D'Alonzo, Valentina (2019) A Spatial Decision Support System for thermal energy planning at the regional scale. PhD thesis, University of Trento.
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The focus of the Ph.D. dissertation is on the thermal part of the energy planning issue since the space conditioning (heating and cooling – H&C) of buildings represents about 75% of the energy consumed by European residential buildings and only 16% of the heating and cooling consumption is covered by renewable energy sources (RES). At the same time, the increased complexity of the spatial planning process when energy issues are involved has made clear the need for new “energy-aware” tools and methods used in this field. The proposed methodology is GIS (Geographical Information System)-based and performed at regional scale given that the movement of energy planning activities from national to regional and local scale allows a much more detailed analysis of both the energy demand and supply, balancing them more effectively. The integration of the spatial dimension within energy analyses can also provide the decision-makers with a spatially-explicit approach towards the energy transition and the development of sustainable energy plans and strategies. The general aim of the Ph.D. thesis is to develop a Spatial Decision Support System (SDSS) allowing the decision-makers to take into account (during the planning process) both the improvement of the energy production from RES and the energy renovation of the existing building stock. The SDSS aims also to connect the energy planning (supply side) with spatial planning (demand side) by seeking synergies between the two fields. This connection is made taking advantage of the framework of the Strategic Environmental Assessment (SEA). The Ph.D. thesis is partially developed within a European co-financed project included in the Interreg Alpine Space programme. The GRETA project was designed to foster the use of shallow geothermal energy (SGE) in energy plans and strategies along the Alps. SGE is a low-carbon source for H&C of buildings, which exploits the heat stored within the ground, a local source widely available and less dependent from changes in time compared to other RES. Despite this, its exploitation is not yet diffused and its growth is limited mainly by factors such as scarce knowledge, complicated and fragmented legislation, and high installation costs. Considering all these issues, the research questions that shaped the Ph.D. activities are: ➢ How to estimate the thermal energy demand of the residential building stock at the regional scale, as a starting point for developing sustainable energy strategies aimed at the reduction of the thermal energy consumption in the existing buildings. ➢ How to integrate this appraisal in the energy planning of a region in order to elaborate different scenarios for the energy balance between thermal demand and supply, fostering the use of shallow geothermal energy (SGE) that is a renewable source still not well-known and not exploited. ➢ How to encourage the connection between energy planning and spatial planning towards the common goal of sustainable energy transition, helping to fill the gap between the development of plans and strategies and their implementation, thanks to the Strategic Environmental Assessment (SEA) framework. The proposed methodology has been applied in a case study, i.e. Valle d’Aosta, an Italian alpine region. Almost all the data processing is performed with open-source software (GRASS GIS, QGIS, Python, and R) and applying a spatially-explicit approach, for pushing the integration of the spatial dimension in the energy analysis. The spatial units of analysis are the single building and the census tract. The single building has been chosen as the smallest unit available for ensuring a better characterization of the thermal energy demand and of the potential energy production from SGE. Moreover, the scenario analysis for the energy renovation of buildings is better performed at the building level; so, it is particularly suitable for developing an SDSS. Nevertheless, some data processing is done at the census tract level, using aggregated and statistical information to estimate the required values at the building level. The reason for this twofold scale of analysis is that the data availability often changes depending on time, space and data provider. For instance, for the case study area only little data was available at the building level for the whole region. Therefore, the methodology integrates data from different sources to fill this knowledge gap. The methodology applied in the case study is divided into two parts: 1) The first one concerns the data collection and processing for the spatial estimation of the space heating demand of the existing building stock. At the end of it, the technical and economic suitability of SGE (performed within the GRETA project) for covering the energy demand of buildings and replacing some fossil fuels is evaluated. 2) The second one is carried out in the framework of SEA, by defining common objectives and developing scenarios for the integration of SGE in the energy planning process, as the short-term objective, and the coordination of energy and spatial planning goals, as the long-term objective. In the Ph.D. thesis, SEA is intended as a conceptual framework for integrating energy and spatial planning, rather than as an evaluation tool. The main outputs of the Ph.D. thesis are: (i) the spatial evaluation of the space heating demand of each residential building of the case study, without using the “archetypes approach”; (ii) the development of a method for the integration of data from different sources and for its estimation if missing at the building level; (iii) the use of SEA as a framework for connecting energy planning and spatial planning fields, to support strategic decision-making processes. Even though the Ph.D. case study is a typical alpine region, (iv) the developed methodology can be applied at different scales and not only on alpine regions but potentially in every kind of context. Since it strongly depends on the availability of data, the replicability of the methodology is quite high. The main expected impacts of these outputs are: (1) SDSS allows to reach a trade-off between the number of input data and the level of detail often required by decision-makers; (2) SDSS can support the decision-makers allowing them to analyse from various viewpoints different energy scenarios and also to localise where is better to address the energy measures; (3) the results at the building level represent a starting point for defining and developing strategies for the energy transition of settlements at different scales; (4) SEA used as a strategic tool for integrating energy and spatial planning, by coordinating strategic objectives, and linking the thesis outputs to the energy decision-making process.
|Doctoral Thesis (PhD)
|Civil, Environmental and Mechanical Engineering
|Area 08 - Ingegneria civile e Architettura > ICAR/20 TECNICA E PIANIFICAZIONE URBANISTICA
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