Stochastic optimal charging of electric-drive vehicles with renewable energy

The paper presents the stochastic optimization algorithm that may eventually be used by electric energy suppliers to coordinate charging of electric-drive vehicles (EDVs) in order to maximize the use of renewable energy in transportation. Due to the stochastic nature of transportation patterns, the Monte Carlo simulation is applied to model uncertainties presented by numerous scenarios. To reduce the problem complexity, the simulated driving patterns are not individually considered in the optimization but clustered into fleets using the GAMS/SCENRED tool. Uncertainties of production of renewable energy sources (RESs) are presented by statistical central moments that are further considered in Hong’s 2-point + 1 estimation method in order to define estimate points considered in the optimization. Case studies illustrate the application of the proposed optimization in achieving maximal exploitation of RESs in transportation by EDVs.

► Optimization model for EDV charging applying linear programming.
► Formation of EDV fleets based on the driving patterns assessment applying the GAMS/SCENRED.
► Consideration of uncertainties of RES production and energy prices in the market.
► Stochastic optimization.
► Application of Hong’s 2-point + 1 estimation method.