Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation

Aggregated probabilistic wind power forecasting is important for power system operations. In this paper, an improved aggregated probabilistic wind power forecasting framework based on spatio-temporal correlation is developed. A Q-learning enhanced deterministic wind power forecasting method is used to generate deterministic wind power forecasts for individual wind farms. The spatio-temporal correlation between the member wind farms and the aggregated wind power is modeled by a joint distribution model based on the copula theory. The marginal distributions of actual aggregated wind power and forecasted power of member wind farms are built with Gaussian mixture models. Then, a conditional distribution of the aggregated wind power is deduced through the Bayesian theory, which is used for aggregated probabilistic forecasts. The effectiveness of the proposed aggregated probabilistic wind power forecasting framework is validated by using the Wind Integration National Dataset Toolkit. Numerical results of case studies at nine locations show that the developed aggregated probabilistic forecasting methodology has improved the pinball loss metric score by up to 54% compared to three benchmark models.