Dissertations / Theses on the topic 'Power systems resilience'

This thesis explores the question of how social power is accounted for in the theory and practice of resilience. Beginning with a critical assessment of the social ecological systems (SES) perspective that underpins much of the theory and study of resilience, this thesis develops a framework, based on Gaventa’s powercube, for understanding power that also incorporates a much less hierarchical understanding of the dimensions of space and time. This revised ‘powerplane’ framework is applied to two empirical case studies of practices of resilience. Applying the powerplane to the case of government-led Scottish community emergency resilience planning finds that while the practices of resilience result in greater levels of engagement and interaction between local and regional levels of government, a gap exists between local government and the public it represents. Applying the powerplane to the grassroots case of Transition Town Peterborough, Canada, shows that intimate knowledge of local social and political institutions can allow a grassroots organisation to introduce resilience ideas into social and political community life. Together the two case studies reveal three key insights from resilience practices aimed at local contexts, rooted in: (1) institutionalising community engagement practices; (2) differences between formal and informal understandings of resilience; and (3) the scope of the risks resilience is aimed at mitigating. Critically exploring these issues in turn helps to illuminate questions about the efficacy, as well as the social and political implications of the resilience practice in question. For theory, the research shows that reconsidering hierarchical notions of scale and time in SES resilience can provoke new thinking about the role of power in resilience practices. In doing so, insights from this research offer novel challenges and complementarities to they way existing critiques of resilience approaches to account for social power issues.

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.

Despite interest from policymakers and the telecommunications sector to deliver superfast broadband to the whole of the UK, rural areas remain underserved, decreasing their ability to benefit from broadband-enabled services. Public intervention, primarily structured as national subsidies, is active across the UK to respond to this rural market failure. Complementing such practices are local-level strategies framed as community-led broadband initiatives. Their inclusion within wider superfast broadband installation strategies has not yet been examined. This doctoral research examines two of these initiatives, their structure and impact on the community to develop an understanding of their potential as replicable rural broadband delivery mechanisms. I analyse both the process of installing superfast broadband technology from community-led perspective and the subsequent engagement with superfast broadband through a qualitative longitudinal approach. A conceptual framework of 'social resilience' is developed as a contemporary analytical tool for examining these individual and community processes. The findings reveal an inherent complexity to rural community-led broadband provision. Community-led broadband reflects a 'localism' development approach, and this process has strengthened local rural identity. Following the adoption of superfast broadband, rural users experienced a growth in digital knowledge and individual resilience. However, the initiatives themselves are often discussed as 'separate from', or incompatible with, the telecommunications industry, as well as sitting outside the scope of current government interventions. In doing so, barriers to external networking and extra-local partnerships are built, limiting the opportunities for community-led broadband networks to become a substantive part of rural broadband delivery across the UK. Throughout the thesis, an understanding of these various tensions, impacting the success, use and replicability of rural community-led broadband, is developed and community-led broadband is shown to be another example of uneven rural development. I conclude by making recommendations for future digital policy interventions in the UK.

The resource mix for the U.S. electrical power grid is undergoing rapid change with increased levels of solar photovoltaic (PV) and wind turbine electricity generating capacity. There are potential negative impacts to grid resilience resulting from hurricane damage to wind and solar power stations connected to the power transmission grid. Renewable power sources are exposed to the environment more so than traditional thermal power sources. To our knowledge, damage to power generating stations is not included in studies on hurricane damage to the electrical power grid in the literature. The lack of a hurricane wind damage prediction model for power stations will cause underestimation of predicted hurricane wind damage to the electrical grid with high percentages of total power generation capacity provided by solar photovoltaic and wind turbine power stations.

Modeling hurricane wind damage to the transmission grid and power stations can predict damage to electrical grid components including power stations, the resultant loss in power generation capacity, and restoration costs for the grid. This Praxis developed models for hurricane exposure, fragility curve-based damage to electrical transmission grid components and power generating stations, and restoration cost to predict resiliency factors including power generation capacity lost and the restoration cost for electrical transmission grid and power generation system damages. Synthetic grid data were used to model the Energy Reliability Council of Texas (ERCOT) electrical grid. A case study was developed based on Hurricane Harvey. This work is extended to evaluate the changes to resiliency as the percentage of renewable sources is increased from 2017 levels to levels corresponding to the National Renewable Energy Lab (NREL) Futures Study 2050 Texas scenarios for 50% and 80% renewable energy.

This thesis presents the study and development of distinct data-driven techniques to support event detection, fault location, and resilience assessment towards enhancements in power systems. It is divided in three main parts as follows. The first part investigates improvements in power system monitoring and event detection methods with focus on dimensionality reduction techniques in wide-area monitoring systems. The second part focuses on contributions to fault location tasks in power distribution networks, relying on information about the network topology and its electrical parameters for short-circuit simulations over a range of scenarios. The third part assesses enhancements in power system resilience to high-impact, lowprobability events associated with extreme weather conditions and human-made attacks, relying on information about the system topology combined with simulations of representative scenarios for impact assessment and mitigation. Overall, the proposed data-driven algorithms contribute to event detection, fault location, and resilience assessment, relying on electrical measurements recorded by intelligent electronic devices, historical data of past events, and representative scenarios, together with information about the network topology, electrical parameters, and operating status. The validation of the algorithms, implemented in MATLAB, is based on computer simulations using network models implemented in OpenDSS and Simulink
Esta tesis presenta el estudio y el desarrollo de distintas técnicas basadas en datos para respaldar las tareas de detección de eventos, localización de fallos y resiliencia hacia mejoras en sistemas de energía eléctrica. Los contenidos se dividen en tres partes principales descritas a continuación. La primera parte investiga mejoras en el monitoreo de sistemas de energía eléctrica y métodos de detección de eventos con enfoque en técnicas de reducción de dimensionalidad en wide-area monitoring systems. La segunda parte se centra en contribuciones a tareas de localización de fallos en redes eléctricas de distribución, basándose en información acerca de la topología de la red y sus parámetros eléctricos para simulaciones de cortocircuito en una variedad de escenarios. La tercera parte evalúa mejoras en la resiliencia de sistemas de energía eléctrica ante eventos de alto impacto y baja probabilidad asociados con condiciones climáticas extremas y ataques provocados por humanos, basándose en información sobre la topología del sistema combinada con simulaciones de escenarios representativos para la evaluación y mitigación del impacto. En general, los algoritmos propuestos basados en datos contribuyen a la detección de eventos, la localización de fallos, y el aumento de la resiliencia de sistemas de energía eléctrica, basándose en mediciones eléctricas registradas por dispositivos electrónicos inteligentes, datos históricos de eventos pasados y escenarios representativos, en conjunto con información acerca de la topología de la red, parámetros eléctricos y estado operativo. La validación de los algoritmos, implementados en MATLAB, se basa en simulaciones computacionales utilizando modelos de red implementados en OpenDSS y Simulink

