Evaluation of the Influence of Different Forecast Horizons on District Energy Systems

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. The necessary
build rates for the different technologies can also be derived from the model. In addition, the
models can be used to give indications about the relevance of different energy technologies. Accordingly,
these models are often used to support regional energy planning. Most of these models assume
perfect foresight. This means that all future developments, e.g. CO2- or fuel prices, are known to the
optimizer from the beginning. Thus, this method of linear optimization always finds the system with the
lowest costs. However, this long-term perspective contradicts the short-term decisions made by companies.
Due to the high uncertainties in the development of certain input values, the investment decisions
in perfect foresight models do not reflect those of reality. In the real world, decision-makers do not have
accurate information about the development of future prices and costs. These uncertainties, as well as
high investment costs for energy infrastructure projects, could lead to a postponement of long-term strategically
important decisions, which can result in a failure to achieve climate targets. For this reason,
models with limited foresight have been developed. In this work, the influence of the forecast horizon on
the simulation result should be examined. Therefore, the rolling horizon method should be applied to a
small regional 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