The size and composition of the Swedish electricity generation are changing. This, in combination with new legal requirements from regulatory entities including the EU Directive 2019/944, creates several challenges for the design of the future system. Among other things, the directive suggests that flexibility solutions are to be integrated into grids to increase the degree of utilization and avoid congestions, when socio-economically profitable. This thesis evaluates what this could mean in a Swedish context, in combination with providing a basic understanding of the contradictions that can arise between a desired efficient grid use in an energy system that goes towards more distributed and intermittent energy generation sources. The work is carried out in collaboration with Vattenfall Eldistribution AB, focusing on the geographical area of Uppsala and Stockholm, the Swedish region hit hardest by local congestions. The work assumes that the economic value of a flexibility solution is at most equivalent to the cost of a conventional new construction aimed at capacity strengthening, or the Value of Lost Load (VoLL). The report’s most important deliverable is a model based on this view. The model is used to evaluate the economic value of flexibility per kWh, in three regional grid construction projects within the mentioned region.  The results show that there is a great potential for using flexibility resources to increase utilization in grids and also to optimize the costs that society pays for this infrastructure by such methods. However, the work concludes that the usage of flexible technologies primarily is to adapt electric consumption with intermittent energy generation, rather than being used to solve local grid capacity shortages.

No description available.

Diese Dissertation widmet sich der Erarbeitung verschiedener Managementstrategien für ein leistungsflussoptimierendes Energiemanagement von dezentralen Energieversorgungssystemen in zukünftigen Niederspannungsnetzstrukturen. Als dezentrales Energieversorgungssystem ist dabei eine beliebige Kombination von Erzeuger-, Verbraucher- und Speichereinheiten zu sehen. Die Optimierung des Leistungsflusses auf Niederspannungsebene wird durch zwei Managementansätze untersucht. In einem verlustoptimierten Managementansatz stehen die bekannten Leitverluste, verursacht durch die wirksamen Leitungsresistanzen, im Fokus der Betrachtung. Durch einen zweiten Ansatz, dem clusterbasierten Managementansatz, wird nochmals eine Fokussierung auf die wirksamen Leitungsresistanzen durch eine Cluster-Bildung von Hausanschlüsen erreicht, welche nur durch geringe wirksame Leitungsresistanzen voneinander getrennt sind. Anhand realitätsnaher Netzabbildungen sowie unterschiedlicher Erzeuger- und Verbrauchersituationen wird der Einfluss eines solchen Energiemanagements auf die Integration dezentraler Erzeuger und der Leitverluste gezeigt sowie die Möglichkeit einer netzoptimierten Betriebsweise untersucht. / This thesis includes two different options to manage the situation of consumption and supply in a low-voltage grid. On the one hand the energy management is based on the optimization of the losses in the low-voltage grid caused by the resistance of the branchs. On the other hand the resistance between consumption and supply is the optimized parameter. This is implemented with a clustering of the whole low-voltage grid in groups of households. Based on realistic models of low-voltage grids and different situations of consumption and supply the two management strategies are tested. These tests shows the influence on the losses, the integration of distributed suppliers and the controlled power flow to the medium-voltage grid.

