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

• Published in: IET renewable power generation Vol. 13; no. 3; pp. 475 - 481
• Main Authors: Xie, Min, Ke, Shaojia, Xiong, Jing, Cheng, Peijun, Liu, Mingbo
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 25.02.2019
• Subjects: conventional variables; correlated wind farms; day‐ahead dynamic economic‐dispatching problem; decoupled dynamic economic‐dispatching model; dimensional disaster; dynamic economic dispatch; dynamic economic dispatching problem; dynamic updating; expected compensation cost; high‐dimensional correlation; high‐dimensional wind farms; large‐scale wind farms; multiple wind farms; multiwind farms; optimisation; power generation dispatch; power generation economics; power grid; power grids; power‐system dispatch; recursive dynamic multivariable linear regression method; recursive dynamic regression‐based two‐stage compensation algorithm; regression analysis; Research Article; stochastic processes; stochastic programming; stochastic variables; traditional stochastic optimisation methods; two‐stage compensation stochastic optimisation algorithm; two‐stage model; two‐stage stochastic optimisation algorithm; wind power plants; wind‐power generation; two-stage compensation stochastic optimisation algorithm; day-ahead dynamic economic-dispatching problem; dynamic economic dispatching problem; stochastic programming; regression analysis; wind-power generation; power grid; traditional stochastic optimisation methods; stochastic variables; power generation economics; optimisation; correlated wind farms; dynamic updating; power grids; stochastic processes; wind power plants; large-scale wind farms; dimensional disaster; dynamic economic dispatch; high-dimensional correlation; recursive dynamic multivariable linear regression method; power-system dispatch; high-dimensional wind farms; two-stage stochastic optimisation algorithm; power generation dispatch; two-stage compensation stochastic-optimisation algorithm; expected compensation cost; multiple wind farms; multiwind farms; conventional variables; two-stage model; decoupled dynamic economic-dispatching model; recursive dynamic regression-based two-stage compensation algorithm
• ISSN: 1752-1416; 1752-1424; 1752-1424
• DOI: 10.1049/iet-rpg.2018.5494

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.