A demanda e, consequentemente, a geração de energia elétrica são questões de suma importância para o desenvolvimento econômico e social dos países. Modelos para previsão destes parâmetros no longo e médio prazo são empregados com a finalidade de antever possíveis cenários e propor estratégias para a realização de um planejamento energético adequado. Neste contexto, o presente estudo tem como objetivo realizar a previsão da geração de energia elétrica no estado do Rio Grande do Sul (RS) em um horizonte de médio prazo (um ano), utilizando Redes Neurais Artificiais (RNA’s) do tipo feedforward com algoritmo de aprendizado supervisionado backpropagation. Para o desenvolvimento deste trabalho elaborou-se um script para executar as simulações necessárias, as quais foram realizadas através do software Matlab®. As variáveis de influência selecionadas como entradas do modelo de previsão referem-se à economia (estadual e nacional), ao balanço de energia elétrica e à meteorologia do estado, durante o período de janeiro de 2009 a março de 2016. Para realizar o treinamento da rede neural, adicionou-se a matriz de entrada este conjunto de dados, com frequência mensal, referentes a janeiro de 2009 a março de 2015 e para previsão foram inseridos dados de abril de 2015 a março de 2016. Por fim, depois de realizada a simulação completa da RNA, comparou-se o resultado observado da geração de energia elétrica do estado com o obtido através do modelo de previsão, indicando um erro percentual absoluto médio (MAPE) de 5,86% e um desvio absoluto médio (MAD) de 134,15 MW médio. Os resultados obtidos neste trabalho mostram-se promissores, além de semelhantes aos encontrados na literatura, demonstrando assim confiabilidade e eficácia do método empregado. / The demand and, consequently, the generation of electric power are very important issues for social and economic development of countries. Models to forecast these parameters in long and medium terms are used to anticipate possible sceneries and propose strategies for the energy planning of countries. In this context, the present study aims to forecast the generation of electric energy in Rio Grande do Sul State (RS) in a medium-term horizon (one year) using, Artificial Neural Networks (ANNs) of the feedforward type with algorithm of supervised learning backpropagation. For the development of this work, a script was elaborated in order to execute the necessary simulations, which were carried out through Matlab® software. The selected variables of influence as inputs of forecasting model refer to economy (State and National), to the electric energy balance and to the meteorology State, during the period from January, 2009 to March, 2016. In order to train the neural network, this data set was added to the entrance matrix, with monthly frequency, from January, 2009 to March, 2015 and for prediction, data were inserted from April, 2015 to March, 2016. Finally, after RNA complete simulation, the observed result of the electric power generation of the State was compared with the one obtained through the prediction model, indicating a mean absolute percent error (MAPE) of 5.86% and a mean absolute deviation (MAD) of 134.15 average MW. The obtained results in this work are promising, besides; they are similar to those found in literature, in this way demonstrating the reliability and efficacy of the using method.

Essa pesquisa tem como objetivo propor um novo referencial metodológico para estimar a linha de base para projetos de MDL a serem conectados ao sistema interligado nacional SIN, a partir de uma visão do mix futuro das fontes energéticas que serão responsáveis pelas gerações de energia, nos próximos dez anos. Objetiva também aplicar essa nova abordagem para calcular as emissões deslocadas pelas atividades de projetos de MDL, através do cálculo do fator de substituição, medido em tCO2/MWh. Este fator estima a redução das emissões decorrente da substituição de parte da energia gerada pelas usinas térmicas convencionais, pela entrada no sistema de usinas que geram energia limpa e/ou pela redução da demanda agregada do sistema elétrico pela implementação de programas/medidas de eficiência energética pelo lado da demanda Para tanto, usando um modelo que simula o equilíbrio entre a oferta elétrica e os requisitos de energia previstos para o horizonte de 10 anos de