A Nested Cross Decomposition Algorithm for Power System Capacity Expansion with Multiscale Uncertainties

Zhouchun Huang (), Qipeng P. Zheng () and Andrew L. Liu ()
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Zhouchun Huang: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 211100 Nanjing, China
Qipeng P. Zheng: Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, Florida 32816
Andrew L. Liu: School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907

INFORMS Journal on Computing, 2022, vol. 34, issue 4, 1919-1939

Abstract: Modern electric power systems have witnessed rapidly increasing penetration of renewable energy, storage, electrical vehicles, and various demand response resources. The electric infrastructure planning is thus facing more challenges as a result of the variability and uncertainties arising from the diverse new resources. This study aims to develop a multistage and multiscale stochastic mixed integer programming (MM-SMIP) model to capture both the coarse-temporal-scale uncertainties, such as investment cost and long-run demand stochasticity, and fine-temporal-scale uncertainties, such as hourly renewable energy output and electricity demand uncertainties, for the power system capacity expansion problem. To be applied to a real power system, the resulting model will lead to extremely large-scale mixed integer programming problems, which suffer not only the well-known curse of dimensionality but also computational difficulties with a vast number of integer variables at each stage. In addressing such challenges associated with the MM-SMIP model, we propose a nested cross decomposition algorithm that consists of two layers of decomposition—that is, the Dantzig–Wolfe decomposition and L-shaped decomposition. The algorithm exhibits promising computational performance under our numerical study and is especially amenable to parallel computing, which will also be demonstrated through the computational results.

Keywords: stochastic programming; power system capacity expansion; parallel computing; decomposition; multiscale uncertainties (search for similar items in EconPapers)
Date: 2022
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