques attempt to model the system uncertainties (e.g., forecast prices) and plan accordingly. In this paper, we propose the use of a model-free Reinforcement Learning (RL) technique that can deduce a charging/discharging strategy without requiring any model of the system. We evaluate the performance of the proposed system with real-world data and investigate the learned charging/discharging pattern to show the effectiveness of the proposed method.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCharging (batteries)
Costs
Electric vehicles
Internet of things
Scheduling
Charging/discharging
Demand response programs
Electric Vehicles (EVs)
Real - time scheduling
Reinforcement learning techniques
Scheduling problem
Scheduling techniques
System uncertainties
Reinforcement learning
TitleReal-Time Scheduling for Electric Vehicles Charging/Discharging Using Reinforcement Learning
TypeConference Paper
Pagination6-1
dc.accessType Abstract Only


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