The dissertation presents a generalized control structure for two-stage power converters operated in a renewable energy power system for smart grid and micro grid systems. The generalized control structure is based on the two-loop average-mode-control technique, and created by reconstructing the conventional control structure and feedback configuration. It is broadly used for both dc-dc and dc-ac power conversion based on the two-stage converter architecture, while offering several functionalities required for renewable energy power systems. The generalized control structure improves the performance and reliability of renewable energy power systems with multiple functionalities required for consistent and reliable distributed power sources in the applications of the smart grid and micro grid system. The dissertation also presents a new modeling approach based on a modification of the subsystem-integration approach. The approach provides continuous-time small-signal models for all of two-stage power converters in a unified way. As a result, a modeling procedure is significantly reduced by treating a two-stage power converter as a single-stage with current sinking or sourcing. The difficulty of linearization caused by time-varying state variables is avoided with the use of the quasi-steady state concept. The generalized control structure and modeling approach are demonstrated using the two-stage dc-dc and dc-ac power conversion systems. A battery energy storage system with a thermoelectric source and a grid-connected power system with a photovoltaic source are examined. The large-signal averaged model and small-signal model are developed for the two demonstrated examples, respectively. Based on the modeling results, the control loops are designed by using frequency domain analysis. Various simulations and experimental tests are carried out to verify the compensator designs and to evaluate the generalized control structure performance. From the simulation and experimental results, it is clearly seen that the generalized control structure improves the performance of a battery energy storage system due to the unified control concept. The unified control concept eliminates transient over-voltage or over-current, extra energy losses, power quality issues, and complicated decision processes for multiple-mode control. It is also seen that the generalized control structure improves the performance of a single-phase grid-connected system through increased voltage control loop bandwidth of the active ripple current reduction scheme. As a result of the increased loop bandwidth, the transient overshoot or undershoot of the dc-link voltage are significantly reduced during dynamic load changes. / Ph. D.

The vision of the smart grid is predicated upon pervasive use of modern digital communication techniques in today's power system. As wide area measurements and control techniques are being developed and deployed for a more resilient power system, the role of communication networks is becoming prominent. Advanced communication infrastructure provides much wider system observability and enables globally optimal control schemes. Wide area measurement and monitoring with Phasor Measurement Units (PMUs) or Intelligent Electronic Devices (IED) is a growing trend in this context. However, the large amount of data collected by PMUs or IEDs needs to be transferred over the data network to control centers where real-time state estimation, protection, and control decisions are made. The volume and frequency of such data transfers, and real-time delivery requirements mandate that sufficient bandwidth and proper delay characteristics must be ensured for the correct operations. Power system dynamics get influenced by the underlying communication infrastructure. Therefore, extensive integration of power system and communication infrastructure mandates that the two systems be studied as a single distributed cyber-physical system. This dissertation proposes a global event-driven co-simulation framework, which is termed as GECO, for interconnected power system and communication network. GECO can be used as a design pattern for hybrid system simulation with continuous/discrete sub-components. An implementation of GECO is achieved by integrating two software packages: PSLF and NS2 into the framework. Besides, this dissertation proposes and studies a set of power system applications which can be only properly evaluated on a co-simulation framework like GECO, namely communication-based distance relay protection, all-PMU state estimation and PMU-based out-of-step protection. All of them take advantage of interplays between the power grid and the communication infrastructure. The GECO experiments described in this dissertation not only show the efficacy of the GECO framework, but also provide experience on how to go about using GECO in smart grid planning activities. / Ph. D.

