A scenario probability based method to solve unit commitment of large scale energy storage system and thermal generation in high wind power penetration level system

Massive scenarios of wind power generation make the solving of unit commitment more complex and time-consuming. In this paper, a revised binary particle swarm optimization (RBPSO) to solve scenario probability based unit commitment is proposed. The RBPSO is used to generate the starting/stop state of units in out layer, and in inner layer a revised lambda iteration method (RLIM) is used to give economic dispatch which considers scenario probability, wind power curtailment, load shedding. The RBPSO simplify the iteration process and significantly shorten solving times. Latin hypercube sampling (LHS) is employed to generate scenarios and K-mean cluster technique is developed to realize scenario reduction and scenario probability calculation. The methodology is verified by a 10 units with 24h demand horizon.