La verdadera causa de la Guerra del Peloponeso y la “Pentecontecia”

At the beginning of the thesis, global environmental concerns and built environment’s role and share in such concerns are addressed. The environmental concerns are than narrowed down to energy related problems, and lastly to infrastructure level energy issues. Chapter 1 of this thesis draws a picture of the environmental problems and points out the policy actions taken to cope with the stated concerns. Transition in grid technology is highlighted as one of the key drivers of environmental policy. Deployment strategy of smart grids is observed to be a “gray area” as there is no solid

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methodology exists. In the light of this argument, the researcher has identified there is a gap in smart grid deployment. Once the frame of the topic area has been drawn, the research question is crystallized, and research aim and objectives are identified accordingly. Additionally, a research methodology that governs the overall research is briefly introduced (See Chapter 4 for in-depth research methodology guidance). Lastly, chapter 1 is concluded with addressing the scope and limitations of the research, and a brief guidance to the thesis.

After framing the research in Chapter 1, a two-step background research has been carried out with the intention of forming a solid knowledge base. The first step (Covered in Chapter 2), is conducting a comprehensive literature review in order to gain insight into energy related environmental problems, and challenges in electricity grid infrastructure. Smart grid concept is examined in detail as it has been addressed as a solution to stated environmental concerns.

Moving on with the second step in background research (elaborated in Chapter 3), decision making concept is reviewed and an appropriate decision making method (in this case it is AHP) has been identified with the intention of meeting the decision making requirements addressed with the aim of this research. As the conducted research lies within spatial context, geospatial dimensions are also addressed. Additionally, visualisation of data is found out to be a suitable method for disseminating knowledge regarding geospatial decision making.

After gaining adequate background knowledge on the milestones of conducting a decision making study regarding smart grid applicability in spatial domain, it is time to plan and structure the way to conduct the proposed research. In other words, a suitable methodology has been formulated (See Chapter 4). Mentioned methodology presents all phases of research ranging from philosophical stance, to characteristics of

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adopted data collection and analyses methods. In brief, researcher’s approach to the conducted research overlaps with “objectivism” and “positivism”, and research methodology addresses that exploratory case studies are conducted and quantitative methods are observed as the dominant tool for data collection and analysis. It is important to highlight that, qualitative methods are also used where appropriate, such as in analysis of focus group study conducted for validation purposes. Questionnaires and interviews are carried out for gathering data in an inductive way.

In Chapter 5, data requirements of the study is fulfilled. Initially, criteria required for smart grid applicability are identified. Stated criteria are as follows:

• Energy Performance Certificates (EPC) • Energy Consumption of Buildings

• Climate Data (Renewable resource potential) • Smart Meter availability

• Smart Appliances

The next step is to conduct a questionnaire survey targeting experts from industry and academia with the intention of obtaining criteria weights (Rate of Importance). Additionally, interviews are carried out with experts in order to strengthen the data obtained through questionnaire surveys. Data reliability and validity has been maintained via applying relevant indices and triangulation of data sources. The criterion with the highest weighting score appears to be smart meter criterion, whereas on the other hand smart appliance criterion achieved the least weighting score.

In Chapter 6, site selection problems in smart grid deployment projects are covered, and geospatial decision support models are addressed as the assisting technology. In this regard, a Geospatial Decision Support Model for Smart Grid Applicability (GDSM4SGA) has been proposed in order to assist decision makers when making decisions on the priory of locations for smart grid deployment. An AHP algorithm for

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the overall assessment is elaborated, and presented via mathematical expressions. A conceptual model comprising use of AHP as the main selection mechanism, linking data layers obtained through data bases and questionnaire surveys, and ranking of alternative neighbourhoods is presented. Ontology regarding linking data layers are developed. Additionally, standardized representations of the stated GDSM4SGA are prepared via Unified Modeling Language (UML), so that specifications of the proposed model are modeled in a way that it is independent from any particular programming languages.

In Chapter 7, proposed GDSM4SGA has been run. A case study comprising assessment of each alternative neighbourhood has been conducted with the intention of mastering the model. A further iteration of AHP algorithm has been supplied, and in turn a formula for obtaining Smart Grid Applicability Score (SGAS) is developed. Polygon (neighbourhood) average data are restructured in accordance with AHP scale, so that SGAS formula can be applied to alternative neighbourhoods. As a next step, SGAS are calculated for each neighbourhood and a ranking has been obtained. It is highlighted that SGAS ranking is the rank of area profiles, and it should be reversed for obtaining priority ranking. As a final step, geo-visualisation of polygon data has been supplied with the use of an earth browser.

Lastly, the proposed model (GDSM4SGA) has been validated. In Chapter 8, presented work covers the output obtained from a focus group study that has been held with the intention of model validation purposes. After explaining the model and underlying assessment mechanism, a demonstration of the GDSM4SGA (via spreadsheets and earth browser) has been made. In return, experts participated in the focus group study are individually asked to evaluate the model by applying a SWOT analysis. Obtained feedbacks indicate that the model is straightforward, and simple, and yet it has sound scientific footing. Addressed issues like lacking environmental

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and socioeconomic parameters are all beyond the scope of this study, and they are highly related to the predefined limitations. On the overall, GDSM4SGA has been validated as a viable tool for assisting decision makers on the allocation of key resources in smart grid projects.