An Innovative Tech Stack for District Heating Network Planning in Lisbon

As cities race to meet ambitious climate targets, the complexity of urban energy transition is pushing traditional planning methods to their limits. With buildings accounting for a massive share of global CO2 emissions, the need for agile, data-driven district heating network planning has never been more urgent.

Recently published in Frontiers in Sustainable Cities, an award-winning case study in Lisbon’s Vale de Santo António district highlights a paradigm shift in how urban energy infrastructure is designed. By leveraging a collaborative, multi-layered software stack, researchers and municipal stakeholders successfully modeled a green-field energy project that balances economic viability with long-term carbon neutrality.

Here is an inside look at how this integrated methodology is setting a new standard for modern energy planning.

A Cross-Sector Collaboration for Urban Innovation

The Vale de Santo António project stands out not just for its technical rigor, but for its deeply collaborative foundation. The research brought together leading academic and civic minds, featuring a joint effort between researchers from ETH Zurich, EPFL and the Technical University of Lisbon (Instituto Superior Técnico), working directly with planners from the Municipal Chamber of Lisbon (Câmara Municipal de Lisboa).

This cross-pollination of academic research and municipal energy planning ensured that the resulting energy models were not just theoretically sound, but practically applicable to the realities of a growing European capital.

The Ultimate Tech Stack: Software for District Heating Planning

To tackle the lack of existing building stock data and accurately simulate complex intervention scenarios, the project team developed a novel, sequential framework integrating three complementary platforms:

1. Urbio: Transforms static data into actionable infrastructure designs. Acting as a “single point of truth”, it centralizes all data into one unified digital twin, then generates optimized energy infrastructure scenarios to supply the neighborhood.

2. City Energy Analyst (CEA): Tasked to simulate neighborhood energy services, calculating demand for heating, cooling, domestic hot water, and electricity (including future EV charging loads).

3. QGIS: Utilized to define the comprehensive building database, mapping construction solutions, building uses, and occupancy schedules.

Crucially, the data flow remains entirely interoperable. Urbio is fully compatible with industry-standard GIS tools, including QGIS and ArcGIS by Esri, allowing users to seamlessly export and import all infrastructure results and geometries in standard .SHP format for broader municipal use.

This integrated approach highlights the capabilities of modern software for district heating planning. As noted in the peer-reviewed paper:

"As for Urbio, it enables a swift exploration of the decision space and provides a deeper understanding of the supply alternatives, and how they relate to external conditions such as fuel prices or carbon intensity, but also to urban parameters such as the distribution of GFA between occupancy types."

Designing the Optimal Network: The Power of Sensitivity Analyses

One of the standout findings of the Lisbon case study was the strategic optimization of the thermal network layout. Instead of defaulting to a single, sprawling district heating layout, the software calculations revealed that a two-layout structure (separating northern and southern networks) drastically minimized pumping demand and heat distribution losses.

Furthermore, the framework enabled a robust sensitivity analysis regarding future carbon taxes and the "greening" of the electrical grid leading up to 2050. The data demonstrated that an electricity-based heating mix (combining decentralized air-source heat pumps with maximized rooftop solar PV) was the most future-proof and cost-effective pathway, outperforming centralized gas systems.

Providing decision makers with the ability to rapidly iterate these scenarios is vital. The researchers emphasized:

"Similarly, Urbio’s model of fast generation of energy supply infrastructure provides a complementary method for the decision makers to witness directly the inherent compromises of neighborhood energy infrastructures. In the end, this first-person interaction with the models helps build trust and confidence in the energy design process."

From Academic Excellence to Industry Impact

The methodology driving this case study not only provided actionable blueprints for the city of Lisbon but also earned significant academic recognition. The lead author of the award-winning research, Alexandre Jewell, has since joined the team at Urbio, bringing his expertise in urban building energy modeling (UBEM) directly into the development of the platform.

As the energy sector increasingly relies on artificial intelligence and advanced generative design to navigate the transition, tools that can bridge the gap between complex data and actionable urban infrastructure planning are essential.

As urban energy needs evolve, the criteria for what makes a 'top' tool change, too. We are proud that this peer-reviewed framework includes Urbio on its list of best district heating planning software, showcasing how our platform helps utilities, consultants and public planners move past simple simulations and toward actionable infrastructure.

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Read the full original academic paper here:

Alexandre Jewell, Nils Schüler, Sébastien Cajot, Ricardo Gomes, Carlos Santos Silva, François Maréchal. Designing a District Energy Infrastructure - a Case-Study in Lisbon. Frontiers in Sustainable Cities. 2022.