Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.

A critical requirement of any remote microgrid is its capability to control the balance between electric generation and load within the confines of the microgrid itself. The integration of significant amounts of “as available” renewable generation to any electric grid (macro or micro) makes it more difficult to maintain this balance and can result in large frequency deviations on a microgrid. Ancillary services provide the resources required to maintain the instantaneous and ongoing balance between generation and load. Battery energy storage systems (BESS) can provide regulating reserves, a type of ancillary service, by modulating active power for frequency control, referred to as load frequency control (LFC), to reduce frequency deviations caused by sudden changes in renewable generation. Historically, the most common methodology for reducing frequency disturbances exacerbated by wind plants with BESS systems is ramp rate control and more recently lead compensation. This thesis proposed a modified lead compensator for use in microgrid applications. A PSS®E microgrid model, based upon existing validated models, was developed to test the effectiveness of the LFC controllers used to dispatch the BESS as a regulating resource to allow increased wind energy penetration levels on remote microgrids. A model of the remote microgrid of the island of Maui, Hawaii was chosen as the basis for the designs. Daily wind power data from 2012 was classified and indexed on an hourly basis by severity of variation. The worst hour for power variation from the wind plants was identified from this indexing and used as the basis for simulating the LFC controllers. The results compared the effectiveness of droop, ramp rate, lead compensation, and modified lead compensation controllers in reducing the variability in the grid frequency caused by changes in wind power generation. An RMS of variation with respect to an average over different time windows was used as the comparison metric. The combined modified lead compensator with ramp rate control showed the best performance of the overall system behavior. / text

In today's highly competitive and deregulated electricity market, companies in the generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers' load profiles. This strategic information is vital in load forecasting, demand-side management planning and long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: 1) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. 2) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for load profile characterization and classification. 3) The frequency domain load profile statistics and forecasting models. Two different models were introduced in this dissertation: the first one is the wavelet load forecast model and the other one is a stochastic model that incorporates local weather condition and frequency domain load profile statistics to perform medium term load profile forecast. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately. / Ph. D.

Made available in DSpace on 2015-03-26T13:53:22Z (GMT). No. of bitstreams: 1 texto completo.pdf: 3187392 bytes, checksum: ac25bb90dac8a3f59cd1983b0f9610a8 (MD5) Previous issue date: 2011-07-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The pyrolysis has been evaluated as a mean of producing energy from biomass and organic wastes as an alternative to fossil fuels. The charcoal produced in this process is highly recalcitrant. Its incorporation in soil can contribute to carbon (C) sequestration and also improve soil quality for agricultural use. Little is known about the characteristics of charcoal and how they relate to their behavior. The objective of this study was to evaluate the effect of different pyrolysis conditions on some physical and chemical characteristics of charcoal produced. For this, the pyrolysis of Eucalyptus cloeziana (7 years of age) samples was carried out at nine temperatures between 300 and 700 °C under three heating rates (5, 22.5 and 40 °C/min) and carbonization time of 15 min. The bio-oil production reaches its maximum at temperatures above 400 °C for the heating rate of 5 °C/min and 450°C for the heating rates of 22.5 and 40 °C/min. By increasing the heating rate and temperature increase the occurrence of fractures in the structure of charcoals, reducing its physical resistance. The charcoal O and H levels significantly decreased by increasing pyrolysis temperature, due to preferential thermodecomposition of aliphatic and O alkyl groups, leaving a predominantlyn aromatic structure. The loss of O alkyl functional groups decreased the CEC of charcoals produced above 400 °C. It was reduced to values of about 1 cmolc/kg. With the higher aromaticity of charcoals produced at higher temperatures, there was an increase of its recalcitrance as demonstrated by higher resistance to thermal and chemical oxidation by dichromate in acid medium. Therefore it is not possible to select a combination of heating rate and pyrolysis temperature that allows producing a chemically active and recalcitrant charcoal. / O uso da pirólise como forma de aproveitamento energético da biomassa e resíduos orgânicos vem sendo avaliada como uma alternativa aos combustíveis fósseis. O carvão gerado neste processo é altamente recalcitrante, e sua incorporação ao solo pode contribuir para o sequestro de C, além de também melhorar a qualidade do solo para fins de uso agrícola. No entanto, pouco se conhece ainda sobre as características do carvão e como estas se relacionam com o seu comportamento. O objetivo deste trabalho foi avaliar o efeito de diferentes condições de pirólise sobre algumas características físicas e químicas do carvão produzido. Para isso, amostras de madeira de Eucalyptus cloeziana (7 anos de idade) foram pirolisadas em nove temperaturas entre 300 e 700 °C, sob três taxas de aquecimento (5, 22,5 e 40 °C/min), com tempo de carbonização de 15 min. A produção de bio-óleo atinge o máximo de rendimento em temperaturas acima de 400 °C na taxa de aquecimento de 5 °C/min, e 450 °C nas taxas de aquecimento de 22,5 e 40 °C/min. Com o aumento da temperatura e da taxa de aquecimento, aumenta a ocorrência de rupturas na estrutura dos carvões, com consequente redução da resistência física destes. Os teores de O e H reduziram significativamente com o aumento da temperatura de pirólise, devido à termodecomposição preferencial dos grupos alifáticos e O alquil, restando uma estrutura de composição predominantemente aromática. Com a perda de grupos funcionais O alquil, a CTC reduziu para valores de aproximadamente 1 cmolc/kg nos carvões produzidos acima de 400 °C. No entanto, o aumento da aromaticidade dos carvões produzidos em maiores temperaturas resulta no aumento da recalcitrância destes, demonstrada pelo aumento da resistência térmica e à oxidação por dicromato em meio ácido. Portanto, não é possível selecionar uma combinação de temperatura de pirólise e taxa de aquecimento que permita produzir um carvão quimicamente ativo e ao mesmo tempo recalcitrante.

