Abstract
A source-load coordination scheduling strategy is proposed in this paper to reduce the system operation cost and wind power curtailment. Firstly, the scheduling model of the power system with wind power is established. To solve the scheduling problem, the binary particle swarm optimization (BPSO) algorithm is used to determine the ON/OFF states of generations; the continuous particle swarm optimization (CPSO) algorithm is used to deal with the economic load dispatch problem; and the constraints are properly handled by adjustment methods. Secondly, in order to maximize the wind power accommodation rate, the power system adopts the time-of-use price program, an optimization model of electricity price is established based on price elasticity matrix. The CPSO algorithm and parallel computing are used to optimize the time-of-use price schedules. According to the results of the case study, the demand response program plays an important role in reducing the peak-valley difference, wind power curtailment, and system operating cost. The proposed scheduling strategy and algorithm are proven to have a good optimization performance, calculation speed and stability.
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