Internship Power Quality

General description

ElaadNL is the knowledge and innovation centre in the field of Smart Charging and the charging infrastructure in the Netherlands and is an initiative of the Dutch grid operators The increasing amount of electric vehicles (EVs) in our electricity grid introduces new challenges for the grid operators. One of them concerns Power Quality (PQ); electric vehicle chargers can for example introduce harmonic distortions, supraharmonic distortions and voltage unbalance. This can result in additional losses, overheating and even failure of network components.

The goal of this project is to obtain better insight in the power quality effects electric vehicles have in practice and to see if and how the impact can be minimized by using smart charging algorithms. To achieve this, permanent measurement set-ups have been installed at different large charging hubs in Zeist, Rotterdam, Utrecht, Hoofddorp and Culemborg, from which relevant PQ data is continuously collected. To improve the insight in the PQ impact of charging EVs, this data needs to be analysed in detail and in the end clustered and generalized. Based on this data, a proposal can be made on how to reduce the PQ impact of several EVs charging and verified with simulations. If possible, the proposed algorithm will also be implemented in practice.

Requirements

• Electric Power Systems course completed (or similar experience / education in Electrical Engineering or Electrical Energy Systems
• Basic knowledge on Power Quality is a pre
• Experience with Matlab, Simulink or Python is a pre

Student task description

The student tasks consist of:

• Literature review on power quality effects caused by EV-charging
• Analysis and clustering of long-term PQ-measurement data from (different) charging plaza(s)
• Assessment of PQ effects and the relation between impact and nr. of EVs or charging power
• Proposal of an algorithm for reducing the PQ effects

Student task description for extension (if applicable)

In addition to the abovementioned:

• Design and implementation of the proposed smart algorithm(s) using simulations
• Verification of the smart algorithm(s) by simulations
• (if possible; verification of the algorithm(s) in practice)