Unit commitment with vehicle-to-Grid using particle swarm optimization.pdf

Unit Commitment with Vehicle-to-Grid using Particle Swarm Optimization Ahmed Yousuf Saber, Member, IEEE and Ganesh Kumar Venayagamoorthy, Senior Member, IEEE Abstract— Vehicle-to-Grid (V2G) technology has drawn great interest in the recent years. Success of the V2G research depends on efficient scheduling of gridable vehicles in limited parking lots. V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning reserves and reliability. As number of gridable vehicles in V2G is much higher than small units of existing systems, unit commitment (UC) with V2G is more complex than basic UC for thermal units. Particle swarm optimization (PSO) is used to solve the UC with V2G, as PSO can reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit. In the proposed model, binary PSO is used to optimize the on/off states of power generating units and in the same model, discrete version of PSO is used to optimize the scheduling of the gridable vehicles in the parking lots to reduce the dimension of the problem. Finally, simulation results show a considerable amount of profit for using V2G after proper UC with V2G scheduling of gridable vehicles in constrained parking lots. Index Terms— Unit commitment, V2G, particle swarm opti- mization, gridable vehicles, generating units, parking lots, profit. I. INTRODUCTION UNIT commitment (UC) involves efficiently schedulingon/off states of all available resources in a system. Unit commitment with vehicle-to-grid (UC-V2G) involves intelligently scheduling existing generating units and large number of gridable vehicles for V2G technology in limited and restricted parking lots so that maximum benefit can be achieved. In addition to fulfill a large number of practical constraints, the opti