The changing nature of the energy grid in recent years has prompted key stakeholders to think of ways to address incoming challenges. Transactive energy is an approach that promises to dynamically align active grid elements coming up in the previously inactive consumers' side to achieve a reliable and smarter grid. This work models the distribution grid structure as a combination of microgrids. A blockchain-in-the loop simulation framework is modelled and simulated for a residential microgrid using power system simulators and transactive agents. Blockchain smart contracts are used to coordinate peer-to-peer energy transactions in the microgrid. The model is used to test three market coordination schemes: a simple auction-less scheme, an auction-less scheme with a normalized sorting metric and an hour ahead single auction scheme with penalties for unfulfilled bids. Case studies are presented of a microgrid with 30 homes, at different levels of solar and energy storage penetration within the microgrid, all equipped with responsive and unresponsive appliances and transactive agents for the HVAC systems. The auction-less scheme with a normalized sorting metric is observed to provide a fairer advantage to smaller solar installations in comparison to the simple auction-less method. It is then concluded that the auction-less schemes are most beneficial to users, as they would not need sophisticated forecasting technology to reduce penalties from bid quantity inaccuracies, as long as the energy mix within the microgrid is diverse enough. / Master of Science / The legacy energy industry involved the bulk transfer of energy from huge generation plants through long transmission lines to the end consumers. However, with the onset of improved renewable energy and information technologies, energy is now being generated closer to the consumer side with appliances capable of actively participating in the energy system now widely available. Transactive energy with blockchain has been proposed in order to dynamically coordinate these systems to work towards a more reliable and smarter grid using economic value in a transparent and secure way. This work models a transactive power grid as a combination of microgrids using a blockchain network to coordinate hourly peer-to-peer energy transactions. The blockchain-in-the-loop simulation model is used to compare three different market mechanisms in a residential microgrid of 30 homes with varying levels of solar panels, batteries and transactive thermostats installed. Two auction-less schemes - one with a normalized sorting metric - and an hour ahead single auction mechanism are analyzed. While the auction-less scheme with the normalized metric is seen to be fairer than the simple auction-less scheme, it is concluded that the auction-less schemes are most beneficial to residents. This is because sophisticated forecasting technology would not be needed like in the hour ahead auction scheme, provided that the microgrid has participants with diverse energy consumption and production profiles throughout the day.

To mitigate the technical challenges faced by the next-generation smart power grid, in this thesis, novel frameworks are developed for optimizing energy management and trading between power companies and grid consumers, who own renewable energy generators and storage units. The proposed frameworks explicitly account for the effect on demand-side energy management of various consumer-centric grid factors such as the stochastic renewable energy forecast, as well as the varying future valuation of stored energy. In addition, a novel approach is proposed to enhance the resilience of consumer-centric energy trading scenarios by analyzing how a power company can encourage its consumers to store energy, in order to supply the grid’s critical loads, in case of an emergency. The developed energy management mechanisms advance novel analytical tools from game theory, to capture the coupled actions and objectives of the grid actors and from the framework of prospect theory (PT), to capture the irrational behavior of consumers when faced with decision uncertainties. The studied PT and game-based solutions, obtained through analytical and algorithmic characterization, provide grid designers with key insights on the main drivers of each actor’s energy management decision. The ensuing results primarily characterize the difference in trading decisions between rational and irrational consumers, and its impact on energy management. The outcomes of this thesis will therefore allow power companies to design consumer-centric energy management programs that support the sustainable and resilient development of the smart grid by continuously matching supply and demand, and providing emergency energy reserves for critical infrastructure. / Master of Science

With the ever-growing concerns of environmental and climate concerns for energy consumption in our society, it is crucial to develop novel solutions that improve the efficient utilization of distributed energy resources for energy efficiency and demand response (DR). As such, there is a need to develop targeted energy programs, which not only meet the requirement of energy goals for a community but also take the energy use patterns of individual households into account. To this end, a sound understanding of the energy behavior of customers at the neighborhood level is needed, which requires operational analytics on the wealth of energy data from customers and devices. In this dissertation, we focus on data-driven solutions for customer energy behavior characterization with applications to distributed energy management and flexibility provision. To do so, the following problems were studied: (1) how different customers can be segmented for DR events based on their energy-saving potential and balancing peak and off-peak demand, (2) what are the opportunities for extracting Time-of-Use of specific loads for automated DR applications from the whole-house energy data without in-situ training, and (3) how flexibility in customer demand adoption of renewable and distributed resources (e.g., solar panels, battery, and smart loads) can improve the demand-supply problem. In the first study, a segmentation methodology form historical energy data of households is proposed to estimate the energy-saving potential for DR programs at a community level. The proposed approach characterizes certain attributes in time-series data such as frequency, consistency, and peak time usage. The