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

Adrian Eisenmann, Tim Streubel and Krzysztof Rudion
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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

Energies, 2022, vol. 15, issue 4, 1-24

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.

Keywords: power quality; genetic algorithm; operational planning; demand-side management; multi-objective optimization; industry 4.0; fourth industrial revolution; artificial intelligence; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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