With the recent proliferation of electric vehicles (EVs), maintaining power quality within acceptable limits in future distribution grids will become a challenging task. A specific concern is the spread of Supraharmonics in the range from 2 to 150 kHz, generated by modern power electronic devices. In this paper, the long term Supraharmonic distortion from three differently sized electric vehicle charging infrastructures is analyzed in frequency and time domain. At the monitored sites several interruptions of EV charging processes were observed due to poor power quality. It was found that vehicles disconnect when exposed to high levels of harmonic distortion. Moreover, the impact of the charging EVs on the Supraharmonic distortion and the interaction with the background distortion for the individual sites is discussed. Results show that a general increase in Supraharmonics emission can be expected due to the rising number of EVs. However, measurements also indicate that damping effects can occur for certain load configurations.
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.
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