Research Article
Generator Scheduling Model for Optimization using Genetic Algorithm with Multiparent Crossover (Ga-Mpc)
@INPROCEEDINGS{10.4108/eai.11-10-2022.2325488, author={Rudi Salman and Irfandi Irfandi and Arwadi Arwadi and Sayuti Rahman}, title={Generator Scheduling Model for Optimization using Genetic Algorithm with Multiparent Crossover (Ga-Mpc)}, proceedings={Proceedings of the 4th International Conference on Innovation in Education, Science and Culture, ICIESC 2022, 11 October 2022, Medan, Indonesia}, publisher={EAI}, proceedings_a={ICIESC}, year={2022}, month={12}, keywords={genetic algorithm optimization generator scheduling genetic algorithm with multiparent crossover}, doi={10.4108/eai.11-10-2022.2325488} }- Rudi Salman
Irfandi Irfandi
Arwadi Arwadi
Sayuti Rahman
Year: 2022
Generator Scheduling Model for Optimization using Genetic Algorithm with Multiparent Crossover (Ga-Mpc)
ICIESC
EAI
DOI: 10.4108/eai.11-10-2022.2325488
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 Multiparent 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 R2010a as a simulation tool. The IEEE standard electric power system with 5-Unit System was used for model testing.
