The energy transition is moving forward. This is despite setbacks, targets that seem difficult to achieve, and a geopolitical situation that tends to push the issue into the background in public discourse. Large or small, Europe’s regions and cities are working to achieve the large-scale reductions in CO₂ emissions that they have set for themselves.

It’s not an easy job. Nor are the questions administrations must answer: Which technologies should they invest in? Which sectors should be supported by effective policies? And how much does the transition to more sustainable ways of producing and consuming energy cost? For years, a simulation model developed by Eurac Research has been helping regions identify the ideal solution to decarbonize their territories—meeting climate goals while spending less.

The results tell us something unexpected: reducing emissions by up to 80 % compared to current levels would cost the system only slightly more than today yet deliver significant benefits for local economies. “Spending on fossil fuels decreases—fuels that are mostly not produced within regional territories in Italy or Europe—while investments grow in skills, labor, and local supply chains,” explains Matteo Prina, a researcher at the Institute for Renewable Energy who has been working for over a decade on the energy-scenario simulation models implemented by Eurac Research. The model has been used by the regions of Lower Austria and Salzburg, South Tyrol, and Piedmont. The next “customers” on the list are the Lombardy Region and four other European regions within the PLANtoACT project. Matteo Prina’s commitment has also earned further recognition: he is the current winner of the South Tyrol Junior Research Award, which celebrates the excellent results of young researchers at the beginning of their scientific careers.

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When we think of an electric-car battery or a photovoltaic panel, the first thing that comes to mind is usually not an Italian or European company. “When it comes to renewable-energy systems, obviously not all, but part of the supply chain – that is, the set of processes that bring these technologies to market and enable their use – has a positive impact on the region, more so than fossil fuels,” explains Matteo Prina. “Even if, for example, the photovoltaic panel is produced abroad, there is a whole part of the installation and maintenance that remains in the local area. This is even more true when it comes to the energy efficiency of buildings.”

By carrying out simulations of energy scenarios for South Tyrol as part of the PNRR INEST project, researchers Steffi Misconel and Matteo Prina noted that by reducing CO2 emissions by 55 percent by 2030 – and thus achieving the climate targets set by the province in the South Tyrol Climate Plan – the total expenditure of the energy system would increase by about 15 percent compared to today. However, the expenditure reinvested in the region would increase from 12 percent to 36 percent.

“In all the regions where we have used our simulation model, the final message is similar,” says Prina. “CO2 emissions could be reduced by up to about 80 percent by spending little more than today – and with greater benefits for the local economy.” With the necessary and important differences, the general recipe is similar in all regions: renewables must be installed, and sectors such as transport and heating must be electrified as much as possible. The final steps—reducing emissions to 100 percent and becoming a truly zero-emission society—are, however, extremely more expensive. “You’d need to produce other energy carriers like hydrogen, and electrify highly complex sectors (called ‘hard to abate’), such as steel, cement, and paper industries using high-temperature processes, as well as maritime and air transport,” he explains.

Underlying these considerations and collaborations with the regions is a robust mathematical model that allows for the simulation of highly detailed future energy scenarios – on an hourly basis and for an entire year, it also calculates their comparison with the current scenario. From the thousands of scenarios available, the model then identifies the ideal solution that allows for the greatest reduction in emissions at the lowest cost (see box).

The most recent region to use the model was Piedmont, in a highly productive collaboration between the regional office responsible for energy transition and Eurac Research’s team, led in this case by Valentina D’Alonzo, who works on urban and regional energy systems. “We worked from the very beginning with the technical office, collecting data together and discussing assumptions. We considered questions like: How many hectares can still be allocated for renewable plants? How many electric vehicles can realistically be expected? What percentage of buildings can be renovated in the next decade?”
Prina explains that this iterative process allowed the region to obtain energy scenarios that can genuinely be implemented by policy. “In return, we gained a better understanding of the needs of public administrations using our tool.”This is completely different from how energy scenario simulation is often understood by administrations: the scientific part creates a mathematical model, finds the technical-economic optimum, and provides this scenario to policy makers. “The risk is that, in this way, this scenario is often not really taken into consideration or implemented because there is no transparency on how the model works and the proposed scenarios do not realistically reflect the possibilities of the territory,” comments the researcher.

technical documentation

An advanced mathematical model for simulating energy scenarios

The model used for the simulations was developed by Eurac Research – based on EnergyPLAN, software created by Aalborg University – and has been improved year after year. The model initially creates an image of the status quo: the hourly trend of electricity, heat, and transport production and consumption over an entire year, hour by hour. The model therefore includes the electricity demand of individual aggregate users and that of industries divided by sector, mobility as well as building heating and cooling demands. Additionally, the model considers the energy sources that, hour by hour, have met this demand.

This current overview is being used to create energy scenarios for specific future years: for example, 2030 or 2050 which are the years that have been identified by international organizations as markers for achieving the next climate targets. The energy scenarios are created by changing around twenty decision variables within selected intervals: these are the assumptions about the future to be agreed upon with the administrations using the model. The variables relate to ways of producing energy: the installation of photovoltaic power, wind power, cogeneration plants, biogas, thermoelectric power plants using fossil fuels, and so on. Other variables relate to the consumption of electricity and heat, to storage, losses in energy transport, and the generation of other energy carriers such as hydrogen. From the thousands of plausible future scenarios generated in this way, the model ultimately finds the optimal solution in terms of reducing emissions and costs incurred by the energy system.

For further information: Machine learning as a surrogate model for EnergyPLAN: Speeding up energy system optimization at the country level Optimization method to obtain marginal abatement cost curve through EnergyPLAN software

Matteo Prina says that just over ten years ago, when he first began working on this issue, the focus of his work was mainly on the accuracy of the models: that is, how close a simulation is to the real world. Today, accuracy is extremely high, and the focus has shifted to two other aspects: the robustness of the mathematical model and its comprehensibility. “When looking into the future, prediction becomes difficult: geopolitical contexts can shift, and few people would have predicted a pandemic in 2020. For this reason, the model has to be robust to numerous external factors—it must incorporate and manage uncertainties so its outputs remain useful even in changing contexts,” he says. The comprehensibility of the model means that it is essential that the results of the modeling do not remain on a computer but become a basis for writing policies that will implement the energy transition.

Something that goes beyond the mere technical aspect: arriving at a model in which the assumptions are already discussed and understood by the entire community through participatory systems. A model that also integrates simulations at the regional level with those at the individual city level. This concept is at the heart of the Life+ PLANtoACT project, which has just been launched and will involve the Lombardy Region, the Oberland region in Germany, the Porto Metropolitan Area, Alba County in Romania, and the French administrative region of Auvergne-Rhône-Alpes over the next five years.

Winner of the Junior Research Award 2025

Matteo Prina is head of the research group on Energy Systems Modeling and E- Mobility at the Institute for Renewable Energy at Eurac Research.

After completing his PhD at the Polytechnic University of Milan, he joined Eurac Research in 2014, where he has developed this field of research by integrating artificial intelligence, optimization algorithms, and participatory methods. He works to improve the robustness and comprehensibility of energy transition scenarios in order to enable faster, more transparent, and responsive decision-making processes. In particular, he helps administrations identify and implement clean energy transition strategies, with a strong emphasis on coupling the transport sector with a broader energy system.

After placing second in the Euregio Young Researchers Award in 2021, Matteo Prina won this year’s South Tyrol Junior Research Award.