Energy Scenarios South Tyrol
The energy transition in South Tyrol is not only about climate protection, but also about economic opportunity. Today, millions of euros leave the region to pay for fossil fuels. Future investments in renewable energy, by contrast, can create local value in South Tyrol.
Scenario status: 2025. These scenarios remain the reference until an updated version is published.
Most energy costs flow out of the region for fossil fuels
Initial investments in renewable energy and efficiency remain in the region
Complete decarbonization with maximum local value creation through local investment
Pathway to Climate Neutrality
The path to decarbonization proceeds step by step with concrete milestones
South Tyrol Climate Plan 2040
The Provincial Government adopted the South Tyrol Climate Plan 2040 in July 2023. The targets of a 55% reduction in CO₂ emissions by 2030 and climate neutrality by 2040 are therefore officially established and form the basis for the scenarios presented here.
- — Mt CO₂ emissions
- High dependency on fossil fuels
- €— million in local value creation
- Beginning of energy transition planning
- Expansion of rooftop PV systems
- First heat pump subsidies
- Pilot projects for e-mobility
- Energy efficiency renovations start
- —% CO₂ reduction achieved
- 45% electric cars
- Massive heat pump installation
- €— million in local investment
- Natural gas phase-out in buildings begins
- Hydrogen infrastructure established
- Industrial electrification advanced
- District heating fully decarbonized
- —% CO₂ reduction achieved
- Complete e-mobility (cars)
- Bidirectional charging (V2G)
- €— million in local value creation
- Energy autonomy through renewables
- South Tyrol as a model for Alpine regions
Scenarios at a glance
The following scenarios show different decarbonization pathways for South Tyrol. Explore their impacts on CO₂ emissions, total costs, and local value creation.
Select Scenario
Cost structure
Final energy consumption [TWh]
CO₂ emissions by sector
Comparison of INEMAR 2019 with the model for 2019
Key Technologies Explained
Clear explanations of the most important energy transition technologies
Heat Pumps
Efficient heating technology that uses ambient heat
Heat pumps extract heat from the environment (air, ground, water) and raise it to a higher temperature level. With 1 kWh of electricity they produce 3–5 kWh of heat. They replace oil and gas heating and are especially efficient in combination with underfloor heating and good insulation.
Vehicle-to-Grid (V2G)
EVs as mobile storage
V2G allows electric vehicles not only to charge, but also to feed electricity back into the grid. Their batteries act as flexible storage for surplus solar and wind power. This is especially valuable in South Tyrol, where PV generation is high during the day and charging demand rises in the evening.
Battery Storage
Storage of renewable energy
Lithium-ion batteries store surplus electricity from PV systems for use at night or during cloudy periods. They can increase self-consumption from 30% to as much as 70% and stabilize the grid thanks to their fast response times.
Power-to-Gas (Hydrogen)
Converting electricity into storable gas
Electrolyzers split water into hydrogen and oxygen using surplus green electricity. Green hydrogen can be stored, transported, and used for industrial high‑temperature processes or as a fuel. It is important for sectors that are hard to electrify.
Sector Coupling
Linking electricity, heat and mobility
Sector coupling intelligently links electricity, heat and transport. Surplus renewable electricity can be used for heat pumps, e-mobility or hydrogen production. This increases system flexibility and enables higher shares of renewables.
Energy Efficiency
Less energy for the same output
Energy‑efficient building retrofits (insulation, new windows) reduce heat demand by 50–80%. LED lighting saves 80% electricity compared to incandescent bulbs. Efficient industrial processes lower energy consumption. Energy efficiency is often the most cost‑effective form of decarbonization.
Methodology & Modeling
Scientifically grounded scenarios for South Tyrol’s energy future
Scenario status: 2025. These scenarios remain the reference until an updated version is published.
How does energy system modeling work?
To plan the best energy future for South Tyrol, we use a computer-based model that evaluates thousands of possible technology combinations. For each combination, it simulates how much energy is produced and consumed in every hour of the year—similar to a detailed weather forecast, but for the energy system.
Hourly calculation of energy generation and consumption for each technology mix
Automatic search for the best solutions between CO₂ reduction and costs
Selection of optimal pathways for 2030 (-55% CO₂) and 2040 (climate neutrality)
EnergyPLAN
Simulation software from Aalborg University for hourly energy flows
EPLANopt
Optimization algorithm from Eurac Research for best technology mixes
8,760 hours
Each hour of the year is simulated individually for maximum accuracy
Sector coupling
Integration of electricity, heat, transport and industry
Link to Climate Plan Monitoring
These energy scenarios complement the official South Tyrol climate plan monitoring. Visit www.eurac.edu/en/data-in-action/climate-change-monitoring for current data on climate development in South Tyrol.
