Implementing optimal Low Impact Development (LID) practices has grown in popularity as a means of mitigating the adverse effects of urbanization and climate change. As such incorporating aspects of resilience for optimal LID design has become paramount. This study focuses on identifying the current LID optimization strategies and associated research gaps as well as assessing whether a quantitative approach to measure LID resilience exists. To do so, a systematic and bibliometric literature review on LIDs optimization and resilience is first conducted, based on which resilience, climate change, and uncertainty are recognised as hotspot keywords. The review also showed that no LID resilience quantification technique was available. Based on the latter outcome and to facilitate LID’s optimal design in future, this research proposes a new resilience quantification approach of LID by developing set of equations using Analytical Probabilistic Approach (APA) and continuous simulation approach using SWMM. The equations consider LID’s functionality and assess resilience using three indices: robustness, rapidity and serviceability. A new overall resilience index (the product of robustness and serviceability) and reliability index (the product of volumetric, occurrence, and temporal reliability) are proposed using different area ratios between contributing catchment and LID area to assure a resilient and safe LID system. LID costing tool of the Sustainable Technologies Evaluation Program (STEP) is subsequently utilized to estimate the capital cost of LID. Finally, a user-oriented design guideline is proposed for a cost-effective, resilient, and reliable LID system. Although this study adopts bioretention (BR) as a demonstration of the approach utility, the developed approach is applicable to any form of LID practices. / Thesis / Master of Applied Science (MASc) / There is a critical need to develop and implement optimal low-impact development (LID) practices in the field of stormwater management to mitigate the adverse effects of urbanization and climate change. This thesis is focused on developing quantitative resilient measurement approach of LID designs. A comprehensive literature review is first carried out, focusing on identifying various optimization methodologies, relevant gaps, and resilience assessment techniques. Subsequently, a novel resilience evaluation approach is developed, using bioretention (BR). By constructing a new reliability index, the entire BR system's reliability can also be assessed. Finally, a cost-effective, resilient and reliable design guideline for BR system is proposed. Although bioretention (BR) is used as an example in this study, the developed approach opens the gate to quantify the resilience of all types of LID practices.

Modern societies are fully dependent on critical infrastructures networks to support the economy, security, and prosperity. Energy infrastructure network is of paramount importance to our societies. As a pillar of the economy, it is necessary that energy infrastructure networks continue to operate safely and be resilient to provide reliable power to other critical infrastructure networks. Nonetheless, frequent large-scale blackouts in recent years have highlighted the vulnerability in the power grids, where disruptions can trigger cascading failures causing a catastrophic regional-level blackout. Such catastrophic blackouts call for a systemic risk assessment approach whereby the entire network/system is assessed against such failures considering the dynamic power flow within. However, the lack of detailed data combining both topological and functional information, and the computational resources typically required for large-scale modelling, considering also operational corrective actions, have impeded large-scale resilience studies. In this respect, the research in the present dissertation focuses on investigating, analyzing, and evaluating the vulnerability of power grid infrastructure networks in an effort to enhance their resilience. Through a Complex Network Theory (CNT) lens, the power grid robustness has been evaluated against random and targeted attacks through evaluating a family of centrality measures. The results shows that CNT models provide a quick and potential indication to identify key network components, which support regulators and operators in making informed decisions to maintain and upgrade the network, constrained by the tolerable risk and allocated financial resources. Furthermore, a dynamic Cascade Failure Model (CFM) has been employed to develop a Physical Flow-Based Model (PFBM). The CFM considers the operational corrective actions in case of failure to rebalance the supply and demand (i.e., dispatch and load shedding). The CFM was subsequently utilized to construct a grid vulnerability map function of the Link Vulnerability Index (LVI), which can be used to rank the line maintenance priority. In addition, a Node Importance Index (NII) has been developed for power substations ranking according to the resulting cascade failure size. The results from CNT and CFM approaches were compared to address the impact of considering the physical behavior of the power grid. The comparison results indicate that relying solely on CNT topology-based model could result in erroneous conclusions pertaining to the grid behavior. Moving forward, a systemic risk mitigation strategy based on the Intentional Controlled Islanding (ICI) approach has been introduced to suppress the failure propagation. The proposed mitigation strategy integrated the operation- with structure-guided strategies has shown excellent capabilities in terms of enhancing the network robustness and minimizing the possibility of catastrophic large-scale blackouts. This research demonstrates the model application on a real large-scale network with data ranging from low to high voltage. In the future, the CFM model can be integrated with other critical infrastructure network systems to establish a network-of-networks interaction model for assessing the systemic risk throughout and between multiple network layers. Understanding the interdependence between different networks will provide stakeholders with insight on enhancing resilience and support policymakers in making informed decisions pertaining to the tolerable systemic risk level to take reliable actions under abnormal conditions. / Thesis / Doctor of Philosophy (PhD)

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