A multivariate framework to study spatio‐temporal dependency of electricity load and wind power

Abstract

With massive wind power integration, the spatial distribution of electricity load centers and wind power plants make it plausible to study the inter‐spatial dependence and temporal correlation for the effective working of the power system. In this paper, a novel multivariate framework is developed to study the spatio‐temporal dependency using vine copula. Hourly resolution of load and wind power data obtained from a US regional transmission operator spanning 3 years and spatially distributed in 19 load and two wind power zones are considered in this study. Data collection, in terms of dimension, tends to increase in future, and to tackle this high‐dimensional data, a reproducible sampling algorithm using vine copula is developed. The sampling algorithm employs k‐means clustering along with singular value decomposition technique to ease the computational burden. Selection of appropriate clustering technique and copula family is realized by the goodness of clustering and goodness of fit tests. The paper concludes with a discussion on the importance of spatio‐temporal modeling of load and wind power and the advantage of the proposed multivariate sampling algorithm using vine copula.

Scenario Generation by Physical Model-Free Approaches for Multiple Renewables

“…In the context of power systems, the Copula framework facilitates the modelling of spatiotemporal dependencies within power outputs by employing a covariance matrix. Subsequent scenario generation is performed through inversetransform sampling methods, such as Monte Carlo simulations, which enable the practical application of the model [11]. random vector 𝑼 = (𝑈 1 , 𝑈 2 , … , 𝑈 𝑚 ) = (𝐹 1 (𝑋 1 ), 𝐹 2 (𝑋 2 ), … , 𝐹 𝑚 (𝑋 𝑚 )) is with uniformly distributed, ranging from 0 to 1, marginals.…”