The increasing share of intermittent renewable energy sources leads to new challenges and opportunities in the operation of energy systems. Nowadays, electricity prices can rise and fall by an order of magnitude within one day. Thus power grid customers have an incentive to shift production in order to benefit from intervals with low prices. To enable load-shifting for processes with nonlinear dynamics and constraints on states, sophisticated control methods are needed. Using deep reinforcement learning (RL), complex control policies can be learned, but this requires large amounts of data and thus many interactions with the environment.
Your task for this thesis will be to implement a specialized deep RL algorithm for industrial demand response applications and compare its performance to standard state-of-the-art RL-algorithms.
- Current master studies in engineering, computer science, simulation science, data science or a comparable field of study
- Good programming skills in Python
- Ideally experience in a major deep learning framework such as PyTorch
- Interest in deep reinforcement learning
- Fluent in English or German (spoken and written)
- Independent working style and good analytical skills
- Willingness to familiarise yourself with new methods
We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:
- Intensive supervision (if desired) by experienced academic colleagues
- Excellent opportunities for home office
- A large research campus with green spaces, offering the best possible means for networking with colleagues and pursuing sports alongside work
- An interesting and socially relevant topic for your thesis with future-oriented themes
- Ideal conditions for gaining practical experience alongside your studies
- An interdisciplinary collaboration on projects in an international, committed and collegial team
- Excellent technical equipment and the newest technology
- Qualified support through your scientific colleagues
- The chance to independently prepare and work on your tasks
- Reasonable remuneration, if work is done (partially) in our office in Jülich
We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.
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