Location Determination of Electric Vehicles Parking Lot With Distribution System by Mexican AXOLOTL Optimization and Wild Horse Optimizer

In this manuscript proposes a hybrid approach for locating and sizing Electric vehicle charging stations (EVCSs) optimally and managing the vehicle charging process. The proposed hybrid approach is to work in conjunction with Mexican Axolotl optimization (MAO) and Wild Horse Optimizer (WHO) hence it is named as MAOWHO approach. The major purposes of the proposed approach are to site and size of the electric vehicle parking lot (EVPL) and to improve the benefit of EVPL for the participation of the reserve market. In addition, power loss and voltage fluctuations occur due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) demand load, which is reduced by the proposed approach. To optimally determine the size of the parking lot, the MAOWHO approach is adopted. The integration of the EV and PV systems, especially in parking lots, enhances the reliability and flexibility of the electrical system at critical moments. Multiple objective optimization problems are calculated to achieve objective variables to reduce power losses, voltage fluctuations, charging and supply costs, and EV costs. The location and capacity of the RES and EV charging stations in this optimization problem are objective variables. The MAOWHO approach enhances Solar Powered Electric Vehicle Parking Lot (EVSPL) participation in various energy and ancillary service markets that includes the effects of capacity payments. Besides, the implementation of MAOWHO approach is done by the MATLAB/Simulink platform and the performance of the MAOWHO approach is compared to the existing approaches. From the simulation outcome, it concludes that the proposed approach based performance provides a profit of 880 €compared to other approaches like SMO, CGO, SBLA.