Doctoral Dissertations (FEAS)

Permanent URI for this collectionhttps://hdl.handle.net/10155/401

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Browsing Doctoral Dissertations (FEAS) by Author "Abu-Rayash, Azzam"

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    Development of integrated performance indicators and integrated energy systems for smart cities
    (2021-04-01) Abu-Rayash, Azzam; Dincer, Ibrahim
    The urge to develop and innovate net zero energy systems for smart city applications has never been more pressing. As the world population is rapidly growing, the global energy demand expands exponentially. The setback with higher energy consumption is the substantial greenhouse gas emissions associated with using fossil-based fuels, which make up the primary energy sources in the world today. This thesis has two main aspects: it introduces a novel and comprehensive smart city concept composed of 8 domains and 32 indicators, which are computed to make the Smart City Index. On the other hand, it introduces four innovative and integrated net zero energy systems for smart city applications. While simple integration of information communication technology (ICT) applications is integral in the development of smart cities, the concept is much more comprehensive to include smart environment, economy, society, governance, infrastructure, transportation, energy, and pandemic resiliency. In this thesis, 20 cities have been analyzed using four distinct weighing scenarios. Based on the sustainability triad scheme, the city with the highest SCI is Toronto at 0.77, whereas the city with the lowest SCI is Abuja at 0.31. Toronto, Vancouver, and Montreal remain part of the top 5 cities in the equal weighting, sustainability triad and energy focused schemes. In fact, the energy focused scheme places four Canadian cities at the highest SCI, with Montreal at the top, scoring 0.7 and Oshawa at 0.66. Life cycle assessment results show that systems 2 and 3 have higher environmental impacts due to their electricity generation through the Organic Rankine Cycle. Human toxicity is the impact category most affected, followed by global warming and acidification. Furthermore, system optimization is completed in order to reach to the optimal design parameters and model selection. System 4 has the highest energy and exergy efficiencies of 74.6% and 62.9% respectively. This system also accounts for the lowest exergy destruction of only 12% of the total systems destruction. Genetic algorithm optimization results reached a plateau after the 20th generation, with the environment, transportation and pandemic categories having the highest scores.