energia calcula-se, inicialmente, as emissões dos GEE do sistema elétrico sem considerar a entrada do projeto de MDL. Na seqüência, as emissões do sistema elétrico são novamente calculadas, considerando a entrada do projeto MDL. Atualmente a linha de base do SIN é calculada, mensalmente, com base no Tool to calculate the emission factor for an eletricity system, que é uma ferramenta metodológica aprovada pelo CE do MDL, para determinar o fator de emissão de sistemas elétricos interligados. Essa ferramenta determina o fator de emissão de atividades de projetos que substituem eletricidade gerada na rede elétrica. Sustenta-se nesta pesquisa que é pouco provável, pelo menos no SIN, que as condições observadas em anos recentes e/ou atuais se reproduzam no futuro. Ao contrário do que ocorria até poucos anos atrás, em que a expansão do sistema elétrico era basicamente assentada em empreendimentos hidrelétricos, na atualidade desenha-se uma clara tendência à fossilização da matriz do setor elétrico nacional. Os resultados do trabalho mostraram que há uma tendência de elevação das emissões do SIN, embora o comportamento do Fator de Substituição, em termos anuais apresente variações importantes, em função das reais necessidades do despacho das térmicas inflexíveis que servem ao SIN a cada ano. No entanto, considerando todo o período estudado, os resultados encontrados são coerentes com o aumento da participação das UTE emissoras dos GEE no mix futuro das fontes energéticas que fornecerão eletricidade ao SIN. Os valores obtidos dos fatores de substituição (FSSINp) para todos os experimentos, entre 2008 e 2017, são significativamente superiores à linha de base do SIN de 2007, calculada com base no Tool to calculate the emission factor for an electricity system. / This research has as a goal propose a new methodological reference to assess the baseline for CDM projects designed to be connected to the national connected system SIN, coming from a foreseen sight of the energetic sources mix that will be responsible for the energy generation, in the next ten years and apply the news approach to calculate the emissions move to distinct amounts of entry energies of SIN, either by generation of new plants that do not generate emissions of GHE, except concern nuclear plants, or by the reduction of future demand of electrical energy, originated from the implantation of programs/measures of energetic efficiency considering demand. The factor of emission replacement factor of the electrical system measures the energy movement generated by the plants that serve the electrical system by the entrance of new plants that do not generate emissions of GHE. Thus, using a model that simulates the balance between supply and requirements for electric energy provided to the horizon of 10 years of energy it is estimated, initially, the GHG emissions of the electric system without considering the input of the CDM project. Following the emissions of the electric system are again calculated, considering the entry of the CDM project. Currently, the SIN baseline is calculated, monthly as a base tool to calculate the emission factor for an electricity system, which is a methodological tool approved by the CDM Executive Board, to determine emission factor of electrical systems. This thesis sustains that it is unlikely, at last in the SIN that the conditions noticed in recent years and/or conditions, will reproduce in the future. On the contrary of what has occurred a few years ago, in which the expansion of the electrical system was basically set up in hydro electrical enterprise, nowadays there is a trend toward fossilization of matrix of national electrical sector. The results of the study showed that there is a trend of increased emissions of SIN, but behavior of Factor Substitution in the year, vary in important ways, depending on the needs of the order of thermal inflexible to serve the SIN each year. However, considering the whole period studied, the results are consistent with the increased participation of the GHG emission UTEs in future mix of energy sources that will provide electricity to the SIN. The values of the factors of substitution (FSSINp) for all experiments, between 2008 and 2017 are significantly above the baseline of SIN, 2007, calculated on the Tool do calculate the emission factor for an electricity system.