empirical evaluation of real energy data of 400 households shows the successful ranking of different subsets of consumers according to their peak energy reduction potential for the DR event. Specifically, it was shown that the proposed approach could successfully identify the 20-30% of customers who could achieve 50-70% total possible demand reduction for DR. Furthermore, the rebound effect problem (creating undesired peak demand after a DR event) was studied, and it was shown that the proposed approach has the potential of identifying a subset of consumers (~5%-40% with specific loads like AC and electric vehicle) who contribute to balance the peak and off-peak demand. A projection on Austin, TX showed 16MWh reduction during a 2-h event can be achieved by a justified selection of 20% of residential customers. In the second study, the feasibility of inferring time-of-use (ToU) operation of flexible loads for DR applications was investigated. Unlike several efforts that required considerable model parameter selection or training, we sought to infer ToU from machine learning models without in-situ training. As the first part of this study, the ToU inference from low-resolution 15-minute data (smart meter data) was investigated. A framework was introduced which leveraged the smart meter data from a set of neighbor buildings (equipped with plug meters) with similar energy use behavior for training. Through identifying similar buildings in energy use behavior, the machine learning classification models (including neural network, SVM, and random forest) were employed for inference of appliance ToU in buildings by accounting for resident behavior reflected in their energy load shapes from smart meter data. Investigation on electric vehicle (EV) and dryer for 10 buildings over 20 days showed an average F-score of 83% and 71%. As the second part of this study, the ToU inference from high-resolution data (60Hz) was investigated. A self-configuring framework, based on the concept of spectral clustering, was introduced that automatically extracts the appliance signature from historical data in the environment to avoid the problem of model parameter selection. Using the framework, appliance signatures are matched with new events in the electricity signal to identify the ToU of major loads. The results on ~1500 events showed an F-score of >80% for major loads like AC, washing machine, and dishwasher. In the third study, the problem of demand-supply balance, in the presence of varying levels of small-scale distributed resources (solar panel, battery, and smart load) was investigated. The concept of load complementarity between consumers and prosumers for load balancing among a community of ~250 households was investigated. The impact of different scenarios such as varying levels of solar penetration, battery integration level, in addition to users' flexibility for balancing the supply and demand were quantitatively measured. It was shown that (1) even with 100% adoption of solar panels, the renewable supply cannot cover the demand of the network during afternoon times (e.g., after 3 pm), (2) integrating battery for individual households could improve the self-sufficiency by more than 15% during solar generation time, and (3) without any battery, smart loads are also capable of improving the self-sufficiency as an alternative, by providing ~60% of what commercial battery systems would offer. The contribution of this dissertation is through introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility of the aggregate daily energy load profiles for a community. When combined, the findings of this research can serve to the field of utility-scale energy analytics for the integration of DR and improved reshaping of network energy profiles (i.e., mitigating the peaks and valleys in daily demand profiles). / Doctor of Philosophy / Buildings account for more than 70% of electricity consumption in the U.S., in which more than 40% is associated with the residential sector. During recent years, with the advancement in Information and Communication Technologies (ICT) and the proliferation of data from consumers and devices, data-driven methods have received increasing attention for improving the energy-efficiency initiatives. With the increased adoption of renewable and distributed resources in buildings (e.g., solar panels and storage systems), an important aspect to improve the efficiency by matching the demand and supply is to add flexibility to the energy consumption patterns (e.g., trying to match the times of high energy demand from buildings and renewable generation). In this dissertation, we introduced data-driven solutions using the historical energy data of consumers with application to the flexibility provision. Specific problems include: (1) introducing a ranking score for buildings in a community to detect the candidates that can provide higher energy saving in the future events, (2) estimating the operation time of major energy-intensive appliances by analyzing the whole-house energy data using machine learning models, and (3) investigating the potential of achieving demand-supply balance in communities of buildings under the impact of different levels of solar panels, battery systems, and occupants energy consumption behavior. In the first study, a ranking score was introduced that analyzes the historical energy data from major loads such as washing machines and dishwashers in individual buildings and group the buildings based on their potential for energy saving at different times of the day. The proposed approach was investigated for real data of 400 buildings. The results for EV, washing machine, dishwasher, dryer, and AC show that the approach could successfully rank buildings by their demand reduction potential at critical times of the day. In the second study, machine learning (ML) frameworks were introduced to identify the times of the day that major energy-intensive appliances are operated. To do so, the input of the model was considered as the main circuit electricity information of the whole building either in lower-resolution data (smart meter data) or higher-resolution data (60Hz). Unlike previous studies that required considerable efforts for training the model (e.g, defining specific parameters for mathematical formulation of the appliance model), the aim was to develop data-driven approaches to learn the model either from the same building itself or from the neighbors that have appliance-level metering devices. For the