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Integrated Modeling for Optimizing Energy Consumption and Market Interaction: Enhancing Tata Steel's Energy Transition Strategy!
Currently, the steel industry heavily depends on fossil fuels. Tata Steel Nederland aims to produce steel with 35%-40% less carbon emissions by 2030 and zero carbon emissions by 2045, requiring replacing coal and natural gas with carbon-free fuels like hydrogen and electricity from renewable sources. This requires both the development of new steel production processes and new operational strategies to account for future renewable energy markets, which are expected to be much more volatile due to the intermittency of solar and wind power.
The primary goal of this assignment is to develop bidding strategies (algorithms) for Tata Steel in the day-ahead wholesale electricity market. The developed algorithm(s) will be encapsulated in Tata Steel’s current operational model. Its performance will be validated by linking it to TNO’s model for the Dutch day-ahead wholesale electricity market, which has other interacting agents (market participants). The resulting integrated electricity market model will be used to study Tata Steel’s interaction with the market, to analyze different energy transition scenarios, and to help Tata Steel Nederland select the most efficient path to reach the aim of decarbonization by 2045.
This project is part of the DEMOSES (DEsigning and MOdeling future Systems of Energy Systems) research project, led by Prof. Laurens de Vries with focus on studying flexibility procurement from electricity consumers and other market participants. Thus, the output of this MSc thesis will be used for the DEMOSES project and also support Tata Steel’s energy transition.
The assignment is designed for 9 months duration, for students interested in contributing to the energy transition and with a background in computer science, Data science and AI technology, or any relevant engineering field. Experience with scientific programming (any programming language, preferably Python) is required, and a background knowledge in reinforcement learning, optimization, and/or model coupling (co-simulation) is an advantage.
As we have been working from home for most of the past two years, we are looking forward to welcoming you in our office where we maintain an energetic, informal working atmosphere. You will get the equipment needed for your work.
You will work in the Thermal Processes knowledge group in Research and Development, which consists of about 20 researchers from various backgrounds. The main goal of this project is to develop the interface between energy consumption- and market models, improving the sustainable energy strategy of Tata Steel Nederland. In addition, you will collaborate with different stakeholders to ensure the quality of model coupling code, and analyse the interaction between the energy market and the energy transition at Tata Steel Nederland.
Tata Steel produceert, bewerkt en distribueert hoogwaardig staal voor producten die het leven vergemakkelijken. Onze missie: bouwen van het toonaangevende Europese staalbedrijf dat duurzaam is in alle opzichten. Wij willen voorop lopen in wat we doen en werken daarbij actief samen met onze klanten, leveranciers en researchinstituten. We bieden behalve goede arbeidsvoorwaarden een technische productie-omgeving in een menselijke en sociale cultuur.
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