Due to Vietnam’s economic development its energy demand will continue to rise by 12–16% annually over the next few years. The government has realized that supply problems in the energy sector pose a significant threat to further development. Therefore, it is making concerted efforts to modernize the existing energy sector and expand the generating structure. There are ambitious expansion plans in the field of renewable energy sources, too. Owing to its very high potential, biomass could play a key role in energy production. This paper attempts to analyze the current status of biomass based energy production in Vietnam addressing variety of aspects such as biomass potential, legal framework as well as financial aspect. Section 4 contains an overview of ongoing bioenergy projects. Instead of providing a complete picture, these examples are intended to illustrate the various ways in which biomass can be used in different economic sectors. Finally existing barriers as well as action to incentivise bioenergy are discussed. / Do phát triển kinh tế, nhu cầu năng lượng của Việt Nam sẽ tiếp tục tăng 12-16% mỗi năm trong vài năm tới. Chính phủ đã nhận ra rằng vấn đề cung cấp trong lĩnh vực năng lượng gây ra một mối đe dọa đáng kể cho sự phát triển tiếp theo. Vì vậy, có các nỗ lực để hiện đại hóa ngành năng lượng hiện có và mở rộng cấu trúc sản sinh năng lượng. Cũng có những kế hoạch mở rộng đầy tham vọng trong lĩnh vự nguồn năng lượng tái tạo. Do có tiềm năng rất cao, sinh khối có thể đóng một vai trò quan trọng trong sản xuất năng lượng. Bài viết này cố gắng phân tích tình trạng hiện tại của sản xuất năng lượng sinh khối tại Việt Nam giải quyết nhiều khía cạnh nhưtiềm năng sinh khối, khuôn khổ pháp lý cũng như các khía cạnh về tài chính. Tổng quan về các dự án năng lượng sinh học đang diễn ra được trình bày trong phần 4. Thay vì cung cấp một bức tranh hoàn chỉnh, các ví dụ được dùng để minh họa cho những cách khác nhau, trong đó sinh khối có thể được sử dụng trong các lĩnh vực kinh tế khác nhau. Rào cản cuối cùng hiện tại cũng nhưhành động để khuyến khích năng lượng sinh học sẽ được thảo luận.

In the United States, 40% of energy use was for electricity generation in 2011, but two thirds of the energy used to produce electricity was lost as heat. Combined heat and power systems are an energy technology that provides electrical and thermal energy at high efficiencies by utilizing excess heat from the process of electricity generation. This technology can offer a decentralized method of energy generation for urban regions which can provide a more reliable, resilient and efficient power supply, and has a lower impact on the environment compared to certain centralized electricity generation systems. In order for the use of combined heat and power systems to become more widespread and mainstream, studies must be performed which analyze their use in various conditions and applications. This work examines the use of a combined heat and power system with a microturbine as the prime mover in residential and commercial scenarios and analyzes the technical and economic feasibility of various system configurations. Energy models are developed for R1, R6 and 2-story office building scenarios using eQUEST, and these results give the electrical and thermal energy requirements for each building. Combined heat and power system models are then developed and presented for each scenario, and the building energy requirements and system component sizes available are considered in order to determine the optimal configurations for each system. The combined heat and power system models designed for each scenario are analyzed to find energy savings, water impacts, and emissions impacts of the system, and each model is examined for economic and environmental feasibility. The models created provide information on the most technically and economically efficient configurations of combined heat and power systems for each scenario examined. Data on system component sizing, system efficiencies, and environmental impacts of each system were determined, as well as how these scenarios compared to the use of traditional centralized energy systems. Combined heat and power has the potential to significantly improve the resiliency, reliability and efficiency of the current energy system in the U.S., and by studying and modeling its uses we more completely understand its function in a range of scenarios and can deploy the systems in a greater number of environments and applications.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv. / Härtill 6 uppsatser.

