Renewable Scenario Generation Based on the Hybrid Genetic Algorithm with Variable Chromosome Length

Determining the operation scenarios of renewable energies is important for power system dispatching. This paper proposes a scenario generation method based on hybrid genetic algorithm with variable chromosome length (HGAVCL). The discrete wavelet transform (DWT) used to divide original data into linear and fluctuant parts according time scales. HGAVCL designed optimally part different sections. Additionally, each section described by autoregressive integrated moving average (ARIMA) model. With consideration temporal correlation, Copula joint probability density function established model part. Based attained ARIMA function, number are generated Monte Carlo method, autocorrelation, offset rate, climbing similarity indexes assess quality scenarios. A case study conducted verify effectiveness proposed approach. calculated 0.0515, 0.0396, 0.9035, respectively, which shows superior performance