Potential Diffusion of Renewables-Based DH Assessment through Clustering and Mapping: A Case Study in Milano

Abstract

This work aims at developing a methodology for the assessment of district heating (DH) potential through the mapping of energy demand and waste heat sources. The presented method is then applied to the Metropolitan City of Milano as a case study in order to investigate the current and, especially, the future sustainability of DH with the foreseen building refurbishment and consequent heat demand reduction. The first step is the identification of the areas the most interesting from a heat density and an economic point of view through a clustering algorithm, in which lies the main novelty of the work. The potential is then assessed by investigating their synergy with the available heat sources, which are mapped and analyzed in terms of recoverable thermal energy and costs. In future scenarios with foreseen heat demand reduction, low-temperature networks and excess heat sources are considered, such as metro stations and datacenters, together with the conventional sources, such as thermoelectric plants. The outcomes prove that lower heat demand corresponds to higher network costs with consequently reduced district heating potential but also prove that the properties of low-temperature district heating can potentially compensate for the drop in its cost-effectiveness. Another interesting finding is that the renovation of buildings in an area should be not performed evenly but with criteria; for instance, in synergy with DH diffusion.

District heating distribution grid costs: a comparison of two approaches

“…In a similar work, Spirito et al applied the effective width concept to estimate the DH distribution grid cost, which later on was used to calculate the potential diffusion of renewables-based DH for the case study of Milan. To identify most suitable DH areas, they used DBSCAN clustering algorithm [7].…”