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Power Quality Mitigation via Smart Demand-Side Management Based on a Genetic Algorithm

Author

Listed:
  • Adrian Eisenmann

    (Institute of Power Transmission and High Voltage Technology (IEH), 70569 Stuttgart, Germany)

  • Tim Streubel

    (Institute of Power Transmission and High Voltage Technology (IEH), 70569 Stuttgart, Germany)

  • Krzysztof Rudion

    (Institute of Power Transmission and High Voltage Technology (IEH), 70569 Stuttgart, Germany)

Abstract

In modern electrical grids, the number of nonlinear grid elements and actively controlled loads is rising. Maintaining the power quality will therefore become a challenging task. This paper presents a power quality mitigation method via smart demand-side management. The mitigation method is based on a genetic algorithm guided optimization for smart operational planning of the grid elements. The algorithm inherits the possibility to solve multiple, even competing, objectives. The objective function uses and translates the fitness functions of the genetic algorithm into a minimization or maximization problem, thus narrowing down the complexity of the addressed high cardinality optimization problem. The NSGA-II algorithm is used to obtain feasible solutions for the auto optimization of the demand-side management. A simplified industrial grid with five different machines is used as a case study to showcase the minimization of the harmonic distortion to normative limits for all time steps during a day at a specific grid node, while maintaining the productivity of the underlying industrial process.

Suggested Citation

  • Adrian Eisenmann & Tim Streubel & Krzysztof Rudion, 2022. "Power Quality Mitigation via Smart Demand-Side Management Based on a Genetic Algorithm," Energies, MDPI, vol. 15(4), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1492-:d:751764
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    References listed on IDEAS

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    1. Kamran Daniel & Lauri Kütt & Muhammad Naveed Iqbal & Noman Shabbir & Ateeq Ur Rehman & Muhammad Shafiq & Habib Hamam, 2022. "Current Harmonic Aggregation Cases for Contemporary Loads," Energies, MDPI, vol. 15(2), pages 1-15, January.
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    Cited by:

    1. Fatima Zahra Zahraoui & Mehdi Et-taoussi & Houssam Eddine Chakir & Hamid Ouadi & Brahim Elbhiri, 2023. "Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids," Energies, MDPI, vol. 16(19), pages 1-26, September.
    2. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
    3. Yukun Dong & Yu Zhang & Fubin Liu & Zhengjun Zhu, 2022. "Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 15(8), pages 1-18, April.
    4. Karol Jakub Listewnik, 2022. "A Method for the Evaluation of Power-Generating Sets Based on the Assessment of Power Quality Parameters," Energies, MDPI, vol. 15(14), pages 1-24, July.
    5. Seyedamin Valedsaravi & Abdelali El Aroudi & Jose A. Barrado-Rodrigo & Walid Issa & Luis Martínez-Salamero, 2022. "Control Design and Parameter Tuning for Islanded Microgrids by Combining Different Optimization Algorithms," Energies, MDPI, vol. 15(10), pages 1-25, May.

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