Multi-objective optimization based robust scheduling of electric vehicles aggregator

Nowadays, uncertainty modeling of upstream grid price by the electric vehicles aggregator is the most important section of electricity market in the smart grid. Therefore, in this paper an interval optimization approach is proposed to model upstream grid price uncertainty. This study presents a novel method in order to solve the problem of electric vehicles aggregators uncertainty which uncertainty-based profit function is changed to deterministic multi-objective problem considering deviation and average profits as conflicting objective functions that average profit should reach the maximum while deviation profit should reach the minimum. Furthermore, the epsilon-constraint method is applied to solve the two-dimensional equation to generate optimal Pareto solutions. Finally, to choose trade-off solution from Pareto solutions as a goal, the fuzzy satisfying approach is implemented. Also, the deterministic approach is compared with the proposed interval optimization approach to demonstrate how considered approach is capable. According to achieved interval multi-objective optimization results, the average profit of electric vehicles aggregators is slightly decreased by 2.94% while the deviation profit is reduced 50% in comparison with the deterministic approach which is more robust versus upstream grid price uncertainty. The mixed-integer linear programming model is solved by the CPLEX solver under the GAMS optimization software.