Climate change studies and examinations of increasing sea levels and temperatures show storm intensity and frequency are increasing. As these storms are increasing in intensity and frequency, the effects of these storms must be monitored to determine the probable damages or impacts to critical infrastructure [2, 35]. These storms suddenly create new demands and requirements upon already stressed critical infrastructure sectors [1]. A combined and interdisciplinary effort must be made to identify these stresses and to mitigate any failures. This effort is needed so that the 21st Century Smart Grid is robust and resilient enough to ensure that the grid is secured against all hazards. This project focuses on anticipating loss of above ground electrical power due to extreme wind speeds. This thesis selected a study region of Indiana, Illinois, Kentucky, and Tennessee to investigate the skill of fragility curve generation for this region, during Hurricane Irene, in the Fall of 2011. Three published fragility techniques are compared within the Midwest study region to determine the best skilled technique for the low wind speeds experienced in this region in August 2011. The three techniques studied are: 1) Powerline Technique [6], a correlation between “as published” state based construction standards and surface wind speeds sustained for greater than one minute; 2) the ANL Headout Technique [37], a correlation of Hurricane Irene three second wind gusts with DOE situation reports of outages; and 3) the Walker Technique [1], a correlation of utility reported outages in the Eastern Seaboard counties with three second surface gusts. The deliverable outcomes for this project include: 1) metrics for determining the method best for the study region, from the archival data during Hurricane Irene timeframe; 2) a fragility curve methodology description for each technique; and 3) a mathematical representation for each technique suitable for inclusion in automated forecast algorithms. Overall, this project combines situational awareness modeling to provide distinct fragility techniques that can be used by the public and private sectors to improve emergency management, restoration processes, and critical infrastructure all-hands-preparedness. This work was supported by Western Kentucky University (WKU) and the National Oceanic Atmospheric Administration (NOAA)

The objective of the proposed research is to develop an intelligent load modeling, identification, and prediction technology to provide granular load energy consumption and performance details and drive building energy reduction, demand reduction, and proactive equipment maintenance. Electricity consumption in commercial and residential sectors accounts for about 70% of the total electricity generation in United States. Buildings are the most important consumers, and contribute to over 80% of the consumptions in these two sectors. To reduce electrical energy spending and carbon emission, several studies from Pacific Northwest National Lab (PNNL) and National Renewable Energy Lab (NREL) prove that if equipped with the proper technologies, a commercial or a residential building can potentially improve energy savings of buildings by up to about 10% to 30% of their usage. However, the market acceptance of these new technologies today is still not sufficient, and the reason is generally acknowledged to be the lack of solution to quantify the contributions of these new technologies to the energy savings, and the invisibility of the loads in buildings. A non-intrusive load monitoring (NILM) system is proposed in this dissertation, which can identify every individual load in buildings and record the energy consumption, time-of-day variations and other relevant statistics of the identified load, with no access to the individual component. The challenge of such a non-intrusive load monitoring is to find features that are unique for a particular load and then to match a measured feature of an unknown load against a database or library of known. Many problems exist in this procedure and the proposed research is going to focus on three directions to overcome the bottlenecks. They are respectively fundamental load studies for a model-driven feature extraction, adaptive identification algorithms for load space extendibility, and the practical simplifications for the real industrial applications. The simulation results show the great potentials of this new technology in building energy monitoring and management.

By 2040, global CO2 emissions and energy consumption are expected to increase by 40%. In the US, buildings account for 40% of national CO2 emissions and energy consumption, of which 75% is met by fossil fuels. Reducing this impact on the environment requires both improved building energy efficiency and increased renewable utilization. To this end, this dissertation presents a demand-supplystorage- based decision framework to enable strategic energy management in smart buildings. This framework includes important but largely unaddressed aspects pertaining to building demand and supply such as occupant plugloads and the integration of weather forecast-based solar prediction, respectively. We devote the first part of our work to study occupant plugloads, which account for up to 50% of demand in high performance buildings. We investigate the impact of plugload control mechanisms based on the analysis of real-world data from experiments we conducted at NASA Ames sustainability base and Carnegie Mellon University (SV campus). Our main contribution is in extending existing demand response approaches to an occupant-in-the-loop paradigm. In the second part of this work, we describe methods to develop weather forecastbased solar prediction models using both local sensor measurements and global weather forecast data from the National Ocean and Atmospheric Administration (NOAA).We contribute to the state-of-the-art solar prediction models by proposing the incorporation of both local and global weather characteristics into their predictions. This weather forecast-based solar model plus the plugload-integrated demand model, along with an energy storage model constitutes the weather-driven plugloadintegrated decision-making framework for energy management. To demonstrate the utility of this framework, we apply it to solve an optimal decision problem with the objective of minimizing the energy-related operating costs associated with a smart building. The findings indicate that the optimal decisions can result in savings of up to 74% in the expected operational costs. This framework enables inclusive energy management in smart buildings by accounting for occupants-in-the-loop. Results are presented and discussed in the context of commercial office buildings.

