Stochastic Control for Intra-Region Probability Maximization of Multi-Machine Power Systems Based on the Quasi-Generalized Hamiltonian Theory

Abstract

With the penetration of renewable generation, electric vehicles and other random factors in power systems, the stochastic disturbances are increasing significantly, which are necessary to be handled for guarantying the security of systems. A novel stochastic optimal control strategy is proposed in this paper to reduce the impact of such stochastic continuous disturbances on power systems. The proposed method is effective in solving the problems caused by the stochastic continuous disturbances and has two significant advantages. First, a simplified and effective solution is proposed to analyze the system influenced by the stochastic disturbances. Second, a novel optimal control strategy is proposed in this paper to effectively reduce the impact of stochastic continuous disturbances. To be specific, a novel excitation controlled power systems model with stochastic disturbances is built in the quasi-generalized Hamiltonian form, which is further simplified into a lower-dimension model through the stochastic averaging method. Based on this Itô equation, a novel optimal control strategy to achieve the intra-region probability maximization is established for power systems by using the dynamic programming method. Finally, the intra-region probability increases in controlled systems, which confirms the effectiveness of the proposed control strategy. The proposed control method has advantages on controlling the fluctuation of system state variables within a desired region under the influence of stochastic disturbances, which means improving the security of stochastic systems. With more stochasticity in the future, the proposed control method based on the stochastic theory will play a novel way to relieve the impact of stochastic disturbances.

The Evolution of Probability Density Function for Power System Excited by Fractional Gaussian Noise

“…Ju et al [29] discussed the effect of additive Gaussian white noise on power system dynamics based on the stochastic averaging method. Lin et al [30] established a novel optimal control strategy for power systems under Gaussian white noise excitation using the dynamic programming method.…”