There has been a large body of statements claiming that the large scale deployment of Distributed Energy Resources (DERs) will eventually reshape the future distribution grid operation in numerous ways. However, there is a lack of evidence specifying to what extent the power system operation will be alternated. In this project, quantitative results in terms of how the future distribution grid will be changed by the deployment of distributed generation, active demand and electric vehicles, are presented. The quantitative analysis is based on the conditions for both a radial and a meshed distribution network. The input parameters are on the basis of the current and envisioned DER deployment scenarios proposed for Sweden. The simulation results indicate that the deployment of DERs can significantly reduce the power losses and voltage drops by compensating power from the local energy resources, and limiting the power transmitted from the external grid. However, it is notable that the opposite results (e.g., severe voltage uctuations, larger power losses) can be obtained due to the intermittent characteristics of DERs and the irrational management of different types of DERs in the DNs. Subsequently, this will lead to challenges for the Distribution System Operator (DSO).

Neste trabalho foi desenvolvida uma metodologia para realizar a gestão ativa da demanda de energia elétrica de consumidores, providos de armazenamento de energia elétrica e geração solar fotovoltaica, inseridos em redes inteligentes. Tal metodologia pode ser utilizada em instalações residenciais e comerciais. Utilizando estratégias de otimização e inteligência artificial, a metodologia configura um sistema de tomada de decisão para o gerenciador do conversor da bateria, que realiza a gestão da energia armazenada, visando reduzir o custo com energia elétrica para o consumidor final. Esta gestão propicia contribuição com a distribuidora em forma de aumento da reserva de capacidade da rede elétrica nos casos em que a tarifa de energia elétrica for mais cara no horário de pico. De qualquer forma, há potencial postergação da necessidade de expansão da rede elétrica e redução de impactos ambientais advindos da geração convencional de energia elétrica, uma vez que tal gestão de energia propicia redução de consumo de energia elétrica da rede. O mesmo sistema de tomada de decisão do gerenciador do conversor da bateria pode ser utilizado em vários consumidores com características semelhantes (mesmo tipo, localização e tarifação de energia elétrica, e perfil de consumo similar), uma vez que tal sistema é composto por uma rede neural treinada com dados locais. Estudo de caso foi conduzido considerando consumidor residencial na cidade de São Paulo. Foram construídos quinze perfis de consumo, que foram combinados com três perfis de geração solar. A metodologia apresentou desempenho satisfatório, tanto na avaliação da etapa de otimização quanto de treinamento da rede neural, uma vez que as curvas de armazenamento de energia apresentaram comportamentos próximos aos esperados. O sistema de tomada de decisão também respondeu de forma adequada, alterando a curva de carga do consumidor vista pela rede de forma a reduzir o custo diário com energia elétrica e o consumo de energia no horário de pico da residência em todos os casos estudados. A análise econômica apontou a necessidade de encontrar formas de tornar a iniciativa positiva do ponto de vista econômico no estudo de caso realizado. / This work presents a methodology developed to perform the active demand side management for consumers, provided with energy storage and solar photovoltaic power, inserted in smart grids. Such methodology can be used in residential and commercial installations. Using optimization and artificial intelligence strategies, the methodology sets up a decision-making system for the battery converter manager, which performs energy storage management, in order to reduce the cost with electricity for the final consumer. This management contributes with the utility increasing the grid reserve capacity when the electricity tariff is more expensive during peak hours. Anyway, there is potential postponement of the need to expand the grid, and environmental impacts reduction from conventional power generation, since such power management provides a reduction of the grid electricity consumption. The same decision-making system of the battery converter manager can be used in several consumers with similar characteristics (same type, location and electricity tariff, and similar consumption profile), since this system is composed by a neural network trained with local data. A case study was conducted considering household in the city of São Paulo. Fifteen consumption profiles were built, which were combined with three solar generation profiles. The methodology presented satisfactory performance both in the evaluation of the optimization stage and the neural network training stage, since the energy storage curves presented behaviors close to those expected. The decision-making system also responded adequately, changing the consumer load curve seen by the grid in order to reduce the daily electricity cost, and energy consumption at peak hours of the household in all cases studied. The economic analysis pointed to the need to find ways to make the initiative positive from an economic point of view in the case study carried out.

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

Neste trabalho foi desenvolvida uma metodologia para realizar a gestão ativa da demanda de energia elétrica de consumidores, providos de armazenamento de energia elétrica e geração solar fotovoltaica, inseridos em redes inteligentes. Tal metodologia pode ser utilizada em instalações residenciais e comerciais. Utilizando estratégias de otimização e inteligência artificial, a metodologia configura um sistema de tomada de decisão para o gerenciador do conversor da bateria, que realiza a gestão da energia armazenada, visando reduzir o custo com energia elétrica para o consumidor final. Esta gestão propicia contribuição com a distribuidora em forma de aumento da reserva de capacidade da rede elétrica nos casos em que a tarifa de energia elétrica for mais cara no horário de pico. De qualquer forma, há potencial postergação da necessidade de expansão da rede elétrica e redução de impactos ambientais advindos da geração convencional de energia elétrica, uma vez que tal gestão de energia propicia redução de consumo de energia elétrica da rede. O mesmo sistema de tomada de decisão do gerenciador do conversor da bateria pode ser utilizado em vários consumidores com características semelhantes (mesmo tipo, localização e tarifação de energia elétrica, e perfil de consumo similar), uma vez que tal sistema é composto por uma rede neural treinada com dados locais. Estudo de caso foi conduzido considerando consumidor residencial na cidade de São Paulo. Foram construídos quinze perfis de consumo, que foram combinados com três perfis de geração solar. A metodologia apresentou desempenho satisfatório, tanto na avaliação da etapa de otimização quanto de treinamento da rede neural, uma vez que as curvas de armazenamento de energia apresentaram comportamentos próximos aos esperados. O sistema de tomada de decisão também respondeu de forma adequada, alterando a curva de carga do consumidor vista pela rede de forma a reduzir o custo diário com energia elétrica e o consumo de energia no horário de pico da residência em todos os casos estudados. A análise econômica apontou a necessidade de encontrar formas de tornar a iniciativa positiva do ponto de vista econômico no estudo de caso realizado. / This work presents a methodology developed to perform the active demand side management for consumers, provided with energy storage and solar photovoltaic power, inserted in smart grids. Such methodology can be used in residential and commercial installations. Using optimization and artificial intelligence strategies, the methodology sets up a decision-making system for the battery converter manager, which performs energy storage management, in order to reduce the cost with electricity for the final consumer. This management contributes with the utility increasing the grid reserve capacity when the electricity tariff is more expensive during peak hours. Anyway, there is potential postponement of the need to expand the grid, and environmental impacts reduction from conventional power generation, since such power management provides a reduction of the grid electricity consumption. The same decision-making system of the battery converter manager can be used in several consumers with similar characteristics (same type, location and electricity tariff, and similar consumption profile), since this system is composed by a neural network trained with local data. A case study was conducted considering household in the city of São Paulo. Fifteen consumption profiles were built, which were combined with three solar generation profiles. The methodology presented satisfactory performance both in the evaluation of the optimization stage and the neural network training stage, since the energy storage curves presented behaviors close to those expected. The decision-making system also responded adequately, changing the consumer load curve seen by the grid in order to reduce the daily electricity cost, and energy consumption at peak hours of the household in all cases studied. The economic analysis pointed to the need to find ways to make the initiative positive from an economic point of view in the case study carried out.