In Italy, the expression “scoprire l’acqua calda,” if literally translated into English, stands for “discovering hot water.” It means that what you just discovered is nothing new; indeed, it is pretty obvious. District heating is nothing more than a network of pipes distributing hot water. So, I thought that “scoprire l’acqua calda” could be a funny expression to stress this article’s aim: to inspire you, dear readers, and make you aware of the benefits this technology can bring in a global context. A novel, replicable methodology for assessing district heating potential will be presented.
By Giulia Spirito, PhD Student at the Energy Department of Politecnico di Milano, Italy
District heating (DH) is a well-known technology, in a way in vented by the ancient Romans thousands of years ago. However, despite the energy, economic and social benefits it can bring, it still is a niche technology. In this sense, district heating should be “discovered”: anyone should become aware of its potential to promote its diffusion. The methodology I will present has been developed based on open-source data and software to make it replicable in other contexts and so that results can be available for everyone.
In the following, the main steps of the method and then the results obtained by applying it in Italy will be illustrated. The focus was on district heating based on renewables and excess heat sources. The main novelty stands in the high spatial resolution achieved, with which it was possible not to overlook local parameters. It is, in fact, essential, when planning a DH network, to properly consider its local nature.
The novel methodology has been developed in a project fund ed by AIRU, the Italian District Heating Association. It was con ducted by the research group “ReLab” of Politecnico di Milano and by Politecnico di Torino.
It consists of 5 main steps that are illustrated in Figure 1. Step 1 illustrates the quantification and the mapping of the heat demand, and step 2 its spatial aggregation. In this way, clusters of heat demand are generated. They identify areas where the heat demand is high and very dense and, thus, where DH is expected to be feasible. The distribution net work’s length and topology are estimated in each cluster, so that heat losses and costs related to the heat distribution can be computed. In step 3, the available heat sources are identified, and the amount of recoverable heat is estimated. At this point, the transmission network connecting sources and heat demand cluster can also be designed in step 4. In step 4a, a triangulation algorithm generates the energy graph in which all the previously identified heat demand and heat sources are connected. Step 4b uses a routing algorithm to turn the linear connection into paths along the streets. In this way, more realistic costs associated with the transmission network can be estimated and considered in the ultimate step, step 5. Here, an optimization algorithm is applied to identify, for each cluster, what is the most economically feasible heating technology among DH and the individual solutions. The strength of this method, and this algorithm, in particular, is the capability of considering the spatial distribution of the elements that make up the whole system, with the possibility to take into account all the associated aspects and costs related to their location and mutual position.
For each demand cluster, the total cost associated with DH, thus the sum of heat generation, heat transportation, and distribution, is compared to the cost that would be paid if the same amount of supplied heat is met by any alternative individual heating solution (e.g., natural gas boilers, air or water heat pumps). The optimization algorithm, aiming to minimize the system’s overall cost, identifies the areas where DH is competitive to any local technology. It indicates the optimal heat demand clusters, the heat sources to be connected, and how (along which network path). The result of the methodology is the definition of DH potential in Italy on an annual basis and based on an optimally designed network, thus with a high spatial resolution.
This section deals with the results obtained by applying the developed methodology to the case study in Italy. DH potential in the country in terms of quantity is presented in Figure 2. At the same time, the identified optimal paths are shown in Figure 3 in a portion of the Italian map for greater clarity. All the results can be explored interactively in the web map created in ArcGIS: https://arcg.is/0vvO4H.
Renewables- and excess heat-based DH in Italy can meet a heat demand of 38 TWh annually and given the minimum cost for the overall system. It corresponds to 12% of the estimated heat demand, about 329 TWh/year. A four-fold expansion is envisaged since DH currently covers only 3% of the overall heat demand.