Energy management strategies for microgrids, containing energy storage, renewable energy sources (RES), and electric vehicles (EVs); which interact with the grid on an individual basis; are presented in Chapter 3. An optimization problem to reduce cost, formulated over a rolling time horizon, using predicted values of load demand, EV connection/disconnection times, and charge levels at time of connection, is described. The solution provides the on-site storage and EV charge/discharge powers. For the first time, both bidirectional and unidirectional charging are considered for EVs and a controller which accommodates uncertainties in EV energy levels and connection/disconnection times is presented. In Chapter 4, a stochastic chance constraints based optimization is described. It affords significant improvement in robustness, over the conventional controller, to uncertainties in system parameters. Simulation results demonstrate that the stochastic controller is at least twice as effective at meeting the desired EV charge level at specific times compared to the non-stochastic version, in the presence of uncertainties. In Chapter 5, a network of microgrids, containing RES and batteries, which trade energy among themselves and with the utility grid is considered. A novel distributed energy management system (EMS), based on a central EMS using a Multi-Objective (MO) Rolling Horizon (RH) scheme, is presented. It uses Alternating Direction Method of Multipliers (ADMM) and Quadratic Programming (QP). It is inherently more data-secure and resilient to communication issues than the central EMS. It is shown that using an EMS in the network provides significant economic benefits over MGs connected directly to the grid. Simulations demonstrate that the distributed scheme produced solutions which are very close to those of the central EMS. Simulation results also reveal that the faster, less memory intensive distributed scheme is scalable to larger networks -- more than 1000 microgrids as opposed to a few hundreds for the central EMS. / Thesis / Doctor of Philosophy (PhD)

Recurrent neural networks (RNNs) have been widely used for supervised pattern recognition and exploring the underlying spatio-temporal correlation. However, due to the vanishing/exploding gradient problem, training a fully connected RNN in many cases is very difficult or even impossible. The difficulties of training traditional RNNs, led us to reservoir computing (RC) which recently attracted a lot of attention due to its simple training methods and fixed weights at its recurrent layer. There are three different categories of RC systems, namely, echo state networks (ESNs), liquid state machines (LSMs), and delayed feedback reservoirs (DFRs). In this dissertation a novel structure of RNNs which is inspired by dynamic delayed feedback loops is introduced. In the reservoir (recurrent) layer of DFR, only one neuron is required which makes DFRs extremely suitable for hardware implementations. The main motivation of this dissertation is to introduce an energy efficient, and easy to train RNN while this model achieves high performances in different tasks compared to the state-of-the-art. To improve the energy efficiency of our model, we propose to adopt spiking neurons as the information processing unit of DFR. Spiking neural networks (SNNs) are the most biologically plausible and energy efficient class of artificial neural networks (ANNs). The traditional analog ANNs have marginal similarity with the brain-like information processing. It is clear that the biological neurons communicate together through spikes. Therefore, artificial SNNs have been introduced to mimic the biological neurons. On the other hand, the hardware implementation of SNNs have shown to be extremely energy efficient. Towards achieving this overarching goal, this dissertation presents a spiking DFR (SDFR) with novel encoding schemes, and defense mechanisms against adversarial attacks. To verify the effectiveness and performance of the SDFR, it is adopted in three different applications where there exists a significant Spatio-temporal correlations. These three applications are attack detection in smart grids, spectrum sensing of multi-input-multi-output(MIMO)-orthogonal frequency division multiplexing (OFDM) Dynamic Spectrum Sharing (DSS) systems, and video-based face recognition. In this dissertation, the performance of SDFR is first verified in cyber attack detection in Smart grids. Smart grids are a new generation of power grids which guarantee a more reliable and efficient transmission and delivery of power to the costumers. A more reliable and efficient power generation and distribution can be realized through the integration of internet, telecommunication, and energy technologies. The convergence of different technologies, brings up opportunities, but the challenges are also inevitable. One of the major challenges that pose threat to the smart grids is cyber-attacks. A novel method is developed to detect false data injection (FDI) attacks in smart grids. The second novel application of SDFR is the spectrum sensing of MIMO-OFDM DSS systems. DSS is being implemented in the fifth generation of wireless communication systems (5G) to improve the spectrum efficiency. In a MIMO-OFDM system, not all the subcarriers are utilized simultaneously by the primary user (PU). Therefore, it is essential to