Our society is heavily dependent on commodities, as water and electricity, supplied to final users by engineered systems, which are known as critical infrastructures. In such context, the understanding of how such systems handle damaging events is an important aspect and is a current concern of researchers, public agents, and society. How much of performance a system loses due to damages is related to its vulnerability, and the ability to absorb and recover successfully from damages is its resilience. In this study, approaches to assess the vulnerability and resilience of power distribution systems by evaluating dynamic features, as the processes of failure and repair, and system reconfiguration for vulnerability, and the effects of extreme weather scenarios for resilience together with the processes of failure of repair are presented. Such approaches were applied on systems previously presented in the literature, and also on a Brazilian power distribution system. A Monte Carlo simulation was applied to evaluate this systems, models for time-to-failure and time-to-repair under different circumstances were obtained from historical data, and a method to use the models of time-to-failure during the vulnerability analysis was introduced. In addition, an assessment of the impact of reconfiguration capability on vulnerability is also carried out, and a resilience assessment under different climate scenarios has been developed. The time-to-failure and repair models highlighted how external factors modifies the Brazilian system failure and repair dynamics, the use of time-to-failure models during vulnerability analysis showed that the consideration of the failure dynamic of the types of elements give different results, and the time domain allows new analysis\' perspectives. The investigation indicated that the vulnerability reduction due to reconfiguration is affected by the number of switches and also the maximum load capacity of the distribution system feeders. The resilience assessment showed that for structural connectivity, larger distribution networks are less resilient, while for electricity delivery, a set of features, related with the topological and electrical organization of such networks, seems to be associated with the network service resilience, such information is useful for system planning and management. The dynamics evaluated in this study are relevant to vulnerability and resilience of such systems, and also to other critical infrastructures. Moreover, the developed approaches can be applied to other systems, as transportation and water distribution. In future studies, other power distribution systems features, as distributed generation and energy storage, will be considered in both, vulnerability and resilience analysis.
Nossa sociedade é altamente dependente de commodities, como água e eletricidade, fornecidas para os usuários por sistemas de engenharia, conhecidos como infraestruturas críticas. A compreensão de como tais sistemas lidam com eventos prejudiciais é uma preocupação atual de pesquisadores, agentes públicos e sociedade. A perda de desempenho de um sistema devido a danos é relacionada à sua vulnerabilidade, e a capacidade de absorver e se recuperar dos danos é a resiliência. Neste estudo, são apresentadas abordagens para avaliar a vulnerabilidade e resiliência de sistemas de distribuição de energia considerando características dinâmicas, como os processos de falha e reconfiguração do sistema, para a vulnerabilidade, e os efeitos de climas extremos na resiliência com os processos de falha e reparo. Tais abordagens foram aplicadas em sistemas previamente apresentados na literatura, e também em um sistema brasileiro. Simulação de Monte Carlo foi utilizada para avaliar as dinâmicas de falha e reparo do sistema utilizando de modelos obtidos a partir de dados históricos, e um método para usar os modelos de tempo-até-falha durante a análise de vulnerabilidade também foi apresentado. Além disso, uma avaliação do impacto da dinâmica de reconfiguração na vulnerabilidade foi realizada e uma avaliação de resiliência sob diferentes cenários climáticos foi desenvolvida. Os modelos tempo-para-falha e reparo destacaram como fatores externos modificam as dinâmicas de falha e reparo do sistema brasileiro, o uso de modelos de confiabilidade na análise de vulnerabilidades mostrou que a consideração dos diferentes tipos de elementos geram resultados diferentes e o domínio de tempo permite novas perspectivas de análise. A investigação da reconfiguração indicou que a redução da vulnerabilidade devido à reconfiguração é afetada pelo número de chaves e também pela máxima capacidade de carga dos alimentadores do sistema de distribuição. A avaliação de resiliência mostrou que, para conectividade estrutural, redes de distribuição maiores são menos resilientes, enquanto que para fornecimento de energia, um conjunto de características, relacionados com a organização topológica e elétrica dessas redes parece ser associado à resiliência do serviço, informação útil para o planejamento. As dinâmicas avaliadas neste estudo são relevantes para a vulnerabilidade e resiliência de tais sistemas, e também para outras infraestruturas críticas. Além disso, essas abordagens podem ser aplicadas a outros sistemas, como transporte e distribuição de água. Em estudos futuros, outras características de sistemas de distribuição de energia, como geração distribuída e armazenamento de energia, serão consideradas nas análises de vulnerabilidade e resiliência.

Electrical Engineering
Ph.D.
This research focuses on developing novel approaches in load balancing and restoration problems in electrical power distribution systems. The first approach introduces an inter-connected network topology, referred to as (n, k)-star topology. While power distribution systems can be constructed in different communication network topologies, the performance and fault assessment of the networked systems can be challenging to analyze. The (n, k)-star topologies have well defined performance and stability analysis metrics. Typically, these metrics are defined based on: i) degree, ii) diameter, and iii) conditional diagnosability of a faulty node. These parameters could be evaluated and assessed before a physical (n, k)-star topology power distribution system is constructed. Moreover, in the second approach, we evaluate load balancing problems by using a decentralized algorithm, i.e., the Multi-Agent System (MAS) based consensus algorithm on an (n, k)-star power topology. With aforementioned research approaches, an (n, k)-star power distribution system can be assessed with proposed metrics and assessed with encouraging results compared to other topology networked systems. Other encouraging results are found in efficiency and performance enhancement during information exchange using the decentralized algorithm. It has been proven that a load balance solution is convergent and asymptotically stable with a simple gain controller. The analysis can be achieved without constructing a physical network to help evaluate the design. Using the (n, k)-star topology and MAS, the load balancing/restoration problems can be solved much more quickly and accurately compared to other approaches shown in the literature.
Temple University--Theses

Cette thèse présente un modèle résilient pour piloter un avion basé sur une logique non monotone. Ce modèle est capable de gérer des solutions à partir d’informations incomplètes, contradictoires et des exceptions. C’est un problème très connu en Intelligence Artificial, qui est étudié depuis plus de 40 ans. Pour ce faire, nous utilisons la logique des défauts pour formaliser la situation et trouver des conclusions possibles. Grâce à cette logique, nous pouvons transformer les règles de pilotage en défauts. Ensuite, lorsque nous calculons les solutions, plusieurs options peuvent en résulter. À ce stade, il existe un critère de décision opportuniste pour choisir la meilleure solution. Le contrôle du système se fait via la propriété de résilience. Nous redéfinissons cette propriété comme l’intégration de la logique non monotone dans le modèle de Minsky. En conséquence, il est démontré que le modèle de résilience proposé pourrait être généralisé aux systèmes intégrant une connaissance du monde contenant des situations, des objectifs et des actions. Enfin, nous présentons les résultats expérimentaux et la conclusion de la thèse en discutant des perspectives et des défis pour les orientations futures. Différentes applications dans d’autres domaines sont prises en compte pour l’intérêt du comportement du modèle
This thesis presents a resilient model to pilot an aircraft based on a non-monotonic logic. This model is capable of handling solutions from incomplete, contradictory information and exceptions. This is a very well known problem in Artificial Intelligence, which has been studied for more than 40 years. To do this, we use default logic to formalise the situation and find possible conclusions. Thanks to this logic we can transform the piloting rules to defaults. Then, when we calculate the solutions, several options could result. At this point an opportunistic decision criteria takes place to choose the better solution. The control of the system is done via the property of resilence, we redefine this property as the integration of the non-monotonic logic in the Minsky’s model. As a result, it is shown that the proposed resilient model could be generalised to systems that incorporate a knowledge of the world that contains situations, objectives and actions. Finally, we present the experimental results and conclusion of the thesis discussing the prospects and challenges that exist for future directions. Different applications in other fields are taken into account for the interest of the model’s behavior