lower-resolution data, the objective was that, if a few samples of buildings have already access to plug meters (i.e., appliance level data), one could estimate the operation time of major appliances through ML models by matching the energy behavior of the buildings, reflected in their smart meter information, with the ones in the neighborhood that have similar behaviors. For the higher-resolution data, an algorithm was introduced that extract the appliance signature (i.e., change in the pattern of electricity signal when an appliance is operated) to create a processed library and match the new events (i.e., times that an appliance is operated) by investigating the similarity with the ones in the processed library. The investigation on major appliances like AC, EV, dryer, and washing machine shows the >80% accuracy on standard performance metrics. In the third study, the impact of adding small-scale distributed resources to individual buildings (solar panels, battery, and users' practice in changing their energy consumption behavior) for matching the demand-supply for the communities was investigated. A community of ~250 buildings was considered to account for realistic uncertain energy behavior across households. It was shown that even when all buildings have a solar panel, during the afternoon times (after 4 pm) in which still ~30% of solar generation is possible, the community could not supply their demand. Furthermore, it was observed that including users' practice in changing their energy consumption behavior and battery could improve the utilization of solar energy around >10%-15%. The results can serve as a guideline for utilities and decision-makers to understand the impact of such different scenarios on improving the utilization of solar adoption. These series of studies in this dissertation contribute to the body of literature by introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility in energy consumption patterns.

Among many smart grid technologies, demand response (DR) is gaining increasing popularity. Many utility companies provide a variety of programs to encourage DR participation. Under these circumstances, various building energy management (BEM) systems have emerged to facilitate the building control during a DR event. Nonetheless, due to the cost and return on investment, these solutions mainly target homes and large commercial buildings, leaving aside small- and medium-sized commercial buildings (SMCB). SMCB, however, accounts for 90% of commercial buildings in the US, and offer great potential of load reduction during peak hours. With the advent of Internet-of-Things (IoT) devices and technologies, low cost smart building solutions have become possible for the SMCB; nonetheless, related intelligent algorithms are not widely available. This dissertation work investigates automated building control algorithms, tailored for the SMCB, to realize automatic device control during DR events. To be specific, a control framework for Air-Conditioning (AC) units' coordination is proposed. The goal of such framework is to reduce the aggregated AC power consumption while maintaining the thermal comfort inside a building during DR events. To achieve this goal, three major components of the framework were studied: building thermal property modeling, AC power consumption modeling and control algorithms design. Firstly, to consider occupants' thermal comfort, a reverse thermal model was designed to predict the indoor temperature of thermal zones under different AC control signals. The model was trained with supervised learning using coarse-grained temperature data recorded by smart thermostats; thus, it requires no lengthy configuration as a forward model does. The cost efficiency and plug-and-play feature of the model make it appropriate for SMCB. Secondly, a power disaggregation algorithm is proposed to model the power-outdoor temperature relationship of multiple AC units, using data from a single power meter and thermostats. Finally, algorithms based on mixed integer linear programming (MILP) and reinforcement learning (RL) were devised to coordinate multiple AC units in a building during a DR event. Integrated with the thermal model and AC power consumption model, these algorithms minimize occupants' thermal discomfort while restricting the aggregated AC power consumption below the DR limit. The efficiency of these control algorithms was tested, which demonstrate that they can generate AC control schedule in short notice (5 minutes) ahead of a DR event. Verification and validation of the proposed framework was conducted in both simulation and actual building environments. In addition, though the framework is designed for SMCBs, it can also be applied to large homes with multiple AC units to coordinate. This work is expected to give an insight into the BEM sector, helping the popularization of implementing DR in buildings. The research findings from this dissertation work shows the validity of the proposed algorithms, which can be used in BEM systems and cloud-based smart thermostats to exploit the untapped DR resource in SMCB. / PHD / For power system operation, the demand and supply should be equal at all time. During peak hours, the demand becomes very high. One way to keep the balance is to provide more generation capacity, and thus more expensive and less efficient generators are brought online, which causes higher production cost and more pollution. Instead, an alternative is to encourage the load reduction via demand response (DR): customers reduce load upon receiving a signal sent by the utility company, usually in exchange for some monetary payback. For buildings to participate in DR, an affordable automation system and related control algorithms are needed. This dissertation proposed a cost-effective, self-learning and data-driven framework to facilitate small- and medium-sized commercial buildings or large homes in air-conditioner (AC) units control during DR events. The devised framework requires little human configuration; it learns the building behavior by analyzing the operation data. Two algorithms are proposed to coordinate multiple AC units in a building with two goals: firstly, reducing the total AC power consumption below certain limit, as agreed between the building owners and their utility company. Secondly, minimizing occupants’ thermal discomfort caused by limiting AC operation. The effectiveness of the framework is investigated in this dissertation based on data collected from a real building.