Detailed Results
Select scenario
Conclusions for Future Scenarios (2030-2040)
HEAT
Natural gas phase-out:
Drastic reduction or complete phase-out of gas boilers, leading to a sharp decline in natural gas imports and related CO₂ emissions.
Massive heat pump deployment:
Heat pumps (air-source and ground-source) become the dominant heating technology, especially in residential and tertiary sectors.
Thermal storage integration:
Increased use of water-based thermal storage (tanks, district heating storage) to balance peak loads and integrate variable renewable electricity.
CHP biomass:
Remains an important renewable base-load source for district heating networks and rural areas, but will likely be optimized for seasonal balancing rather than daily peak coverage.
ELECTRICITY
Rising demand:
Overall electricity demand increases significantly (often +30-70% in scenarios), driven by electrification of heating, transport, and industry.
PV boom:
Strong growth in distributed photovoltaic systems (residential, commercial, and agri-PV), making solar, alongside hydropower, one of the pillars of local electricity generation in South Tyrol.
Need for flexibility:
- Battery storage (stationary and mobile via EVs)
- Flexible consumers (heat pumps with smart control, industry with demand response)
- Electricity exchange (with neighboring regions to balance periods of surplus and shortage)
INDUSTRY
Electrification push:
Industrial heat pumps cover low- to medium-temperature processes (<150°C).
Hydrogen uptake:
Targeted use of green hydrogen for medium- to high-temperature processes where electrification is less viable (metal processing, ceramics). At the same time, local power-to-hydrogen infrastructure, including electrolyzers and H2 storage, is being developed.
Sector coupling:
Hydrogen-to-power, via fuel cells or hydrogen-ready CHP plants, provides flexible backup capacity and supports the stability of the electricity system.
TRANSPORT
BEV dominance:
Battery electric vehicles become the standard for passenger transport; combustion cars decline sharply.
Bidirectional charging (V2G):
Plays a key role in providing grid services such as frequency regulation and peak shaving. In South Tyrol, it offers strong potential thanks to the widespread use of PV systems, which makes it possible to align EV charging with solar generation.
Public transport and logistics:
Electrification of local public buses and light commercial vehicles.
For heavy-duty and long-haul transport, hydrogen or bio-LNG may still be considered as complementary solutions.
Applications of the Modelling Methodology
Our modelling methodology has been successfully applied across various regions and countries in Europe.
Lower Austria
Regional energy modelling for the Austrian state of Lower Austria, with a focus on sustainable energy planning.
Salzburg
Regional energy modelling for the Austrian state of Salzburg to support the energy transition at state level.
Italy
National energy scenario analysis for Italy, with a focus on decarbonization pathways.
Piedmont Region
Energy modelling for the Piedmont region to support regional energy planning.
7 EU Countries
Comparative analysis for Germany, Spain, France, Italy, Netherlands, Poland, and Sweden.
PLANtoACT LIFE – 5 EU Regions
Auvergne-Rhône-Alpes (FR), Lombardy (IT), Alba County (RO), Oberland (DE), Porto metropolitan area (PT).
Energy scenarios for your region
In South Tyrol, we have shown how research, data, and policy can come together to form a robust energy-transition strategy. We transfer this expertise to other regions and co-develop scenarios that make pathways to climate neutrality tangible.
Further Information
Scientific publications, data sources, and further materials on the methodology and the energy scenarios for South Tyrol
Modeling & Methodology
Multi-objective optimization algorithm coupled to EnergyPLAN software: The EPLANopt model
Prina, M.G., Cozzini, M., Garegnani, G., Manzolini, G., Moser, D., Filippi Oberegger, U., et al. • Energy, 2018; 149: 213–221
Development of the EPLANopt model for multi-objective optimization of energy systems.
Transition pathways optimization methodology through EnergyPLAN software for long-term energy planning
Prina, M.G., Lionetti, M., Manzolini, G., Sparber, W., Moser, D. • Applied Energy, 2019; 235: 356–368
Methodology for optimizing long-term energy transition pathways using EnergyPLAN.
Evaluating near-optimal scenarios with EnergyPLAN to support policy makers
Prina, M.G., Johannsen, R., Sparber, W., Østergaard, P.A. • Smart Energy, 2023; 100100
Evaluation of near-optimal energy scenarios for evidence-based policy support.
Machine learning as a surrogate model for EnergyPLAN: speeding up energy system optimization at the country level
Prina, M.G., Dallapiccola, M., Moser, D., Sparber, W. • Energy, 2024: 132735
Using machine learning to accelerate country-level energy system optimization.