Comme la puissance de calcul des systèmes de calcul haute performance continue de croître, en utilisant un grand nombre de cœurs CPU ou d’unités de calcul spécialisées, les applications hautes performances destinées à la résolution des problèmes de très grande échelle sont de plus en plus sujettes à des pannes. En conséquence, la communauté de calcul haute performance a proposé de nombreuses contributions pour concevoir des applications tolérantes aux pannes. Cette étude porte sur une nouvelle classe d’algorithmes numériques de tolérance aux pannes au niveau de l’application qui ne nécessite pas de ressources supplémentaires, à savoir, des unités de calcul ou du temps de calcul additionnel, en l’absence de pannes. En supposant qu’un mécanisme distinct assure la détection des pannes, nous proposons des algorithmes numériques pour extraire des informations pertinentes à partir des données disponibles après une pannes. Après l’extraction de données, les données critiques manquantes sont régénérées grâce à des stratégies d’interpolation pour constituer des informations pertinentes pour redémarrer numériquement l’algorithme. Nous avons conçu ces méthodes appelées techniques d’Interpolation-restart pour des problèmes d’algèbre linéaire numérique tels que la résolution de systèmes linéaires ou des problèmes aux valeurs propres qui sont indispensables dans de nombreux noyaux scientifiques et applications d’ingénierie. La résolution de ces problèmes est souvent la partie dominante; en termes de temps de calcul, des applications scientifiques. Dans le cadre solveurs linéaires du sous-espace de Krylov, les entrées perdues de l’itération sont interpolées en utilisant les entrées disponibles sur les nœuds encore disponibles pour définir une nouvelle estimation de la solution initiale avant de redémarrer la méthode de Krylov. En particulier, nous considérons deux politiques d’interpolation qui préservent les propriétés numériques clés de solveurs linéaires bien connus, à savoir la décroissance monotone de la norme-A de l’erreur du gradient conjugué ou la décroissance monotone de la norme résiduelle de GMRES. Nous avons évalué l’impact du taux de pannes et l’impact de la quantité de données perdues sur la robustesse des stratégies de résilience conçues. Les expériences ont montré que nos stratégies numériques sont robustes même en présence de grandes fréquences de pannes, et de perte de grand volume de données. Dans le but de concevoir des solveurs résilients de résolution de problèmes aux valeurs propres, nous avons modifié les stratégies d’interpolation conçues pour les systèmes linéaires. Nous avons revisité les méthodes itératives de l’état de l’art pour la résolution des problèmes de valeurs propres creux à la lumière des stratégies d’Interpolation-restart. Pour chaque méthode considérée, nous avons adapté les stratégies d’Interpolation-restart pour régénérer autant d’informations spectrale que possible. Afin d’évaluer la performance de nos stratégies numériques, nous avons considéré un solveur parallèle hybride (direct/itérative) pleinement fonctionnel nommé MaPHyS pour la résolution des systèmes linéaires creux, et nous proposons des solutions numériques pour concevoir une version tolérante aux pannes du solveur. Le solveur étant hybride, nous nous concentrons dans cette étude sur l’étape de résolution itérative, qui est souvent l’étape dominante dans la pratique. Les solutions numériques proposées comportent deux volets. A chaque fois que cela est possible, nous exploitons la redondance de données entre les processus du solveur pour effectuer une régénération exacte des données en faisant des copies astucieuses dans les processus. D’autre part, les données perdues qui ne sont plus disponibles sur aucun processus sont régénérées grâce à un mécanisme d’interpolation
As the computational power of high performance computing (HPC) systems continues to increase by using huge number of cores or specialized processing units, HPC applications are increasingly prone to faults. This study covers a new class of numerical fault tolerance algorithms at application level that does not require extra resources, i.e., computational unit or computing time, when no fault occurs. Assuming that a separate mechanism ensures fault detection, we propose numerical algorithms to extract relevant information from available data after a fault. After data extraction, well chosen part of missing data is regenerated through interpolation strategies to constitute meaningful inputs to numerically restart the algorithm. We have designed these methods called Interpolation-restart techniques for numerical linear algebra problems such as the solution of linear systems or eigen-problems that are the inner most numerical kernels in many scientific and engineering applications and also often ones of the most time consuming parts. In the framework of Krylov subspace linear solvers the lost entries of the iterate are interpolated using the available entries on the still alive nodes to define a new initial guess before restarting the Krylov method. In particular, we consider two interpolation policies that preserve key numerical properties of well-known linear solvers, namely the monotony decrease of the A-norm of the error of the conjugate gradient or the residual norm decrease of GMRES. We assess the impact of the fault rate and the amount of lost data on the robustness of the resulting linear solvers.For eigensolvers, we revisited state-of-the-art methods for solving large sparse eigenvalue problems namely the Arnoldi methods, subspace iteration methods and the Jacobi-Davidson method, in the light of Interpolation-restart strategies. For each considered eigensolver, we adapted the Interpolation-restart strategies to regenerate as much spectral information as possible. Through intensive experiments, we illustrate the qualitative numerical behavior of the resulting schemes when the number of faults and the amount of lost data are varied; and we demonstrate that they exhibit a numerical robustness close to that of fault-free calculations. In order to assess the efficiency of our numerical strategies, we have consideredan actual fully-featured parallel sparse hybrid (direct/iterative) linear solver, MaPHyS, and we proposed numerical remedies to design a resilient version of the solver. The solver being hybrid, we focus in this study on the iterative solution step, which is often the dominant step in practice. The numerical remedies we propose are twofold. Whenever possible, we exploit the natural data redundancy between processes from the solver toperform an exact recovery through clever copies over processes. Otherwise, data that has been lost and is not available anymore on any process is recovered through Interpolationrestart strategies. These numerical remedies have been implemented in the MaPHyS parallel solver so that we can assess their efficiency on a large number of processing units (up to 12; 288 CPU cores) for solving large-scale real-life problems

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters.

Low-power wireless technology is a part and parcel of our daily life, shaping the way in which we behave, interact, and more generally live. The ubiquity of cheap, tiny, battery-powered devices augmented with sensing, actuation, and wireless communication capabilities has given rise to a ``smart" society, where people, machines, and objects are seamlessly interconnected, among themselves and with the environment. Behind the scenes, low-power wireless protocols are what enables and rules all interactions, organising these embedded devices into wireless networks, and orchestrating their communications. The recent years have witnessed a persistent increase in the pervasiveness and impact of low-power wireless. After having spawned a wide spectrum of powerful applications in the consumer domain, low-power wireless solutions are extending their influence over the industrial context, where their adoption as part of feedback control loops is envisioned to revolutionise the production process, paving the way for the Fourth Industrial Revolution. However, as the scale and relevance of low-power wireless systems continue to grow, so do the challenges posed to the communication substrates, required to satisfy ever more strict requirements in terms of reliability, responsiveness, and energy consumption. Harmonising these conflicting demands is far beyond what is enabled by current network stacks and control architectures; the need to timely bridge this gap has spurred a new wave of interest in low-power wireless networking, and directly motivated our work. In this thesis, we take on this challenge with a main conceptual and technical tool: concurrent transmissions (CTX), a technique that, by enforcing nodes to transmit concurrently, has been shown to unlock unprecedented fast, reliable, and energy efficient multi-hop communications in low-power wireless networks, opening new opportunities for protocol design. We first direct our research endeavour towards industrial applications, focusing on the popular IEEE 802.15.4 narrowband PHY layer, and advance the state of the art along two different directions: interference resilience and aperiodic wireless control. We tackle radio-frequency noise by extensively analysing, for the first time, the dependability of CTX under different types, intensities, and distributions of reproducible interference patterns, and by devising techniques to push it further. Specifically, we concentrate on CRYSTAL, a recently proposed communication protocol that relies on CTX to rapidly and dependably collect aperiodic traffic. By integrating channel hopping and noise detection in the protocol operation, we provide a novel communication stack capable of supporting aperiodic transmissions with near-perfect reliability and a per-mille radio duty cycle despite harsh external interference. These results lay the ground towards the exploitation of CTX for aperiodic wireless control; we explore this research direction by co-designing the Wireless Control Bus (WCB), our second contribution. WCB is a clean-slate CTX-based communication stack tailored to event-triggered control (ETC), an aperiodic control strategy holding the capability to significantly improve the efficiency of wireless control systems, but whose real-world impact has been hampered by the lack of appropriate networking support. Operating in conjunction with ETC, WCB timely and dynamically adapts the network operation to the control demands, unlocking an order-of-magnitude reduction in energy costs w.r.t. traditional periodic approaches while retaining the same control performance, therefore unleashing and concretely demonstrating the true ETC potential for the first time. Nevertheless, low-power wireless communications are rapidly evolving, and new radios striking novel trade-offs are emerging. Among these, in the second part of the thesis we focus on ultra-wideband (UWB). By providing hitherto missing networking primitives for multi-hop dissemination and collection over UWB, we shed light on the communication potentialities opened up by the high data throughput, clock precision, and noise resilience offered by this technology. Specifically, as a third contribution, we demonstrate that CTX not only can be successfully exploited for multi-hop UWB communications but, once embodied in a full-fledged system, provide reliability and energy performance akin to narrowband. Furthermore, the higher data rate and clock resolution of UWB chips unlock up to 80% latency reduction w.r.t. narrowband CTX, along with orders-of-magnitude improvements in network-wide time synchronization. These results showcase how UWB CTX could significantly benefit a multitude of applications, notably including low-power wireless control. With WEAVER, our last contribution, we make an additional step towards this direction, by supporting the key functionality of data collection with an ultra-fast convergecast stack for UWB. Challenging the internal mechanics of CTX, WEAVER interleaves data and acknowledgements flows in a single, self-terminating network-wide flood, enabling the concurrent collection of different packets from multiple senders with unprecedented latency, reliability, and energy efficiency. Overall, this thesis pushes forward the applicability and performance of low-power wireless, by contributing techniques and protocols to enhance the dependability, timeliness, energy efficiency, and interference resilience of this technology. Our research is characterized by a strong experimental slant, where the design of the systems we propose meets the reality of testbed experiments and evaluation. Via our open-source implementations, researchers and practitioners can directly use, extend, and build upon our contributions, fostering future work and research on the topic.