Electric Vehicles (EVs) are active loads as they increase the demand for electricity and introduce several challenges to electrical distribution feeders during charging. Demand Response (DR) or performing load control in commercial buildings along with the deployment of solar photovoltaic (PV) and ice storage systems at the building level can improve the efficiency of electricity grids and mitigate expensive peak demand/energy charges for buildings. This research aims to provide such a solution to make EV penetration transparent to the grid. Firstly, this research contributes to the development of an integrated control of major loads, i.e., Heating Ventilation and Air Conditioning (HVAC), lighting and plug loads while maintaining occupant environmental preferences in small- and medium-sized commercial buildings which are an untapped DR resource. Secondly, this research contributes to improvement in functionalities of EnergyPlus by incorporating a 1-minute resolution data set at the individual plug load level. The research evaluates total building power consumption performance taking into account interactions among lighting, plug load, HVAC and control systems in a realistic manner. Third, this research presents a model to study integrated control of PV and ice storage on improving building operation in demand responsive buildings. The research presents the impact of deploying various combinations of PV and ice storage to generate additional benefits, including clean energy generation from PV and valley filling from ice storage, in commercial buildings. Fourth, this research presents a coordinated load control strategy, among participating commercial buildings in a distribution feeder to optimally control buildings' major loads without sacrificing occupant comfort and ice storage discharge, along with strategically deployed PV to absorb EV penetration. Demand responsive commercial building load profiles and field recorded EV charging profiles have been added to a real world distribution circuit to analyze the effects of EV penetration, together with real-world PV output profiles. Instead of focusing on individual building's economic benefits, the developed approach considers both technical and economic benefits of the whole distribution feeder, including maintaining distribution-level load factor within acceptable ranges and reducing feeder losses. / Ph. D.

The rapid growth of internet-connected smart devices capable of exchanging energy price information and adaptively controlling the consumption of connected loads, has paved the way for transactive control to make inroads in the modern grid. Transactive control frameworks integrate the wholesale and retail energy markets, and enable active participation of end users, thereby playing a key role in managing the rising number of distributed assets.However, the use of internet for the communication of data among the building, distribution,and transmission levels makes the system susceptible to external intrusions. A skilled adversary can potentially manipulate the exchanged data with the intention to inflict damage to the system or increase financial gains. In this thesis, the effect of such data integrity attacks on information exchanged between the distribution systems operator and end-users is investigated. Impact on grid operations is evaluated using different categories like operational, financial, user comfort and reliability parameters. It is shown that attack impact depends on a number of factors like attack duration, time of attack, penetration rate etc besides the attack magnitude. The effect of an attack continues to persist for some time after its removal and hence effective detection and mitigation strategies will be required to ensure system resilience and robustness. / Master of Science / Transactive energy is a framework where price-responsive loads adjust their energy consumption at a certain time according to the real-time energy price sent by the utility. Field demonstrations in recent years have shown that transactive control can effectively manage grid objectives and also monetarily benefit both the electric utility and end-users. Therefore, transactive energy is expected to make inroads into conventional grid operations in the next few years. As successful operation of such a market depends on the information exchanged among different stakeholders, a malicious adversary may try to inject false data and affect system operations. This thesis investigates how manipulating data in the transactive energy platform affects system operations and financial gains of different stakeholders. Understanding system behavior under attack conditions will help in formulating effective detection and mitigation strategies and enhancing system resilience.

