Jobid=A.0.056
The power and energy systems community is increasingly interested in leveraging emerging computing technologies, including quantum computing, to address challenges in power system optimization, particularly in the context of the Energy Transition. Transitioning to a power system heavily reliant on weather-dependent renewable energy to achieve environmental targets introduces a critical dimension of uncertainty in power system operations, necessitating complex decision-making processes for system operators. Mixed-integer stochastic programming formulations are commonly used to model these uncertainties, but they pose computational challenges.
While some power system optimization problems have been adapted for quantum computing, current hardware limitations restrict their scalability and prevent us from studying large-scale problem instances where a potential quantum advantage may be observed. Instead, this project aims to develop a hybrid quantum-classical algorithmic framework, combining classical high-performance computing (HPC) and quantum processing units, to enhance the computational viability of mixed-integer stochastic programs in power system modeling. The focus will be on characterizing the convergence properties and potential computational advantages of these algorithms, as well as identifying their limitations, particularly in noisy quantum computing environments. Your work will involve experimentation with gate-based quantum computers to solve operational power system optimization problems.
You will primarily contribute to the Intelligent Energy Systems program within the group. This project is supported by and, therefore, you will have the opportunity to interact with teams of experts from SURF.
Specifications
Eindhoven University of Technology (TU/e)
Requirements
You must possess a comprehensive knowledge of quantum computation with an emphasis on optimization algorithms. You must have a proven ability to rigorously develop and apply quantitative decision-making methods (e.g., mathematical programming or data-driven/AI approaches), as evidenced by a relevant PhD thesis or publications. Furthermore, familiarity with topics such as surrogate modelling, mathematical decomposition methods and distributed optimization techniques is highly desirable. Familiarity with electrical power systems is also desirable but not necessary.
Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
