nPro emerged from research and continues to be used there: as a calculation core for techno-economic case studies, as a reference for validating researchers’ own models and as a tool in reviews of district heating planning. This page presents the works that use or cite nPro and explains what the tool was used for in each of them.
Why scientific traceability matters
Planning decisions for district energy systems and heating networks have to be robust – towards clients, funding bodies and in committees. nPro therefore not only makes its calculation methods transparent in the documentation, but also bases them on methods that have previously been published in peer-reviewed journals. Anyone calculating with nPro can thus support their approach with citable references.
In the meantime, a body of literature has grown up around the tool. It can be divided into three groups, which the following sections address one after the other:
- The reference paper on nPro – the central description of the tool.
- Applications – case studies, research projects and theses that calculate with nPro.
- Reviews and tool comparisons – overview articles that place nPro within the tool landscape.
The reference paper on nPro
The basis of all references is the article that describes the tool itself:
The paper documents the chain that nPro works through in a project: the generation of load profiles for space heating, domestic hot water, space cooling, process cooling and electricity including electric mobility, the subsequent dimensioning and simulation of thermal networks up to 5GDHC systems as well as the optimization of the energy center. In particular, it describes the MILP-based design of the generators (see optimization model) and the quasi-static network simulation with a parameterization that is physically sound while remaining manageable for planners.
nPro in case studies and research projects
Operating temperatures in a 4th generation district heating network
Using a real German network, the study investigates what a reduction of the operating temperature really costs and delivers. Three operating strategies are compared: a constant supply temperature of 60 °C, one of 50 °C as well as a sliding supply temperature between 40 and 50 °C. nPro serves as the calculation core for the network and the energy center and represents how the heat supply shifts from the central heat pump to decentralized reheating with electric instantaneous water heaters – with the corresponding consequences for network losses, CO₂ emissions, primary energy demand and total costs.
For practitioners, this is a prime example of how a systematic scenario comparison can be set up with the heating curves and temperature levels in nPro, instead of assessing a temperature reduction in blanket terms.
Hybrid off-grid energy systems: nPro and TOPSIS
The authors from Irkutsk Technical University combine nPro with a decision-making method. The subject of the investigation is the supply of the village of Ust-Sobolevka in the Russian Primorye region: 190 inhabitants, 170 km to the nearest district center, supplied so far by a diesel power plant. This is therefore an off-grid island system, and precisely not a classic district heating project.
In the first step, the authors use nPro to calculate ten variants of a hybrid system. The technology portfolio considered comprises wind turbines, photovoltaics, PVT collectors, solar thermal, air-source heat pumps, biomass and electric boilers as well as thermal and electrical storage; the diesel generator remains in the system as a reserve and is represented via the grid connection module – a substitute modelling approach described in the nPro documentation. The set of variants does not arise from manual trial and error, but by successively setting upper limits for capital expenditure and CO₂ emissions; nPro optimizes every variant subject to the respective constraint. In the second step, the authors condense this field of variants into a ranking using the TOPSIS method, assessed by capital expenditure, LCOE, LCOH and CO₂ emissions.
The preferred configuration combines a diesel generator, wind power, photovoltaics, a heat pump, a biomass boiler and thermal storage and achieves an LCOE of 0.118 €/kWh with an LCOH of 0.195 €/kWh. A null result is also interesting: solar thermal was offered but not selected in any of the ten configurations – the optimization consistently opted for PVT or for heat from the heat pump and the boiler.
The work thus shows two things. First, nPro’s energy center optimization can be used as a variant generator for downstream decision-making methods. Second, the authors’ tool comparison highlights the web interface as an advantage over tools requiring a complex local installation.
Optimal energy mix of a small-scale district heating system
The authors from the “Ss. Cyril and Methodius” University in Skopje use nPro to develop a district heating concept for a cluster of existing public buildings in Ohrid (North Macedonia) – schools, kindergartens, offices, a hospital, a student hall of residence, a sports hall and a swimming pool. The buildings exist, the network is planned; the work is therefore a feasibility study for a new-build case, not the analysis of an existing network.
The demand determination is carried out in nPro building by building via areas and specific consumption values. The result: 6,740 MWh of heating demand of the buildings at a peak load of 3,385 kW, plus 749 MWh of network losses – i.e. a heat load of 7,490 MWh at the energy center at 3,470 kW. The electricity demand at the transfer point is 1,710 MWh and 453 kW including pumping electricity.
The energy center combines solar thermal, a natural gas-fired CHP unit, a heat pump, a high-temperature thermal storage unit and a PV system. The design results in 2,125 m² of collector area, 722 kWel of CHP capacity, a 500 kWth heat pump, 1,050 kWp of PV and a storage unit of 1,740 m³ or 40.4 MWh. On the heat side, the heat pump covers 43.7% (3,274 MWh), the CHP unit 31.6% (2,364 MWh) and the solar thermal system 24.7% (1,854 MWh) – with an assumed COP of 4. In summer, the heat supply runs solely via the solar thermal system and the storage unit, while the CHP unit stands still. The economic feasibility calculation over 20 years yields a positive net present value with a payback period of about seven years.
