Modeling

Case Study

A robust model as a basis for decision-making – stable, error-free, ready for use

Challenge

The implementation quality of the existing model was not yet optimal for operational use. Errors in structure and parameterization led to unstable runs and prevented reliable results. The team had to intervene manually on a regular basis to correct model errors. This effort tied up specialist capacities, delayed workflows, and made reliable planning difficult.

Goal

The aim was to create a technically sound and robust model as a basis for informed decisions. Returns were to be assessed more accurately and risks systematically managed. At the same time, expertise was to be built up within the team. The company was looking for a technical implementation partner who could raise the modeling to a high professional and sustainable level.

What we do

We analyzed the existing model structure and identified specific areas for improvement. Together with the customer team, we developed a suitable implementation and implemented it consistently. Findings from the analysis were directly incorporated into the model logic. The exchange was closely integrated into the team's working methods, including transparent knowledge transfer and methodological decisions.

Result

The model works stably and delivers consistent results. Infeasibilities no longer occur. Deadlines are reliably met and workloads are distributed evenly. The implementation complies with the current European model standards and forms a robust basis for decisions affecting earnings and risk management.

Modeling is more than just individual runs

Modeling determines how resilient your scenarios are, how valid sensitivities are assessed, and how confidently investment or system decisions are made.

If you would like to check how robust your model structure is today and where targeted further development would have a concrete impact, let's talk about it.

Modeling with an impact on your decisions

As your technical implementation partner, we work directly in your modeling environment—PLEXOS, BID3, or even PyPSA. We parameterize, implement, further develop, and ensure the methodological quality of your models. In doing so, we draw on experience from national and European contexts and work according to the latest modeling practices.

The results are model runs that run stably, scenarios that remain comparable, and evaluations that fit clearly into decision-making processes.

Stability and speed during operation

Unstable model runs or inconsistent data have a direct impact on results. We start where model logic, data structures, and parameterization intersect. We structure models so that they work in a reproducible, traceable, and resilient manner.

Model runs become plannable, sensitivities systematically comparable, and results consistently interpretable. Decisions are based on clearly derived assumptions and transparent model structures.

Modeling in line with current European standards

Models in the electricity and energy market operate within the framework of clear European guidelines. We are familiar with these requirements from practical application and implement them methodically and accurately.

Whether scenario development, sensitivities, or further development of existing models: we transfer current standards to your model landscape and ensure that results are consistent with the regulatory and European context.

Seamlessly integrated into your structures

We work directly in your existing systems and processes. Our experts take on clearly defined modeling tasks and contribute their experience in a targeted manner. Depending on your needs, implementation takes place on your infrastructure or in a specially designed environment.

The collaboration is designed for continuity. Models are further developed, assumptions are reviewed, and scenarios are adjusted. Knowledge remains documented and available within the company. This creates a modeling basis that is sustainable in the long term.

Reliable modeling as the basis for sound decisions

Assumptions, parameterizations, and results are set up in such a way that they can be transparently traced and discussed internally. Model logic and scenario frameworks are clearly documented and technically derived.

This creates an effective basis for decision-making. Investments are evaluated more precisely, risks are systematically classified, and strategic options are reliably compared. Modeling thus becomes an active management tool in your company.