An optimal solution to unit commitment problem of realistic integrated power system involving wind and electric vehicles using chaotic slime mould optimizer

Abstract Plug-in electric vehicles (PEVs) could be integrated into power networks to meet rising demand as well provide mobile storage help the grid operate more efficiently. The most efficient charging and discharging of PEVs are required for effective utilization this potential. with poor management may see a spike in peak demand, resulting increased generation. To take advantage off-peak benefits avoid load shedding, must intelligently scheduled. This paper offers solution optimal generation scheduling impact vehicle (V2G) operation presence wind renewable energy source using chaotic slime mould algorithm (CSMA). Further, effectiveness proposed simulation results 10-unit system incorporating V2G has been compared other well-known optimization techniques such harmony search (HAS), chemical reaction optimization(CRO), genetic artificial neural network(GA-ANN), particle swarm (PSO), cuckoo (CS). comparative analysis reveals significant cost savings