Type: Master assignment
Educational programme: Computer Science, Applied Mathematics
Contact (internal): Johann Hurink and Gerwin Hoogsteen
Company: TotalEnergies
Contact (external): Jules van Dijk
Context
Electric Vehicles (EVs) will play a key role in the energy transition towards a sustainable energy system. With a projection of 10 million EVs in the Netherlands in 2050, the EV fleet forms a significant source of distributed, controllable energy storage and flexibility. Through smart charging policies, EVs can be charged or discharged at various rates to maximize the utilization of renewable energy sources, reduce CO2 emissions, balance the grid, or prevent grid congestion.
As part of its pivot towards renewable energy, energy giant TotalEnergies operates as a Charge Point Operator (CPO), managing a portfolio of about 8,500 public charge points in the Netherlands, and aims to operate 30,000 public charge points in 2025. In order to reduce physical impact on the grid and due to economic incentives for participating in energy trading and flexibility markets, TotalEnergies already has developed and deployed smart charging models and optimization algorithms.
Assignment
A growing demand of our customers is the optimal combined control and deployment of energy generation (e.g. PV solar), storage (e.g. battery storage, EV) and load (e.g. building or EV).
Your assignment is to develop, implement, and evaluate (using simulations) an energy management system that, based on a selected number of parameters (input), optimizes the scheduling and control of multiple assets. The optimization approach should be able to optimize (output) for multiple objectives such as energy price optimization, maximization of self-consumption, peak shaving, grid balancing and grid congestion reduction. You will be provided with a significant historical database of detailed charge session information and battery usage, a solid foundation of existing optimization models and theory and your models can be tested on one of the largest public EV charge point networks in the Netherlands.
Workload:
- 30% theory
- 30% implementation
- 20% simulating
- 20% writing
For more information, contact Gerwin Hoogsteen or Jules van Dijk:
J.F. van Dijk (Jules)
+31621149588
jules.van-dijk@totalenergies.com