Considerando condições anormais que o Sistema Elétrico de Potência (SEP) está sujeito, a proteção de seus elementos é um tópico importante. Dentre os equipamentos a serem protegidos, destacam-se os geradores devido a representarem elevado custo de investimento e estarem sujeitos a multas por paradas não programadas. Desta forma, com base em pesquisa bibliográfica, observa-se que não existem estudos abrangentes para a proteção individual de máquinas síncronas aplicadas à geração eólica. Além disso, considerando o contexto das smart grids, a presença de baterias e a possibilidade da operação ilhada podem alterar a dinâmica das situações de falta. Portanto, faz-se necessário um estudo do comportamento dos aerogeradores em situações de falha, sabendo que o esquema de proteção depende do tipo de gerador e da maneira como este está conectado ao SEP. Neste sentido, esta pesquisa propôs incluir uma bateria para operar com um gerador eólico de velocidade variável de forma complementar, suavizando a potência de saída e tornando o sistema de conversão de energia eólica forte o suficiente para operar no modo ilhado. A metodologia estabelece vários tipos de falhas para investigar o comportamento da turbina eólica em tais condições. Para realizar as simulações de falta, foi utilizado um simulador digital de tempo real (RTDS®). Com base nisso, um esquema composto por funções de proteção convencionais foi especificado e testado usando o software MATLAB®. Além disso, simulações em laço fechado foram realizadas com relés comercial e universal. Os resultados obtidos com o esquema proposto são bastante promissores. / Considering abnormal conditions to which the Electric Power System (EPS) may be subjected, the protection of its elements is an important topic. Among the equipments to be protected, the generators are highlighted, because they represent a high investment cost and are subjected to penalties for unscheduled stoppages. Hence, based on literature, it is observed that there are no comprehensive studies and standards for individual protection of Synchronous Generators (SGs) applied to Wind Energy Conversion System (WECS). Furthermore, considering the smart grids context, the presence of batteries and the possibility of island operation may change the dynamic of fault situations. Therefore, it is necessary to study and analyse the behavior of wind turbines in fault situations, knowing that the protection scheme is dependent on the generator type and the way it is connected to the EPS. In order to study these issues, this research proposed to include a battery to operate with a full-variable speed wind generator in a complementary way, smoothing the output power and making the WECS strong enough to operate in the island mode. The methodology establishes several fault types to investigate the wind turbine behavior in such conditions. In order to conduct the fault simulations, a real time digital simulator (RTDS®) was used. Based on this, a scheme composed by conventional protection functions were specified and tested using the MATLAB® software. Furthermore, hardware-in-the-loop simulations were performed with commercial and universal relays. Very good results in favor of the proposed scheme are presented.

A importância das novas tecnologias de informação, automação, monitoramento e sistemas eletrônicos inteligentes têm aumentado significativamente nos últimos anos. Essas tecnologias desempenham um papel fundamental na sociedade moderna e contribuem de forma decisiva para a resolução de importantes desafios para uma sociedade que quer ser mais próspera, internacionalmente competitiva, saudável, segura e sustentável. Como eixo de \"inovação\", essas tecnologias são fatores importantes para todos os setores produtivos da economia. O motor destas tecnologias, entretanto, é a energia, particularmente a eletricidade. Assim, em uma sociedade cujo estilo de vida é fortemente dependente dela, desenvolver tecnologias que permitam não somente a geração, mas também a distribuição de energia de forma barata e limpa e que garantam seu fornecimento ao longo do tempo com a máxima eficiência é uma questão prioritária. Os sistemas baseados em redes inteligentes (do inglês: Smart Grid) vêm, justamente, atender a esses requisitos, representando o que há de mais moderno no setor elétrico, com aumento e diversificação de fontes de geração distribuída na forma de pequenos geradores, maior interação consumidor-distribuidor de energia, integração de diferentes fontes de geração renováveis (ex.: solar, eólica etc.). O cenário energético nacional está avançando de forma muito rápida. Nas distribuidoras, o foco claramente está na redução de perdas comerciais e de custos operacionais, principalmente por meio da modernização dos ativos e da crescente instalação de dispositivos eletrônicos