sense the idle frequency bands and assign them to the secondary user (SU). The effectiveness of SDFR in capturing the spatio-temporal correlation of MIMO-OFDM time-series and predicting the availability of frequency bands in the future time slots is studied as well. In the third application, the SDFR is modified to be adopted in video-based face recognition. In this task, the SDFR is leveraged to recognize the identities of different subjects while they rotate their heads in different angles. Another contribution of this dissertation is to propose a novel encoding scheme of spiking neurons which is inspired by the cognitive studies of rats. For the first time, the multiplexing of multiple neural codes is introduced and it is shown that the robustness and resilience of the spiking neurons is increased against noisy data, and adversarial attacks, respectively. Adversarial attacks are small and imperceptible perturbations of the input data, which have shown to be able to fool deep learning (DL) models. So far, many adversarial attack and defense mechanisms have been introduced for DL models. Compromising the security and reliability of artificial intelligence (AI) systems is a major concern of government, industry and cyber-security researchers, in that insufficient protections can compromise the security and privacy of everyone in society. Finally, a defense mechanism to protect spiking neurons against adversarial attacks is introduced for the first time. In a nutshell, this dissertation presents a novel energy efficient deep spiking recurrent neural network which is inspired by delayed dynamic loops. The effectiveness of the introduced model is verified in several different applications. At the end, novel encoding and defense mechanisms are introduced which improve the robustness of the model against noise and adversarial attacks. / Doctor of Philosophy / The ultimate goal of artificial intelligence (AI) is to mimic the human brain. Artificial neural networks (ANN) are an attempt to realize that goal. However, traditional ANNs are very far from mimicking biological neurons. It is well-known that biological neurons communicate with one another through signals in the format of spikes. Therefore, artificial spiking neural networks (SNNs) have been introduced which behave more similarly to biological neurons. Moreover, SNNs are very energy efficient which makes them a suitable choice for hardware implementation of ANNs (neuromporphic computing). Despite the many benefits that are brought about by SNNs, they are still behind traditional ANNs in terms of performance. Therefore, in this dissertation, a new structure of SNNs is introduced which outperforms the traditional ANNs in three different applications. This new structure is inspired by delayed dynamic loops which exist in biological brains. The main objective of this novel structure is to capture the spatio-temporal correlation which exists in time-series while the training overhead and power consumption is reduced. Another contribution of this dissertation is to introduce novel encoding schemes for spiking neurons. It is clear that biological neurons leverage spikes, but the language that they use to communicate is not clear. Hence, the spikes require to be encoded in a certain language which is called neural spike encoding scheme. Inspired by the cognitive studies of rats, a novel encoding scheme is presented. Lastly, it is shown that the introduced encoding scheme increases the robustness of SNNs against noisy data and adversarial attacks. AI models including SNNs have shown to be vulnerable to adversarial attacks. Adversarial attacks are minor perturbations of the input data that can cause the AI model to misscalassify the data. For the first time, a defense mechanism is introduced which can protect SNNs against such attacks.

The security and resiliency of electric power supply to serve critical facilities are of high importance in today's world. Instead of building large electric power grids and high capacity transmission lines, an intelligent microgrid (or smart grid) can be considered as a promising power supply alternative. In recent years, multi-agent systems have been proposed to provide intelligent energy control and management systems in microgrids. Multi-agent systems offer their inherent benefits of flexibility, extensibility, autonomy, reduced maintenance and more. The implementation of a control network based on multi-agent systems that is capable of making intelligent decisions on behalf of the user has become an area of intense research. Many previous works have proposed multi-agent system architectures that deal with buying and selling of energy within a microgrid and algorithms for auction systems. The others proposed frameworks for multi-agent systems that could be further developed for real life control of microgrid systems. However, most proposed methods ignore the process of sharing energy resources among multiple distinct sets of prioritized loads. It is important to study a scenario that emphasizes on supporting critical loads during outages based on the user's preferences and limited capacity. The situation becomes further appealing when an excess DER capacity after supplying critical loads is allocated to support non-critical loads that belong to multiple users. The previous works also ignore the study of dynamic interactions between the