While the newly envisioned smart grid will result in a more efficient and reliable power grid, its use of fine-grained meter data has widely raised concerns of consumer privacy. This thesis implements a data obfuscation approach to preserve consumer privacy and assesses its feasibility on a large-scale advanced metering infrastructure (AMI) network built upon the new IEEE 802.11s wireless mesh standard. This obfuscation approach preserves consumer privacy from eavesdroppers and the utility companies while preserving the utility companies' ability to use the fine-grained meter data for state estimation. The impact of this privacy approach is assessed based on its impact on data throughput and delay performance. Simulation results have shown that the approach is feasible to be used even when the network size grows. Additional adaptations to the approach are analyzed for their feasibility in further research.

Recently, there has been an increase in the deployment of Phasor Measurement Units (PMUs) which has enabled real time, wide area monitoring of power systems. PMUs can synchronously measure operating parameters across the grid at typically 30 samples per second, compared to 1 sample per 2-5 seconds of a conventional Supervisory Control And Data Acquisition (SCADA) system. Such an explosion of data in power systems has provided an opportunity to make electrical grids more reliable. Additionally, it has brought a challenge to extract information from the massive amount of data. In this research, several data mining algorithms are used to extract information from synchrophasor data for improving situational awareness of power systems. The extracted information can be used for event detection, for reducing the dimension of data without losing information, and also to use it as heuristic to process future measurements. The methods proposed in this research work can be broadly classified into two parts: a) stream mining and b) dimension reduction. Stream mining algorithms provide solution utilizing state-of-the-art data stream mining algorithms such as Hoeffding Trees (HT). HT algorithm builds a decision tree by scanning the incoming data stream only once. The tree itself holds sufficient statistics in its leaves to grow the tree and also to make classification decisions of incoming data. Instead of using a large number of samples, which leads to a tree too large to accommodate in memory, the number of samples that are needed to split at each node is determined using Hoeffding bound (HB). HB keeps the size of the decision tree within bounds while also maintaining accuracies statistically competitive to traditional decision trees. Dimension reduction algorithms reduce dimension of the synchrophasor data by extracting maximum information from a huge data set without losing information. In this dissertation, both online and offline dimension reduction algorithms have been studied. The online dimension reduction uses an unsupervised method using principal components of the time series data. The offline method optimizes unique mutual information between the state of the power system and synchrophasor measurements. It optimizes the criteria by reducing redundant information while maximizing relevant information.

Recent technological developments have led to significant changes in the power grid. Increasing consumption, widespread adoption of Distributed Energy Resources (DER), installation of smart meters, these are some of the many factors that characterize the changing distribution network. These transformations taking place at the edge of the grid call for improved planning and operation practices. In this context, this thesis aims to improve the grid edge functionality by putting forth a method to address the problem of high demand during peak period by identifying customer groups for participation in demand response programs, which can lead to significant peak shaving for the utility. A possible demand response strategy for peak shaving makes use of Photovoltaic (PV) and Battery energy storage system (BESS). In the process, this work also examines the approach to computation of hosting capacity (HC) for small PV and quantifies the difference obtained in HC when a detailed Low voltage (LV) network is available and included in HC studies. Most PV hosting studies assess the impact on system feeders with aggregated LV loads. However, as more residential customers adopt rooftop solar, the need to include secondary network models in the analysis is studied by performing a comparative study of hosting capacity for a feeder with varying loading information available. / Master of Science / Today, with significant technological advancements, as we proceed towards a modern grid, a mere change in physical infrastructure will not be enough. With the changes in kinds of equipment installed on the grid, a wave of transformation has also begun to flow in the planning and operation practices for a smarter grid. Today, the edge of the grid where the customer is interfaced to the power system has become extremely complex. Customers can use rooftop solar PV to generate their own electricity, they are more informed about their consumption behavior due to installation of smart meters and also have options to integrate other technology like battery energy storage system and electric vehicles. Like with any good technology, adoption of these advancements in the system brings with itself a greater need for reform in operation and planning of the system. For instance, increasing installation of rooftop solar at the customer end calls for review of existing methods that determine the maximum level of PV deployment possible in the network without violating the operating conditions. So, in this work, a comparative study is done to review the PV hosting capacity of a network with varying levels of information available. And the importance of utilities to have secondary network models available is emphasized. With PV deployed in the system, enhanced demand response strategies can be formulated by utilities to tackle high demand during peak period. In a bid to identify customers for participation in such programs, in this work, a computationally efficient strategy is developed to identify customers with high demand during peak period, who can be incentivized to participate in demand response programs. With this, a significant peak shaving can be achieved by the utility, and in turn stress on the distribution network is reduced during peak hours.

