MUWOS - Multiple use water optimization system for the power generation and navigation trade-offs analysis

Nelio Moura de Figueiredo, Claudio José Cavalcante Blanco, Lúcio Carlos Pinheiro Campos Filho and André Luiz Amarante Mesquita

Renewable Energy, 2023, vol. 203, issue C, 205-218

Abstract: This article aimed to develop a model for managing multiple uses of water to minimize conflicts of use related to the operation of reservoir systems in hydroelectric watersheds. The MUWOS model – Multiple Uses Water Optimization System, which consists of an optimization model based on non-linear programming, developed and structured in GAMS (General Algebraic Modeling System) using the MINOS solver, for conflict mitigation and minimization from the modeling and operational optimization for different navigation and hydrological scenarios. The MUWOS was applied to Tapajós watershed for the future Hydropower Project – HPP São Luiz do Tapajós, Itaituba, state of Pará, Brazil. For power generation and navigation water depth, considering inflows for dry, medium and wet hydrological scenarios, MUWOS showed, in relation to reference levels for the low, medium and high navigation scenarios, water depths dropped below the minimum for average generations of 2411 MW; 2939 MW and 3586 MW, respectively. For energy generation and transported cargo capacity, considering the same hydrological scenarios, MUWOS demonstrated that, in relation to the reference levels of the low, medium and high navigation scenarios, average generations above 2869 MW; 3508 MW and 4740 MW, respectively, result in no gains in transported cargo capacity, and average generations below 1344 MW; 1622 MW, and 2056 MW, respectively, make cargo transport unfeasible. The developed model represents a tool of fundamental importance for the operational optimization of reservoir systems with multiple uses, allowing the optimization of generation and outflow in HPPs, with the maintenance of navigability conditions downstream of dams.

Keywords: Water management; Reservoir operation; Optimization; Nonlinear programming; Amazon (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:203:y:2023:i:c:p:205-218

DOI: 10.1016/j.renene.2022.12.004

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