agents and the physical systems. It is important to study the interaction and time delay when an agent issues a control signal to control a physical device in a microgrid and when the command is executed. Agents must be able to respond to the information sensed from the external environment quickly enough to manage the microgrid in a timely fashion. The ability of agents to disconnect the microgrid during emergencies should also be studied. These issues are identified as knowledge gaps that are of focus in this thesis. The objective of this research is to design, develop and implement a multi-agent system that enables real-time management of a microgrid. These include securing critical loads and supporting non-critical loads belonging to various owners with the distributed energy resource that has limited capacity during outages. The system under study consists of physical (microgrid) and cyber elements (multi-agent system). The cyber part or the multi-agent system is of primary focus of this work. The microgrid simulation has been implemented in Matlab/Simulink. It is a simplified distribution circuit that consists of one distributed energy resources (DER), loads and the main grid power supply. For the multi-agent system implementation, various open source agent building toolkits are compared to identify the most suitable agent toolkit for implementation in the proposed multi-agent system. The agent architecture is then designed by dividing overall goal of the system into several smaller tasks and assigning them to each agent. The implementation of multi-agent system was completed by identifying Roles (Role Modeling) and Responsibilities (Social and Domain Responsibilities) of agents in the system, and modeling the Knowledge (Facts), rules and ontology for the agents. Finally, both microgrid simulation and multi-agent system are connected together via TCP/IP using external java programming and a third party TCP server in the Matlab/Simulink environment. In summary, the multi-agent system is designed, developed and implemented in several simulation test cases. It is expected that this work will provide an insight into the design and development of a multi-agent system, as well as serving as a basis for practical implementation of an agent-based technology in a microgrid environment. Furthermore, the work also contributes to new design schemes to increase multi-agent system's intelligence. In particular, these include control algorithms for intelligently managing the limited supply from a DER during emergencies to secure critical loads, and at the same time supporting non-critical loads when the users need the most. / Master of Science

Cyber-physical systems (CPSs) are large-scale systems that seamlessly integrate physical and human elements via a cyber layer that enables connectivity, sensing, and data processing. Key examples of CPSs include smart power systems, smart transportation systems, and the Internet of Things (IoT). This wide-scale cyber-physical interconnection introduces various operational benefits and promises to transform cities, infrastructure, and networked systems into more efficient, interactive, and interconnected smart systems. However, this ubiquitous connectivity leaves CPSs vulnerable to menacing security threats as evidenced by the recent discovery of the Stuxnet worm and the Mirai malware, as well as the latest reported security breaches in a number of CPS application domains such as the power grid and the IoT. Addressing these culminating security challenges requires a holistic analysis of CPS security which necessitates: 1) Determining the effects of possible attacks on a CPS and the effectiveness of any implemented defense mechanism, 2) Analyzing the multi-agent interactions -- among humans and automated systems -- that occur within CPSs and which have direct effects on the security state of the system, and 3) Recognizing the role that humans and their decision making processes play in the security of CPSs. Based on these three tenets, the central goal of this dissertation is to enhance the security of CPSs with human actors by developing fool-proof defense strategies founded on novel theoretical frameworks which integrate the engineering principles of CPSs with the mathematical concepts of game theory and human behavioral models. Towards realizing this overarching goal, this dissertation presents a number of key contributions targeting two prominent CPS application domains: the smart electric grid and drone systems. In smart grids, first, a novel analytical framework is developed which generalizes the analysis of a wide set of security attacks targeting the state estimator of the power grid, including observability and data injection attacks. This framework provides a unified basis for solving a broad set of known smart grid security problems. Indeed, the developed tools allow a precise characterization of optimal observability and data injection attack strategies which can target the grid as well as the derivation of optimal defense strategies to thwart these attacks. For instance, the results show that the proposed framework provides an effective and tractable approach for the identification of the sparsest stealthy attacks as well as the minimum sets of measurements to defend for protecting the system. Second, a novel game-theoretic framework is developed to derive optimal defense