Optimization of Vehicle-to-Grid Scheduling in Constrained Parking Lots
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An automatic Vehicle-to-Grid (V2G) technology can contribute to the utility grid. V2G technology has drawn great interest in the recent years. Success of the sophisticated automatic V2G research depends on efficient scheduling of gridable vehicles in constrained parking lots. Parking lots have constraints of space and current limits for V2G. However, 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 only thermal units. Particle swarm optimization (PSO) is proposed to solve the V2G, as PSO has been demonstrated to 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 optimizes the on/off states of power generating units easily. Vehicles are presented by signed integer number instead of 0/1 to reduce the dimension of the problem. Typical discrete version of PSO has less balance between local and global searching abilities to optimize the number of charging/discharging gridable vehicles in the constrained system. In the same model, balanced PSO is proposed to optimize the V2G part in the constrained parking lots. Finally, results show a considerable amount of profit for using proper scheduling of gridable vehicles in constrained parking lots.