Determinar quais são os aparelhos eletrônicos de uma residência que possuem maior influência na conta de luz não é tarefa trivial. As Redes de Sensores Sem Fios (RSSF) auxiliam os usuários nessa tarefa, permitindo descobrir se há algum tipo de desperdício no ambiente monitorado e assim, auxiliá-los a fazer as devidas correções. Por isso, é fundamental usar nas smart grids métodos que detectam novidades, também conhecido como anomalias , de forma individual e autônoma, para os usuários quando algo anômalo surge no consumo de energia dos equipamentos eletrônicos. Tais anomalias podem surgir, por exemplo, quando um equipamento consome energia acima do esperado, o que pode indicar um defeito. Nesse contexto, este trabalho propõe um método inteligente, nomeado como Novelty Detection Power Meter (NodePM), para detectar as novidades no consumo de energia dos equipamentos eletrônicos monitorados por uma smart grid. O NodePM detecta as novidades considerando a entropia de cada equipamento monitorado, a qual é calculada com base em um modelo de cadeia de markov que é gerado através de um algoritmo de aprendizado de máquina. Para tanto, o NodePM é integrado a uma plataforma de monitoramento remoto de consumo de energia, que consiste de uma RSSF associada a uma aplicação em nuvem. Para validar o desempenho do NodePM foram feitos experimentos utilizando a análise de variância e testes paramétricos e não-paramétricos. Os resultados de tais experimentos, obtidos mediante a análise estatística, evidenciou a viabilidade do NodePM na plataforma desenvolvida / It is not a simple task to determine which pieces of elevtronic equipment have the greatest influence on the electricity bill. The Wireless Sensor Networks (WSN) assist users in this task, allowing to discover if there is any type of a waste in a monitored environment and thus, help them to take proper actions. Hence, it is of crucial importance to use intelligent methods in the smart grids for a novelty detection and to inform the users in an individual and autonomous way when some anomaly has occurred in the energy consumption of electronic equipment. These anomalies can arise, for instance, when a piece of equipment consumes more energy than expected. In this context, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect the novelties in the energy consumption of electonic equipment monitored by a smart grid. The NodePM detects the novelties considering the entropy of each device monitored, which is calculed based on a Markov chain model that is generated through a machine learning algorithm. For this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a WSN associated with a cloud application. To validate the performance of the NodePM, experiments were done using analysis of variance and parametric and non-parametric tests. The result of these tests, which were obtained from a statistical analysis, provided evidence of the feasibility of the NodePM in the platform that was developed

This thesis addresses the problem of local intrusion detection for SCADA (Supervisory Control and Data Acquisition) field devices in the smart grid. A methodology is proposed to detect anomalies in the communication patterns using a combination of n-gram analysis and Bloom Filter. The predictable and regular nature of the SCADA communication patterns is exploited to train the intrusion detection system. The protocol considered to test the proposed approach is MODBUS which is used for communication between a SCADA server and field devices in power system. The approach is tested for attacks like HMI compromise and Man-in-the-Middle. Bloom Filter is chosen because of its strong space advantage over other data structures like hash tables, linked lists etc. for representing sets. The advantage comes from its probabilistic nature and compact array structure. The false positive rates are found to be minimal with careful choice of parameters for Bloom Filter design. Also the memory-efficient property of Bloom Filter makes it suitable for implementation in resource constrained SCADA components. It is also established that the knowledge of physical state of the power system i.e., normal, emergency or restorative state can help in improving the accuracy of the proposed approach.

The digitalization of the electricity grid can provide politicians and different kinds of companies with solid benefits in terms of efficiency, renewable integration and control. However, some part of the deployment of smart grids is only possible with the engagement of consumers who are required to change their behavior significantly. This behavioral change can be induced if energy companies (incumbents and new players) adopt business models that do not sell a technology but use technology to wrap a viable business case around the core task to “solving a customer’s problem”. This paper investigates different business models in the smart grid context on their possibility to diffuse in the market. Furthermore, it stresses that the emergence of smart grids will provide incumbents and new companies with new possibilities to offer new services. However for incumbent companies these new roles, increased competition, and new services also imply challenges. Old paradigms of the traditionally conservative utilities need to be changed. This paper finds out that the electricity consumer of today is not uniform, but rather can be divided into four segments who seem to describe the market well (at least in Germany) and have very different needs and preferences. In order to become agents of change in the future utilities will have to transform their business model, if they are not already on their way to do so. Throughout the paper, a EU (European Union) and North American perspective is considered, with primary focus on Sweden, Germany, and the USA.

