A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems

This paper formulates and solves a probabilistic optimal power flow approach (POPF) for a hybrid power system that includes plug-in electric vehicles (PEV), photovoltaic (PV) and wind energy (WE) sources. In the proposed approach, the Monte Carlo Simulation (MCS) was combined with the antithetic variates method (AVM) to determine the probability distribution function (PDF) of the power generated by the hybrid system. To reduce the computational cost of the optimal power flow calculations, we solved the POPF problem using a master-slave parallel epsilon variable multi objective genetic algorithm (Pev-MOGA). The performance of the proposed approach was assessed using the IEEE 30-bus, 57-bus and 118-bus power systems. Various scenarios incorporating several configurations of WEs, PVs and PEVs sources were considered in the evaluation. Sensitivity analysis was also performed for further assessment. The obtained results along with a comparison analysis with other optimization algorithms confirmed the effectiveness of the proposed approach in accurately providing a set of optimal solutions for the hybrid power system.