A multi-band uncertainty set robust method for unit commitment with wind power generation

Robust optimization (RO) is one of the most effective methods to deal with unit commitment problems of power systems. The most commonly used robust optimizations are classical robust optimization (CRO) and Seng-Cheol Kang robust optimization (SCK-RO). In order to better coordinate the robustness and economy of power system operation, this paper introduces another robust optimization model called multi-band uncertainty robust optimization (MBU-RO). The proposed multi-band uncertainty robust optimization divides the wind power fluctuation interval into several small intervals and weakens the effect of intervals with less probability on robustness and economy of power system operation. In this paper, the basic idea of multi-band uncertainty robust optimization is introduced. The differences between multi-band uncertainty robust optimization and Seng-Cheol Kang robust optimization are analyzed. Besides, this paper improves the parameter setting method of multiband uncertainty robust optimization based on the historical sample of wind power so that the optimization result of multi-band uncertainty robust optimization is more objective. The results are tested on the IEEE-39 power system with one wind farm and the IEEE-118 power system with three wind farms. Compared with the classical robust optimization and Seng-Cheol Kang robust optimization, simulation results show that the proposed multi-band uncertainty robust optimization can better balance the robustness and economy of the power system with wind energy.

Automated summary

This paper introduces a new robust optimization model called multi-band uncertainty robust optimization, which aims to better balance the robustness and economy of power systems with wind energy. By dividing the wind power fluctuation interval into smaller intervals and adjusting the parameter setting method based on historical wind power samples, the proposed multi-band uncertainty robust optimization shows better performance in balancing the system's robustness and economy compared to classical robust optimization and Seng-Cheol Kang robust optimization.