Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power

• Published in: IEEE transactions on sustainable energy Vol. 1; no. 1; pp. 19 - 29
• Main Authors: Chen, Peiyuan, Siano, Pierluigi, Bak-Jensen, Birgitte, Chen, Zhe
• Format: Journal Article
• Language: English
• Published: IEEE 01.04.2010
• Subjects: Constraint optimization; Control systems; Correlation; Monte Carlo; power factor; Power system modeling; Reactive power; stochastic optimization; Stochastic processes; Stochastic systems; time series; Voltage; Wind energy; Wind power generation; wind power generation (WPG); Wind turbines
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2010.2044900

This paper proposes a stochastic optimization algorithm that aims to minimize the expectation of the system power losses by controlling wind turbine (WT) power factors. This objective of the optimization is subject to the probability constraints of bus voltage and line current requirements. The optimization algorithm utilizes the stochastic models of wind power generation (WPG) and load demand to take into account their stochastic variation. The stochastic model of WPG is developed on the basis of a limited autoregressive integrated moving average (LARIMA) model by introducing a cross-correlation structure to the LARIMA model. The proposed stochastic optimization is carried out on a 69-bus distribution system. Simulation results confirm that, under various combinations of WPG and load demand, the system power losses are considerably reduced with the optimal setting of WT power factor as compared to the case with unity power factor. Furthermore, an economic evaluation is carried out to quantify the value of power loss reduction. It is demonstrated that not only network operators but also WT owners can benefit from the optimal power factor setting, as WT owners can pay a much lower energy transfer fee to the network operators.