Les objectifs récents en ce qui concerne la durabilité des systèmes électriques et l'atténuation des menaces liées au changement climatique modifient la portée des exigences de planification de ces systèmes. D'une part, les systèmes durables d'énergie à faible émission de carbone qui comportent une part élevée de sources d'énergie renouvelables intermittentes(IRES) se caractérisent par une forte augmentation de la variabilité intertemporelle et nécessitent des systèmes flexibles capables d'assurer la sécurité de l'approvisionnement électrique. D'autre part, la fréquence et la gravité accrues des phénomènes climatiques extrêmes menacent la fiabilité du fonctionnement des réseaux électriques et exigent des systèmes résilients capables de résister à ces impacts potentiels. Tout en s'assurant que les incertitudes inhérentes au système sont bien prises en compte directement au moment de la prise des décisions de planification à long terme. Dans ce contexte, la présente thèse vise à développer une modélisation technicoéconomique et un cadre d'optimisation robuste pour la planification des systèmes électriques multi-périodes en considérant une part élevée d'IRES et la résilience aux phénomènes climatiques extrêmes. Le problème spécifique de planification considéré est celui du choix de la technologie, de la taille et du programme de mise en service des unités de production conventionnelles et renouvelables sous des contraintes techniques, économiques,environnementales et opérationnelles. Dans le cadre de ce problème, les principales questions de recherche à aborder sont : (i) l'intégration et l'évaluation appropriées des besoins de flexibilité opérationnelle en raison de la variabilité accrue des parts élevées de la production d'IRES, (ii) la modélisation et l'intégration appropriées des exigences de résilience contre les phénomènes climatiques extrêmes dans la planification du système électrique et (iii) le traitement des incertitudes inhérentes de l'offre et la demande dans ce cadre de planification. En résumé, les contributions originales de cette thèse sont :- Proposer un modèle de planification du système électrique intégré multi période avec des contraintes dynamiques et en considérant un pourcentage élevé de pénétration des énergies renouvelables.- Introduire la mesure du déficit de flexibilité prévu pour l'évaluation de la flexibilité opérationnelle.- Proposer un ensemble de modèles linéaires pour quantifier l'impact des vagues de chaleur extrêmes et de la disponibilité de l'eau sur le déclassement des unités de production d'énergie thermique et nucléaire, la production d'énergie renouvelable et la consommation électrique du système.- Présenter une méthode permettant d'intégrer explicitement l'impact des phénomènes climatiques extrêmes dans le modèle de planification du système électrique.- Traiter les incertitudes inhérentes aux paramètres de planification du système électrique par la mise en oeuvre d'un nouveau modèle d'optimisation adaptatif robuste à plusieurs phases.- Proposer une nouvelle méthode de solution basée sur l'approximation des règles de décision linéaires du modèle de planification robuste.- Appliquer le cadre proposé à des études de cas de taille pratique basées sur des projections climatiques réalistes et selon plusieurs scénarios de niveaux de pénétration des énergies renouvelables et de limites de carbone pour valider la pertinence de la modélisation globale pour des applications réelles
Recent objectives for power systems sustainability and mitigation of climate change threats are modifying the breadth of power systems planning requirements. On one hand, sustainable low carbon power systems which have a high share of intermittent renewable energy sources (IRES) are characterized by a sharp increase in inter-temporal variability and require flexible systems able to cope and ensure the security of electricity supply. On the other hand, the increased frequency and severity of extreme weather events threatens the reliability of power systems operation and require resilient systems able to withstand those potential impacts. All of which while ensuring that the inherent system uncertainties are adequately accounted for directly at the issuance of the long-term planning decisions. In this context, the present thesis aims at developing a techno-economic modeling and robust optimization framework for multi-period power systems planning considering a high share of IRES and resilience against extreme weather events. The specific planning problem considered is that of selecting the technology choice, size and commissioning schedule of conventional and renewable generation units under technical, economic, environmental and operational constraints. Within this problem, key research questions to be addressed are: (i) the proper integration and assessment of the operational flexibility needs due to the increased variability of the high shares of IRES production, (ii) the appropriate modeling and incorporation of the resilience requirements against extreme weather events within the power system planning problem and (iii) the representation and treatment of the inherent uncertainties in the system supply and demand within this planning context. In summary, the original contributions of this thesis are: - Proposing a computationally efficient multiperiod integrated generation expansion planning and unit commitment model that accounts for key short-term constraints and chronological system representation to derive the planning decisions under a high share of renewable energy penetration. - Introducing the expected flexibility shortfall metric for operational flexibility assessment. - Proposing a set of piece-wise linear models to quantify the impact of extreme heat waves and water availability on the derating of thermal and nuclear power generation units, renewable generation production and system load. - Presenting a method for explicitly incorporating the impact of the extreme weather events in a modified power system planning model. - Treating the inherent uncertainties in the electric power system planning parameters via a novel implementation of a multi-stage adaptive robust optimization model. - Proposing a novel solution method based on ``information basis'' approximation for the linear decision rules of the affinely adjustable robust planning model. - Applying the framework proposed to a practical size case studies based on realistic climate projections and under several scenarios of renewable penetration levels and carbon limits to validate the relevance of the overall modeling for real applications

