PhD Oral Exam - Aya Doma, Building Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

The global shift towards electrification transforms buildings from passive electricity consumers into active prosumers capable of energy generation, storage, and trading. This evolution expands the building-to-grid (B2G) interactions, moving beyond conventional demand-side management (DSM) strategies toward dynamic peer-to-peer (P2P) energy markets. Such markets necessitate innovative solutions to align energy supply with fluctuating demand through enhanced building flexibility. This thesis aims to integrate occupant-related information in this context. It develops occupancy-informed energy management strategies that dynamically respond to real-time occupancy variations, optimizing energy use and facilitating effective interactions between buildings and the local grid, as well as with peer buildings. The research primary objectives are to: (1) develop approaches for generating representative occupancy schedules for the urban modelling of various building types; (2) formulate occupancy-informed control algorithms for residential and non-residential buildings aiming at enhancing the energy flexibility of these buildings; (3) evaluate the impact of these strategies at both building and grid-distribution levels, addressing roles of both consumers and prosumers; and (4) assess the feasibility of the developed strategies within the current energy policies and future P2P market contexts.

Utilizing open-source datasets, such as mobile positioning data (GPS) and smart thermostat data, combined with advanced modelling, this thesis developed occupancy schedule generators (OSGs) suitable for urban energy simulations. Then, a comprehensive framework was developed to dynamically model urban building energy performance, considering occupancy variations, interactions among buildings, local grids, and neighboring infrastructure within varying market structures. Applying this framework to urban-scale case studies demonstrated significant reductions in peak energy demand, achieving up to 35% savings in residential buildings and over 17% in non-residential buildings, alongside notable energy cost reductions. Ultimately, this research provides foundational methods and practical tools to leverage building energy flexibility strategically, facilitating optimized interactions within evolving sustainable energy systems.