strategies to thwart stealthy data injection attacks on the smart grid, launched by multiple adversaries, while accounting for the limited resources of the adversaries and the system operator. The analytical results show the existence of a diminishing effect of aggregated multiple attacks which can be leveraged to successfully secure the system; a novel result which leads to more efficiently and effectively protecting the system. Third, a novel analytical framework is developed to enhance the resilience of the smart grid against blackout-inducing cyber attacks by leveraging distributed storage capacity to meet the grid's critical load during emergency events. In this respect, the results demonstrate that the potential subjectivity of storage units' owners plays a key role in shaping their energy storage and trading strategies. As such, financial incentives must be carefully designed, while accounting for this subjectivity, in order to provide effective incentives for storage owners to commit the needed portions of their storage capacity for possible emergency events. Next, the security of time-critical drone-based CPSs is studied. In this regard, a stochastic network interdiction game is developed which addresses pertinent security problems in two prominent time-critical drone systems: drone delivery and anti-drone systems. Using the developed network interdiction framework, the optimal path selection policies for evading attacks and minimizing mission completion times, as well as the optimal interdiction strategies for effectively intercepting the paths of the drones, are analytically characterized. Using advanced notions from Nobel-prize winning prospect theory, the developed framework characterizes the direct impacts of humans' bounded rationality on their chosen strategies and the achieved mission completion times. For instance, the results show that this bounded rationality can lead to mission completion times that significantly surpass the desired target times. Such deviations from the desired target times can lead to detrimental consequences primarily in drone delivery systems used for the carriage of emergency medical products. Finally, a generic security model for CPSs with human actors is proposed to study the diffusion of threats across the cyber and physical realms. This proposed framework can capture several application domains and allows a precise characterization of optimal defense strategies to protect the critical physical components of the system from threats emanating from the cyber layer. The developed framework accounts for the presence of attackers that can have varying skill levels. The results show that considering such differing skills leads to defense strategies which can better protect the system. In a nutshell, this dissertation presents new theoretical foundations for the security of large-scale CPSs, that tightly integrate cyber, physical, and human elements, thus paving the way towards the wide-scale adoption of CPSs in tomorrow's smart cities and critical infrastructure. / Ph. D. / Enhancing the efficiency, sustainability, and resilience of cities, infrastructure, and industrial systems is contingent on their transformation into more interactive and interconnected smart systems. This has led to the emergence of what is known as cyber-physical systems (CPSs). CPSs are widescale distributed and interconnected systems integrating physical components and humans via a cyber layer that enables sensing, connectivity, and data processing. Some of the most prominent examples of CPSs include the smart electric grid, smart cities, intelligent transportation systems, and the Internet of Things. The seamless interconnectivity between the various elements of a CPS introduces a wealth of operational benefits. However, this wide-scale interconnectivity and ubiquitous integration of cyber technologies render CPSs vulnerable to a range of security threats as manifested by recently reported security breaches in a number of CPS application domains. Addressing these culminating security challenges requires the development and implementation of fool-proof defense strategies grounded in solid theoretical foundations. To this end, the central goal of this dissertation is to enhance the security of CPSs by advancing novel analytical frameworks which tightly integrate the cyber, physical, and human elements of a CPS. The developed frameworks and tools enable the derivation of holistic defense strategies by: a) Characterizing the security interdependence between the various elements of a CPS, b) Quantifying the consequences of possible attacks on a CPS and the effectiveness of any implemented defense mechanism, c) Modeling the multi-agent interactions in CPSs, involving humans and automated systems, which have a direct effect on the security state of the system, and d) Capturing the role that human perceptions and decision making processes play in the security of CPSs. The developed tools and performed analyses integrate the engineering principles of CPSs with the mathematical concepts of game theory and human behavioral models and introduce key contributions to a number of CPS application domains such as the smart electric grid and drone systems. The introduced results enable strengthening the security of CPSs, thereby paving the way for their wide-scale adoption in smart cities and critical infrastructure.