Machine-to-Machine (M2M) communications is an emerging communication paradigm that provides ubiquitous connectivity between devices along with an ability to communicate autonomously without human intervention. M2M communications acts as an enabling technology for the practical realization of Internet-of-Things (IoT). However, M2M communications differs from conventional Human-to-Human (H2H) communications due to its unique features such as massive number of connected devices, small data transmissions, little or no mobility, requirements of high energy efficiency and reliability, etc. These features create various challenges for existing communication networks which are primarily optimized for H2H communications. Therefore, novel solutions are required to meet the key requirements of M2M communications. In addition, enhancements are required at different layers of the protocol stack to support co-existence of M2M devices and H2H users. The main objective of this research is to investigate the challenges of M2M communications in two broad types of M2M networks; capillary M2M and cellular M2M networks. The primary focus is on developing novel solutions, algorithms, and protocol enhancements for successfully enabling M2M communications. Since cognitive radio technology is very promising for M2M communications, special emphasis is on capillary M2M networks with cognitive radio based Physical layer. Besides, the focus is also on exploring new frontiers in M2M communications. This thesis covers different aspects of M2M communications. Considering the motivation for cognitive M2M and service requirements of M2M devices, two cognitive MAC protocols have been proposed. The first protocol is centralized in nature and utilizes a specialized frame structure for co-existence with the primary network as well as handling different Quality-of-Service (QoS) requirements of M2M devices. The second protocol is a distributed cognitive MAC protocol, which is specially designed to provide high energy efficiency and reliability for M2M devices operating in challenging wireless environments. Both protocols explicitly account for the peculiarities of cognitive radio environments. The protocols have been evaluated using analytical modeling and simulation studies. Recently IETF has standardized a specially designed routing protocol for capillary M2M networks, known as RPL (Routing for Low Power and Lossy Networks). RPL is emerging as the de facto routing protocol for many M2M applications including the smart grid. On the other hand, the application of cognitive radio for smart grid communication is under active investigation in the research community. Hence, it is important to investigate the applicability and adaptation of RPL in cognitive radio environments. In this regard, an enhanced RPL based routing protocol has been proposed for cognitive radio enabled smart grid networks. The enhanced protocol provides novel modifications to RPL for protecting the primary users along with meeting the utility requirements of the secondary network. An important challenge in LTE-based cellular networks with M2M communications is the uplink radio resource management as available resources are shared between M2M devices and H2H users, having different and often conflicting QoS requirements. Apart from this, energy efficiency requirements become critically important. Further, the specific constraints of Single Carrier Frequency Division Multiple Access (SC-FDMA) complicate the resource allocation problem. In this respect, an energy efficient resource allocation algorithm for the uplink of LTE networks with M2M/H2H co-existence under statistical QoS guarantees has been developed, that is based on canonical duality theory. The proposed algorithm outperforms classical algorithms in terms of energy efficiency while satisfying the QoS requirements of M2M devices and H2H users. A new frontier in M2M communications is the nano-M2M communications, which is envisioned to create the Internet-of-Nano-Things (IoNT). Molecular communication (MC) is a promising communication technique for nano-M2M communications. In literature, no model for error performance of MC exists. Therefore, an error performance model has been developed that explicitly accounts for noise and interference effects. Since relaying and network coding based solutions are gaining popularity for nano-M2M networks, the error performance of a network coded molecular nano-M2M network has been evaluated as well. Finally, the thesis is concluded based on the overall picture of the research conducted. In addition, some directions for future work are included as well.

Renewable energy is key to a sustainable future. However, the intermittency of most renewable sources and lack of sufficient storage in the current power grid means that reliable integration of significantly more renewables will be a challenging task. Moreover, increased integration of renewables not only increases uncertainty, but also reduces the fraction of traditional controllable generation capacity that is available to cope with supply-demand imbalances and uncertainties. Less traditional generation also means less rotating mass that provides very short term, yet very important, kinetic energy storage to the system and enables mitigation of the frequency drop subsequent to major contingencies but before controllable generation can increase production. Demand, on the other side, has been largely regarded as non-controllable and inelastic in the current setting. However, there is strong evidence that a considerable portion of the current and future demand, such as electric vehicle load, is flexible. That is, the instantaneous power delivered to it needs