A demanda e, consequentemente, a geração de energia elétrica são questões de suma importância para o desenvolvimento econômico e social dos países. Modelos para previsão destes parâmetros no longo e médio prazo são empregados com a finalidade de antever possíveis cenários e propor estratégias para a realização de um planejamento energético adequado. Neste contexto, o presente estudo tem como objetivo realizar a previsão da geração de energia elétrica no estado do Rio Grande do Sul (RS) em um horizonte de médio prazo (um ano), utilizando Redes Neurais Artificiais (RNA’s) do tipo feedforward com algoritmo de aprendizado supervisionado backpropagation. Para o desenvolvimento deste trabalho elaborou-se um script para executar as simulações necessárias, as quais foram realizadas através do software Matlab®. As variáveis de influência selecionadas como entradas do modelo de previsão referem-se à economia (estadual e nacional), ao balanço de energia elétrica e à meteorologia do estado, durante o período de janeiro de 2009 a março de 2016. Para realizar o treinamento da rede neural, adicionou-se a matriz de entrada este conjunto de dados, com frequência mensal, referentes a janeiro de 2009 a março de 2015 e para previsão foram inseridos dados de abril de 2015 a março de 2016. Por fim, depois de realizada a simulação completa da RNA, comparou-se o resultado observado da geração de energia elétrica do estado com o obtido através do modelo de previsão, indicando um erro percentual absoluto médio (MAPE) de 5,86% e um desvio absoluto médio (MAD) de 134,15 MW médio. Os resultados obtidos neste trabalho mostram-se promissores, além de semelhantes aos encontrados na literatura, demonstrando assim confiabilidade e eficácia do método empregado. / The demand and, consequently, the generation of electric power are very important issues for social and economic development of countries. Models to forecast these parameters in long and medium terms are used to anticipate possible sceneries and propose strategies for the energy planning of countries. In this context, the present study aims to forecast the generation of electric energy in Rio Grande do Sul State (RS) in a medium-term horizon (one year) using, Artificial Neural Networks (ANNs) of the feedforward type with algorithm of supervised learning backpropagation. For the development of this work, a script was elaborated in order to execute the necessary simulations, which were carried out through Matlab® software. The selected variables of influence as inputs of forecasting model refer to economy (State and National), to the electric energy balance and to the meteorology State, during the period from January, 2009 to March, 2016. In order to train the neural network, this data set was added to the entrance matrix, with monthly frequency, from January, 2009 to March, 2015 and for prediction, data were inserted from April, 2015 to March, 2016. Finally, after RNA complete simulation, the observed result of the electric power generation of the State was compared with the one obtained through the prediction model, indicating a mean absolute percent error (MAPE) of 5.86% and a mean absolute deviation (MAD) of 134.15 average MW. The obtained results in this work are promising, besides; they are similar to those found in literature, in this way demonstrating the reliability and efficacy of the using method.

A demanda e, consequentemente, a geração de energia elétrica são questões de suma importância para o desenvolvimento econômico e social dos países. Modelos para previsão destes parâmetros no longo e médio prazo são empregados com a finalidade de antever possíveis cenários e propor estratégias para a realização de um planejamento energético adequado. Neste contexto, o presente estudo tem como objetivo realizar a previsão da geração de energia elétrica no estado do Rio Grande do Sul (RS) em um horizonte de médio prazo (um ano), utilizando Redes Neurais Artificiais (RNA’s) do tipo feedforward com algoritmo de aprendizado supervisionado backpropagation. Para o desenvolvimento deste trabalho elaborou-se um script para executar as simulações necessárias, as quais foram realizadas através do software Matlab®. As variáveis de influência selecionadas como entradas do modelo de previsão referem-se à economia (estadual e nacional), ao balanço de energia elétrica e à meteorologia do estado, durante o período de janeiro de 2009 a março de 2016. Para realizar o treinamento da rede neural, adicionou-se a matriz de entrada este conjunto de dados, com frequência mensal, referentes a janeiro de 2009 a março de 2015 e para previsão foram inseridos dados de abril de 2015 a março de 2016. Por fim, depois de realizada a simulação completa da RNA, comparou-se o resultado observado da geração de energia elétrica do estado com o obtido através do modelo de previsão, indicando um erro percentual absoluto médio (MAPE) de 5,86% e um desvio absoluto médio (MAD) de 134,15 MW médio. Os resultados obtidos neste trabalho mostram-se promissores, além de semelhantes aos encontrados na literatura, demonstrando assim confiabilidade e eficácia do método empregado. / The demand and, consequently, the generation of electric power are very important issues for social and economic development of countries. Models to forecast these parameters in long and medium terms are used to anticipate possible sceneries and propose strategies for the energy planning of countries. In this context, the present study aims to forecast the generation of electric energy in Rio Grande do Sul State (RS) in a medium-term horizon (one year) using, Artificial Neural Networks (ANNs) of the feedforward type with algorithm of supervised learning backpropagation. For the development of this work, a script was elaborated in order to execute the necessary simulations, which were carried out through Matlab® software. The selected variables of influence as inputs of forecasting model refer to economy (State and National), to the electric energy balance and to the meteorology State, during the period from January, 2009 to March, 2016. In order to train the neural network, this data set was added to the entrance matrix, with monthly frequency, from January, 2009 to March, 2015 and for prediction, data were inserted from April, 2015 to March, 2016. Finally, after RNA complete simulation, the observed result of the electric power generation of the State was compared with the one obtained through the prediction model, indicating a mean absolute percent error (MAPE) of 5.86% and a mean absolute deviation (MAD) of 134.15 average MW. The obtained results in this work are promising, besides; they are similar to those found in literature, in this way demonstrating the reliability and efficacy of the using method.

