Alliander aims to play a decisive role in the energy transition to a more sustainable future. The company seeks new technologies and responds to new developments in the energy sector with the goal to use the grid more efficiently and boost productivity. It allows us to connect as many renewables, electric vehicles, heat pumps and new customers to the grid as possible.
At Alliander we have a low number of measurements in the lower part of the grid (e.g. at the street level and at secondary substations), therefore we use so called bottom-up models, where we use measurements at large customers or average profiles per customer to estimate the loads on secondary substations, see e.g. ANDES: grid capacity planning using a bottom-up, profile-based load forecasting approach, van de Sande et al. In this way we create pseudo-measurements that at least give some insight in what is happening in the grid
For a certain class of customers we have more information to estimate their loads than we’re actually using. For these medium-sized customers we know per month their energy consumption (and/or production) and their maximum and minimum load during a 15-minute period during each month. The only information we are using so far is the total energy consumption (production) per year, we multiply this by an average profile per customer type. Due to the fact that we’re using average profiles this could lead to an underestimation of the real load.
It turns out that using both sources of information (total and maximum/minimum consumption/production) is not trivial. Nonlinear scaling methods are non-stable, while regression methods usually cannot deal with aggregated information over an interval such as the total or maximum load.
One idea to solve this problem is to use a combination of Gaussian Processes and Bayesian Networks to estimate load profiles + uncertainty bands that fit both the sum and the maximum of consumed energy. But also other methods could be tested in order to solve the problem.
You are a university student with a mathematical, artificial intelligence and/or computer science background. 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 and/or machine learning, for example in Python or R
Interest in the energy system and the challenges of the energy transition
What do we offer you?
This is a challenging and highly varied internship in an organization that is at the center of the energy transition. Alliander is at the forefront of applying Data Science in a technical environment. Obviously, this includes a good internship allowance and we support you with all means to perform your work well. We have more than enough data available and ready to perform this assignment.
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.
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).