inteligentes nos clientes de baixa tensão (ex.: medidores eletrônicos, dispositivos eletrônicos inteligentes para monitoramento e diagnóstico, relés digitais etc.). Esta tese de doutorado apresenta uma solução com tecnologia nacional que disponibiliza todos os benefícios do Smart Grid através dos equipamentos mais importantes e estratégicos presentes na topologia das Redes de Distribuição Subterrânea Secundária Trifásica: os Protetores de Redes. A partir do centro nevrálgico das Redes de Distribuição Subterrâneas (RDS), cuja topologia está presente nos centros de alta concentração de carga das principais metrópoles do Brasil, a solução desenvolvida pode viabilizar técnica e economicamente a modernização da automação da RDS, com tecnologia nacional de baixo custo, proporcionando igualmente a incorporação dos avanços do Smart Grid e da Geração Distribuída. Este salto tecnológico significaria para as distribuidoras de energia elétrica entre outros benefícios: Melhor controle do processo para uma melhor otimização da rede, desde integração das intermitentes fontes renováveis até uma interação mais dinâmica com os consumidores; Maior flexibilidade às concessionárias em relação ao uso da energia para atingir o grande objetivo social de redução do efeito estufa e otimização do consumo de energia reduzindo perdas e desperdícios; No curto prazo, os benefícios diretos da melhoria do gerenciamento da indisponibilidade, gerenciamento otimizado dos ativos e do capital, melhoria no planejamento, processos e serviços de fornecimento e usos finais de energia, aumento de eficiência de manutenção, redução de perdas técnicas e comerciais, otimização do investimento na compra de novos protetores com menores custos podendo superar a demanda reprimida pelos altos custos de alternativas importadas. / The importance of new technologies in the field of, automation, monitoring, information technology and electronic systems have increased significantly in recent years. These technologies play a basic role in the modern society and contribute of decisive way for the resolution of important challenges for a society that is in search of a more prosperous life, internationally competitive, healthful, safe and sustainable. As a key of \"innovation\", these technologies are key factors for all the productive sectors of the economy in the society. The fuel for the engine of these technologies, however, is the energy, particularly the electricity. Thus, in a society whose life style is strongly dependent of electricity, to develop technologies that not only allow the generation, but also the distribution of energy in a cheap and clean way and which could guarantee its supply throughout the time with the maximum efficiency is a priority issue. The systems based on intelligent networks fully meet these requirements, representing what there is of most modern in the electric sector. The Brazilian energy scenario is quickly changing over the recent years toward modernization, with more distributed generation, in the form of smaller generators, more customer interaction, the integration of more variable resources such as wind and solar, and more renewables overall. For the Power Utilities, especially in the Distribution Sector, the focus is clearly in the reduction of commercial losses and operational costs, mainly by means of the modernization of the assets and an increase in the installation of intelligent electronic devices at consumers side (e.g.: electronic energy meters, intelligent electronic devices for condition monitoring, digital relays etc.). This work presents a solution developed based on Brazilian technology that incorporates all the benefits of smart grid to the most important equipment that is present in the topology of the Low-Voltage Secondary Network Distribution System: the Network Protector. From the neuralgic center of these Low-Voltage Secondary Network Systems, which topology is used in the most important cities in Brazil, which has a high load concentration, the solution presented here make it feasible technically and economically the use of smart grid topology profiting from its great benefits such as: Allow utilities to better optimize the grid to support a number of public policies, from intermittent renewable integration to more dynamic interfaces with customers; Offer utilities more flexibility relative to how they use energy toward the greater societal objectives of reducing greenhouse gases and energy consumption. In the short and mid term, a smarter grid offers utilities operational benefits (outage management, improved processes, maintenance and workforce efficiency, reduced losses, etc.) as well as benefits associated with improved asset management (system planning, better capital asset utilization, etc.), lower investment to acquire new Network Protectors.

