Dealing with Negative Inflows in the Long-Term Hydrothermal Scheduling Problem

Paulo Vitor Larroyd, Renata Pedrini, Felipe Beltrán, Gabriel Teixeira, Erlon Cristian Finardi and Lucas Borges Picarelli
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Paulo Vitor Larroyd: Norus, Florianópolis 88036-003, Brazil
Renata Pedrini: Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianopolis 88040-900, Brazil
Felipe Beltrán: Norus, Florianópolis 88036-003, Brazil
Gabriel Teixeira: Norus, Florianópolis 88036-003, Brazil
Erlon Cristian Finardi: Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianopolis 88040-900, Brazil
Lucas Borges Picarelli: Norte Energia S.A., Brasilia 70390-025, Brazil

Energies, 2022, vol. 15, issue 3, 1-19

Abstract: The long-term hydrothermal scheduling (LTHS) problem seeks to obtain an operational policy that optimizes water resource management. The most employed strategy to obtain such a policy is stochastic dual dynamic programming (SDDP). The primary source of uncertainty in predominant hydropower systems is the reservoirs inflow, usually a linear time series model (TSM) based on the order- p periodic autoregressive [PAR( p )] model. Although the linear PAR( p ) can represent the seasonality and autocorrelation of the inflow datasets, negative inflows may appear during SDDP iterations, leading to water balance infeasibilities in the LTHS problem. Different from other works, the focus of this paper is not avoiding negative inflows but instead dealing with the negative values that cause infeasibilities. Hence, three strategies are discussed: ( i ) inclusion of a slack variable penalized in the objective function, ( ii) negative inflow truncation to zero, and ( iii ) optimal inflow truncation, among which the latter is a novel approach. The strategies are compared individually and combined. Methodological conditions and evidence of the algorithm convergence are presented. Out-of-sample simulations show that the choice of negative inflow strategy significantly impacts the performance of the resultant operational policy. The combination of strategy ( i) and ( iii ) reduces the expected operation cost by 15%.

Keywords: time series model; river inflow; hydrothermal scheduling; SDDP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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