Optimal non-anticipative scenarios for nonlinear hydro-thermal power systems

Gislaine A. Periçaro, Elizabeth W. Karas, Clóvis C. Gonzaga, Débora C. Marcílio, Ana Paula Oening, Luiz Carlos Matioli, Daniel H.M. Detzel, Klaus de Geus and Marcelo R. Bessa

Applied Mathematics and Computation, 2020, vol. 387, issue C

Abstract: The long-term operation of hydro-thermal power generation systems is modeled by a large-scale stochastic optimization problem that includes nonlinear constraints due to the head computation in hydroelectric plants. We do a detailed development of the problem model and state it by a non-anticipative scenario analysis, leading to a large-scale nonlinear programming problem. This is solved by a filter algorithm with sequential quadratic programming iterations that minimize quadratic Lagrangian approximations using exact hessians in L∞ trust regions. The method is applied to the long-term planning of the Brazilian system, with over 100 hydroelectric and 50 thermoelectric plants, distributed in 5 interconnected subsystems. This problem with 50 synthetically generated inflow scenarios and a horizon of 60 months, amounting to about one million variables and 15000 nonlinear constraints was solved by the filter algorithm in a standard 2016 notebook computer in 10 h of CPU.

Keywords: Nonlinear optimization; Filter method; Non-anticipative scenario analysis; Hydro-thermal power systems (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:387:y:2020:i:c:s0096300319308124

DOI: 10.1016/j.amc.2019.124820

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