Face à l'explosion du trafic mobile entraînée par le succès des smartphones, les opérateurs de réseaux mobiles (MNOs) densifient leurs réseaux à travers le déploiement massif des stations de base à faible portée (SBS), capable d’offrir des services très haut débit et de remplir les exigences de capacité et de couverture. Cette nouvelle infrastructure, appelée réseau cellulaire hétérogène (HetNet), utilise un mix de stations de base hiérarchisées, comprenant des macro-cellule à forte puissance et des SBS à faible puissance.La prolifération des HetNets soulève une nouvelle préoccupation concernant leur consommation d'énergie et empreinte carbone. Dans ce contexte, l'utilisation de technologies de production d'énergie dans les réseaux mobiles a suscité un intérêt particulier. Les sources d'énergie respectueuses de l'environnement couplées à un système de stockage d'énergie ont le potentiel de réduire les émissions carbone ainsi que le coût opérationnel énergétique des MNOs.L'intégration des énergies renouvelables (panneau solaire) et du stockage d'énergie (batterie) dans un SBS gagne en efficacité grâce aux leviers technologiques et économiques apportés par le smart grid (SG). Cependant, l'architecture résultante, que nous appelons Green Small-Cell Base station (GSBS), est complexe. Premièrement, la multitude de sources d'énergie, le phénomène de viellissement du système et le prix dynamique de l'électricité dans le SG sont des facteurs qui nécessitent planification et gestion pour un fonctionnement plus efficace du GSBS. Deuxièmement, il existe une étroite dépendance entre le dimensionnement et le contrôle en temps réel du système, qui nécessite une approche commune capable de résoudre conjointement ces deux problèmes. Enfin, la gestion holistique d’un HetNet nécessite un schéma de contrôle à grande échelle pour optimiser simultanément les ressources énergétiques locales et la collaboration radio entre les SBSs.Par conséquent, nous avons élaboré un cadre d'optimisation pour le pré-déploiement et le post-déploiement du GSBS, afin de permettre aux MNOs de réduire conjointement leurs dépenses d'électricité et le vieillissement de leurs équipements. L'optimisation pré-déploiement consiste en un dimensionnement du GSBS qui tient compte du vieillissement de la batterie et de la stratégie de gestion des ressources énergétiques. Le problème associé est formulé et le dimensionnement optimal est approché en s'appuyant des profils moyens (production, consommation et prix de l'électricité) à travers une méthode itérative basée sur le solveur non-linéaire “fmincon”. Le schéma de post-déploiement repose sur des capacités d'apprentissage permettant d'ajuster dynamiquement la gestion énergétique du GSBS à son environnement (conditions météorologiques, charge de trafic et coût de l'électricité). La solution s'appuie sur le fuzzy Q-learning qui consiste à combiner le système d'inférence floue avec l'algorithme Q-learning. Ensuite, nous formalisons un système d'équilibrage de charge capable d'étendre la gestion énergétique locale à une collaboration à l'échelle réseau. Nous proposons à ce titre un algorithme en deux étapes, combinant des contrôleurs hiérarchiques au niveau du GSBS et au niveau du réseau. Les deux étapes s'alternent pour continuellement planifier et adapter la gestion de l'énergie à la collaboration radio dans le HetNet.Les résultats de la simulation montrent que, en considérant le vieillissement de la batterie et l'impact mutuel de la conception du système sur la stratégie énergétique (et vice-versa), le dimensionnement optimal du GSBS est capable de maximiser le retour sur investissement. En outre, grâce à ses capacités d'apprentissage, le GSBS peut être déployé de manière plug-and-play, avec la possibilité de s'auto-organiser, d'améliorer le coût énergétique du système et de préserver la durée de vie de la batterie. / Pushed by an unprecedented increase in data traffic, Mobile Network Operators (MNOs) are densifying their networks through the deployment of Small-cell Base Stations (SBS), low-range radio-access transceivers that offer enhanced capacity and improved coverage. This new infrastructure – Heterogeneous cellular Network (HetNet) -- uses a hierarchy of high-power Macro-cell Base Stations overlaid with several low-power (SBSs).The augmenting deployment and operation of the HetNets raise a new crucial concern regarding their energy consumption and carbon footprint. In this context, the use of energy-harvesting technologies in mobile networks have gained particular interest. The environment-friendly power sources coupled with energy storage capabilities have the potential to reduce the carbon emissions as well as the electricity operating expenditures of MNOs.The integration of renewable energy (solar panel) and energy storage capability (battery) in SBSs gain in efficiency thanks to the technological and economic enablers brought by the Smart Grid (SG). However, the obtained architecture, which we call Green Small-Cell Base Station (GSBS), is complex. First, the multitude of power sources, the system aging, and the dynamic electricity price in the (SG) are factors that require design and management to enable the (GSBS) to efficiently operate. Second, there is a close dependence between the system sizing and