The evaluation of buildings energy consumption and the optimization of district heating networks: a GIS-based model

Int J Energy Environ Eng (2016) 7:343–351 DOI 10.1007/s40095-015-0161-5 ORIGINAL RESEARCH The evaluation of buildings energy consumption and the optimization of district heating networks: a GIS-based model Chiara Delmastro • Guglielmina Mutani • Laura Schranz Received: 31 July 2014 / Accepted: 31 December 2014 / Published online: 27 January 2015  The Author(s) 2015. This article is published with open access at Springerlink.com Abstract The European buildings occupy a key place among the major energy consumer sectors, with high savings potential. The development of urban planning tools helpful to understand the right policy strategies turning the settled sustainable targets into real energy consumption savings is now a real challenge. Into this paper is described a methodology, for the mid-long term scenarios analysis, able to asses the buildings energy consumption of a municipality by means of a simulation approach and of a geo-referenced characterization of the stock. A thermal model, based on real consumption data, has been used to evaluate space heating energy demand; different savings opportunities have been simulated. Moreover, from the geo-referenced representation of the district heating network, through the integrated procedure, it is possible to perform the optimization of the network layout. A case study application in Turin is presented. Main results are the evaluation of energy consumptions, total costs of the interventions and the release of policy suggestions. Thanks to geo-referenced maps is allowed to put in evidence criticalities and policy effects through thematic maps. The methodology highlights the advantages of coupling a geographical information system application and energy demand forecasting model to build up a tool aimed at supporting decision-making. Keywords Energy consumption  Residential buildings  District heating  Optimization  GIS Published in the Special Issue ‘‘8th AIGE Conference (Italian Association for Energy Management)’’. C. Delmastro  G. Mutani  L. Schranz (&) DENERG, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy e-mail: Introduction Nowadays in Europe, the building sector is one of the most energy consumers: the individuation of suitable measures to reach the fixed reduction target for 2020 is a real challenge for policy makers. Consequently, great attention is now focused into the planning tools for the forecasting of the effects of different interventions and measures. For the analysis of the energy systems of a Country or a region and, less frequently, of a local system like a metropolitan area, energy models like those of the MARKAL-TIMES [1] and MESSAGE [2] families have been (and are) widely used. Such models are a technology-rich tool for evaluating long-term trajectories of multi-regional energy systems. Together with the diffusion of geographic information systems (GIS) applications, the growing interest in local energy planning and the increase of the power of the computing tools have encouraged the development of new exercises in the field [3]. The integration of space variable is a skill necessary in almost every energy planning process; in fact, it is helpful to assist the siting of new generation facilities, to determine the optimum route for new distribution and transmission network lines, and to develop emergency evacuation plans around facilities. The energy planning process of an urban settlements or a district first require defining its reference energy system: to evaluate the energy demand by end use and by fuel, to identify the existing end-use technologies and to describe the local supply plants and infrastructures. Thanks to the GIS tool, all these data are the geo-referenced. The presented paper is focused on the retrofit of buildings and the refurbishment of the district heating network in the Centro Residenziale Europa area of Turin–CRE 123 344 Int J Energy Environ Eng (2016) 7:343–351 (with a gross heated volume of about 650,000 m3). The analysis has been performed using a specific GIS-integrated modeling tool able to identify and characterize a suitable mix of measures/retrofit/technologies/infrastructures in the direction of a ‘‘smart’’ and sustainable built environment. Three scenarios are proposed: Baseline, Medium and Advanced. In all the scenarios, the renovation of the district heating network is included; the difference among them is related to the level of buildings retrofit. Moreover, for that exercise only water heating and space heating services have been taken into account. In Italy, typically, these services are supplied by individual heating systems, using boilers (gas, oil, solid fuels) or electric heating system; contrarily, district heating is widely adopted in Turin. The CRE district, built from 1968 to 1974, is sited near to the Fiat Mirafiori factory, in the South-West of the city, between Tazzoli, Orbassano and Reni axes. The new prefabrication system, produced by the UPIR factory and very diffused at that period, made available elegant apartments type at lower price in comparison to other contemporary private constructions. The resulting district, is characterized by blocks of flats—eleven floors tall—arranged in a U around the border of the affected area. After more than 40 years, the Centro Residenziale Europa district heating (DH) network is quite old and characterised by high heat losses that justify the desire of the inhabitants to promote a refurbishment. Materials and methods The first step of the procedure is the collection of the Base Year (2010) data: global domestic product (GDP), population, floor area, per capita income, energy use, energy policies, base maps for the GIS analysis. Moreover, it is necessary to describe the building stock (construction period, shape factor…)—identification of reference buildings—and to characterize the district heating network (existing and planned). Using the GIS software, all the buildings and the networks are geo-referenced. It allows the creation of a detailed representative GIS-Database with the characteristics of the buildings and their end-use technologies (efficiency and life) [4] (Fig. 1). GIS data could be open source data, free data made available by private companies, fee-based data sources; they are available from many sources including federal, state, and local government database. Through an embedded procedure of the Model, in absence of existing local network or in case of the refurbishment of the existing one, it is possible to find (under suitable user-defined constraints) the path that minimizes 123 the total cost. This procedure is the core of the project; it starts by use, for each geo-referenced building, a thermal model to evaluate the space heating and hot water energy demands. Then, for all the buildings, using bottom-up models, it is possible to describe and to project a certain number of reference processes supplied by a series of commodities (coal, natural an (...truncated)