Maintenance of generation units coordinated with annual hydrothermal scheduling using a hybrid technique

Abstract

This paper presents a hybrid technique for solving the generation maintenance scheduling coordinated with middle term hydrothermal dispatch based on the Chu-Beasley genetic algorithm and linear programming. It takes into consideration the non-linearity derived from the cost of the fuel for thermal power plants. The output of the genetic algorithm is a proposal for the start week of the generation maintenance plan that minimises the hydrothermal dispatch cost. This work has two main contributions: on one hand proposing a solution that integrates hydrothermal dispatch and maintenance scheduling in a single model, therefore a new mathematical model and, on the other hand the proposed solution applies a specialised genetic algorithm that has not been applied before to solve this problem. The test system to validate the methodology is composed of three hydro plants and two thermal plants split into 22 generation units, considering three types of preventive maintenance, in a planning term of one year (52 weeks). This methodology combines an exact technique and a specialised genetic algorithm, which favours convergence. RESUMEN:Este artículo presenta una técnica híbrida para resolver la programación del mantenimiento de las unidades de generación coordinado con el despacho hidrotérmico de mediano plazo. La solución se basa en el algoritmo genético de Chu-Beasley y en la técnica de programación lineal. Tiene en cuenta no linealidades derivadas del costo de los combustibles utilizados por las centrales térmicas. La salida del algoritmo genético es una propuesta de la semana de inicio del plan de mantenimiento de cada unidad de generación que minimiza el costo del despacho hidrotérmico. Las dos principales contribuciones de este trabajo son que propone un modelo matemático que coordina dos problemas que en la literatura se han resuelto de forma separada, y que aplica un algoritmo genético especializado que aún no ha sido utilizado para resolver el problema coordinado. El sistema de prueba para validar la metodología se compone de tres centrales hidroeléctricas y dos centrales térmicas dividas en 22 unidades de generación, teniendo en cuenta el mantenimiento preventivo, en un horizonte de planeamiento de un año (52 semanas). Esta metodología combina una técnica exacta con un algoritmo genético especializado, lo que favorece la convergencia.18

A Mathematical Program for Scheduling Preventive Maintenance of Cogeneration Plants with Production

“…Their integer programming-based PMS maximizes units' availability during a specified planning horizon while satisfying both demand and technical constraints. Ramírez-Martínez et al [12] generated a one-week maintenance plan that matches hydrothermal transmission. They minimized the cost of operating the hydrothermal transmission cost, using a hybrid of a genetic algorithm and non-linear programming, where the nonlinearity is caused by the fuel cost of the thermal power plants.…”

“…For cogeneration systems, a panoply of optimization approaches varying from approximate to exact methods are available. Approximate approaches vary from simple heuristics, tabu search to genetic algorithms [12][13][14][16][17][18][19] whereas exact approaches are mostly integer programming based [20][21][22][23][24][25][26].…”

Tema I____gestão Da Produção

“…Os resultados indicam que o HNSGA-II apresenta melhores soluções em relação ao NSGA-II ordinal em termos do ponto de vista das funções objetivas, enquanto os resultados são obviamente revertidos em grau de equilíbrio para alcançar os dois objetivos simultaneamente. RAMÍREZ-MARTÍNEZ et. al.…”