Mathematical Optimisation for Modern Energy Systems
The research conducted by Prof. Dr Liers’ group focuses on optimisation under uncertainty. It encompasses combinatorial, mixed-integer and non-linear optimisation models. Particular emphasis is placed on the development of new algorithmic methods, which increasingly incorporate data-driven approaches and machine learning techniques. In doing so, the group not only designs powerful algorithms but also develops and analyses methods with verifiable quality guarantees. The aim is to provide mathematically sound decision-making and optimisation methods capable of solving the complex and challenging problems of modern applications, particularly in the energy sector, as efficiently and reliably as possible.
The research has particular applications in energy systems:
- electricity systems
- gas systems
integrated energy systems
as well as in the natural and material sciences and in logistics
- Development of practical and efficient algorithms for mathematical optimisation problems under uncertainty
- Research into the integration of data-driven approaches and machine learning methods in optimisation
- Applications in energy systems, materials science, engineering and logistics
- Development of bespoke optimisation models and algorithms for operational planning problems
- Analysis and improvement of planning processes through mathematical modelling and data analysis
- Development of methods for managing uncertainty in decision-making processes
- Collaborative and research projects on industrial applications