Yes / Introduction: There are risks to the safety of medicines management when patient care is transferred between healthcare organisations, for example when a patient is discharged from hospital. Using the theoretical concept of resilience in healthcare, this study aimed to better understand the proactive role that patients can play in creating a safer, resilient medicines management at a common transition of care. Methods: Qualitative interviews with 60 cardiology patients six weeks after their discharge from two UK hospitals explored patients’ experiences with their discharge medicines. Data were initially subjected to an inductive thematic analysis and a subsequent theory-guided deductive analysis. Results: During interviews twenty-three patients described medicines management resilience strategies in two main themes: identifying system vulnerabilities; and establishing self-management strategies. Patients could anticipate problems in the system that supplied them with medicines and took specific actions to prevent them. They also identified when errors had occurred both before and after medicines had been supplied and took corrective action to avoid harm. Some reported how they had not foreseen problems or experienced patient safety incidents. Patients recounted how they ensured information about medicines changes was correctly communicated and acted upon, and identified their strategies to enhance their own reliability in adherence and resource management. Conclusion: Patients experience the impact of vulnerabilities in the medicines management system across the secondary-primary care transition but many are able to enhance system resilience through developing strategies to reduce the risk of medicines errors occurring. Consequently, there are opportunities – with caveats – to elicit, develop and formalise patients’ capabilities which would contribute to safer patient care and more effective medicines management.

Due to the growing number of diverse power systems disruptions, including extreme weather events, technical factors, and human factors, assessing and quantifying the resilience of electric power subsystems has become an indispensable step to develop an efficient strategic plan to enhance the resilience and reliability of these systems and to endure the diverse interruptions. In this study, factors and sub-factors that may have either direct or indirect impact on the resilience of biomass-based combined heat and power systems are identified, and the interdependencies among them are determined as well. A Bayesian network model is implemented to quantify the resilience of a bCHP system, and the results are analyzed by applying three different techniques, which are sensitivity analysis, forward propagation analysis, and backward propagation analysis.

This dissertation aims to develop decision-making tools that aid power grid operators in mitigating extreme events. Two distinct areas are focused on: a) improving grid performance after a severe disturbance, and b) enhancing grid monitoring to facilitate timely preventive actions. The first part of the dissertation presents a proactive islanding strategy to split the bulk power transmission system into smaller self-adequate islands in order to arrest the propagation of cascading failures after an event. Heuristic methods are proposed to determine in what sequence should the island boundary lines be disconnected such that there are no operation constraint violations. The idea of optimal partitioning is further extended to the distribution network. A planning problem for determining which parts of the existing distribution grid can be converted to microgrids is formulated. This partitioning formulation addresses safety limits, uncertainties in load and generation, availability of grid-forming units, and topology constraints such as maintaining network radiality. Microgrids help maintain energy supply to critical loads during grid outages, thereby improving resilience. The second part of the dissertation focuses on wide-area monitoring using Phasor Measurement Unit (PMU) data. Strategies for data imputation and prediction exploiting the spatio-temporal correlation in PMU measurements are outlined. A deep-learning-based methodology for identifying the location of temporary power systems faults is also illustrated. As severe weather events become more frequent, and the threats from coordinated cyber intrusions increase, formulating strategies to reduce the impact of such events on the power grid becomes important; and the approaches outlined in this work can find application in this context. / Doctor of Philosophy / The modern power grid faces multiple threats, including extreme-weather events, solar storms, and potential cyber-physical attacks. Towards the larger goal of enhancing power systems resilience, this dissertation develops strategies to mitigate the impact of such extreme events. The proposed schemes broadly aim to- a) improve grid performance in the immediate aftermath of a disruptive event, and b) enhance grid monitoring to identify precursors of impending failures. To improve grid performance after a disruption, we propose a proactive islanding strategy for the bulk power grid, aimed at arresting the propagation of cascading failures. For the distribution network, a mixed-integer linear program is formulated for identifying optimal sub-networks with load and distributed generators that may be retrofitted to operate as self-adequate microgrids, if supply from the bulk power systems is lost. To address the question of enhanced monitoring, we develop model-agnostic, computationally efficient recovery algorithms for archived and streamed data from Phasor Measurement Units (PMU) with data drops and additive noise. PMUs are highly precise sensors that provide high-resolution insight into grid dynamics. We also illustrate an application where PMU data is used to identify the location of temporary line faults.

Across many domains, systems suppliers are challenged by the complexity of their systems and the speed at which their systems must be changed in order to meet the needs of customers or the societies which the systems support. Stakeholder needs are ever more complex: appearing, disappearing, changing and interacting faster than solutions able to address them can be instantiated. Similarly, the systems themselves continually change as a result of both external and internal influences, such as damage, changing environment, upgrades, reconfiguration, replacement, etc. In the event of situations unforeseen at design time, personnel (for example maintainers or operators) close to the point of employment may have to modify systems in response to the evolving situation, and to do this in a timely manner so that the system and/or System-of-Systems (SoS: a set of systems that have to interoperate) can achieve their aims. This research was motivated by the problem of designing-in re-configurability to the constituent systems of a SoS to enable the SoS and its systems to effectively and efficiently counter the effects of unforeseen events that adversely affect fitness-for purpose whilst operational. This research shows that a SoS does not achieve or maintain fitness-for-purpose because it cannot implement the correct, timely and complete transfer of Material, Energy and Information (MEI) between its constituents and with its external environment that is necessary to achieve a desired outcome; i.e. the purpose.

<p dir="ltr">The safe and reliable delivery of electricity is critical for the functioning of our modern society. However, high-impact, low-probability (HILP) catastrophic events (such as extreme weather caused by climate change, or cyber-physical attacks) pose an ever-growing threat to the power grid. At the same time, modern advancements in computational capabilities, communication infrastructure, and measurement technologies provide opportunities for new operation and control strategies that enhance the resilience of electric power systems to such HILP events. In this work, optimization-based operation and control approaches are proposed to improve resilience in two power systems applications. First, a real-time linearized-trajectory model-predictive controller (LTMPC) is developed for ensuring voltage, frequency, and transient (rotor angle) stability in systems engineered to operate as microgrids. Such microgrids are capable of seamlessly transitioning from grid-connected operation to an islanded mode and thus, enhance system resilience. The proposed LTMPC enables rapid deployment of such systems by reducing engineering costs and development time while maintaining stable operation. On the other hand, some power systems, such as distribution feeders, are not designed to operate as standalone microgrids. For these cases, a method is proposed for forming ad-hoc microgrids from intact sections of the damaged feeder in the aftermath of a HILP event. A feeder operating center-on-a-laptop (FOCAL) is introduced that coordinates the control of possibly hundreds of inverter-interfaced distributed energy resources (e.g., rooftop solar, battery storage) to improve system resilience. Theoretical analysis as well as numerical case studies and simulations of the proposed strategies are presented for both applications.</p>