Sina Baghali - Academia.edu (original) (raw)
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Papers by Sina Baghali
IEEE Transactions on Power Systems
The increasing market penetration of electric vehicles (EVs) may pose significant electricity dem... more The increasing market penetration of electric vehicles (EVs) may pose significant electricity demand on power systems. This electricity demand is affected by the inherent uncertainties of EVs' travel behavior that makes forecasting the daily charging demand (CD) very challenging. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips, and develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance. These parameters are later used to model the temporal charging behavior of EVs. The simulation results show that the proposed modeling can effectively estimate the daily CD pattern based on travel behavior of EVs, and simple machine learning techniques can forecast the travel parameters with acceptable accuracy.
The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for ... more The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for inventing an alternative eco-friendly transportation fleet. Electrical vehicles (EVs) were introduced as an eligible substitute for the conventional vehicle fleet. The significant uncertainties associated with EVs usage, however, make their performance unpredictable. In this paper, we introduce a methodological framework for scheduling smart charging of EVs which considers the colligated uncertainties. Furthermore, the framework includes an ecumenical model for charging and discharging of EVs which is utilized in a 21-node sample distribution network which contains wind turbine as a Distributed Generation units. Our proposed approach extracted that the welfare of Vehicle to Grid effectuation in the network is much more beneficial than the uncoordinated mode. The simulation result indicates that smart charging effectuation is economical.
IEEE Transactions on Power Systems
The increasing market penetration of electric vehicles (EVs) may pose significant electricity dem... more The increasing market penetration of electric vehicles (EVs) may pose significant electricity demand on power systems. This electricity demand is affected by the inherent uncertainties of EVs' travel behavior that makes forecasting the daily charging demand (CD) very challenging. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips, and develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance. These parameters are later used to model the temporal charging behavior of EVs. The simulation results show that the proposed modeling can effectively estimate the daily CD pattern based on travel behavior of EVs, and simple machine learning techniques can forecast the travel parameters with acceptable accuracy.
The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for ... more The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for inventing an alternative eco-friendly transportation fleet. Electrical vehicles (EVs) were introduced as an eligible substitute for the conventional vehicle fleet. The significant uncertainties associated with EVs usage, however, make their performance unpredictable. In this paper, we introduce a methodological framework for scheduling smart charging of EVs which considers the colligated uncertainties. Furthermore, the framework includes an ecumenical model for charging and discharging of EVs which is utilized in a 21-node sample distribution network which contains wind turbine as a Distributed Generation units. Our proposed approach extracted that the welfare of Vehicle to Grid effectuation in the network is much more beneficial than the uncoordinated mode. The simulation result indicates that smart charging effectuation is economical.