not to be bound to a specific trajectory. In this thesis, we focus on harnessing demand flexibility as a key to enabling more renewable integration and cost reduction. We start with a data driven analysis of the potential of flexible demands, particularly plug-in electric vehicle (PEV) load. We first show that, if left unmanaged, these loads can jeopardize grid reliability by exacerbating the peaks in the load profile and increasing the negative correlation of demand with wind energy production. Then, we propose a simple local policy with very limited information and minimal coordination that besides avoiding undesired effects, has the positive side-effect of substantially increasing the correlation of flexible demand with wind energy production. Such local policies could be readily implemented as modifications to existing "grid friendly" charging modes of plug-in electric vehicles. We then propose improved localized charging policies that counter balance intermittency by autonomously responding to frequency deviations from the nominal frequency and show that PEV load can offer a substantial amount of such ancillary services. Next, we consider the case where real-time prices are employed to provide incentives for demand response. We consider a flexible load under such a pricing scheme and obtain the optimal policy for responding to stochastic price signals to minimize the expected cost of energy. We show that this optimal policy follows a multi-threshold form and propose a recursive method to obtain these thresholds. We then extend our results to obtain optimal policies for simultaneous energy consumption and ancillary service provision by flexible loads as well as optimal policies for operation of storage assets under similar real-time stochastic prices. We prove that the optimal policy in all these cases admits a computationally efficient form. Moreover, we show that while optimal response to prices reduces energy costs, it will result in increased volatility in the aggregate demand which is undesirable. We then discuss how aggregation of flexible loads can take us a step further by transforming the loads to controllable assets that help maintain grid reliability by counterbalancing the intermittency due to renewables. We explore the value of load flexibility in the context of a restructured electricity market. To this end, we introduce a model that economically incentivizes the load to reveal its flexibility and provides cost-comfort trade-offs to the consumers. We establish the performance of our proposed model through evaluation of the price reductions that can be provided to the users compared to uncontrolled and uncoordinated consumption. We show that a key advantage of aggregation and coordination is provision of "regulation" to the system by load, which can account for a considerable price reduction. The proposed scheme is also capable of preventing distribution network overloads. Finally, we extend our flexible load coordination problem to a multi-settlement market setup and propose a stochastic programming approach in obtaining day-ahead market energy purchases and ancillary service sales. Our work demonstrates the potential of flexible loads in harnessing renewables by affecting the load patterns and providing mechanisms to mitigate the inherent intermittency of renewables in an economically efficient manner. / text

In the past decades, wireless Machine-to-Machine (M2M) networks have been developed in various industrial and public service areas and envisioned to improve our daily life in next decades, e.g., energy, manufacturing, transportation, healthcare, and safety. With the advantage of low cost, flexible deployment, and wide coverage as compared to wired communications, wireless communications play an essential role in providing information exchange among the distributed devices in wireless M2M networks. However, an intrinsic problem with wireless communications is that the limited radio spectrum resources may result in unsatisfactory performance in the M2M networks. With the number of M2M devices projected to reach 20 to 50 billion by 2020, there is a critical need to solve the problems related to the design and applications in the wireless M2M networks. In this dissertation work, we study the wireless M2M networks design from three closely related aspects, the wireless M2M communication reliability, efficiency, and Demand Response (DR) control in smart grid, an important M2M application taking the advantage of reliable and efficient wireless communications. First, for the communication reliability issue, multiple factors that affect communication reliability are considered, including the shadowing and fading characteristics of wireless channels, and random network topology. A general framework has been proposed to evaluate the reliability for data exchange in both infrastructure-based single-hop networks and multi-hop mesh networks. Second, for the communication efficiency issue, we study two challenging scenarios in wireless M2M networks: one is a network with a large number of end devices, and the other is a network with long, heterogeneous, and/or varying propagation delays. Media Access Control (MAC) protocols are designed and performance analysis are conducted for both scenarios by considering their unique features. Finally, we study the DR control in smart grid. Using Lyapunov optimization as a tool, we design a novel demand response control strategy considering consumer’s comfort requirements and fluctuations in both the renewable energy supply and customers’ load demands. By considering those unique features of M2M networks in data collection and distribution, the analysis, design and optimize techniques proposed in this dissertation can enable the deployment of wireless M2M networks with a large number of end devices and be essential for future proliferation of wireless M2M networks. / Graduate / 0544 / flintlei@gmail.com