Orientador: Anna Diva Plasencia Lotufo / Resumo: Os sistemas de energia elétrica estão passando por transformações. Aos poucos, técnicas de sistemas de informação estão sendo incorporadas aos sistemas atuais de energia. Basicamente este é o conceito de smart grid. Esta incorporação visa aumentar a eficiência dos sistemas de energia elétrica, pois os diversos agentes envolvidos em todo o sistema terão à disposição informações mais completas, precisas e de forma praticamente instantânea. Como consequência, haverá um aumento significativo de dados disponíveis para serem empregados de variadas formas. Um exemplo do uso de dados é a previsão de demanda de energia elétrica. De uma forma geral, previsões servem como suporte para suprir demandas, estimar custos ou justificar investimentos futuros. No campo de previsão de demanda de cargas elétricas existem diversos modelos na literatura, a grande maioria se concentra em níveis mais agregados, que atendem a grandes consumidores em que o fornecimento de energia é feito, por exemplo, por uma subestação. Uma smart grid também coloca à disposição as informações de consumo de energia em níveis cada vez menos agregados, como uma residência ou um prédio comercial. Realizar previsões neste nível é um desafio, pois essas demandas são muito influenciadas pelo comportamento humano. Diferentemente dos níveis mais agregados, modelos de previsão para níveis menos agregados, ou desagregados, ainda são poucos. O objetivo deste trabalho é fazer a previsão de cargas elétricas não residenciais mistas ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Electrical power systems are in transformation nowadays. Gradually, information system technology are being introduced to the energy systems. Basically, this is the concept of smart grid. This new concept aims to improve the efficiency of the energy systems, once the evolved agents will provide complete and precise information instantaneously. This way, a significant increase in data will be available to be employed in several forms. One example in using these data is electric energy demand forecasting. In general, predictions are support to provide electric load demand, estimate costs or justify future investments. Concerning electric load demand, there are several models in the literature, and the majority is concentrated in aggregated levels, attending large consumers, where, for example, the energy supply is provided by a substation. Considering the smart grid, there are consumption information in less aggregated levels as for example residences or commercial buildings. Therefore, realizing predictions in these levels (less aggregated) is a challenge, once the demand is influenced by the human behavior. The models for predicting loads in aggregated levels are common, in the contrary of less aggregated that are few. This work aims to predict short term mixed nonresidential electric loads using data from a Brazilian University. Firstly, Fuzzy ARTMAP Neural Network is chosen to execute the predictions, and afterwards a hybrid methodology containing Fuzzy ARTMAP and Square Mi... (Complete abstract click electronic access below) / Doutor

