Two-stage and multi-stage decompositions for the medium-term hydrothermal scheduling problem: A computational comparison of solution techniques. (May 2021)

• Record Type: Journal Article
• Authors: Beltrán, F.; Finardi, E.C.; de Oliveira, W.
• Is Part Of: International journal of electrical power & energy systems. Volume 127(2021)
• Publication Date: 2021-05
• Subjects: Generation scheduling problem -- Stochastic programming -- Level bundle method; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.106659
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: We propose a MTGS model with a multi-period scenario tree with non-common samples. Hourly periods in the first week are considered to include the thermal UC constraints. A two-stage decomposition and the level bundle method are applied to solve the MTGS. The two-stage decomposition provides time reductions in order of 80%. The use of the level bundle method provides time reductions in the order of 20%. Abstract: In hydro-based power systems, the Generation Scheduling (GS) problem is usually divided into a series of coupled models aiming to obtain an optimal operating policy over a multi-year planning horizon. The medium-term GS (MTGS) problem belongs to these models, linking the long-term GS (LTGS) with the short-term GS (STGS) problem. To carry out a proper coupling, we propose a stochastic MTGS model with a multi-period scenario tree with non-common samples, and hourly discretization in the first week to include the nonconvex thermal unit-commitment constraints. These considerations lead to solving a very challenging large-scale stochastic optimization problem with mixed-binary variables in the first week. In this context, efficient solution techniques must be explored to ensure a suitable trade-off between solution accuracy and computational performance. In this paper, we propose a two-stage decomposition for the MTGS that is solved by the extended level bundle method. Every second-stage subproblem is itself a multi-period stochastic problem involving only continuous variables. Numerical assessments on the large-scale Brazilian MTGS problem indicate that our approach significantly outperforms the Nested decomposition and Stochastic Dual Dynamic Programming (SDDP) algorithm in terms of computational burden, providing CPU time reductions in the order of 19%.