Doctor of Philosophy / Department of Electrical and Computer Engineering / Don Gruenbacher / Caterina Scoglio / Cyber physical systems emerge when physical systems are integrated with communication networks. In particular, communication networks facilitate dissemination of data among components of physical systems to meet key requirements, such as efficiency and reliability, in achieving an objective. In this dissertation, we consider one of the most important cyber physical systems: the smart grid. The North American Electric Reliability Corporation (NERC) envisions a smart grid that aggressively explores advance communication network solutions to facilitate real-time monitoring and dynamic control of the bulk electric power system. At the distribution level, the smart grid integrates renewable generation and energy storage mechanisms to improve reliability of the grid. Furthermore, dynamic pricing and demand management provide customers an avenue to interact with the power system to determine electricity usage that satisfies their lifestyle. At the transmission level, efficient communication and a highly automated architecture provide visibility in the power system; hence, faults are mitigated faster than they can propagate. However, higher levels of reliability and efficiency rely on the supporting physical communication infrastructure and the network technologies employed. Conventionally, the topology of the communication network tends to be identical to that of the power network. In this dissertation, however, we employ a Demand Response (DR) application to illustrate that a topology that may be ideal for the power network may not necessarily be ideal for the communication network. To develop this illustration, we realize that communication network issues, such as congestion, are addressed by protocols, middle-ware, and software mechanisms. Additionally, a network whose physical topology is designed to avoid congestion realizes an even higher level of performance. For this reason, characterizing the communication infrastructure of smart grids provides mechanisms to improve performance while minimizing cost. Most recently, algebraic connectivity has been used in the ongoing research effort characterizing the robustness of networks to failures and attacks. Therefore, we first derive analytical methods for increasing algebraic connectivity and validate these methods numerically. Secondly, we investigate impact on the topology and traffic characteristics as algebraic connectivity is increased. Finally, we construct a DR application to demonstrate how concepts from graph theory can dramatically improve the performance of a communication network. With a hybrid simulation of both power and communication network, we illustrate that a topology which may be ideal for the power network may not necessarily be ideal for the communication network. To date, utility companies are embracing network technologies such as Multiprotocol Label Switching (MPLS) because of the available support for legacy devices, traffic engineering, and virtual private networks (VPNs) which are essential to the functioning of the smart grid. Furthermore, this particular network technology meets the requirement of non-routability as stipulated by NERC, but these benefits are costly for the infrastructure that supports the full MPLS specification. More importantly, with MPLS routing and other switching technologies, innovation is restricted to the features provided by the equipment. In particular, no practical method exists for utility consultants or researchers to test new ideas, such as alternatives to IP or MPLS, on a realistic scale in order to obtain the experience and confidence necessary for real-world deployments. As a result, novel ideas remain untested. On the contrary, OpenFlow, which has gained support from network providers such as Microsoft and Google and equipment vendors such as NEC and Cisco, provides the programmability and flexibility necessary to enable innovation in next-generation communication architectures for the smart grid. This level of flexibility allows OpenFlow to provide all features of MPLS and allows OpenFlow devices to co-exist with existing MPLS devices. Therefore, in this dissertation we explore a low-cost OpenFlow Software Defined Networking solution and compare its performance to that of MPLS. In summary, we develop methods for designing robust networks and evaluate software defined networking for communication and control in cyber physical systems where the smart grid is the system under consideration.

I en traditionellt konservativ bransch rör det sig nu snabbt. En snabb ökning av andelen förnybar elproduktion gör att det uppstår en rad nya utmaningar som måste lösas. Den här studien är en undersökning av framtidens distributionsnät och har ett fokus på nätstationerna. Studien visar att en ökad mängd distribuerad generering kan göra att dagens skydd inte fungerar som tänkt. Samtidigt ökar kraven på våra elnät. Detta medför att nya skydd kan behövas. Man ser också att automation ökar vilket minskar avbrottstiderna. Det undersöks också vilka elenergilagringssystem som finns och kan vara aktuellt i distributionsnätet. Studien visar att ett Li-Jon batterilager kan fylla flera olika viktiga funktioner åt olika aktörer, detta öppnar för flera olika inkomstkällor samtidigt som kostnaderna sjunker. Detta gör att det kan bli möjligt att energilager blir en vanligt förekommande komponent i distributionsnätet.