Power system is the backbone of modern society. Traditionally, over 90% of the electrical energy is produced by power generation systems driven by steam turbines. Recently, with the development of renewable energy resources, wind energy conversion systems are the proven solutions for the next generation sustainable energy resources. Stability and performance of these power systems are the primary concerns of power system engineers. To better characterize the dynamical behaviors of power systems in practical applications, time delays in the feedback state variables, systems modeling uncertainties, and external disturbances are included in the state space model of the power system in this work. Linear matrix inequality based robust and resilient controllers satisfying the H_infinty performance objective for time delayed power systems are proposed. Fixed time delays are assumed to exist within the system state and input signals. The system model is assumed to have unstructured bounded uncertainties and L_2 type of disturbances. Furthermore, controller gain perturbations are assumed to be of additive type. The proposed control techniques have been applied to variable speed permanent magnet synchronous generator based wind energy conversion systems, and electrical power generation systems driven by steam turbine. Computer simulations conducted in MATLAB show the eectiveness of the proposed control algorithms.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 99-108).
Many critical infrastructures, such as transportation and electric energy networks, and health care, are now becoming highly integrated with information and communication technology, in order to be more efficient and reliable. These cyber-physical systems (CPS) now face an increasing threat of cyber-attacks. Intelligent attackers can leverage their knowledge of the system, disruption, and disclosure resources to critically damage the system while remaining undiscovered. In this dissertation, we develop a defense strategy, with the ability to uncover malicious and intelligent attacks and enable resilient operation of cyber-physical systems. Specifically, we apply this defense strategy to power systems, described by linear frequency dynamics around the nominal operating point. Our methodology is based on the notion of data aggregation as a tool for extracting internal information about the system that may be unknown to the attacker. As the first step to resilience and security, we propose several methods for active attack detection in cyber-physical systems. In one approach we design a clustering-based moving-target active detection algorithm and evaluate it against stealthy attacks on the 5-bus and 24-bus power grids. Next, we consider an approach based on Interaction Variables (IntVar), as another intuitive way to extract internal information in power grids. We evaluate the eectiveness of this approach on Automatic Generation Control (AGC), a vital control mechanism in today's power grid. After an attack has been detected, mitigation procedures must be put in place to allow continued reliable operation or graceful degradation of the power grid. To that end, we develop a resilient state estimation algorithm, that provides the system operator with situational awareness in the presence of wide-spread coordinated cyber-attacks when many system measurements may become unavailable.
by Ana Jevtić.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

With more than 75% of its population experiencing poverty, Zimbabwe was in 2012 considered one of the world's poorest countries. The country sits at the centre of the global HIV/AIDS epidemic and remains one of the hardest hit countries accounting for 5% of all new infections in sub-Saharan Africa. Zimbabwe's 15% HIV prevalence rate was 19 times the global average by 2012, and the total years of life lost due to premature mortality increased by over 150% between 1990 and 2010 because of HIV/AIDS. This study draws on notions of 'governmentality' to ask how the 'framing' of the value of PLWHA has influenced their treatment by the Zimbabwean government and society. Four questions are posed: first the study asks, in what ways do health policy decision-makers in Zimbabwe frame the value of people living with HIV/AIDS (PLWHA)? Secondly, the study questions the ways in which people not infected by HIV (Non-PLWHA) frame the value of PLWHA. Thirdly, the study turns to PLWHA and asks how they frame their own value. Finally, the study investigates the implications of valuing PLWHA, for their lives, or conversely, their deaths. The study draws upon primary research undertaken through interviews, focus group discussions, observations and document review. While there are some contradictions within and between groups of study participants in the ways they frame the value of PLWHA; the study finds consensus within and between these groups in the manner in which they tend to value PLWHA. Analysing these findings, there are five ways people in Zimbabwe frame the value of PLWHA. Firstly, from a 'citizen' perspective, PLWHA are both legal and political citizens who can identify as equal members of society like other citizens. They have social rights; participate, belong and can access HIV treatment that can reduce risks of death. Secondly, from a 'client' standpoint; PLWHA are customers, gaining access to health services through individual monetary payments or social payments such as Government budget allocations. This introduces a degree of 'rationing', forcing the clients (PLWHA) to behave in ways that increase their chances of receiving services. Those with lower purchasing power struggle to access expensive life-saving anti-retrovirals, thus individual wealth confers value on the lives of the wealthy. Thirdly, framing from a Statistical Representation perspective - through statistics, PLWHA can be used as a means of bargaining for government to gain access to international funding, to increase the chances of survival for PLWHA by bringing services such as antiretroviral therapy (ART). Fourthly, the 'Expendable populations' perspective views subgroups of PLWHA who fail to adhere to norms of behaviour prescribed by the government, including those unable to purchase services, such as the poor and homosexuals, sex workers and prisoners, as populations that may be allowed to die. Finally, the study shows that PLWHA lament the discursive space of technocrats with a counter-narrative of their value in which they emerge not as expendable victims but as victors reframed as an indefatigable population - 'Resiliencers'. PLWHA create a narrative of disobedient materiality, challenging totalising notions of governmentality. This study concludes by considering the relevance in the Zimbabwean context of the concept of 'Allowable Death' as a premature, avoidable death despite consciously crafted narratives that the death happened because nothing could have been done under the prevailing conditions to prevent it.

With the More Electric Aircraft paradigm, commercial commuter aircraft are increasing the size and complexity of electrical power systems by increasing the number of electrical loads. With this increase in complexity comes a need to analyze electrical power systems using new tools. The Hybrid Power System Optimizer (HyPSO) developed by Airbus SAS is a simulator designed to analyze new aircraft power systems. This thesis project will first provide a method to assess the reliability of complex aircraft electrical power systems before and after failure and reconfiguration events. Next, an add-on to HyPSO is developed to integrate the previously developed reliability calculations. Proof-of-concepts including new data visualizations are performed and provided.

A resilient power grid system is important to ensure the delivery of power to consumers while minimizing the cost of new technologies. Due to the increase of electricity consumption and CO2 emission, renewable energies and energy storage system are a compelling alternative. We started to identify decisions that need to be made, and parameters associated to model a power grid system expansion plan. Then, we investigated a utility company demand for the next 15 years. Also, we identified their current resources, and used that as a starting point. Then, we formulated an optimization model for a power grid expansion with different types of renewable energies, such as solar and wind, to meet the demand and minimize the cost of installation; as well as, a battery storage system (Lithium-ion) that is considered to come up with an optimal solution of a resilient power grid. Moreover, uncertainties of renewables are considered in the model, and robust optimization formulation is used to model them. Existing coal facilities are considered as a part of the model as well, and this part is designed on the optimization model in a way that would help decrease the use of such facilities and still manage them to meet demand. Numerical experiments are performed on several scenarios, and compared to what the utility company has forecasted in terms of cost, and renewable energies integration.A resilient power grid system is important to ensure the delivery of power to consumers while minimizing the cost of new technologies. Due to the increase of electricity consumption and CO2 emission, renewable energies and energy storage system are a compelling alternative. We started to identify decisions that need to be made, and parameters associated to model a power grid system expansion plan. Then, we investigated a utility company demand for the next 15 years. Also, we identified their current resources, and used that as a starting point. Then, we formulated an optimization model for a power grid expansion with different types of renewable energies, such as solar and wind, to meet the demand and minimize the cost of installation; as well as, a battery storage system (Lithium-ion) that is considered to come up with an optimal solution of a resilient power grid. Moreover, uncertainties of renewables are considered in the model, and robust optimization formulation is used to model them. Existing coal facilities are considered as a part of the model as well, and this part is designed on the optimization model in a way that would help decrease the use of such facilities and still manage them to meet demand. Numerical experiments are performed on several scenarios, and compared to what the utility company has forecasted in terms of cost, and renewable energies integration.

The electricity network is undergoing significant changes to cater to environmental-deterioration and fuel-depletion issues. Consequently, an increasing number of renewable resources in the form of distributed generation (DG) are being integrated into medium-voltage distribution networks. The DG integration has created several technical and economic challenges for distribution network operators. The main challenge is basically the problem of managing network voltage profile and congestion which is caused by increasing demand and intermittent DG operations. The result of all of these changes is a paradigm shift in the way distribution networks operate (from passive to active) and are managed that is not limited only to the distribution network operator but actively engages with network users such as demand aggregators, DG owners, and transmission-system operators. This thesis expands knowledge on the active distribution system in three specific areas and attempts to fill the gaps in existing approaches. A comprehensive active network management framework in active distribution systems is developed to allow studies on (i) the flexibility of network topology using modern power flow controllers, (ii) the benefits of centralised thermal electricity storage in achieving the required levels of flexibility and resiliency in an active distribution system, and (iii) system resiliency toward fault occurrence in hybrid AC/DC distribution systems. These works are implemented within the Advanced Interactive Multidimensional Modelling Systems (AIMMS) software to carry out optimisation procedure. Results demonstrate the benefit provided by a range of active distribution system solutions and can guide future distribution-system operators in making practical decisions to operate active distribution systems in cost-effective ways.

