The randomness, volatility and weak regulation characteristics of renewable energy have brought new challenges to the static voltage safety and stability of a power system. In view of this, an online probability evaluation method of static voltage stability margin of a power system with a high proportion of renewable energy considering the bilateral uncertainties of source and load is proposed. First, the influence of a large number of renewable energies replacing traditional units on static voltage stability margin is analyzed based on the difference of reactive power regulation characteristics between them. Then the influence of renewable energy output uncertainty on the distribution range of the stability margin is analyzed, and source and load uncertainty models are established to generate typical scenarios. Finally, in order to deal with the rapid fluctuation of stability margin brought by renewable energy, an online probability assessment method of stability margin based on optimized ELM-KDE is proposed. The stability margin of typical scenarios is predicted by an optimized extreme learning machine (ELM), and its probability distribution function is accurately obtained by kernel density estimation (KDE). The expected margin of static voltage stability and the risk of static voltage stability are constructed to characterize the results. Simulation tests are carried out on the New England 39 and IEEE 300 node systems, and the results are compared with the traditional Monte Carlo calculation results to verify the effectiveness of the proposed method.

This work is supported by the National Key Research and Development Program of China (No. 2020YFB0905900).