Description

Problem

With the increasing penetration of variable renewable energy sources such as wind and photovoltaic (PV) systems, there is a growing need for approaches that can continuously evaluate system reliability and provide feedback on potential supply-demand mismatches. By integrating district-level generation data with reliability evaluation metrics such as Loss of Load Probability (LOLP) and Expected Energy Not Served (EENS), and incorporating automated reliability assessment, it is possible through feedback to improve the detection and mitigation of potential supply issues in modern power systems.

Goal

Develop a data-driven model for automating reliability assessment of district-level power supply using renewable generation data and synthetic demand data. The work will aim to compute reliability metrics such as LOLP and EENS using probabilistic methods, such as discrete-event simulation. The focus will be on implementing automated feedback and updated reliability assessments based on power system characteristics. The final goal is to validate and improve the accuracy of the model

Requirements

• Modeling and simulation.
• Basic knowledge in probabilistic modeling.
• Understanding of reliability concepts (e.g., LOLP, EENS).
• Basic understanding of energy systems.
• Knowledge in Python programming language.

Sources

[1] Python model for renewable generation data (my master thesis)

[2] https://github.com/PyPSA/pypsa-de

[3] Weather data from ERA5