Sustainable Energy System Planning for an Industrial Zone by Integrating Electric Vehicles as Energy Storage

Renewable energy sources and electric vehicles are promising solutions for reducing fossil fuel consumption and environmental impacts within the electricity and transportation sectors. In this study, a new electric vehicle aggregator framework is proposed and four different electric vehicle charging scenarios have been modelled to analyse the impact of electric vehicles on the considered industrial microgrid. The remarkable point of this aggregator is reducing the computational time in the presence of a high penetration level of EVs. The EV charging scenarios include; no electric vehicle, uncoordinated electric vehicle charging, unidirectional and bidirectional vehicle to the grid. Diverse types of renewable energy sources such as wind turbines, solar PV panels, and geothermal units are considered, and an energy scheduling model is implemented. Uncertainties of renewable energy sources and electric vehicles behaviour (arrival time, departure time, and travelled distance) are modelled with their probability distribution function from related historical data. Also, for demand-side management, the Time of Use (TOU) strategy is implemented and three cost levels are defined, which contain off-peak, mid-peak, on-peak, and their cost levels. Results show that the suggested vehicle to grid model is effectively reduced the operation cost and maximize renewable energy share. Eventually, a sensitivity analysis is executed, and the result illustrates that a model with an increase in the penetration level of electric vehicles from (100 to 2000) in the industrial microgrid have better effectiveness in relative operation cost in electric vehicle smart charging schemes. It means relative operation cost, which is related to the unidirectional and bi-directional decrease from 0.996 to 0.95 and 0.988 to 0.831, respectively.