Electrical and Computer Engineering
Ph.D.
Power networks are one of the most complex systems in the field of electrical and computer engineering. In power networks, power supply-demand balancing can be achieved in a static or a dynamic model. In a static model, the power network cannot be easily adapted to intentional or unintentional network topology changes because the network design is predetermined, whereas in a dynamic model, the power network can be dynamically constructed and reconfigured at run-time, which leads to a more nimble, flexible, and stable system. In this dissertation, a novel Market-based Multi-agent System (MMS) is proposed to solve supply-demand balancing and power restoration problems in a dynamic model. The power network is modeled as a market environment consisting of Belief-Desire-Intention (BDI) agents representing three characters: 1) consumer, 2) supplier, and 3) middleman. The BDI agents are able to negotiate power supply and demand of the power network, with consumers exploring the market and exchanging power information with neighboring middlemen and suppliers. So long as all consumers and suppliers establish supply-demand relationships represented in tree data structures, a qualified minimal access structure is found as the lower bound of the system reliability. When contingencies occur, the agents can quickly respond and restore loads guided by the relationships using minimum computational resource. Based on case studies and simulation results, the proposed approach delivers more effective performance of contingencies response and better computation time efficiency as the scale of the power network expands. The proposed MMS shows promises for solving various real-world power supply-demand and restoration problems, and serves as a solid foundation for future power networks refinement and improvement.
Temple University--Theses

Variability affects the performance and power of a circuit. Along with static variations, dynamic variations, which occur during chip operation, necessitate a safety margin. The safety margin makes it difficult to meet the target performance within a limited power budget. This research explores methodologies to minimize the safety margin, thereby improving the energy efficiency of a system. The safety margin can be reduced by either minimizing the variation or adapting to the variation. This research explores three different methods to compensate for variations efficiently. First, post-silicon tuning methods for minimizing variations in 3D ICs are presented. Design methodologies to apply adaptive voltage scaling and adaptive body biasing to 3D ICs and the associated circuit techniques are explored. Second, non-design-intrusive circuit techniques are proposed for adaptation to dynamic variations. This work includes adaptive clock modulation and bias-voltage generation techniques. Third, design-intrusive methods to eliminate the safety margin are proposed. The proposed methodologies can prevent timing-errors in advance with a minimized performance penalty. As a result, the methods presented in this thesis minimize static variations and adapt to dynamic variations, thereby, enabling robust low-power operation of digital systems.

Power distribution systems have underwent a lot of significant changes in the last two decades. Wide-scale integration of Distributed Energy Resources (DERs) have made the distribution grid more resilient to abnormal conditions and severe weather induced outages. These DERs enhance the reliability of the system to bounce back from an abnormal situation rather quickly. However, the conventional notion of a radial system with unidirectional power flow does not hold true due to the addition of these DERs. Bidirectional power flow has challenged the conventional protection schemes in place. The most notable effects on the protection schemes can be seen in the field of islanding or Loss of Mains(LOM) detection and general fault identification and isolation. Adaptive protection schemes are needed to properly resolve these issues. Although, previous works in this field have dealt with this situation, a more comprehensive approach needs to be taken considering multiple topologies for developing adaptive protection schemes. The most common protective devices widely deployed in the distribution system such as overcurrent relays, reverse power relays at Point of Common Coupling(PCC), fuses, reclosers and feeder breakers need to studied in implementing these schemes. The work presented in this dissertation deals with simulation based and analytical approaches to tackle the issues of islanding and adaptive protection schemes. First we propose a multiprinciple passive islanding detection technique which relies on local PCC measurements, thus reducing the need of additional infrastructure and still ensuring limited Non Detection Zone (NDZ). The next step to islanding detection would be to sustain a islanded distribution system in order to reduce the restoration time and still supply power to critical loads. Such an approach to maintain generator load balance upon islanding detection is studied next by appropriate shedding of non-critical and low priority critical loads based upon voltage sensitivity analysis. Thereafter, adaptive protection schemes considering limited communication dependency is studied with properly assigning relay settings in directional overcurrent relays (DOCRs), which are one of the most widely deployed protective devices in distribution systems by catering to multiple topologies and contingencies. A simulation based technique is discussed first and then an analytical approach to solve the conventional optimal relay coordination problem using Mixed Integer Linear Programming (MILP) with the usage of multiple setting groups is studied. All these approaches make the distribution more robust and resilient to system faults and ensure proper fault identification and isolation, ensuring overall safety of system.
Doctor of Philosophy
With widespread integration of inverter-based distributed energy resources (DERs) in the distribution grid, the conventional protection and control schemes no longer hold valid. The necessity of an adaptive protection scheme increases as the DER penetration in the system increases. Apart from this, changes in system topology and variability in DER generation, also change the fault current availability in the system in real-time. Hence, the protection schemes should be able to adapt to these variations and modify their settings for proper selectivity and sensitivity towards faults in the system, especially in systems with high penetration of DERs. These protection schemes need to be modified in order to properly identify and isolate faults in the network as well as correctly identify Loss of Mains (LOM) or islanding phenomenon. Special attention is needed to plan the next course of action after the islanding occurrence. Additionally, the protective devices in distribution system should be utilized to their maximum capability to create an adaptive and smart protection system. This document elaborately explains the research work pertaining to these areas.

Near-Threshold Computing embodies an intriguing choice for mobile processors due to the promise of superior energy efficiency, extending the battery life of these devices while reducing the peak power draw. However, process, voltage, and temperature variations cause a significantly high failure rate of Level One cache cells in the near-threshold regime a stark contrast to designs in the super-threshold regime, where fault sites are rare. This thesis work shows that faulty cells in the near-threshold regime are highly clustered in certain regions of the cache. In addition, popular mobile benchmarks are studied to investigate the impact of run-time workloads on timing faults manifestation. A technique to mitigate the run-time faults is proposed. This scheme maps frequently used data to healthy cache regions by exploiting the application cache behaviors. The results show up to 78% gain in performance over two other state-of-the-art techniques.

A continuous increase in demands from the utility grid and traction applications have steered public attention toward the integration of energy storage (ES) and hybrid ES (HESS) solutions. Modern technologies are no longer limited to batteries, but can include supercapacitors (SC) and flywheel electromechanical ES well. However, insufficient control and algorithms to monitor these devices can result in a wide range of operational issues. A modern day control platform must have a deep understanding of the source. In this dissertation, specialized modular Energy Storage Management Controllers (ESMC) were developed to interface with a variety of ES devices. The EMSC provides the capability to individually monitor and control a wide range of different ES, enabling the extraction of an ES module within a series array to charge or conduct maintenance, while remaining storage can still function to serve a demand. Enhancements and testing of the ESMC are explored in not only interfacing of multiple ES and HESS, but also as a platform to improve management algorithms. There is an imperative need to provide a bridge between the depth of the electrochemical physics of the battery and the power engineering sector, a feat which was accomplished over the course of this work. First, the ESMC was tested on a lead acid battery array to verify its capabilities. Next, physics-based models of lead acid and lithium ion batteries lead to the improvement of both online battery management and established multiple metrics to assess their lifetime, or state of health. Three unique HESS were then tested and evaluated for different applications and purposes. First, a hybrid battery and SC HESS was designed and tested for shipboard power systems. Next, a lithium ion battery and SC HESS was utilized for an electric vehicle application, with the goal to reduce cycling on the battery. Finally, a lead acid battery and flywheel ES HESS was analyzed for how the inclusion of a battery can provide a dramatic improvement in the power quality versus flywheel ES alone.

