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要旨:
Renewable energy sources in modern power systems pose a serious challenge to the power system stability in the presence of stochastic fluctuations. Many efforts have been made to assess power system stability from the viewpoint of the bifurcation theory. However, these studies have not covered the dynamic evolution of renewable energy integrated, non-autonomous power systems. Here, we numerically explore the transition phenomena exhibited by a non-autonomous stochastic bi-stable power system oscillator model. We use additive white Gaussian noise to model the stochasticity in power systems. We observe that the delay in the transition observed for the variation of mechanical power as a function of time shows significant variations in the presence of noise. We identify that if the angular velocity approaches the noise floor before crossing the unstable manifold, the rate at which the parameter evolves has no control over the transition characteristics. In such cases, the response of the system is purely controlled by the noise, and the system undergoes noise-induced transitions to limit-cycle oscillations. Furthermore, we employ an emergency control strategy to maintain the stable non-oscillatory state once the system has crossed the quasi-static bifurcation point. We demonstrate an effective control strategy that opens a possibility of maintaining the stability of electric utility that operates near the physical limits.
The recent paradigm shift in integrating intermittent renewable energy (RE) and plug-in electric vehicles has increased uncertainties in power systems. These uncertainties alter the operational schedule and stability margin of the system. Therefore, there is significant research interest in power system dynamics in the presence of uncertain fluctuations. Studies on the impact of fluctuations hitherto are based on the approximation of the system as autonomous. In this work, we investigate the effect of noise on the power system transition characteristics for different operating environments, preserving the non-autonomous behavior of the system. We also propose a control strategy for the power system model considered. Considering power system operation close to the physical limits and the chances of cascading failures affecting major sectors of the society, a control strategy which allows one to regain the stability is highly pertinent.