Avec le développement des technologies de communication sans fil, les réseaux de capteur à radio cognitive (CRSNs) représentent une solution efficace pour le déploiement des réseaux électriques intelligents, connus aussi sous le nom de smart grids. La technologie de radio cognitive permet aux nœuds capteurs d’utiliser les bandes de fréquences non utilisées par des utilisateurs avec licence afin de contourner les limitations des bandes de fréquences sans licence. Dans ce contexte, plusieurs problèmes de communication freinent le déploiement des CRSNs pour les smart grids tel que la coexistence de différentes applications électriques ainsi que l’hétérogénéité des disponibilités des bandes de fréquence avec licence entre les nœuds capteurs. Les travaux de recherche menés dans cette thèse se focalisent essentiellement sur l’allocation des ressources spectrales pour les CRSNs déployés pour contrôler des smart grids. Nous proposons des nouvelles techniques d’allocation de ressources spectrales qui prennent en considération des topologies de déploiement possibles des CRSNs dans les smart grids tout en assurant d’une manière distribuée l’équité entre les nœuds capteurs déployés. Tout au long de notre travail, l’allocation des canaux est effectuée sans faire appel à un canal de contrôle en commun pour le partage des messages de contrôle avant chaque accès au spectre. L'évaluation de performances des différentes solutions développées montre qu'elles réalisent effectivement une allocation opportuniste des ressources spectrales d’une manière distribuée et équitable tout en considérant différentes caractéristiques du système sous-jacent aux réseaux électriques intelligents. / With the advances in wireless communication technologies, cognitive radio sensor networks (CRSNs) stand as an efficient spectrum solution in the development of intelligent electrical power networks, the smart grids. The cognitive radio (CR) technology provides the sensors with the ability to use the temporally available licensed spectrum in order to escape the unlicensed spectrum resource scarcity problem. In this context, several challenging communication issues face the CRSN deployment for smart grids such as the coexistence of different electrical applications and the heterogeneous opportunities to access available licensed channels between smart grid sensors. The work conducted in this thesis focuses on spectrum resource allocations for CRSNs in smart grids. We concentrate our efforts on the development of new spectrum resource sharing paradigms for CRSNs in smart grids. The developed solutions focus on distributed and balanced spectrum sharing among smart grid sensors and on eventual CRSN deployment scenarios in smart grid areas. All along the thesis, channels are assigned without relying on a predefined common control channel (CCC) to exchange control messages before each spectrum access trial. All along the thesis, channels are assigned without relying on a predefined common control channel (CCC) to exchange control messages before each spectrum access trial. Performance evaluation of the different proposed channel assignment solutions shows their ability to achieve a distributed and fair opportunistic spectrum assignment in a way to consider different smart grid system characteristics.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2015-12-16T11:21:48Z No. of bitstreams: 1 igordelgadodemelo.pdf: 4931795 bytes, checksum: cf03c45f0f2492c6cf9186af1b3866a2 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2015-12-16T11:57:23Z (GMT) No. of bitstreams: 1 igordelgadodemelo.pdf: 4931795 bytes, checksum: cf03c45f0f2492c6cf9186af1b3866a2 (MD5) / Made available in DSpace on 2015-12-16T11:57:23Z (GMT). No. of bitstreams: 1 igordelgadodemelo.pdf: 4931795 bytes, checksum: cf03c45f0f2492c6cf9186af1b3866a2 (MD5) Previous issue date: 2015-09-18 / Este trabalho objetiva apresentar uma metodologia capaz de estimar os componentes harmônicos em sistemas de distribuição com topologia radial utilizando PMUs (Phasor Measurement Units). Os estados a serem estimados serão as correntes passantes em todas as linhas do sistema em coordenadas retangulares. Uma vez que essas correntes são obtidas, torna-se possível o cálculo de outras grandezas elétricas através das equações de fluxo de potência e leis de Kirchhoff. A metodologia considera poucas unidades de medição fasorial (as PMUs) instaladas efetuando a leitura dos sinais de tensões nodais e correntes nas linhas com distorção harmônica. A fim de restaurar a observabilidade do sistema por completo, são considerados dados históricos de demanda de potência ativa/reativa, os quais serão tratados como restrições de desigualdades excursionando entre um valor mínimo