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Master These Intern

Geplaatst 23 mrt. 2024
Werkervaring
0 tot 2 jaar
Full-time / part-time
Full-time
Soort opleiding
Taalvereisten
Engels (Vloeiend)
Nederlands (Vloeiend)

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Are you looking for a Master thesis assignment? Do you want to gain more knowledge about smart charging on available solar power and develop an algorithm? Do you want to show the benefits of savings on CO2 in an App? Then TotalEnergies haves the Assignment for you! We are looking for a

Master Thesis Intern

(Research on a PV-Algoritm)

TotalEnergies Charging Solutions Netherlands realizes electric charging infrastructure and charging solutions. The company is part of TotalEnergies. With a network of over 12,000 public EV charging points, we are one of the largest players in the Dutch market for safe, clean and affordable energy. Our customers are provinces, cities, gas stations and (larger) business customers. You will be working closely with the EV-Sales teams for solutions to achieve the long-term plan objective in 2030. We have a solid growth objective and are therefore looking for a Master Thesis Intern.

First scope:

The goal of this thesis is to (1) develop an optimization algorithm for smart charging on available solar power in the public domain, (2) ensure a stable energy supply to connected EV drivers/users and (3) be able to show a driver/user the optimized benefit it has gained in e.g., CO2 through an existing charging App.

Background

In 2023 TotalEnergies will implement smart charging based on local solar production. This project is related to a solar park that will be realized in/near a city in The Netherlands. Balancing charging and production ensure that electric vehicles are charged when there is enough green (solar) energy available in the energy system. However, smart charging mid-day has limitations due to the relative shorter time of charging sessions compared to evening/night charging sessions. Therefore, it is important that the EV driver (user) receives a guaranteed minimum amount of energy, while expressing his charging preferences (departure time, amount of energy) through a device (App). The provided flexibility from the EV driver should be rewarded.

Problem definition:

Current smart charging propositions are mostly focused on forecasted passive pre-defined energy optimizations - indirectly caused by solar production - and active intraday/imbalance optimizations, influencing energy sales output to vehicles.

The scope of this thesis is to specifically optimize on the forecasted energy produced by (local) PV based on reference that full capacity is given to the vehicles when PV production is high. This optimization should have the precondition that minimum amount of energy capacity is always supplied to the vehicle. EV drivers (users) will be informed by the amount of energy they have received, and the gains derived from the optimisation through the TotalEnergies charging App.

There are three domains to review :

1. Implement a forecasting method/model for solar produced in The Netherlands (energy mix) in a day ahead model (as exits) and use this model to define the steering algorithm of these related chargers in the city, while take charging limitations and preconditions into account.

A base (existing) algorithm will be provided to be improved. As the App is used as a direct input device we define “Active smart charging on a solar energy profile where forecasted energy data and weather data are used to define this base solar profile for the city.”

2. Create a dynamic layer on the actual intraday optimizations in CO2 and/or ancillary services to the power grid. Define how the algorithm should interact on these additional parameters.

3. Create a theoretical reflection on the two topics above when the energy supplied is negative due to the absence of sun and when the vehicle (in the future) will supply energy back to the grid (V2G).

Research aspect/challenge:

As TotalEnergies is launching this mid-2023, there is an optimization framework needed on the existing smart charging algorithm as the current models do not provide in this solar domain alone. The model must be adapted aiming to satisfy both the EV drivers (users) on energy supplied and CO2 reduced with maximum share of solar in the mix over duration of 6 hours.

The optimization will be done by analyzing and creating an algorithm which is based on the input data from the large-scale PV plant, solar market data, charging data from the municipality and energy market data imports. The base can be the current smart charging algorithm used, that will be modified. The integration and coupling of the energy market, PV power plant & chargers is the key aspect within this research. The challenge is to balance all new variables in the algorithm to meet the goal of the base steering profiles used on these public city chargers.

Method:

By extensive literature review/research and through (historic) data analysis of current charging infrastructure of a city, PV data and expected performance of the TotalEnergies solar field to be created resulting in an optimal energy scheduling framework can be created in preferable Python.

Expected outcome:

A theoretical defined and described algorithm, validated on historic charging data of this specific city. If the algorithm can reduce the CO2 footprint over the energy supplied in combination with minimum amount of energy supplied to the vehicle, this model is a next step into the future of smart charging.

Your Profile:

- Affinity with sustainable energy (technology) and the energy transition

- Ability to work on independently on a task

- Open to involve colleagues/experts in the process and operate in a team

- Focused on the task, ability to connect and see relations with other

- A proven good knowledge the energy market and the English language (in speech and writing).

- Experience with modelling in Python is a pre

Why you should join us!

We are a solid company with clear growth ambitions and have a major stake in TotalEnergies’ #NetZero2050 climate ambition. The position is challenging with room for initiative and responsibility. You have career opportunities to expand beyond this role. Within TotalEnergies we strive for diversity and welcome open-minded, daring and caring people. We offer an attractive internship compensation. And last, but not least, you will have the opportunity to work with us to create a better future!

Interested?

Are you interested? That’s great! If your skills and experience match this job description, please send us your CV and motivation letter. For more information about the project/assignment, you can contact Corporate Recruiter Ewoud Heineken via 06-21380612 or Ewoud.heineken@totalenergies.com.

TotalEnergies is al bijna 100 jaar actief in energie. Met 100.000 medewerkers verspreid over 130 landen begrijpen wij de energiewereld als geen ander. Een groot gedeelte van onze energie produceren wij zelf. En een groeiend aandeel komt uit alternatieve bronnen, zoals windenergie en zonne-energie. Samen streven we naar duurzame mobiliteit met schone brandstoffen en dragen bij aan de #NetZero2050 klimaatambitie.
Kijk op https://services.totalenergies.nl/ voor onze activiteiten in Nederland.

Energie
Den Haag
Actief in 130 landen
600 medewerkers
60% mannen - 40% vrouwen
Gemiddeld 40 jaar oud