The concern about the consequences of carbon-intensive activities across all socio-economic sectors is accelerating the path towards renewables-based power systems. However, larger renewable energy penetration allied with unknown future demand adds vulnerability and uncertainty to the design of power systems. This work assesses the impact of climate variability and energy demand in renewables-based power systems. An hourly-based modelling tool is used to simulate the power system for Portugal in 2050. A multiyear model calibration is proposed, enabling a more reliable simulation. Regarding climate, two representative concentration pathways (RCP4.5 and RCP8.5), totaling 473 climate realizations, are tested. Five electricity demand-flexibility scenarios are tested for each activity sector, assuming diverging levels for electricity demand, storage and demand-side management. The impacts of climate variability on supply and demand are simultaneously analyzed and quantified. Energy demand plays a crucial role in the power system. Results show that residential demand may increase between 4 and 60%, which are used to define scenarios. The cross-border interconnection needs quadruplicate from low to high demand, while the renewable generation share decreases 16 p.p. Climate variability, depending on the scenario, leads to changes in residential demand between -8 to +5% around its median, while renewables generation share might oscillate between -15 and +15 p.p. Cross-border interconnection energy trading needs may vary by a factor of two due to climate variability, from -62 to +226% around its median. Fully renewables-based power systems are especially vulnerable to climate. The system power capacity required under a climatic median year varies 3-fold according to demand-flexibility scenarios. For that same system to be resilient under unfavorable years, it is required an increase of up to 200-fold in storage or doubling of cross-border interconnection. A power system designed for unfavorable years requires 54% more installed capacity. Hence, future climate variability will be critical in the power systems’ operation, thus pivotal to evaluate and consider in its planning.

abstract: The electric power system (EPS) is an extremely complex system that has operational interdependencies with the water delivery and treatment system (WDTS). The term water-energy nexus is commonly used to describe the critical interdependencies that naturally exist between the EPS and water distribution systems (WDS). Presented in this work is a framework for simulating interactions between these two critical infrastructure systems in short-term and long-term time-scales. This includes appropriate mathematical models for system modeling and for optimizing control of power system operation with consideration of conditions in the WDS. Also presented is a complete methodology for quantifying the resilience of the two interdependent systems. The key interdependencies between the two systems are the requirements of water for the cooling cycle of traditional thermal power plants as well as electricity for pumping and/or treatment in the WDS. While previous work has considered the dependency of thermoelectric generation on cooling water requirements at a high-level, this work considers the impact from limitations of cooling water into network simulations in both a short-term operational framework as well as in the long-term planning domain. The work completed to set-up simulations in operational length time-scales was the development of a simulator that adequately models both systems. This simulation engine also facilitates the implementation of control schemes in both systems that take advantage of the knowledge of operating conditions in the other system. Initial steps for including the influence of anticipated water availability and water rights attainability within the combined generation and transmission expansion planning problem is also presented. Lastly, the framework for determining the infrastructural-operational resilience (IOR) of the interdependent systems is formulated. Adequately modeling and studying the two systems and their interactions is becoming critically important. This importance is illustrated by the possibility of unforeseen natural or man-made events or by the likelihood of load increase in the systems, either of which has the risk of putting extreme stress on the systems beyond that experienced in normal operating conditions. Therefore, this work addresses these concerns with novel modeling and control/policy strategies designed to mitigate the severity of extreme conditions in either system.
Dissertation/Thesis
Doctoral Dissertation Electrical Engineering 2020

abstract: Resilience is emerging as the preferred way to improve the protection of infrastructure systems beyond established risk management practices. Massive damages experienced during tragedies like Hurricane Katrina showed that risk analysis is incapable to prevent unforeseen infrastructure failures and shifted expert focus towards resilience to absorb and recover from adverse events. Recent, exponential growth in research is now producing consensus on how to think about infrastructure resilience centered on definitions and models from influential organizations like the US National Academy of Sciences. Despite widespread efforts, massive infrastructure failures in 2017 demonstrate that resilience is still not working, raising the question: Are the ways people think about resilience producing resilient infrastructure systems? This dissertation argues that established thinking harbors misconceptions about infrastructure systems that diminish attempts to improve their resilience. Widespread efforts based on the current canon focus on improving data analytics, establishing resilience goals, reducing failure probabilities, and measuring cascading losses. Unfortunately, none of these pursuits change the resilience of an infrastructure system, because none of them result in knowledge about how data is used, goals are set, or failures occur. Through the examination of each misconception, this dissertation results in practical, new approaches for infrastructure systems to respond to unforeseen failures via sensing, adapting, and anticipating processes. Specifically, infrastructure resilience is improved by sensing when data analytics include the modeler-in-the-loop, adapting to stress contexts by switching between multiple resilience strategies, and anticipating crisis coordination activities prior to experiencing a failure. Overall, results demonstrate that current resilience thinking needs to change because it does not differentiate resilience from risk. The majority of research thinks resilience is a property that a system has, like a noun, when resilience is really an action a system does, like a verb. Treating resilience as a noun only strengthens commitment to risk-based practices that do not protect infrastructure from unknown events. Instead, switching to thinking about resilience as a verb overcomes prevalent misconceptions about data, goals, systems, and failures, and may bring a necessary, radical change to the way infrastructure is protected in the future.
Dissertation/Thesis
Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2018

abstract: The phrase water-energy nexus is commonly used to describe the inherent and critical interdependencies between the electric power system and the water supply systems (WSS). The key interdependencies between the two systems are the power plant’s requirement of water for the cooling cycle and the water system’s need of electricity for pumping for water supply. While previous work has considered the dependency of WSS on the electrical power, this work incorporates into an optimization-simulation framework, consideration of the impact of short and long-term limited availability of water and/or electrical energy. This research focuses on the water supply system (WSS) facet of the multi-faceted optimization and control mechanism developed for an integrated water – energy nexus system under U.S. National Science Foundation (NSF) project 029013-0010 CRISP Type 2 – Resilient cyber-enabled electric energy and water infrastructures modeling and control under extreme mega drought scenarios. A water supply system (WSS) conveys water from sources (such as lakes, rivers, dams etc.) to the treatment plants and then to users via the water distribution systems (WDS) and/or water supply canal systems (WSCS). Optimization-simulation methodologies are developed for the real-time operation of water supply systems (WSS) under critical conditions of limited electrical energy and/or water availability due to emergencies such as extreme drought conditions, electric grid failure, and other severe conditions including natural and manmade disasters. The coupling between WSS and the power system was done through alternatively exchanging data between the power system and WSS simulations via a program control overlay developed in python. A new methodology for WDS infrastructural-operational resilience (IOR) computation was developed as a part of this research to assess the real-time performance of the WDS under emergency conditions. The methodology combines operational resilience and component level infrastructural robustness to provide a comprehensive performance assessment tool. The optimization-simulation and resilience computation methodologies developed were tested for both hypothetical and real example WDS and WSCS, with results depicting improved resilience for operations of the WSS under normal and emergency conditions.
Dissertation/Thesis
Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2019