control, which requires an approach to address these problems simultaneously. Finally, the achievement of a holistic management in a (HetNet) requires a network-level energy-aware scheme that jointly optimizes the local energy resources and radio collaboration between the SBSs.Accordingly, we have elaborated pre-deployment and post-deployment optimization frameworks for GSBSs that allow the MNOs to jointly reduce their electricity expenses and the equipment degradation. The pre-deployment optimization consists in an effective sizing of the GSBS that accounts for the battery aging and the associated management of the energy resources. The problem is formulated and the optimal sizing is approximated using average profiles, through an iterative method based on the non-linear solver “fmincon”. The post-deployment scheme relies on learning capabilities to dynamically adjust the GSBS energy management to its environment (weather conditions, traffic load, and electricity cost). The solution is based on the fuzzy Q-learning that consists in tuning a fuzzy inference system (which represents the energy arbitrage in the system) with the Q-learning algorithm. Then, we formalize an energy-aware load-balancing scheme to extend the local energy management to a network-level collaboration. We propose a two-stage algorithm to solve the formulated problem by combining hierarchical controllers at the GSBS-level and at the network-level. The two stages are alternated to continuously plan and adapt the energy management to the radio collaboration in the HetNet.Simulation results show that, by considering the battery aging and the impact of the system design and the energy strategy on each other, the optimal sizing of the GSBS is able to maximize the return on investment with respect to the technical and economic conditions of the deployment. Also, thanks to its learning capabilities, the GSBSs can be deployed in a plug-and-play fashion, with the ability to self-organize, improve the operating energy cost of the system, and preserves the battery lifespan.

Considerando condições anormais que o Sistema Elétrico de Potência (SEP) está sujeito, a proteção de seus elementos é um tópico importante. Dentre os equipamentos a serem protegidos, destacam-se os geradores devido a representarem elevado custo de investimento e estarem sujeitos a multas por paradas não programadas. Desta forma, com base em pesquisa bibliográfica, observa-se que não existem estudos abrangentes para a proteção individual de máquinas síncronas aplicadas à geração eólica. Além disso, considerando o contexto das smart grids, a presença de baterias e a possibilidade da operação ilhada podem alterar a dinâmica das situações de falta. Portanto, faz-se necessário um estudo do comportamento dos aerogeradores em situações de falha, sabendo que o esquema de proteção depende do tipo de gerador e da maneira como este está conectado ao SEP. Neste sentido, esta pesquisa propôs incluir uma bateria para operar com um gerador eólico de velocidade variável de forma complementar, suavizando a potência de saída e tornando o sistema de conversão de energia eólica forte o suficiente para operar no modo ilhado. A metodologia estabelece vários tipos de falhas para investigar o comportamento da turbina eólica em tais condições. Para realizar as simulações de falta, foi utilizado um simulador digital de tempo real (RTDS®). Com base nisso, um esquema composto por funções de proteção convencionais foi especificado e testado usando o software MATLAB®. Além disso, simulações em laço fechado foram realizadas com relés comercial e universal. Os resultados obtidos com o esquema proposto são bastante promissores. / Considering abnormal conditions to which the Electric Power System (EPS) may be subjected, the protection of its elements is an important topic. Among the equipments to be protected, the generators are highlighted, because they represent a high investment cost and are subjected to penalties for unscheduled stoppages. Hence, based on literature, it is observed that there are no comprehensive studies and standards for individual protection of Synchronous Generators (SGs) applied to Wind Energy Conversion System (WECS). Furthermore, considering the smart grids context, the presence of batteries and the possibility of island operation may change the dynamic of fault situations. Therefore, it is necessary to study and analyse the behavior of wind turbines in fault situations, knowing that the protection scheme is dependent on the generator type and the way it is connected to the EPS. In order to study these issues, this research proposed to include a battery to operate with a full-variable speed wind generator in a complementary way, smoothing the output power and making the WECS strong enough to operate in the island mode. The methodology establishes several fault types to investigate the wind turbine behavior in such conditions. In order to conduct the fault simulations, a real time digital simulator (RTDS®) was used. Based on this, a scheme composed by conventional protection functions were specified and tested using the MATLAB® software. Furthermore, hardware-in-the-loop simulations were performed with commercial and universal relays. Very good results in favor of the proposed scheme are presented.