The ongoing digitalization affects all sectors and changes the competitive landscape. A sector that is often seen upon as traditional, with low digital maturity is the energy sector. Hence, existing literature has focused on overcoming technical difficulties associated with the digitalization and lacks reasoning concerning the implications on existing business models. The purpose of the study is therefore to investigate how the digitalization affects companies in the Swedish energy sector when it comes to innovations in the business model, how companies creates, delivers and captures value. This purpose is addressed through an exploratory multiple case study including some of the most prominent actors on the Swedish energy market. The result of the study shows that the digitalization has had multiple implications in all of the business model´s building blocks, but it is still associated with a lot of uncertainties and the most radical changes are expected to happen in the future. Theoretical implications of this study are the increased understanding to how digitalization drives business model innovations and how application of new technologies can lead to increased business value. Practical implications are deepened knowledge for business managers in how digitalization can be utilized to gain increased value in an industry with an overall low digital maturity. / Syfte – Studiens syfte är att undersöka hur digitaliseringen påverkar företag i svenska energibranschen när det kommer till att skapa, leverera och fånga värde. Detta genom att skapa förståelse genom att undersöka hur digitaliseringen har påverkat företagen i den svenska energibranschen. Studiens syfte, att undersöka hur digitaliseringen driver affärsmodellsinnovationer inom varje del av energibranschens värdekedja, är explorativt. Studiens underlag grundar sig på insamling av empirisk data för att skapa ny kunskap, vilket medför att studiens forskningsansats är induktiv. Metod – Datainsamlingen har huvudsakligen genomförts via semistrukturerade intervjuer som analyserats via tematisk analys. Det selektiva urvalet grundar sig i fem olika kriterier där två av dessa kriterier ansågs som nödvändiga för samtliga av de företag som användes som fallföretag i studien. Därefter har tre kriterier använts för att identifiera viktiga aspekter kopplat till respektive forskningsfråga. Forskningsfrågorna ämnar besvara hur företagen anses använda digitaliseringen för att skapa, leverera eller fånga värde av den vara som de producerar och/eller levererar. Därefter har ett snöbollsurval tillämpats för att identifiera intervjupersoner på respektive fallföretag. Resultat – Resultatet av studien påvisar att svenska energibolag har förändrat sina affärsmodeller utifrån dimensionerna skapa, fånga och leverera värde till följd av digitaliseringen. Detta har genomförts på olika sätt mellan fallföretagen, både genom inkrementell och radikal affärsmodellsinnovation. Teoretiska implikationer – Studien bidrar till förståelsen för hur digitaliseringen vidare driver affärsmodellsinnovationer, där har studien flertalet teoretiska bidrag och tillför insikter i hur digitalisering som fenomen påverkar och förändrar affärsmodeller. Praktiska implikationer – Studien bidrar med insikter hur digitaliseringen påverkar den svenska energibranschen sett från ett perspektiv från företag i framkant inom detta område. Studien har undersökt fallföretag efter ett visst antal kriterier, dessa kriterier har lett till att framstående företag liknande bästa praxis inom området har bidragit, vilket kommer leda till en ökad förståelse för andra bolag i samma bransch. Dessutom kan rapporten nyttjas för att identifiera förbättringspotential i företagen och agera som en katalysator för att digitalt transformera verksamheten.

Nos sistemas de distribuição, a grande ramificação, radialidade, heterogeneidade, dinâmica das cargas e demais particularidades, impõem dificuldades à localização de faltas, representando um desafio permanente na busca por melhores indicadores de continuidade e confiabilidade no fornecimento de energia elétrica. A regulação incisiva dos órgãos do setor, a penetração de geração distribuída e a tendência de modernização trazida pelas redes inteligentes, demandam detalhados estudos para readequação dos sistemas elétricos a conjuntura atual. Neste contexto, esta tese propõe o desenvolvimento de uma metodologia para localização de faltas em sistemas de distribuição empregando a capacidade dos medidores inteligentes de monitoramento e de aquisição de tensão em diferentes pontos da rede elétrica. A abordagem proposta baseia-se na estimação, por ferramentas de aprendizado de máquina, das impedâncias de sequência zero e positiva entre os pontos de alocação dos medidores inteligentes e de ocorrência de falta, e do estado de sensibilização destes medidores frente a correntes de falta. Assim, calculando-se as respectivas distâncias elétricas em função das impedâncias estimadas e definidas as direções das mesmas em relação a topologia da rede, busca-se identificar o ponto ou área com maior sobreposição de distâncias elétricas como o local ou a região de maior probabilidade da falta em relação aos medidores inteligentes. Para tanto, faz-se uso combinado de ferramentas convencionais e inteligentes pela aplicação dos conceitos de análise de sistemas elétricos, diagnóstico dos desvios de tensão, e classificação de padrões por meio da técnica de aprendizado de máquina denominada Árvore de Decisão. Os resultados obtidos pela aplicação desta metodologia demonstram que o uso de informações redundantes fornecidas pelos medidores inteligentes minimiza os erros de estimação. Além disso, para a maior parte dos casos testados o erro absoluto máximo de localização da falta se concentra entre 200 m e 1000 m, o que reduz a busca pelo local de ocorrência da falta pelas equipes de manutenção da rede elétrica. / In distribution systems, the dense branching, radial pattern, heterogeneity, dynamic of the loads, and other characteristics create several difficulties in defining the fault location, representing a great challenge in the search for better continuity and reliability indicators of the electrical energy supply. The intense government regulations, the increasing use of distributed generation, and the trend towards modernization via smart grids require a detailed study in order to upgrade the current systems. In this context, this thesis proposes a methodology development for fault location in distribution systems with the use of smart meters monitors and the acquisition of voltage at different points in the electrical network. The proposed method is based on the estimation, using machine learning, of the state of awareness of smart meters across the fault currents and of the zero and positive sequence impedance between the location of these meters and of the fault occurrence. Therefore, by calculating the electrical distances as a function of the estimated impedances and defining its the direction in relation to the network topology, the point/region with the biggest superposition of the electrical distances can be assigned as the point/region with the highest probability of fault occurrence in relation to the smart probes. For this purpose, a machine learning technique named decision tree is used to apply concept analyses to the electrical systems, diagnosis of voltage deviations, and pattern recognition of the electrical systems. The results obtained by the application of this methodology demonstrate that the use of redundant information provided by the smart meters minimizes estimation errors. In addition, for most of the cases tested, the maximum absolute error of the fault location is concentrated between 200 m and 1000 m, which reduces the search for the fault location by the maintenance teams of the electrical network.

