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Iterative Genetic Algorithm to Improve Optimization of a Residential Virtual Power Plant

Author

Listed:
  • Anas Abdullah Alvi

    (Electrical, Electronic and Control Engineering Department, University of Extremadura, 06006 Badajoz, Spain)

  • Luis Martínez-Caballero

    (Institute of Control and Industrial Electronics, Warsaw University of Technology, 00-662 Warsaw, Poland)

  • Enrique Romero-Cadaval

    (Electrical, Electronic and Control Engineering Department, University of Extremadura, 06006 Badajoz, Spain)

  • Eva González-Romera

    (Electrical, Electronic and Control Engineering Department, University of Extremadura, 06006 Badajoz, Spain)

  • Mariusz Malinowski

    (Institute of Control and Industrial Electronics, Warsaw University of Technology, 00-662 Warsaw, Poland)

Abstract

With the increasing penetration of renewable energy such as solar and wind power into the grid as well as the addition of modern types of versatile loads such as electric vehicles, the grid system is more prone to system failure and instability. One of the possible solutions to mitigate these conditions and increase the system efficiency is the integration of virtual power plants into the system. Virtual power plants can aggregate distributed energy resources such as renewable energy systems, electric vehicles, flexible loads, and energy storage, thus allowing for better coordination and optimization of these resources. This paper proposes a genetic algorithm-based optimization to coordinate the different elements of the energy management system of a virtual power plant, such as the energy storage system and charging/discharging of electric vehicles. It also deals with the random behavior of the genetic algorithm and its failure to meet certain constraints in the final solution. A novel method is proposed to mitigate these problems that combines a genetic algorithm in the first stage, followed by a gradient-based method in the second stage, consequently reducing the overall electricity bill by 50.2% and the simulation time by almost 95%. The performance is evaluated considering the reference set-points of operation from the obtained solution of the energy storage and electric vehicles by performing tests using a detailed model where power electronics converters and their local controllers are also taken into account.

Suggested Citation

  • Anas Abdullah Alvi & Luis Martínez-Caballero & Enrique Romero-Cadaval & Eva González-Romera & Mariusz Malinowski, 2025. "Iterative Genetic Algorithm to Improve Optimization of a Residential Virtual Power Plant," Energies, MDPI, vol. 18(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:20:p:5377-:d:1769765
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    References listed on IDEAS

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