The decarbonization of territorial or industrial energy systems requires multi-vector integration (electricity, heat, gas) and optimized management of flexibilities (storage, flexible demand). However, uncertainties in exogenous data and those due to modeling choices limit the confidence in the sizing obtained by classical approaches (e.g., deterministic Mixed Integer Linear Programming models).
The post-doctoral research aims to evaluate and improve the robustness, reliability, and precision of sizing and KPIs (cost, CO2 emissions) under these uncertainties. The post-doctoral work will combine:
- A comparative analysis of existing approaches (scenario analysis, rolling horizon, MPC) using quantitative metrics (inspired, for example, by meteorology: robustness, reliability, precision).
- The implementation of a case study to serve as a benchmark, building upon existing work and generating scenarios and results.
- The development of a sizing approach that reduces the impact of uncertainties, with validation on realistic case studies.