A multi-objective optimization approach to active power control of wind farms

With more and more wind farms integrated into the grid, the stability and security of the grid can be significantly affected by the wind-farm-generated power, due to the intermittent and volatile nature of the wind-farm-generated power. Therefore, control of the wind farm power to meet the stability and quality requirements of the power grid becomes important. Active power control of wind-farm, however, is challenging because the wind-farm output power can only be reliably predicted for a short period of time (i.e., ultra-short term power prediction), and large variations exist in the wind-turbine output power. In this paper, an optimal active power control scheme is proposed to maximize the running time of each individual wind turbine, and minimize the switching of wind turbines. Particularly, the ultra-short term power prediction is utilized to quantify the available output power of each wind turbine, and the wind turbines are classified according to the running conditions and the available powers. Then, the active power allocation is formulated as a multi-objective optimization problem and solved by using an improved genetic algorithm (GA). The proposed approach is illustrated by implementing it to the active allocation of a wind-farm model in simulation. The simulation results demonstrate that the proposed method to the active power allocation outperforms the conventional average method, and optimally distributes active power among the wind turbines.