e máximo considerados em um problema de otimização não linear que visa diminuir a diferença entre os valores monitorados pelas PMUs e os calculados pela metodologia. As mencionadas restrições permitem ao estimador o acompanhamento das variações sofridas ao longo do tempo na curva de carga para a frequência fundamental e também para as demais frequências. A abordagem proposta neste trabalho considera a modelagem trifásica de equipamentos e linhas de distribuição, portanto, são modelados os efeitos de acoplamento mútuo entre fases e a operação não linear de equipamentos de eletrônica de potência tiristorizados. O método demonstra eficiência não apenas em estimar os componentes harmônicos de um certo espectro considerado no estudo, como também se mostra uma ferramenta prática de detecção e identificação de fontes harmônicas no sistema elétrico de potência, além de explicitar um exemplo prático do uso de PMUs no que tange ao monitoramento de redes de distribuição, carentes de acompanhamento em tempo real. A metodologia ainda se mostra capaz de ser aliada a grandes estudos contextualizados em qualidade de energia, uma vez que permite a estimação de índices de distorção harmônica. / This work aims to present a methodology which is capable of estimating harmonic components for distribution systems with radial topology, using PMUs (Phasor Measurement Units). The estimated states will be all branch currents of the system expressed in rectangular coordinates. Once these currents are obtained, it is possible to calculate other electrical quantities using power flow equations and also Kirchhoff’s law. The methodology considers the installation of a few number of phasor measurement units which will measure voltage and branch currents signals distorted by harmonic sources. In order to make the whole system observable, historical data of active/reactive power demand will be treated as inequality constraints varying between minimum and maximum limits described in a non linear optimization problem, which aims to minimize the difference between the values monitored by PMUs and the ones calculated by the methodology. The already mentioned constraints allows the accompaniment of the variations occured in a typical load curve during a period of time for the fundamental frequency and also for their multiples, allowing the accompaniment of the harmonic load curve, normally unknown. The proposed approach considers a three-phase modelling of equipments and distribution lines, subject to their mutual coupling effects caused by mutual impedances between the lines. It will also be considered electronic-based devices using thyristors located along the distribution feeder, injecting harmonic currents in the system. The method demonstrates efficiency in estimating the harmonic states of the net and also in detecting and identifying harmonic sources in an eletric power system, besides showing a practical use of PMUs for the monitoring of distribution systems, lacking in information and real-time accompaniment. The method also enables the estimation of power quality indicators such as total harmonic distortion.

Large penetration of distributed generation from variable renewable energy sources, increased consumption flexibility on the demand side and the electrification of transportation pose great challenges to existing and future electric distribution networks. This thesis studies the roles of several actors involved in electric distribution systems through electricity consumption data analysis and simulation models. Results show that real-time electricity pricing adoption in the residential sector offers economic benefits for end consumers. This occurs even without the adoption of demand-side management strategies, while real-time pricing also brings new opportunities for increasing consumption flexibility. This flexibility will play a critical role in the electrification of transportation, where scheduled charging will be required to allow large penetration of EVs without compromising the network's reliability and to minimize upgrades on the existing grid. All these issues add significant complexity to the existing infrastructure and conventional passive components are no longer sufficient to guarantee safe and reliable network operation. Active distribution networks are therefore required, and consequently robust and flexible modelling and simulation computational tools are needed for their optimal design and control. The modelling approach presented in this thesis offers a viable solution by using an equation-based object-oriented language that allows developing open source network component models that can be shared and used unambiguously across different simulation environments.