Demand participation is a basic ingredient of the next generation of power exchanges in electricity markets. A key challenge in implementing demand response stems from establishing reliable market frameworks so that purchasers can estimate the demand correctly, buy as economically as possible and have the means of hedging the risk of lack of supply. System operators also need ways of estimating responsive load behaviour to reliably operate the grid. In this context, two aspects of demand response are addressed in this study: scheduling and baseline estimation. The thesis presents a market clearing algorithm including demand side reserves in a two-stage stochastic optimization framework to account for wind power production uncertainty. The results confirm that enabling the load to provide reserve can potentially benefit consumers by reducing electricity price, while facilitating a higher share of renewable energy sources in the power system. Two novel methods, Bayesian Linear regression and Kernel adaptive filtering, are proposed for baseline load forecasting in the second part of the study. The former method provides an integrated solution for prediction with full accounting for uncertainty while the latter provides an online sequential learning algorithm that is useful for short term forecasting. / Graduate / 0544 / nimahtehrani@gmail.com

Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / Caterina Scoglio / Many physical and engineered systems (e.g., smart grid, transportation and biomedical systems) are increasingly being monitored and controlled over a communication network. These systems where sensing, communication, computation and real time control are closely integrated are referred to as cyber physical systems (CPS). Cyber physical systems present a plethora of challenges related to their design, analysis, optimization and control. In this dissertation, we present some fundamental methodologies to analyze the optimization of physical systems over a communication network. Specifically, we consider a medium voltage DC shipboard smart grid (SSG) reconfiguration problem as a test case to demonstrate our approach. The main goal of SSG reconfiguration is to change the topology of the physical power system by switching circuit breakers, switches, and other devices in the system in order to route power effectively to loads especially in the event of faults/failures. A majority of the prior work has focused on centralized approaches to optimize the switch configuration to maximize specific objectives. These methods are prohibitively complex and not suited for agile reconfiguration in mission critical situations. Decentralized solutions proposed do reduce complexity and implementation time at the cost of optimality. Unfortunately, none of the prior efforts in this arena address the cyber physical aspects of an SSG. This dissertation aims to bridge this gap by proposing a suite of methods to analyze both centralized and decentralized SSG reconfigurations that incorporate the effect of the underlying cyber infrastructure. The SSG reconfiguration problem is a mixed integer non convex optimization problem for which branch and bound based solutions have been proposed earlier. Here, optimal reconfiguration strategies prioritize the power delivered to vital loads over semi-vital and non vital loads. In this work, we propose a convex approximation to the original non convex problem that significantly reduces complexity of the SSG reconfiguration. Tradeoff between power delivered and number of switching operations after reconfiguration is discussed at steady state. Second, the distribution of end-to-end delay associated with fault diagnosis and reconfiguration in SSG is investigated from a cyber-physical system perspective. Specifically, a cross-layer total (end-to-end) delay analysis framework is introduced for SSG reconfiguration. The proposed framework stochastically models the heterogeneity of actions of various sub-systems viz., the reconfiguration of power systems, generation of fault information by sensor nodes associated to the power system, processing actions at control center to resolve fault locations and reconfiguration, and information flow through communication network to:(1) analyze the distribution of total delay in SSG reconfiguration after the occurrence of faults; and (2) propose design options for real-time reconfiguration solutions for shipboard CPS, that meet total delay requirements. Finally, the dissertation focuses on the quality of SSG reconfiguration solution with incomplete knowledge of the overall system state, and communication costs that may affect the quality (optimality) of the resulting reconfiguration. A dual decomposition based decentralized optimization in which the shipboard system is decomposed into multiple separable subsystems with agents is proposed. Specifically, agents monitoring each subsystem solve a local concave dual function of the original objective while neighboring agents share information over a communication network to obtain a global solution. The convergence of the proposed approach under varying network delays and quantization noise is analyzed and comparisons with centralized approaches are presented. Results demonstrate the effectiveness as well as tradeoffs involved in centralized and decentralized SSG reconfiguration approaches.

