Wind-electricity-heat Correlation and Potential Uncertainty Analysis Based on Copula Function

In the economic dispatch and optimal operation of power system, it is necessary and effective to consider the correlation among heat load, power load and wind power output to formulate a reasonable dispatch plan or to evaluate the reliability of the system. Therefore, based on the theory of Copula correlation analysis, a multivariate Copula analysis toolbox (MvCAT) is proposed to infer Copula parameters and estimate potential uncertainties. Firstly, the mixed evolution Markov Chain Monte Carlo (MCMC) method in Bayes framework calculates the posterior distribution of Copula parameters, evaluate their uncertainties relative to fitting, and then select the appropriate Copula function by goodness-of-fit test. Finally, a the typical daily data of a province as samples for analysis, proposed method solves the limitation that the local optimization method often falls into the local minimum, and quantitative evaluation of the correlation between the response variables and the uncertainty associated with the length of the recorded data is essential for multivariate frequency analysis.