Orientador: Ernesto Ruppert Filho / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-19T04:10:37Z (GMT). No. of bitstreams: 1 Nogueira_ViniciusdeFreitasGomes_M.pdf: 1151425 bytes, checksum: 25421deb7c7d0073c31744365ccb6e53 (MD5) Previous issue date: 2011 / Resumo: Os geradores síncronos trifásicos são atualmente os mais utilizados em geração de energia elétrica em grande escala no mundo todo. Na geração de energia elétrica em pequena escala, geralmente como geração distribuída, ligada à rede de distribuição em média tensão ou até mesmo em baixa tensão, eles tem sido muito usados devido à sua principal qualidade que é a de operar em velocidade constante. Entre as suas aplicações em geração distribuída destacam-se aquelas que usam energias renováveis como em médios e pequenos aproveitamentos hidráulicos, em aproveitamentos da biomassa e nos aproveitamentos eólicos. Neste trabalho estuda-se a modelagem do gerador síncrono, estabilidade transitória utilizando os diferentes modelos de representação do gerador síncrono e o seu desempenho dinâmico em algumas situações de operação em geração distribuída com cargas lineares e com cargas não lineares, com e sem o uso de filtragem ativa / Abstract: The three-phase synchronous generators are currently the most used in generating electricity on a large scale worldwide. In generating electricity on a small scale, usually as distributed generation, connected to the distribution network or even medium voltage low voltage, they have been widely used due to its main quality is to operate at constant speed. Among its applications in distributed generation to include those using renewable energy as in medium and small hydroelectric plants, biomass and hydroelectric in the Windmill. This paper studies the modeling of the synchronous generator, transient stability using different models of representation of the synchronous generator and its dynamic performance in some situations of operation distributed generation with linear loads and nonlinear loads, with and without the use of filtering active / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica

<p>Worldwide concern on fossil fuels depletion and adverse impact on environment pushed researchers to find an alternative energy source. Among various potential systems, electrochemical energy storage devices have attracted significant attraction due to short charge/discharge time, easy relocation, and relatively cheap cost compared to large storage systems. Much research has been reported to suggest a material for electrochemical storage systems. Carbon is a key part of human life in terms of energy source, building materials, daily clothing and foods. The extraordinary characteristics of carbon materials, including good conductivity, good structure stability, relatively low cost, and sustainability, draw interest to carbon application in energy storage systems. </p> <p>The introduction of lithium ion batteries (LIB), using graphite as an anode material, fulfilled the need of alternative energy source and elevated the technologies into next level high-performance applications such as portable devices. While the technology advancement in high performance electronics fosters the development of advanced lithium ion batteries, the introduction of electric vehicles and large intermittent systems seeks energy storage devices with high capacity, sustainability, and low cost. In this thesis, the impact of the characteristics of carbon material on energy storage system performance is studied. The work presented in this thesis not only suggests a cost-effective carbon synthesis for advanced LIB, but also addresses how the carbon structure impact and resolves the systematic issue associated with next generation energy storage systems.</p> <p>Chapter 3 describes a facile, one-step, solvent-free ‘dry autoclaving’ synthesis method utilizing coffee oil as the carbon precursor to obtain micrometer diameter spheroidal carbon particles for lithium ion battery anodes. The spheroidal morphology resulted from the evaporation of liquid oil into a liquid/gas phase interphase at elevated temperature (700 ^oC), followed by solid/gas sublimation interactions during cooling (below 350 ^oC) in a closed autoclave. A mechanism of spheroidal carbon formation is proposed considering the precursor’s composition and chemical interactions during autoclaving. The prepared carbon from dry autoclave has shown successful LIB performance and structure stability after 250 cycles.