Since the last United Nations Climate Change Conference in 2015 in Paris (the COP 21), world leaders acknowledged climate change. There is no need any more to justify the switch from fossil fuel-based to renewable energy sources. Nevertheless, this transition is far from being straightforward. Besides technologies that are not yet mature -- or at least not always financially viable in today's economy -- the power grid is currently not ready for a rapid and massive integration of renewable energy sources. A main challenge for the power grid is the inadequacy between electric production and consumption that will rise along with the integration of such sources. Indeed, due to their dependence on weather, renewable energy sources are intermittent and difficult to forecast with today's tools. As a commodity, electricity is a quite distinct good for which there must be perfect adequacy of production and consumption at all time and characterized by a very inelastic demand. High shares of renewable energy sources lead to high price volatility and a higher risk to jeopardize the security of supply. Additionally, the switch to renewable energy sources will lead to an electrification of loads and transportation, and thus the emergence of new higher-consumption loads such as electric vehicles and heat pumps. These new and higher-consumption loads, combined with the population growth, will cause over-rated power load increases with less predictable load patterns in the future.This work focuses on issues specific to the distribution power grid in the context of the current energy transition. Traditional low-voltage grids are perhaps the most passive circuits in power grids. Indeed, they are designed primarily using a fit and forget approach where power flows go from the distribution transformer to the consumers and no element has to be operated or regularly managed. In fact, low-voltage networks completely lack observability due to very low monitoring. The distribution grid will especially undergo drastic changes from this energy transition. Distributed sources and new high-consumption -- and uncoordinated -- loads result in new power flow patterns, as well as exacerbated evening peaks for which it is not designed. The consequences are power overloads and voltage imbalances that deteriorate grid components, such as a main asset like the medium-to-low voltage transformer. Additionally, the distribution grid is characterized by end-users that pay a price for electricity that does not reflect the grid situation -- that is, mostly constant over a year -- and allow little to no actions on their consumption.These issues have motivated authorities to propose a global approach to ensure security of electricity supply at short and medium-term. The latter requires, among others, the development of demand response programs that encourage users to take advantage of load flexibility. First, we propose adequate electricity pricing structures that will allow users to unlock the potential of such demand response programs; namely, dynamic pricings combined with a prosumer structure. Second, we propose a fast and robust two-level optimization, formulated as a mixed-integer linear program, that coordinates flexible loads. We focus on two types of loads; electric vehicles and heat pumps, in an environment with solar PV panels. The lower level aims at minimizing individual electricity bills while, at the second level, we optimize the power load curve, either to maximize self-consumption, or to smoothen the total power load of the transformer. We propose a parametric study on the trade-off between only minimizing the individual bills versus only optimizing power load curves, which have proven to be antagonist objectives. Additionally, we assess the impact of the rising share of flexible loads and renewable energy sources for scenarios from today until 2050. A macro-analysis of the results allows us to assess the benefits of load flexibility for every actor of the distribution grid, and depending on the choice of a pricing structure. Our optimization has proved to prevent evening peaks, which increases the lifetime of the distribution transformer by up to 200%, while individual earnings up to 25% can be made using adequate pricings. Consequently, the optimization significantly increases the power demand elasticity and increases the overall welfare by 10%, allowing the high shares of renewable energy sources that are foreseen. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished