Calculation of maximum injection power of wind farms based on chance-constrained programming

Due to the stochastic characteristics of wind farms outputs, this paper presents a method based on the stochastic programming theory in calculation of the maximum injection power of large-scale wind farms connected to power systems. Calculation of the maximum injection power can be turned into a chance-constrained programming problem under certain security, stability and power quality constraints by this method. It introduces random variables to describe active powers of wind farms and pays attention to realistic operating situations during short time periods. A hybrid intelligent algorithm combined with the stochastic simulation, neural networks and genetic algorithm is proposed to calculate the maximum injection power. The results for the IEEE 30-bus system with wind farms demonstrate the correctness and effectiveness of the proposed method. The method avoids fussy calculation of the dynamic simulation and conservative results computed by deterministic programming methods, and it ensures the optimization of results.