</p> <p>Chapter 4 illustrates the temperature effect on the structure of biomass derived carbon. In this study, due to its abundance and high porosity, pistachio shells were selected as the primary carbon source and carbonized at a range from 700 to 1500 °C. The temperature effect on carbon structure was analyzed by XRD, Raman, BET, and electron microscopy. To propose an advanced lithium ion battery, pistachio shell-derived carbon was applied as an anode material for a sodium ion battery (SIB). The correlation of carbon structure and SIB electrochemical performance is presented. Pistachio shell carbonized at 1000 °C resulted in highly amorphous structure with specific surface area (760.9 m²/g) and stable cycle performance (225 mAh g^-1 at 10 mA g^-1). With support from Raman, XRD, and BET, the storage mechanism has been studied as well.</p> <p> Chapter 5 describes the impact of carbon structure on resolving the polysulfide shuttling effect in lithium sulfur (Li-S) batteries. Lithium sulfur batteries have received tremendous attention due to its high theoretical capacity (1672 mAh g^-1), sulfur abundance, and low cost. However, main systemic issues, associated with polysulfide shuttling and low Coulombic efficiency, hinder the practical use of sulfur electrodes in commercial batteries. The work in this thesis demonstrated an effective strategy of decorating nano-MnO₂ (less than 10 wt. %) onto a sulfur reservoir in order to further capture the out-diffused polysulfides via chemical interaction, and thereby improve the electrochemical performance of sulfur electrodes without increasing the mass burden of the total battery configuration. Pistachio shell-derived sustainable carbon (PC) was employed as an effective sulfur container due to its structural characteristics (interconnected macro channels and micropores). With the aids of the structural benefits of PC scaffold and the uniform decoration of nano-MnO₂, the polysulfide shuttling effect was significantly suppressed and cycling performance of a sulfur cathode was dramatically improved over 250 cycles.</p> This thesis offers a new prospect in the study of carbon materials applications in various energy storage systems. This concept can be further extended to other applications, such as lithium metal batteries. The intercalation property of carbon structure can reduce the local current density, reducing the risk of lithium dendrite growth, which is the most critical issue of lithium metal battery.

<p>Lithium ion batteries have been regarded as one of the most promising and intriguing energy storage devices in modern society since 1990s. A lithium ion battery contains three main components, cathode, anode, and electrolyte, and the performance of battery depends on each component and the compatibility between them. Electrolyte acts as a lithium ions conduction medium and two electrodes contribute mainly to the electrochemical performance. Generally, cathode is the limiting factor in terms of capacity and cell potential, which attracts significant research interests in this field.Different from conventional slurry thick film cathodes with additional electrochemically inactive additives, binder-free thin film cathode has become a promising candidate for advanced high-performance lithium ion batteries towards applications such as all-solid-state battery, portable electronics, and microelectronics. However, these electrodes generally require modifications to improve the performance due to intrinsically slow kinetics of cathode materials. </p> <p>In this thesis work, pulsed laser deposition has been applied to design thin film cathode electrodes with advanced nanostructures and improved electrochemical performance. Both single-phase nanostructure designs and multi-phase nanocomposite designs are explored. In terms of materials, the thesis focuses on manganese based layered oxides because of their high electrochemical performance. In Chapter 3 of the nanocomposite cathode work, well dispersed Au nanoparticles were introduced into highly textured LiNi_0.5Mn_0.3Co_0.2O₂(NMC532) matrix to act as localized current collectors and decrease the charge transfer resistance. To further develop this design, in Chapter 4, tilted Au pillars were incorporated into Li₂MnO₃ with more effective conductive Au distribution using simple one-step oblique angle pulsed laser deposition. In Chapter 5, the same methodology was also applied to grow 3D Li₂MnO₃ with tilted and isolated columnar morphology, which largely increase the lithium ion intercalation and the resulted rate capability. Finally, in Chapter 6, direct cathode integration of NMC532 was attempted on glass substrates for potential industrial applications. </p>