L’augmentation de la consommation pour soutenir la croissance, le souci de réduction des gaz à effet de serre et les avancées technologiques ont favorisé le développement des sources d’énergie renouvelables depuis les années 90. L’implantation de ces générateurs décentralisés a progressivement modifié l’architecture du réseau en passant d’un modèle vertical à une situation davantage clusterisée. Ce réseau maillé voit ainsi apparaitre de nouveaux acteurs, à la fois producteurs et consommateurs (en anglais, les « prosumers»). Pour ce type de structures, la stratégie classique actuelle consiste à acheter l'ensemble de l'énergie consommée alors que la totalité de la production est vendue séparément à des tarifs intéressants. Avec les progrès réalisés sur les différentes technologies de stockage, de nouveaux degrés de liberté apparaissent et des opérations plus intelligentes deviennent possibles. L’objet de l’étude est un microréseau comprenant un générateur photovoltaïque et un consommateur tertiaire associés à un moyen de stockage. Deux technologies sont envisagées avec des volants d’inertie dans un premier temps et une batterie électrochimique (Li-ion) par la suite. Les domaines d’étude relatifs à ce type de système sont la gestion énergétique par planification, la commande temps réel et le dimensionnement. Les travaux de cette thèse se concentrent d’abord sur la problématique de gestion par optimisation des flux d’énergie. Différents algorithmes sont ainsi utilisés et comparés pour planifier le fonctionnement du microréseau. L’objectif est de diminuer la facture énergétique en tenant compte des données de consommation et production mais également des politiques tarifaires en vigueur et d’éventuelles contraintes de fonctionnement imposées par le fournisseur d’énergie. Dans un second temps la problématique de dimensionnement est abordée avec une démarche de conception optimale intégrant la boucle de gestion dès la phase de design. Nous montrons plus particulièrement comment l’adéquation entre les méthodes d’optimisation utilisées et le modèle du microréseau employé peut permettre la réduction significative des temps de calcul. Une configuration optimale du microréseau, valable sur des horizons temporels longs intégrant les alternances saisonnières, peut finalement se dégager. Les travaux se concluent sur une phase d’analyse avec des dimensionnements établis pour différents contextes tarifaires. Le but est de dégager des domaines permettant de valoriser et justifier l’installation d’un moyen de stockage qui s’avère indispensable pour soutenir le développement des sources d’énergies renouvelables et assurer la transition énergétique. / To face the increasing demand of electrical power in compliance with the liberalization of the electricity market and the need of reducing CO2 emissions, many distributed energy resources have emerged and especially the generation systems that utilize renewable energy sources. In the nearfuture, the grid could be described as an aggregation of several microgrids both consumer and producer. For those "prosumers", a classical strategy consists in selling all the highly subsidized production at important prices while all consumed energy is purchased. Smarter operations now become possible with developments of energy storage technologies and evolving prices policies. The microgrid considered in the thesis is composed of an industrial load and a photovoltaic generator associated to an energy storage. Two technologies are considered with high speed flywheels on one hand and a Li-ion electrochemical battery on the other. The common study referring to such systems allude to the optimal scheduling, the real-time management and the sizing methodology. Firstly in the thesis, the optimal power flow dispatching is performed using various algorithms. Those operations aim at reducing the electrical bill taking account of consumption and production forecasts as well as the different fares and possible constraints imposed by the power supplier. Then the design strategy is investigated. The approach consists in simultaneously integrating the energy management and the sizing of the system. We particularly underline the complexity of the resulting optimization problem and how it can be solved using suitable optimization methods in compliance with relevant models of the microgrid. We specifically show the reduction of the computational time allowing the microgrid simulation over long time durations in the optimization process in order to take seasonal variations into account. In the last part a cost analysis is performed, and different design are computed depending on the prices policies. The goal is to determine a financial context that would encourage the deployment of storage systems that are necessary to favor the development of intermittent renewable energy sources.