For practitioners, the work answers two frequently asked questions. First, nPro can also be usefully applied far below the scale of large city networks – here a district network with an annual heat supply of under 8 GWh. Second, its application does not depend on the German context: climate data for Ohrid and Macedonian energy prices are used directly, and the technology costs can likewise be freely adjusted.
Shallow geothermal energy in an existing district
The joint project investigates the technical, economic and legal boundary conditions under which shallow geothermal energy can be developed in existing districts. It is carried out by a consortium consisting of Fraunhofer IEE and IEG, the Stiftung Umweltenergierecht, the Institute for Solar Energy Research (ISFH), GASAG Solution Plus, Trianel as well as five municipal utilities. The subjects of the investigation are five real inner-city existing districts in Münster, Solingen, Bensheim, Schleswig and Berlin.
In this project, nPro is the tool for the variant comparison. Nine supply variants were calculated for each district: a cold network and a warm network, in each case without regeneration as well as with 50% and 75% solar regeneration of the borehole field, plus a central air-source heat pump and two decentralized variants with an air-source and a brine-to-water heat pump respectively. The economic assessment is carried out in accordance with VDI 2067 over 20 years, including BEG, BEW and progres.NRW subsidies.
Methodologically interesting is the division of labor with the borehole software EED, which proceeds iteratively: in nPro, the load curves for each building are entered with space heating and domestic hot water considered separately, the network is designed and the energy center dimensioned; the simulated load curve of the net source heat is then fed into the borehole design. The resulting source temperatures – formed as hourly 50-year mean values – are then fed back into nPro and the calculation is repeated. The same procedure applies to the PVT collectors used for regeneration, whose heat yield depends on the mean collector temperature. The characteristic curves of the reference heat pumps and the collector parameters come from test rig measurements at the ISFH and were entered directly into nPro.
For analyses that go beyond initial estimates, the nPro tool also proved suitable for practical use in the project.
Technical Report UrbangroundheatFraunhofer IEE
nPro in theses
Theses are a good indicator of how quickly a tool can be learned and how independently it can be worked with – and, when compared with other models, they show where the strengths of the tool lie.
An application tutorial compared with REopt
The thesis is designed as a user manual for two planning tools and, in doing so, delineates their respective scopes: REopt for the site-related optimization of individual properties in the US context, nPro for district energy systems with network modelling and a building-by-building approach.
In the nPro part, Syntrilalas calculates five systems for a district in Chania (Greece). On the electricity side, these are a grid-connected PV-wind system as well as two off-grid variants with a battery and with hydrogen storage respectively; on the heat side, a geothermal supply with thermal storage and a biomass CHP unit with thermal storage. The comparison is made within each of the two groups. In the case of the heating systems, both variants prove to be economically viable; the geothermal solution comes out ahead in terms of net present value despite the higher investment and is also classified as preferable in terms of capital efficiency and internal rate of return.
The value of this work lies less in the result than in the demonstration of how easy the tool is to learn: a diploma student independently builds up the demand determination, the energy center design and the economic feasibility calculation and documents the path there step by step.
Model comparison with a co-simulation
Baars develops a high-resolution co-simulation framework for a planned 4th generation heating network in the Eindhoven pilot district Generalenbuurt-Zuid. Via the FMI interface, EnergyPlus building models are coupled with a network model in Matlab/Simulink, with a communication time step of ten minutes.
To check the plausibility of her own model, the author additionally calculates the same district – 22 terraced houses, 2,816 m², supply 53 °C, return 30 °C, TMY weather data for Eindhoven – in nPro and compares the heating demand curves. Both models show the same annual profile and the same order of magnitude; in the hourly mean, the curves largely overlap during the heating period. On this basis, Baars assesses her co-simulation results as valid.
The work thus describes the division of labor also mentioned by the reviews from the other side: anyone wanting to estimate the demand of a district robustly in the concept phase can arrive, with the standard load profiles and the degree-day method, at results that stand up to a model that is orders of magnitude more computationally intensive.
nPro in reviews and tool comparisons
Overview articles on district heating modelling now regularly list nPro in their tool tables.
The authors combine a systematic literature review of 118 works with a survey of eleven tool developers and experts. nPro is classified there in detail as one of eleven tools – alongside TRNSYS, Dymola, IDA ICE, EnergyPLAN, STANET, ROKA, heatbeat, NetSim, EcoStruxure District Energy and pandapipes.
The classification describes the calculation chain precisely: nPro represents the network components via energy balances, calculates heat and pressure losses branch by branch in hourly resolution and computes quasi-statically, with the hydraulic and the thermal simulation running sequentially. Unlike most of the tools compared, nPro covers modelling, optimization and simulation together. As a strength, the authors note the design of 5GDHC and anergy networks and count nPro among the tools that are able to represent 4th and 5th generation networks at all; the modelling of cooling networks and low-exergy networks with network temperatures from 0 to over 120 °C is explicitly mentioned, as is the technical suitability for multi-energy systems including hydrogen.
Are you working scientifically with nPro?
This overview is continuously expanded. If you have used nPro in a publication, a research project or a thesis, we would be glad to hear about it – we will happily add the work to this list. For teaching and research there are also separate terms of use; simply get in touch with us about this.