Determinants of Urban Energy Performance and Sustainability: An Integrated and Multidimensional Analysis using Agent-based Modeling and Optimization

Abstract

The pursuit of sustainable development puts immense emphasis on cities and energy infrastructure reflected by the growing efforts to achieve the performance goals of energy self-sufficiency and net-zero emissions. However, studies addressing energy performance often lack a systematic consideration of the context such as urban built environment or electricity grid, and the resulting effects on energy system dynamics. Solutions and policies aimed at energy sustainability, such as integration of renewables and storage, rarely account for diverse urban circumstances. This dissertation develops holistic and scalable agent-based and optimization modeling frameworks that incorporate contextual factors in the analysis across multiple performance dimensions (technical, economic, and policy). It leverages the local climate zone (LCZ) classification of typical urban forms, which is rarely used in urban energy planning, and addresses various urban contexts defined by the urban built types and the mix of building and land use-types. It considers the effect of the mode of energy demand and supply matching, and the type of climate on the energy performance of different urban settings. It studies the potential of hybrid energy storage for performance improvement of urban communities of different types, and also for multi-energy trading and arbitrage in energy markets. The analytical methodologies and models are demonstrated through comparative case studies considering different types of urban areas characterized by the built form and function harnessing their technical rooftop solar PV potential, climates, storage technologies, and energy markets. Results unveiled critical insights on variation in performance potential of renewables and storage deployment across various types of urban environments and market settings confirming the distinctive merits of the developed approaches. For instance, low-rise areas as communities can reach energy self-sufficiency of above 65% and reduce energy costs by 15% on average through adoption of rooftop solar and Li-ion battery solutions. Low-rise areas with large buildings (e.g., warehouses) can utilize potentially enormous solar energy surplus in transport sector through hydrogen-based storage systems. High- and mid-rise areas are highly dependent on the electric grid implying renewable energy generation beyond urban boundaries. They need energy storage mainly for 100% self-consumption and demand charge reduction. Multi-energy trading of electricity and hydrogen enabled by reversible fuel cells, possibly combined with Li-ion batteries, is a commercially viable option, unlike electricity-only arbitrage, for grid-scale energy storage deployment in the future. The models and research findings could be applied in the planning and development of context-sensitive solutions, strategies, and policies meant for sustainable cities and energy infrastructure.

Date of Award	14 May 2024
Original language	American English
Supervisor	Ahmad Mayyas (Supervisor)