Determinar quais são os aparelhos eletrônicos de uma residência que possuem maior influência na conta de luz não é tarefa trivial. As Redes de Sensores Sem Fios (RSSF) auxiliam os usuários nessa tarefa, permitindo descobrir se há algum tipo de desperdício no ambiente monitorado e assim, auxiliá-los a fazer as devidas correções. Por isso, é fundamental usar nas smart grids métodos que detectam novidades, também conhecido como anomalias , de forma individual e autônoma, para os usuários quando algo anômalo surge no consumo de energia dos equipamentos eletrônicos. Tais anomalias podem surgir, por exemplo, quando um equipamento consome energia acima do esperado, o que pode indicar um defeito. Nesse contexto, este trabalho propõe um método inteligente, nomeado como Novelty Detection Power Meter (NodePM), para detectar as novidades no consumo de energia dos equipamentos eletrônicos monitorados por uma smart grid. O NodePM detecta as novidades considerando a entropia de cada equipamento monitorado, a qual é calculada com base em um modelo de cadeia de markov que é gerado através de um algoritmo de aprendizado de máquina. Para tanto, o NodePM é integrado a uma plataforma de monitoramento remoto de consumo de energia, que consiste de uma RSSF associada a uma aplicação em nuvem. Para validar o desempenho do NodePM foram feitos experimentos utilizando a análise de variância e testes paramétricos e não-paramétricos. Os resultados de tais experimentos, obtidos mediante a análise estatística, evidenciou a viabilidade do NodePM na plataforma desenvolvida / It is not a simple task to determine which pieces of elevtronic equipment have the greatest influence on the electricity bill. The Wireless Sensor Networks (WSN) assist users in this task, allowing to discover if there is any type of a waste in a monitored environment and thus, help them to take proper actions. Hence, it is of crucial importance to use intelligent methods in the smart grids for a novelty detection and to inform the users in an individual and autonomous way when some anomaly has occurred in the energy consumption of electronic equipment. These anomalies can arise, for instance, when a piece of equipment consumes more energy than expected. In this context, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect the novelties in the energy consumption of electonic equipment monitored by a smart grid. The NodePM detects the novelties considering the entropy of each device monitored, which is calculed based on a Markov chain model that is generated through a machine learning algorithm. For this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a WSN associated with a cloud application. To validate the performance of the NodePM, experiments were done using analysis of variance and parametric and non-parametric tests. The result of these tests, which were obtained from a statistical analysis, provided evidence of the feasibility of the NodePM in the platform that was developed

Emerging Software Defined Networking (SDN) technology has provided excellent flexibility to large-scale networks in terms of control, management, security, and maintenance. On the other hand, recent years witnessed a tremendous growth of the critical infrastructure networks, namely the Smart-Grid, in terms of its underlying communication infrastructure. Such large local networks requires significant effort in terms of network management and security. We explore the potential utilization of the SDN technology over the Smart Grid communication architecture. Specifically, we introduce three novel SDN deployment scenarios in local networks of Smart Grid. Moreover, we also investigate the pertinent security aspects with each deployment scenario along with possible solutions. On the other hand, we conducted experiments by using actual Smart Grid communication data to assess the recovery performance of the proposed SDN-based system. The results show that SDN is a viable technology for the Smart Grid communications with almost negligible delays in switching to backup wireless links.

The IEC Common Information Model (CIM) is of central importance in enabling smart grid interoperability. Its continual development aims to meet the needs of the smart grid for semantic understanding and knowledge representation for a widening domain of resources and processes. With smart grid evolution the importance of information and data management has become an increasingly pressing issue not only because far more data is being generated using modern sensing, control and measuring devices but also because information is now becoming recognised as the ‘integral component’ that facilitates the optimal flexibility required of the smart grid. This thesis looks at the impacts of CIM implementation upon the landscape of smart grid issues and presents research from within National Grid contributing to three key areas in support of further CIM deployment. Taking the issue of Enterprise Information Management first, an information management framework is presented for CIM deployment at National Grid. Following this the development and demonstration of a novel secure cloud computing platform to handle such information is described. Power system application (PSA) models of the grid are partial knowledge representations of a shared reality. To develop the completeness of our understanding of this reality it is necessary to combine these representations. The second research contribution reports on a novel methodology for a CIM-based model repository to align PSA representations and provide a knowledge resource for building utility business intelligence of the grid. The third contribution addresses the need for greater integration of information relating to energy storage, an essential aspect of smart energy management. It presents the strategic rationale for integrated energy modeling and a novel extension to the existing CIM standards for modeling grid-scale energy storage. Significantly, this work has already contributed to a larger body of work on modeling Distributed Energy Resources currently under development at the Electric Power Research Institute (EPRI) in the USA.