Les systèmes pair-à-pair (P2P) sont utilisés par des millions d'usagers tous les jours. Dans beaucoup de cas, un usager souhaite communiquer, échanger du contenu ou des services à travers différents systèmes P2P. Cela requiert de la coopération entre les différents systèmes P2P, ce qui est très souvent difficile ou même impossible à obtenir, à cause des raisons suivantes; in primis, l'absence d'une infrastructure de routage entre les réseaux, ce qui rend la communication étanche et, in secundis, l'incompatibilité des protocoles et des opérations des susdits systèmes. L'objectif principal de cette thèse est celui de permettre la coopération entre systèmes P2P. La thèse introduit un cadre de coopération rétro-compatible entre systèmes P2P hétérogènes constitué de deux parties. La première est un cadre de routage intra-réseaux permettant à des réseaux hétérogènes de communiquer. La deuxième est une application coopérative, conçue à l'aide et au travers du cadre de routage, dont l'objectif est celui de résoudre les incompatibilités protocolaires des systèmes P2P sous-jacents. Comme étude de cas de notre cadre de coopération, on présente une solution complète permettant une coopération entre des réseaux P2P spécialisés dans l'échange des fichiers pouvant s'appliquer aux réseaux P2P actuels. Dans la deuxième partie de la thèse, on présente une étude de cas d'usage de notre architecture d'interconnexion des réseaux P2P pour la collecte et la gestion des données dans réseaux d'électricité intelligents. / Peer-to-peer systems are used by millions of users every day. In many scenarios, it is desirable for the users from different P2P systems to communicate and exchange content or services with each other. This requires co-operation between the P2P systems, which is often difficult or impossible, due to the two following reasons. First is the lack of an inter-overlay routing infrastructure throughout these systems, caused by the incompatibilities in overlay networks on top of which they are built. Second, there are incompatibilities in the application protocols of these systems. The main topic of this thesis is enabling the cooperation between P2P systems. The thesis introduces a cooperation framework for backward-compatible cooperating heterogeneous P2P systems which constitutes two parts. The first one is an inter-overlay routing framework which allows to inter-routing between heterogeneous overlay networks. The second one is the cooperation application, built on the top of the inter-overlay routing framework, which aims at solving the incompatibilities in the application protocols of P2P systems. As a case study of the cooperation framework, we introduce a complete solution for cooperating P2P file-sharing networks which is applicable for all current P2P file-sharing networks. In the second topic of this thesis, we investigate a case study of using inter-connecting P2P overlays for collecting and managing data in smart grid, a typical example of cyber physical system.

Submitted by Fernanda Ciolfi (fernanda.ciolfi@puc-campinas.edu.br) on 2016-07-06T12:44:44Z No. of bitstreams: 1 Tiago Gerard Machado.pdf: 2230326 bytes, checksum: 325bc3f8e5b3ef063d165882ed59d1f3 (MD5) / Made available in DSpace on 2016-07-06T12:44:45Z (GMT). No. of bitstreams: 1 Tiago Gerard Machado.pdf: 2230326 bytes, checksum: 325bc3f8e5b3ef063d165882ed59d1f3 (MD5) Previous issue date: 2016-05-23 / The global energy sector is going through a revolutionary moment with the internet of things in the electric power system, the smart grids in Brazil this movement is still embryonic, but is gaining momentum in recent years driven by the need for operational efficiency and the new standard more participatory consumer.Electrical networks are essential for physical and economic well-being of a nation, in this way, with the implementation of smart grid solutions is imperative that information security is considered to protect critical assets. Traditionally the focus of cybersecurity has always been in IT, in order to protect the information and unauthorized access to information systems, use, modification or some kind of action that would compromise the confidentiality, integrity or availability of information. Cybersecurity for smart grid requires a combined focus of information security for IT systems to the communications network and the physical equipment of the electric grid.Thus, the thesis aims to develop a ranking methodology level cybersecurity maturity in smart grids. The methodology is based on two pillars, the first in identifying assets, threats and impacts and the second in carrying out a assessment for the analysis and classification of the 126 requirements maturity level that are grouped into 16 groups of requirements, always applied in a specific use case.The methodology was applied in two use cases of operating systems in a smart grid of a major power distribution company. The results clearly identify the proprietary system has 51% of the requirements at the lowest level while the new system 47% of applications are at the highest level. / O setor el?trico mundial est? passando por um momento revolucion?rio com da internet das coisas no sistema el?trico de pot?ncia, ou seja, as redes el?tricas inteligentes, no Brasil este movimento ainda ? embrion?rio, mas est? ganhando for?a nos ?ltimos anos impulsionado pela necessidade de efici?ncia operacional e o novo padr?o de consumidor mais participativo.As redes el?tricas s?o essenciais para o bem-estar f?sico e econ?mico de uma na??o, desta forma, com a implanta??o das solu??es de redes el?tricas inteligentes ? imprescind?vel que a seguran?a da informa??o seja considerada para proteger os ativos cr?ticos. Tradicionalmente o foco da seguran?a cibern?tica sempre foi na IT, com o objetivo de proteger as informa??es e os sistemas de informa??o de acessos n?o autorizados, utiliza??o, modifica??o ou algum tipo de a??o de que comprometa a confidencialidade, integridade ou disponibilidade da informa??o. A seguran?a cibern?tica para smart grid requer um foco combinado de seguran?a da informa??o para os sistemas de IT, para a rede de comunica??o e para os equipamentos f?sicos da rede el?trica.Desta maneira, a disserta??o tem por objetivo desenvolver uma metodologia de classifica??o do n?vel de maturidade de seguran?a cibern?tica nas redes el?tricas inteligentes. A metodologia baseia-se em dois grandes pilares, primeiro na identifica??o dos ativos, amea?as e impactos e o segundo na realiza??o de uma an?lise e classifica??o do n?vel de maturidade de 126 requerimentos que s?o agrupados em 16 grupos de requerimentos, sempre aplicados em um caso de uso espec?fico.A metodologia foi aplicada em dois casos uso de sistemas de opera??o de uma rede el?trica inteligente de uma grande companhia de distribui??o de energia. Os resultados permitiram identificar claramente que o sistema propriet?rio possui 51% dos requerimentos no n?vel mais baixo enquanto no novo sistema 47% dos requerimentos est?o no n?vel mais alto.

