Power Plant Scheduling Model For Optimization Using Genetic Algorithm with Multiparent Crossover (Ga-Mpc)
Rudi Salman(a), Irfandi(b), Arwadi Sinuraya(c)

(a)(b)(c) State University of Medan

Abstract

Electric power systems are designed and operated to meet the needs of varying and growing electrical loads. The highest cost in operating an electric power system is the cost of fuel. For this reason, it is necessary to use optimization techniques to reduce these costs. Therefore, the optimization problem, namely minimizing the operating costs of the electric power system, is a significant issue. One of the efforts to reduce the operating costs of power plants is by optimizing the scheduling of power plants, in this case, generator scheduling. Generator scheduling aims to prepare a generator start-up (ON) and shut-down (OFF) schedule hourly to meet previously estimated load requirements while meeting specified constraints. Mathematically, the generator scheduling optimization problem is a complex nonlinear combinatorial optimization problem. So to solve this problem, one way can be to use a Genetic Algorithm with Multi parent Crossover (GA-MPC). A genetic algorithm is a random search technique that provides optimal solutions to optimization problems.
This study aims to build a generator scheduling optimization model using GA-MPC. The research was carried out at the Computer Laboratory of the Electrical Engineering Education Department, using the Matlab software version R2008b as a simulation tool. The IEEE standard electric power system with 14 buses was used for model testing.
The results showed that the generator scheduling model built using GA-MPC for generator scheduling optimization was successfully carried out. This situation is quite good and shows that GA-MPC can be implemented for generator scheduling optimization problems.

Keywords: Genetic Algorithm, Optimization, Generator Scheduling, Genetic Algorithm with Multi parent Crossover (GA-MPC)

Topic: Applied Sciences-Computers and Engineering