Evaluation of Uncertainties in Energy System Simulation using Modeling to Generate Alternatives

Institut

Lehrstuhl für Energiesysteme

Typ

Bachelorarbeit / Semesterarbeit / Masterarbeit /

Inhalt

theoretisch /  

Beschreibung

Energy system optimization models are a widely used tool to provide useful information on the pathway
to achieve carbon reduction targets. Based on the given assumptions as well as the defined boundary
conditions of the model, the most cost-effective pathway to reach climate goals is determined. Accordingly,
these models are often used to support regional energy planning. Due to the high uncertainties in
the development of certain input values such as energy carrier and investment costs, the investment
decisions in models do not consider the reality of uncertainty. In the real world, decision-makers do not
have accurate information about the development of future prices and costs. Especially because of the
increasing coupling of the electricity, heating, cooling, and mobility sectors and the volatility of renewable
energy supply, conventional energy system planning methods used in practice are reaching their limits.
Monte-Carlo Optimization is a popular method that is used to model systems that contain a high level of
uncertainty. In the context of this work, Modeling to Generate Alternatives Approach should be implemented
in PyPSA for a small regional energy system, e.g. the campus Garching. Based on this, the
results of the optimization should be evaluated. The aim is to assess the influence of the various uncertainty
factors. And to make a statement about the relevance of the various factors to the resulting energy
system.

Voraussetzungen

• Interest in energy systems optimization,
• High motivation and independent, structured way of working.
• Experience in programming with Python (PyPSA) and knowledge of optimization is a plus.

Möglicher Beginn

sofort

Kontakt

Lennart Trentmann, M.Sc.
Raum: 3711
Tel.: (089) 289 16287
lennart.trentmanntum.de

Ausschreibung