Demand response (DR) is one of the integral mechanisms of today's smart grids. It enables consumer energy assets such as flexible loads, standby generators and storage systems to add value to the grid by providing cost-effective flexibility. With increasing renewable generation and impending electric vehicle deployment, there is a critical need for large volumes of reliable and responsive flexibility through DR. This poses a new challenge for the electricity sector. Smart grid development has resulted in the availability of large amounts of data from different physical segments of the grid such as generation, transmission, distribution and consumption. For instance, smart meter data carrying valuable information is increasingly available from the consumers. Parallel to this, the domain of data analytics and machine learning (ML) is making immense progress. Data-driven modelling based on ML algorithms offers new opportunities to utilise the smart grid data and address the DR challenge. The thesis demonstrates the use of data-driven models for enhancing DR from large consumers such as commercial and industrial (C&I) buildings. A reliable, computationally efficient, cost-effective and deployable data-driven model is developed for large consumer building load estimation. The selection of data pre-processing and model development methods are guided by these design criteria. Based on this model, DR operational tasks such as capacity scheduling, performance evaluation and reliable operation are demonstrated for consumer energy assets such as flexible loads, standby generators and storage systems. Case studies are designed based on the frameworks of ongoing DR programs in different electricity markets. In these contexts, data-driven modelling shows substantial improvement over the conventional models and promises more automation in DR operations. The thesis also conceptualises an emissions-based DR program based on emissions intensity data and consumer load flexibility to demonstrate the use of smart grid data in encouraging renewable energy consumption. Going forward, the thesis advocates data-informed thinking for utilising smart grid data towards solving problems faced by the electricity sector.

We consider the optimal economic dispatch of power generators in a smart electric grid for allocating power between generators to meet load requirements at minimum total cost. We assume that each generator has a piece-wise linear cost function. We first present a polynomial time algorithm that achieves optimal dispatch. We then present a decentralized algorithm where, each generator independently adjusts its power output using only the aggregate power imbalance in the network, which can be observed by each generator through local measurements of the frequency deviation on the grid. The algorithm we propose exponentially erases the power imbalance, while eventually minimizing the generation cost.

Sensor network web services have recently emerged as promising tools to provide remote management, data collection and querying capabilities for sensor networks. They can be utilized in a large number of elds among which Demand-Side Energy Management (DSEM) is an important application area that has become possible with the smart electrical power grid. DSEM applications generally aim to reduce the cost and the amount of power consumption. In the traditional power grid, DSEM has not been implemented widely due to the large number of households and lack of ne-grained automation tools. However by employing intelligent devices and implementing communication infrastructure among these devices, the smart grid will renovate the existing power grid and it will enable a wide variety of DSEM applications. In this thesis, we analyze various DSEM scenarios that become available with sensor network web services. We assume a smart home with a Wireless Sensor Network (WSN) where the sensors are mounted on the appliances and they are able to run web services. The web server retrieves data from the appliances via the web services running on the sensor nodes. These data can be stored in a database after processing, where the database can be accessed by the utility, as well as the inhabitants of the smart home. We showthat our implementation is e cient in terms of running time. Moreover, the message sizes and the implementation code is quite small which makes it suitable for the memory-limited sensor nodes. Furthermore, we show the application scenarios introduced in the thesis provide energy saving for the smart home.