Les objectifs récents en ce qui concerne la durabilité des systèmes électriques et l'atténuation des menaces liées au changement climatique modifient la portée des exigences de planification de ces systèmes. D'une part, les systèmes durables d'énergie à faible émission de carbone qui comportent une part élevée de sources d'énergie renouvelables intermittentes(IRES) se caractérisent par une forte augmentation de la variabilité intertemporelle et nécessitent des systèmes flexibles capables d'assurer la sécurité de l'approvisionnement électrique. D'autre part, la fréquence et la gravité accrues des phénomènes climatiques extrêmes menacent la fiabilité du fonctionnement des réseaux électriques et exigent des systèmes résilients capables de résister à ces impacts potentiels. Tout en s'assurant que les incertitudes inhérentes au système sont bien prises en compte directement au moment de la prise des décisions de planification à long terme. Dans ce contexte, la présente thèse vise à développer une modélisation technicoéconomique et un cadre d'optimisation robuste pour la planification des systèmes électriques multi-périodes en considérant une part élevée d'IRES et la résilience aux phénomènes climatiques extrêmes. Le problème spécifique de planification considéré est celui du choix de la technologie, de la taille et du programme de mise en service des unités de production conventionnelles et renouvelables sous des contraintes techniques, économiques,environnementales et opérationnelles. Dans le cadre de ce problème, les principales questions de recherche à aborder sont : (i) l'intégration et l'évaluation appropriées des besoins de flexibilité opérationnelle en raison de la variabilité accrue des parts élevées de la production d'IRES, (ii) la modélisation et l'intégration appropriées des exigences de résilience contre les phénomènes climatiques extrêmes dans la planification du système électrique et (iii) le traitement des incertitudes inhérentes de l'offre et la demande dans ce cadre de planification. En résumé, les contributions originales de cette thèse sont :- Proposer un modèle de planification du système électrique intégré multi période avec des contraintes dynamiques et en considérant un pourcentage élevé de pénétration des énergies renouvelables.- Introduire la mesure du déficit de flexibilité prévu pour l'évaluation de la flexibilité opérationnelle.- Proposer un ensemble de modèles linéaires pour quantifier l'impact des vagues de chaleur extrêmes et de la disponibilité de l'eau sur le déclassement des unités de production d'énergie thermique et nucléaire, la production d'énergie renouvelable et la consommation électrique du système.- Présenter une méthode permettant d'intégrer explicitement l'impact des phénomènes climatiques extrêmes dans le modèle de planification du système électrique.- Traiter les incertitudes inhérentes aux paramètres de planification du système électrique par la mise en oeuvre d'un nouveau modèle d'optimisation adaptatif robuste à plusieurs phases.- Proposer une nouvelle méthode de solution basée sur l'approximation des règles de décision linéaires du modèle de planification robuste.- Appliquer le cadre proposé à des études de cas de taille pratique basées sur des projections climatiques réalistes et selon plusieurs scénarios de niveaux de pénétration des énergies renouvelables et de limites de carbone pour valider la pertinence de la modélisation globale pour des applications réelles
Recent objectives for power systems sustainability and mitigation of climate change threats are modifying the breadth of power systems planning requirements. On one hand, sustainable low carbon power systems which have a high share of intermittent renewable energy sources (IRES) are characterized by a sharp increase in inter-temporal variability and require flexible systems able to cope and ensure the security of electricity supply. On the other hand, the increased frequency and severity of extreme weather events threatens the reliability of power systems operation and require resilient systems able to withstand those potential impacts. All of which while ensuring that the inherent system uncertainties are adequately accounted for directly at the issuance of the long-term planning decisions. In this context, the present thesis aims at developing a techno-economic modeling and robust optimization framework for multi-period power systems planning considering a high share of IRES and resilience against extreme weather events. The specific planning problem considered is that of selecting the technology choice, size and commissioning schedule of conventional and renewable generation units under technical, economic, environmental and operational constraints. Within this problem, key research questions to be addressed are: (i) the proper integration and assessment of the operational flexibility needs due to the increased variability of the high shares of IRES production, (ii) the appropriate modeling and incorporation of the resilience requirements against extreme weather events within the power system planning problem and (iii) the representation and treatment of the inherent uncertainties in the system supply and demand within this planning context. In summary, the original contributions of this thesis are: - Proposing a computationally efficient multiperiod integrated generation expansion planning and unit commitment model that accounts for key short-term constraints and chronological system representation to derive the planning decisions under a high share of renewable energy penetration. - Introducing the expected flexibility shortfall metric for operational flexibility assessment. - Proposing a set of piece-wise linear models to quantify the impact of extreme heat waves and water availability on the derating of thermal and nuclear power generation units, renewable generation production and system load. - Presenting a method for explicitly incorporating the impact of the extreme weather events in a modified power system planning model. - Treating the inherent uncertainties in the electric power system planning parameters via a novel implementation of a multi-stage adaptive robust optimization model. - Proposing a novel solution method based on ``information basis'' approximation for the linear decision rules of the affinely adjustable robust planning model. - Applying the framework proposed to a practical size case studies based on realistic climate projections and under several scenarios of renewable penetration levels and carbon limits to validate the relevance of the overall modeling for real applications

abstract: With growing complexity of power grid interconnections, power systems may become increasingly vulnerable to low frequency oscillations (especially inter-area oscillations) and dependent on stabilizing controls using either local signals or wide-area signals to provide adequate damping. In recent years, the ability and potential to use wide-area signals for control purposes has increased since a significant investment has been made in the U. S. in deploying synchrophasor measurement technology. Fast and reliable communication systems are essential to enable the use of wide-area signals in controls. If wide-area signals find increased applicability in controls the security and reliability of power systems could be vulnerable to disruptions in communication systems. Even though numerous modern techniques have been developed to lower the probability of communication errors, communication networks cannot be designed to be always reliable. Given this background the motivation of this work is to build resiliency in the power grid controls to respond to failures in the communication network when wide-area control signals are used. In addition, this work also deals with the delay uncertainty associated with the wide-area signal transmission. In order to counteract the negative impact of communication failures on control effectiveness, two approaches are proposed and both approaches are motivated by considering the use of a robustly designed supplementary damping control (SDC) framework associated with a static VAr compensator (SVC). When there is no communication failure, the designed controller guarantees enhanced improvement in damping performance. When the wide-area signal in use is lost due to a communication failure, however, the resilient control provides the required damping of the inter-area oscillations by either utilizing another wide-area measurement through a healthy communication route or by simply utilizing an appropriate local control signal. Simulation results prove that with either of the proposed controls included, the system is stabilized regardless of communication failures, and thereby the reliability and sustainability of power systems is improved. The proposed approaches can be extended without loss of generality to the design of any resilient controller in cyber-physical engineering systems.
Dissertation/Thesis
Ph.D. Electrical Engineering 2014

This thesis addresses resilience for the future of Canadian suburbs, through the lens of buildings and food, particularly against the backdrop of peak oil and climate change. Food access is an integral part of how a city sustains itself. There is growing evidence that the current global food system, the one that feeds many cities today, is “broken” or at least at risk. It has, in the past, produced an abundance of food. It has also brought along a number of unintended consequences, has neglected to embed equitable distribution patterns, and when faced with peak oil and climate change, risks some form of collapse. This thesis focuses on the food distribution question. It suggests a new food system model for the City of Mississauga that couples the region's local systems with global networks in a set of local/global relationships. The research portion of this work provides an overview of the dynamic historical and present relationship between food and city infrastructure, touches on the issues facing suburban resiliency today, and investigates the challenges facing the food retail industry. It then draws lessons from large-scale typologies of urban agriculture being proposed in recent years by architects and urban designers. This work, specifically at the design stage, identifies the suburban supermarket as a local catalyst for transformation. Today, the City of Mississauga is not food secure – that is, it does not rely on a safe, adequate, sustainable, or appropriate food supply. This thesis investigates how local and sustainable food systems can be integrated into the urban fabric and systems sustaining suburbs today. It further seeks to build on existing conditions, and answer how the suburban big-box typology, preferred by retailers, can contribute to food security.