Recursive dynamic regression‐based two‐stage compensation algorithm for dynamic economic dispatch considering high‐dimensional correlation of multi‐wind farms

Abstract

Large-scale wind farms connected to the power grid have brought great challenges to power-system dispatch. In this study, an improved two-stage compensation stochastic optimisation algorithm based on recursive dynamic regression is proposed to solve the day-ahead dynamic economic-dispatching problem considering the high-dimensional correlation of multiple wind farms. First, a copula function is used to describe the correlation of high-dimensional wind farms. Second, a two-stage compensation stochastic-optimisation algorithm is proposed to convert the dynamic economic-dispatching model to a two-stage model with mutual iteration by decoupling the conventional and stochastic variables. In this decoupled model, the calculation of the expected compensation cost is critical and usually limited by the dimension of the correlated wind farms, which leads to inefficient convergence and long computation times. To solve those problems, a recursive dynamic multivariable linear regression method based on global least squares is proposed to improve the two-stage stochastic optimisation algorithm. This improved two-stage compensation algorithm overcomes the dimensional disaster of traditional stochastic optimisation methods and can solve the dynamic economic dispatching problem considering the high-dimensional correlation of multiple wind farms. Finally, the practicability and efficiency of the proposed algorithm are verified by the examples of an IEEE118 system and an actual provincial system.