Proceedings of the 4th International Conference on Innovation in Education, Science and Culture, ICIESC 2022, 11 October 2022, Medan, Indonesia

Research Article

Generator Scheduling Model for Optimization using Genetic Algorithm with Multiparent Crossover (Ga-Mpc)

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  • @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
Rudi Salman1,*, Irfandi Irfandi1, Arwadi Arwadi1, Sayuti Rahman2
  • 1: Departement of Electrical Engineering Universitas Negeri Medan, Medan, North Sumatera
  • 2: Department of Information Technology, Universitas Harapan Medan, Medan,North Sumatera
*Contact email: rudisalman@unimed.ac.id

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.