Abstract
With the increase in the proportion of multiple renewable energy sources, power electronics equipment and new loads, power systems are gradually evolving towards the integration of multi-energy, multi-network and multi-subject affected by more stochastic excitation with greater intensity. There is a problem of establishing an effective stochastic dynamic model and algorithm under different stochastic excitation intensities. A Milstein-Euler predictor-corrector method for a nonlinear and linearized stochastic dynamic model of a power system is constructed to numerically discretize the models. The optimal threshold model of stochastic excitation intensity for linearizing the nonlinear stochastic dynamic model is proposed to obtain the corresponding linearization threshold condition. The simulation results of one-machine infinite-bus (OMIB) systems show the correctness and rationality of the predictor-corrector method and the linearization threshold condition for the power system stochastic dynamic model. This study provides a reference for stochastic modelling and efficient simulation of power systems with multiple stochastic excitations and has important application value for stability judgment and security evaluation.
Highlights
Driven by energy reform and emerging technologies, the trend of the “three highs” and “three multiples” in smart grids is gradually accelerating [1]
For a power system under the action of stochastic excitations of different intensities, this paper studies the stochastic excitation threshold conditions that can be used to linearize a nonlinear stochastic dynamic model and select the stochastic dynamic model of the power system, while ensuring the accuracy of both the model and solution
The general stochastic differential equation is driven by Brownian motion, while the stochastic dynamic model in this paper is driven by Gaussian white noise
Summary
Driven by energy reform and emerging technologies, the trend of the “three highs” (high proportion of renewable energy source grid connection, high proportion of power electronics equipment and high proportion of new loads) and “three multiples” (multiple energy sources, multiple networks and multiple subjects) in smart grids is gradually accelerating [1]. The linearization process ignores the influence of the nonlinear characteristics of the system, so stability analysis deviation will occur when the stochastic excitation of a power system increases. A stochastic large disturbance is a sudden change of large-capacity load (such as switching of large-capacity load, switching of main system components, or component failure) For such a large disturbance, a nonlinear model is needed, and its stability analysis methods include the extended equal area and pseudo-Hamiltonian system stochastic average methods [1]. This avoids the deviation of stability analysis caused by the linear model solution method when the linearization condition of the stochastic dynamic model is not satisfied It shows the influence of different stochastic excitation intensities on model selection and stability analysis, and provides a reference for power system stochastic modelling and efficient simulation.
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