Internship description
Liander’s low voltage electricity grids is starting to reach its boundaries in more and more occasions due to the rising numbers of solar panels and electric vehicles. More and more grid extensions are carried out, but this is simply not enough to keep up with the pace of the growing demand.
In order to meet future demands it is a prerequisite that we employ data-driven techniques to increase the efficiency in both planning the optimal grid extensions as well as the efficiency of the grid as is. A huge challenge for Liander is that very little is known about the precise currents and voltages in the past. Historically the amount of measurements in the low voltage grid has been very low, but this is starting to change. The most common measurements currently are smart meter voltage measurements and smart meter readings, the latter ones only to be used anonymously.
Especially the voltage measurements can give a direct insight at certain points in the grid, but it is still difficult to extend this to the state of a complete low voltage grid. Within Alliander we recently developed a state-estimator especially suitable for both medium and low voltages grids, (see https://github.com/alliander-opensource/power-grid-model), but currently this only has been extensively tested for medium voltage grids.
Another important issue is that we have smart meters at the vast majority of our low voltage grid customer connections, however reading out the voltages and currents of all of them continuously is currently impossible by both privacy restrictions and the bandwidths of the communication networks. A solution for this would be to keep the data local, e.g. using edge computing. It is expected (however unproven) that this edge computing approach would give enough insights in low voltage grids.
The main questions for this assignment could be:
1. What is the insight on the low voltage grid’s status given by the currently available measurements. Is it possible to get this insight realtime or only up to one day ago? Would it be possible to make decisions on grid investments or the inclusion of smart techniques that enable a more efficient usage of the grid, such as smart charging, voltage control by home batteries, etcetera?
It’s hard to state exactly how much uncertainty is allowed for such decisions, but it is clear that if we know all voltages are currents within 1% of the available capacity we will not gain much from e.g. more measurements, while on the other hand when the uncertainties or errors are more then 10% of the available capacity there is certainly room for improvement.
2. If a better insight is needed, does it have the highest priority to have data from a better quality (both for the grid topology and the measurements) or to have more data (more smart meters or more measurements at secondary substations)?
3. Would a decentralized approach be sufficient to give enough insights in the local grid status?
Requirements
You are a university student with a technical background (such as (Applied) Physics, (Applied) Mathematics, Electrical or Mechanical engineering). You are also interested in modeling complex systems and working with large amounts of data, and you are looking for a graduation or internship.
You also have:
• Programming experience with regard to modeling, for example in Python, R or Matlab
• Knowledge of or interest in the energy system
What do we offer you?
A challenging and highly varied internship in an organization that is in the center of the energy transition. Alliander is at the forefront when it comes to applying Data Science in a technical environment. Obviously, this includes a good internship allowance and we support you with all means, so that you can perform your work well. We have more than enough data available to perform this assignment.
About Alliander
Alliander is a large Dutch distribution system operator (DSO) that ensures that millions of customers have access to electricity and gas every day for living, working, transport and recreation. We stand for an energy supply that gives everyone access to reliable, affordable and sustainable energy under the same conditions. Now and in the future. That is what we work on together every day. We offer our professionals an environment for innovative and smart ideas. An environment for your energy.
You will work at the Research Centre for Digital Technologies, This team is researching smart and innovative technologies that help us do our job better and faster in the field and in the office. We are researching which digital innovations will bring real value to Alliander and which should therefore be implemented on a large scale in (digitalization) teams. This means we're looking for radical digital technological innovations that will help Alliander make great progress. The rapid testing of theory in practice is part of our research, so that the most valuable digital technological innovations are ultimately implemented. Our Research Center innovates in an open manner and with an open mindset: connecting knowledge from outside and inside through collaboration and partnerships.
Screening policy
Alliander screens all applicants. Depending on the position, the screening consists of the following steps: checking references, checking the authenticity of identity papers and diplomas, an integrity check and requesting a certificate of conduct (VOG).
For more information, contact Gerwin Hoogsteen or Johann Hurink:
