Wind-power-heat Correlation and Its Underlying Uncertainty Analysis Based on Copula Function

In economic dispatch of power system, it is necessary and effective to consider the correlation between heat load, electric load and wind power output to save power generation costs and achieve reasonable wind power consumption. Therefore, based on the theory of Copula correlation analysis, a multivariate Copula analysis toolbox (MvCAT) is proposed, which contains various Copula families with different degrees of complexity. The Bayesian framework based on residual Gaussian likelihood function is used to infer Copula parameters and estimates potential uncertainty. First, the model is inferred in the Bayesian framework, and the hybrid evolutionary Markov chain Monte Carlo (MCMC) method is used to estimate the posterior distribution and generate the Copula parameters. Then the performance of Copula is evaluated based on how close the simulated bivariate probability is to its empirical observations, and the optimal Copula model is selected. Finally, the data from a certain province is used as a sample for analysis. The results show that the proposed method can well reflect the rank correlation between variables, and the uncertainty associated with data length can be assessed quantitatively.