THESIS - Optimal bidding of a fleet of electrical-vehicle chargers in the mFRR activation market

Background

The growth of intermittent renewable power productions calls for a higher level of flexibility in the power grid. At the same time, the number of Electric Vehicles (EVs) has been steadily increasing in the past years. The EVs are seen as a source of flexibility that not only helps grid stability, but also generates an additional revenue for EV owners.

Manual Frequency Restoration Reserve (mFRR ) is one type of ancillary grid services. This reserve is activated by the TSO (transmission system operator) on an hourly basis in response to power system energy needs, ensuring that power supply and demand is always balanced. The TSO procures the energy needs through an auction that takes place every hour, 45 minutes before delivery hour. Every market participant submits a bid curve detailing the energy they are willing to provide for a given clearing price. If a bid is accepted by the TSO, then the participant is obliged to deliver the procured volume and is reimbursed by the clearing price of the auction.

The bid curve  submitted to the mFRR market should maximize the expected revenue, yet at the same time it should respect constraints imposed by the charger availability and user preferences (such as ready time, target state-of-charge). These aspects, in addition to uncertainty associated to the EV behaviors, make the bidding problem of EV chargers for grid services different from conventional power plants.

Objective

The thesis project involves the following work:

• Conduct literature study to check for prior works related to optimal bidding of EV chargers, particularly for the mFRR market.

• Analyze historical mFRR price data and fit a stochastic price model

• Develop and validate a method computing the optimal bid, considering prices, charger availability, TSO requirements, etc..

• Evaluate the impact of different source of uncertainties (EV chargers behavior, market prices,..).

Your Qualifications

• Good programming skills (ideally in Python).

• Any course in mathematical optimization (LP, MILP,...), machine learning, statistics or quantitative finance is an advantage.

• Interest into energy markets, optimization and data science topics.

• The project language is English, hence good communication in English is required.

We offer you

• An expert will be appointed to supervise and guide you through the thesis project.

• You have the opportunity to create a network of people within the company as well as with other students doing their thesis with us.

• An interesting thesis, that can lead to a career with us.

Fortum offers a taxable one-time compensation for a well-executed and approved thesis.

Interested?

To apply, register your CV and a personal letter of maximum one page or a Power Point presentation of maximum two slides, in our Recruitment Tool, latest by 24th November 2024. Applications will be evaluated continuously and an applicant may be on-boarded before this date.