Abstract
Currently, large-scale solar farms are being rapidly integrated in electrical grids all over the world. However, the photovoltaic (PV) output power is highly intermittent in nature and can also be correlated with other solar farms located at different places. Moreover, the increasing PV penetration also results in large solar forecast error and its impact on power system stability should be estimated. The effects of these quantities on small-signal stability are difficult to quantify using deterministic techniques but can be conveniently estimated using probabilistic methods. For this purpose, the authors have developed a method of probabilistic analysis based on combined cumulant and Gram– Charlier expansion technique. The output from the proposed method provides the probability density function and cumulative density function of the real part of the critical eigenvalue, from which information concerning the stability of low-frequency oscillatory dynamics can be inferred. The proposed method gives accurate results in less computation time compared to conventional techniques. The test system is a large modified IEEE 16-machine, 68-bus system, which is a benchmark system to study low-frequency oscillatory dynamics in power systems. The results show that the PV power fluctuation has the potential to cause oscillatory instability. Furthermore, the system is more prone to small-signal instability when the PV farms are correlated as well as when large PV forecast error exists.
Highlights
There has been a large proliferation of photovoltaic power generation (PVG) in electric power system and its aggregated production capacity is rapidly approaching the conventional generation capacity
A power oscillation damping controller and power system stabilizer are occasionally used to improve small-signal stability for the test system considered in our work [15, 20]
In this paper, the proposed probabilistic method is applied to analyze the effect of PV uncertainties arising mainly due to stochastic PV fluctuations and forecast error on small-signal stability
Summary
There has been a large proliferation of photovoltaic power generation (PVG) in electric power system and its aggregated production capacity is rapidly approaching the conventional generation capacity. The effect of different power system uncertainties including PVG on small-signal stability using game theory approach was studied in [17]. The effect of correlation between solar farms on PSSS was studied in [18] using copula theory and the authors found that correlation can lead to a decrease in system stability margin. There is a need to quantify the effect of correlation between PV farms on small-signal stability with an accurate and fast method. There is a high need to fill the research gaps described here, especially in the current era of rapid large-scale integration of PVG, and to analyze its effect on small-signal stability of a large system using a fast yet accurate technique. Formulation of a framework to assess probabilistic small-signal stability in less computation time with highly accurate results considering stochastic PV output. Development of a probabilistic model of PV output power using real measured data. Formulation of a framework to assess probabilistic small-signal stability in less computation time with highly accurate results considering stochastic PV output. Study of the impact of correlation between solar farms, solar forecast error, and change in penetration level on PSSS
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