研究目的
To address the problem of probabilistic small signal stability analysis (PSSSA) of a power system consisting of multiple types of renewable energy by proposing a probabilistic collocation method (PCM)-based PSSSA for a power system consisting of wind farms and photovoltaic farms.
研究成果
The study concludes that the 3*3 order PCM meets computational accuracy requirements with much smaller time consumption than MCS, making it suitable for analyzing the PSSS of power systems with wind and PV power. Reducing the output of synchronous generators improves PSSS, while shutting off part of synchronous units has complex effects. The study emphasizes the need for careful planning and possibly installing damping controllers to mitigate risks.
研究不足
The study acknowledges that shutting off part of the synchronous units to increase renewable energy penetration has more complex effects, which may be beneficial or adverse for the PSSS of the system. It is affected by many factors such as the location of PV and wind power, access capacity, wind speed, and light intensity.
1：Experimental Design and Method Selection:
The study adopts the PCM for PSSSA of a power system with wind and PV power generation. The methodology involves selecting parameter samples through the probability distribution of parameters to establish a polynomial relationship between simulation results and uncertain parameters.
2：Sample Selection and Data Sources:
The study uses a 4-machine 2-area system and the New England system for case studies. The probabilistic models of wind and PV power are described, with wind speed obeying a Weibull distribution and light intensity following a Beta distribution.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned in the provided text.
4：Experimental Procedures and Operational Workflow:
The flow chart of PSSSA for a power system with multiple types of renewable energy sources based on PCM is described, including setting up probability models, confirming the order of PCM, conducting small disturbance analysis, and obtaining the probability distribution of damping ratios.
5：Data Analysis Methods:
The study calculates the expectation and standard deviation of the damping ratio, compares the results with Monte Carlo simulations, and uses the probabilistic eigenvalue index to determine the PSSS of the system.
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