Estimación de estado en la central termogas Machala utilizando machine learning
Fecha
Director
Enlace al recurso
DOI
ORCID
Google Scholar
Cvlac
gruplac
Descripción Dominio:
Título de la revista
ISSN de la revista
Título del volumen
Editor
Universidad Santo Tomás. Seccional Bucaramanga
Compartir
Resumen
En el presente trabajo de titulación se analizó el estado de la Central Termogas Machala, la problemática del proyecto es garantizar grandes retos para conseguir una continuidad y garantizar el abastecimiento de energía eléctrica de forma eficiente y aprovechamiento de los recursos naturales y minimizando el impacto ambiental, la central termogas Machala trabaja con ciclo combinado, cuenta con 8 unidades generadoras que corresponden a Machala I y Machala II y la potencia total es 187 MW. En base a la programación en lenguaje Python utilizando la librería de Pyomo para el proceso de optimización, esta permitió analizar las variables de costos de combustible, potencia y Energía eléctrica de la central, la función objetivo corresponde a minimizar los costos de generación de energía eléctrica y las restricciones están asociadas a costos de arranque, parada y el balance de potencia. Por otra parte, para la resolución del problema se hace uso de GNU Linear Programming Kit (GLPK), debido a que el tipo de programación propuesta es entero lineal mixta; a través del análisis realizado se pudo observar qué generadores térmicos pueden operar al mismo tiempo, formar planes de mantenimiento para la salida de generadores de forma programada y cuál es la energía total producida.
In the present titling work, the state of the Machala Termogas Power Plant was analyzed, the problem of the project is to guarantee great challenges to achieve continuity and guarantee the supply of electrical energy efficiently and use of natural resources and minimizing environmental impact, The Machala thermogas plant works with a combined cycle, it has 8 generating units that correspond to Machala I and Machala II and the total power is 187 MW. Based on the programming in Python language using the Pyomo library for the optimization process, this allowed to analyze the variables of costs of fuel, power and electrical energy of the plant, the objective function corresponds to minimizing the costs of electrical energy generation and the restrictions are associated with start-up, stop and power balance costs. On the other hand, to solve the problem, the GNU Linear Programming Kit (GLPK) is used, because the type of programming proposed is mixed linear integer; Through the analysis carried out, it was possible to observe which thermal generators can operate at the same time, form maintenance plans for the output of generators on a scheduled basis and what is the total energy produced.
In the present titling work, the state of the Machala Termogas Power Plant was analyzed, the problem of the project is to guarantee great challenges to achieve continuity and guarantee the supply of electrical energy efficiently and use of natural resources and minimizing environmental impact, The Machala thermogas plant works with a combined cycle, it has 8 generating units that correspond to Machala I and Machala II and the total power is 187 MW. Based on the programming in Python language using the Pyomo library for the optimization process, this allowed to analyze the variables of costs of fuel, power and electrical energy of the plant, the objective function corresponds to minimizing the costs of electrical energy generation and the restrictions are associated with start-up, stop and power balance costs. On the other hand, to solve the problem, the GNU Linear Programming Kit (GLPK) is used, because the type of programming proposed is mixed linear integer; Through the analysis carried out, it was possible to observe which thermal generators can operate at the same time, form maintenance plans for the output of generators on a scheduled basis and what is the total energy produced.
Abstract
Idioma
Palabras clave
combined cycle, Pyomo, Python, optimization, thermogas plant, central termogas, ciclo combinado, Pyomo, Python, optimización