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Deep Dive with Prof. Dr. Tom Brown

How can we map the integration of renewables into the energy system?

The answer is: with the help of complex modeling systems.

Tom Brown is a leading expert in the development of energy system models at TU Berlin (Berlin University of Technology). The open source model Python for Power System Analysis (PyPSA) was developed under his leadership, which was then developed further for Austria in the context of the project ‘zusammEn2040’. The APG project team met with Brown to discuss the opportunities and challenges of modern modeling systems and their use in practice.

More about Prof. Dr. Tom Brown

Interview

APG: Good morning Mr. Brown! Thank you for taking the time to talk to us. We would like to start with a general question: What is your motivation for specializing in energy system modeling?

Brown: The energy system has to and will provide a key contribution to a successful energy transition. The renewable energy sources offer us the opportunity to restructure our energy system. However, the volatility of the electricity generation from PV and wind means that major changes are necessary. To plan how renewables can be integrated into the system, we need the right tools to be able to map the new relationships.

APG: By "mapping" do you mean: we have to be able to simulate these new relationships?

Brown: Yes! The important aspect is that future system planning will always have to deal with great uncertainties. It is therefore crucial that the models are flexible enough to be able to take into account these uncertainties. It must be possible to study what will happen if, for example, wind power facilities are developed to a lesser extend than originally expected. This is also one of the difficulties - comprehensive modeling systems quickly become incredibly complex!

APG: The ‘zusammEn2040’ project uses an energy system model based on PyPSA which you developed. In your opinion, what distinguishes PyPSA from other energy system models?

Brown: When we started developing PyPSA eight years ago, the circumstances were very different. Firstly, there were many different models, but they only looked at individual aspects of the energy system or were primarily developed for examining the deployment of conventional power plants. With the expansion of the renewables, however, the situation has changed and is still changing: The sites of electricity generation are differently distributed. What distinguishes PyPSA is that it has a high geographical and chronological resolution and offers the possibility of coupling different sectors such as heat, transport and industry. The interconnections between the sectors can be easily considered in the model without the need to build interfaces to other models. The second unique selling point is that we worked with an open source approach right from the start. Fortunately, this approach has by now become more and more established.

APG: Since you have mentioned open source. Why is transparency so important in the discussion about the future of the energy transition?

Brown: As a modeler, you have a lot of leeway. If the assumptions used in a model are known, the dialog becomes a completely different one. We know from many discussions in the past that if the method is not made transparent, decision-making is difficult, as different modeling approaches use different methods and objectives, which can even lead to contradictory results. Disclosing these methods and objectives helps to make ensuing decisions more comprehensible.

Comprehensive modeling systems quickly become incredibly complex!

Prof. Dr. Tom Brown Leading expert in the development of energy system models at the Berlin University of Technology (TU Berlin)

APG: What are the biggest challenges in energy system modeling?

Brown: There are three main issues. Firstly, there is the quality of the data. How many heat pumps will there be, where will renewables be expanded, what efficiency levels will there be in the future, what will happen in the industry sector? There are hundreds of questions, but often only approximate answers. At the same time, there has been a tremendous development in this area over the last three to four years. The quality of the data has improved significantly. Some data are only available now. One of the major challenges is to progressively incorporate these data into the models.

Secondly, there is the calculability. The models we use are incredibly complex. Calculations can take several weeks. This makes the work more difficult, for example, because modifications cannot be tested "just like that".

Thirdly, there is the issue of communication. Given the plethora of results, it is important to filter out the relevant messages and not overwhelm people, while at the same time presenting the situation truthfully.

APG: The issue of communication is probably crucial in many respects. What is your experience regarding the reaction to your results? In other words, can you outline the impact of your work?

Brown: As a rule, effects at a political level are often indirect. For example, research informs the TSOs or other institutions and thus indirectly informs politics. But there are also examples where PyPSA has been used directly by decision-makers: For example, Donald Tusk's Civic Platform, which won the 2023 elections in Poland, used modeling results from the think tank Instrat for their energy policy. Something similar can be observed in the UK, where the Labour Party resorts to the modeling results of the think tank Ember. For me as a researcher, it is of course exciting to see where the models are being used - and what results can be obtained.

If you make clever use of the new flexibilities, you can massively reduce costs.

Prof. Dr. Tom Brown

APG: You already mentioned the results: From a content perspective, what are the most important findings or insights that can be gained from energy system modeling?

Brown: One of the most important insights for me is certainly that if you make clever use of the new flexibilities, you can massively reduce costs. It makes much more sense to use surplus production from renewables for electrolysis instead of shutting the facilities down. And during the infamous “dark doldrums” (note: i.e. simultaneous high electricity consumption with low production of wind and solar power, typically on consecutive cold, cloudy but windless winter days), sufficient storage is needed to secure the supply.

APG: As TSO, we can also add in this context that despite the massive grid expansion that is necessary for the integration of the volatile renewables and to handle the increasing electrification, new flexibilities that can also be used to benefit the entire system will be needed in the future.

Brown: Exactly. It is important to ensure that overall system planning is efficient.

APG: Models are often criticized. What can a model-based approach achieve and where do you perhaps also see the limits?

Brown: There are many sensitive issues in the context of the energy transition that quickly can have a ripple effect. From the perspective of economic optimization, the solution space of the models is very large and leaves a lot of leeway for decision-makers. This quickly leads to geopolitical or economic policy issues: How do we manage the decarbonization of the industry sector? To what extent do we want to be dependent on imports from outside of Europe? Of course, these questions cannot be answered from a modeling perspective. But the analyses can be used to support decision-making.

APG: A frequent criticism of models is that they do not adequately reflect reality. How do you bridge the gap between the model and reality when it comes to utilizing the results?

Brown: On the one hand, through validation wherever possible. The results for sample years are checked and compared with reality. We always aspire to approximate reality. On the other hand, there are many uncertainties. Therefore, when interpreting model results, qualitative statements are often more reliable than, for instance, an exact calculation of costs or the quantities of energy sources required. I have already mentioned one example: flexibility and storage will be essential. Models such as PyPSA can provide a good representation of how an energy system behaves as a whole. However, there are other processes for deriving specific infrastructure projects, such as the network development plan of APG or other TSOs in Europe.

APG: What are the major future issues in your research with energy system modeling?

Brown: One of the main issues will certainly be building resilience. Can we and do we want to accept similar dependencies on non-European producers for synthetic gas as we do for natural gas? At the same time, there are other aspects, especially if our system is largely based on renewables. How do we deal with the consequences of climate change - also from a security of supply perspective? How do we deal with natural disasters or events such as dark doldrums? We need to answer questions like these and energy system modeling can play an important role in this context.

APG: Thank you very much for the interview!

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