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· An alternative algorithm to schedule electric vehicles (EVs).
· Optimized routing of EVs at charging satiations to minimize network power loss.
· Solar power prediction using a novel hybrid SARIMA-LSTM model.
· Exploration of scenarios with time and routing flexibility of the car owners.
Electric vehicles (EVs) are going to overrule the transportation sector due to their pollution-free technology and low running costs. However, charging the EVs causes significant power demand and stress on the power delivery network. The challenge can be tackled well when charging and discharging scheduling are coordinated with intelligent EV routing. In this work, two-stage charging and discharging scheduling are proposed. In the first stage, a time scheduling algorithm is structured to identify EV charging/discharging slots at different hours, and at a later stage, the slots are optimally distributed among different charging stations. Routing of the EVs towards the EVCSs has been designed to enhance the useful participation of the EVs in the charging and discharging program. In this regard, a possible number of EVs in the test region has been forecasted with a regression model. The adequacy of the combined charging-discharging and location scheduling model is tested on a typical PV-enhanced 28-bus Indian distribution network. Three case studies containing three sub-cases in each have been performed incorporating the choice of the EV owners towards charging and discharging in different time slots in a day. The case studies have resulted in a peak-to-average ratio (PAR) of 1.151,0, 1.165,0, 1.196,8, 1.165,0, 1.180,9, 1.196,8, 1.196,8, 1.196,8 and 1.196,8 for the 24-h demand pattern in Case-1a, Case-1b, Case-1c, Case-2a, Case-2b, Case-2c, Case-3a, Case-3b and Case-1c respectively in comparison to a PAR of 1.2 for the 24-h demand in base case.
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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
