Electrical Engineering - Energy management and operation
Work description
The optimization of energy community assets is crucial for enabling local decarbonization and enhancing system flexibility. These communities play a key role in supporting system operation by improving local energy balance and enabling demand-side flexibility. However, the asset sizing problem is computationally challenging, particularly when including binary decision variables and flexibility constraints. At INESC TEC, both conventional and metaheuristic approaches have been explored to solve this problem. While metaheuristic methods offer a promising alternative, their full potential has not yet been realized due to the complexity of parameter tuning and integration of flexibility provision. Additional testing and refinement are needed to assess their true performance and feasibility. The expected work in this context is outlined as follows: - Review existing literature on metaheuristic optimization methods applied to energy systems, particularly in the context of energy community planning and asset sizing with flexibility constraints; - Analyze the current implementation of metaheuristic approaches at INESC TEC and identify opportunities for parameter tuning and performance improvement;
Academic Qualifications
Electrical engineering, computer science, applied mathematics, computer science or similar
Minimum profile required
- Basic knowledge of optimization;- Basic knowledge of energy communities;- Knowledge of the Python programming language;- Fluency in English (written and spoken);
Preference factors
-Interest or basic experience in energy community models; -Interest or experience in metaheuristics; -Experience in scientific research activities; -Programming experience in Python;
Application Period
Since 29 May 2025 to 16 Jun 2025
Centre
Power and Energy Systems