Research on Volatility Clustering Algorithm for Intermittent Renewable Energy

Abstract

Abstract The increase in the penetration rate of renewable energy such as wind power and photovoltaics has brought challenges to the stable dispatch and safe operation of the power system. Therefore, based on the short-time fast Fourier transform method, this paper constructs a long-short-term memory neural network model, and proposes a new volatility clustering algorithm for intermittent renewable energy. On this basis, based on the open source data set RTS-GMLC published by the National Renewable Energy Laboratory (NREL), experimental simulations were carried out to verify the effectiveness of the proposed volatility clustering algorithm for intermittent renewable energy. sex. This provides a quantitative decision-making basis for the dispatch and operation of power systems dominated by intermittent renewable energy.