These results appear very promising, and since Italy presents a very peculiar territory, they also suggest obtaining even higher results in countries where the environment may be intrinsically more suitable for a technology like DH. Indeed, Italy presents a variegated territory and different climate conditions, passing from the northern regions with cold winters to central and southern regions characterized by a mild Medi terranean climate. Moreover, it presents a widely uneven demographic distribution, with very dense metropolitan cities such as Milan and Rome and sparsely populated areas along the Alps, the Apennines, and in the two major islands, mainly.
Despite this peculiar territory conformation, a fourfold DH expansion can be obtained based on renewables and existing excess heat sources. This confirms the important role that district heating can have in mitigating climate change and in facing the current energy crisis in Italy and globally.
Regarding the visualization of the results, in the map, it is possible to see the heat demand clusters represent ed as orange polygons, the heat sources described as points, and the optimal heat fluxes as directed arrows.
The considered heat sources are waste incineration plants (in green), wastewater treatment plants (in blue), and low-temperature and high-temperature effluents from industries (in yellow and red, respectively). The size of the points indicates the amount of recoverable heat, while the amount of transported heat along the arrows is specified by their colour.
You may notice that not only sources and heat demand clusters are connected. There are paths linking sources with sources and clusters with clusters. Indeed, if the available heat from a source is more significant than the demand in its vicinity, it can be distributed to multiple clusters; if the heat entering a cluster exceeds its heat demand, this residual heat can be conveyed to one or more adjacent clusters; if a cluster’s demand cannot be met by a single source, multiple sources can be used.
The developed methodology can be improved since some simplification was made, but the results are reliable and encouraging. They can be extrapolated from the map and used as a starting point for further analysis of specific districts in Italy or other countries.
Future efforts will go towards an increased temporal resolution, considering demand and load profiles and heat storages to balance them, and towards sector coupling, thus by considering the interaction of the heating sector with the electrical and transport sectors.
How to make a plan?
The developed methodology enables the assessment of DH potential on a large-scale level by considering the magnitude, location, and costs of all the elements constituting the energy system. The local aspects that are essential when dealing with DH are therefore contemplated.
The outcome of the optimization problem is very promising and can provide a reliable starting point for any decision-making authority, such as policymakers, cities’ mayors, and DH operators. District heating is indeed an energy infrastructure in which local aspects are fundamental for a proper potential assessment and planning. As proved by the great development DH experienced in Denmark since the intervention of the Government in the late 70s, adequate regulation at the national level is required to foster the market uptake of this technology.
But together with policymakers, it is helpful that also engineers, system operators, managers, and even users (non-expert people) are made aware of this technology’s potential. It is important that everyone knows that the advantages of this technology are many and assured, even though a great investment cost is generally required for the construction of the distribution network. Moreover, it is important to stress that everyone can take advantage of the environmental, social, and economic benefits that arise from DH if the system is properly built and properly managed, operated, and used.
That is why results are made available online and open for consultancy. Everyone, even non-experts, can access them and get an insight into a specific area’s potential in terms of DH systems’ ability to provide thermal energy. Everyone can “discover hot water”!
For further information, please contact: Giulia Spirito, email@example.com
What makes this subject exciting to you?
I have focused on DH&C since my degree in 2020. I was motivated and still am because DH’s role in decarbonization appears increasingly relevant, with countless rising opportunities. I am devoting myself to learning more about them, contributing to this development, and disseminating the acquired experience. This article proves it.
What will your findings do for DH?
The intention is to foster DH diffusion by highlighting its benefits. This is done by developing a replicable methodology for assessing DH potential based on RES and excess heat. Fundamentally, each of the involved players of a DH system has a clear vision of the potential advantages of this technology and how they can be maximized while minimizing costs. The results obtained in Italy can be the stepping-stones for many other applications and more detailed research.
Giulia is a Ph.D. student at Politecnico di Milano. Her research focuses on DH at local and national scales, intending to merge these two scales of analysis. She manages tools to design and optimize DH networks holistically based on geo-referenced data. She has been involved in several national and EU-funded projects and is participating in the activities of the IEA. She is a co-author of 6 indexed scientific publications.