Open Source Code
EPLANopt — Open Source Optimization Model
Prina, M.G. (matpri) • GitHub repository
Open-source code for the EPLANopt multi-objective optimization model.
South Tyrol Climate Plan
South Tyrol Climate Plan 2040
Autonomous Province of Bolzano - South Tyrol • Strategy document
Official provincial climate plan with targets and measures.
South Tyrol Climate Plan Monitoring
Eurac Research • Online platform
Monitoring tool for tracking South Tyrol's climate targets.
Sector Coupling & Decarbonization
EnergyPLAN – Advanced analysis of smart energy systems
Lund, H., Thellufsen, J.Z., Østergaard, P.A., Sorknæs, P., Skov, I.R., Mathiesen, B.V. • Smart Energy, 2021; 1: 100007
Advanced analysis capabilities of the EnergyPLAN software for smart energy systems.
Classification and challenges of bottom-up energy system models - A review
Prina, M.G., Manzolini, G., Moser, D., Nastasi, B., Sparber, W. • Renewable and Sustainable Energy Reviews, 2020; 129: 109917
Comprehensive review and classification of bottom-up energy system models.
Electrification of transport and residential heating sectors in support of renewable penetration
Bellocchi, S., Manno, M., Noussan, M., Prina, M.G., Vellini, M. • Energy, 2020; 196
Scenarios for sector coupling through electrification of transport and heating in Italy.
Data Sources
INEMAR - Atmospheric Emissions Inventory
Provincial Agency for Environment and Climate Protection • Emissions inventory 2019
Basis for South Tyrol's CO₂ emissions data.
Questions about the methodology?
For detailed information about modeling and the scenarios, please contact the research team at Eurac Research.
renewable.energy@eurac.eduQuestions & Answers
Answers to the most important questions about decarbonization in South Tyrol
Glossary of Terms
Clear explanations of the most important technical terms
BEV
Battery Electric Vehicle – a vehicle powered solely by electricity stored in a battery.
CO₂eq
CO₂ equivalent – a unit that expresses the climate impact of different gases relative to CO₂.
Decarbonization
Process of reducing CO₂ emissions by replacing fossil fuels with renewable energy sources.
EnergyPLAN
Software developed at Aalborg University for hourly energy system analysis.
EPLANopt
Optimization model that combines EnergyPLAN with a multi‑objective algorithm to identify optimal energy system configurations.
ktoe
Kilotonnes of oil equivalent (ktoe) – an energy unit corresponding to 1,000 tonnes of oil equivalent (approx. 11.63 GWh).
ktCO₂eq
Kilotonnes of CO₂ equivalent – 1,000 tonnes of CO₂ or the equivalent amount of other greenhouse gases.
Pareto Front
A set of optimal solutions where no objective can be improved without worsening another one (e.g. costs vs. CO₂).
Power-to-Gas
Conversion of electricity into gaseous energy carriers such as hydrogen or methane via electrolysis.
Power-to-Heat
Conversion of electricity into heat, e.g., via heat pumps or electric heaters.
Sector Coupling
Smart integration of the electricity, heat and mobility sectors for the efficient use of renewable energy.
V2G
Vehicle-to-Grid – a technology that allows EVs to feed electricity back into the grid and act as mobile storage.
Institute for Renewable Energy
We develop and test the building blocks for a climate-neutral future for South Tyrol. In the Alpine region, we create practical energy solutions that can serve as models worldwide.
Regional model
We study energy systems in their real regional context, with real data, grids, and infrastructure. This yields robust insights for South Tyrol and other regions.
Scalable solutions
From components to buildings to regional energy systems—we provide blueprints for transforming entire regions.
Research meets market
We help companies shorten the path from an idea to a certified, market-ready product through excellent lab infrastructure and specialist expertise.
Shaping the energy future together
Whether it is a research partnership, product development, or knowledge transfer, discover the possibilities for collaboration with our institute.
This research has been carried out within the PNRR research activities of the consortium iNEST (Interconnected North-East Innovation Ecosystem) funded by the European Union Next-GenerationEU (Piano Nazionale di Ripresa e Resilienza (PNRR) Missione 4 Componente 2, Investimento 1.5 D.D. 1058 23/06/2022, ECS_00000043 – Spoke1, RT3A, CUP I43C22000250006).
We would also like to express our sincere gratitude to the Autonomous Province of Bolzano – South Tyrol, in particular the Office for Energy and Climate Protection (Ufficio Energia e tutela del clima), and to Alperia Greenpower Srl for their valuable data provision and continuous support to this research.
This presentation reflects only the authors' views and opinions; neither the European Union nor the European Commission can be considered responsible for them.