In der vorliegenden Arbeit wird ein Modell zur Beschreibung des Energiebedarfs elektrischer Fahrzeuge des ÖPNV auf Basis von Wahrscheinlichkeitsdichten entwickelt, das insbesondere eine Dimensionierung von fahrzeugseitigem Energiespeichersystem und wegseitiger Energieversorgungsinfrastruktur in einem fahrleitungsfreien Betriebskonzept (DockingPrinzip) erlaubt. Im Gegensatz zur deterministischen Energiebedarfsbestimmung ermöglicht die stochastische Modellierung mit einer Kombination aus Markov-Kette und Semi-Markov-Prozess die Berücksichtigung von Zuverlässigkeitsvorgaben im Sinne einer Missionserfüllung. Schließlich kann so die Größe hybrider Fahrzeugenergiespeichersysteme und die Lage von Nachladestationen entlang der Strecke optimiert werden. Die Wirksamkeit der Modellierung wird anhand einer Fallstudie basierend auf Messdaten für ein Straßenbahnfahrzeug demonstriert. Für die Auslegung der wegseitigen Energieversorgungsinfrastruktur werden die Belastungsgänge des Nachladeprozesses in Form von zeitgewichteten Belastungsdauerkurven für charakteristische Netztopographien hergeleitet. Ein Laden des fahrzeugseitigen Energiespeichers aus einer wegseitigen Energie-Vorsammel-Station (Docking-Station) bringt einerseits eine erhebliche Glättung des Leistungsverlaufs beim Energiebezug. Andererseits ist ein elektrischer Anschluss dieser Station an das Niederspannungsnetz in gewöhnlichen städtischen Siedlungsstrukturen innerhalb weniger hundert Meter möglich.

Operational performance of grid-connected microgrid with integrated solar photovoltaic (PV) electricity production and battery energy storage (BES) is investigated.  These distributed energy resources (DERs) have the potential to reduce conventionally produced electrical power and contribute to reduction of greenhouse gas emissions.  This investigation is based upon the DER’s techno-economic specifications and theoretical performance, consumer load data and electrical utility retail and distribution data.  Available literature provides the basis for DER specification and performance.  Actual consumer load profile data is available for residential and commercial consumer sector customers.  The electrical utility data is obtained from Mälarenergi, AB.  The aim is to investigate how to use simulations to specify a grid connected microgrid with DERs (PV production and a BES system) for two consumer sectors considering a range of objectives.  An open-source, MATLAB-based simulation tool called Opti-CE has successfully been utilized.  This package employs a genetic algorithm for multi-objective optimization.  To support attainment of one of the objectives, peak shaving of the consumer load, a battery operational strategy algorithm has been developed for the simulation.  With respect to balancing peak shaving and self-consumption one of the simulations supports specification of a commercial sector application with 117 kWp PV power rating paired with a lithium ion battery with 41.1 kWh capacity.  The simulation of this system predicts the possibility to shave the customer load profile peaks for the month of April by 20%.  The corresponding self-consumption ratio is 88%.  Differences in the relationship between the load profiles and the system performance have been qualitatively noted.  Furthermore, simulation results for lead-acid, lithium-ion and vanadium-redox flow battery systems are compared to reveal that lithium ion delivers the best balance between total annualized cost and peak shaving performance for both residential and commercial applications.

Dans cette thèse, une méthode de résolution de problèmes de commande optimale non linéaires sous contraintes d'état et de commande. Cette méthode repose sur l'adaptation des méthodes de points intérieurs, utilisées en optimisation de dimension finie, à la commande optimale. Un choix constructif de fonctions de pénalisation intérieure est fourni dans cette thèse. On montre que ce choix permet d'approcher la solution d'un problème de commande optimale sous contraintes en résolvant une suite de problèmes de commande optimale sans contraintes dont les solutions sont simplement caractérisées par les conditions de stationnarité du calcul des variations.Deux études dans le domaine de la gestion de l'énergie dans les bâtiments sont ensuite conduites. La première consiste à quantifier la durée maximale d'effacement quotidien du chauffage permettant de maintenir la température intérieure dans une certaine bande de confort, et ce pour différents types de bâtiments classés de mal à bien isolés. La seconde étude se concentre sur les bâtiments BBC et consiste à quantifier la capacité de ces bâtiments à réaliser des effacements électriques complets du chauffage de 6h00 à 22h00 tout en maintenant, là encore, la température intérieure dans une bande de confort. Cette étude est réalisée sur l'ensemble de la saison de chauffe. / This thesis exposes a methodology to solve constrained optimal controlof non linear systems by interior penalty methods. A constructivechoice for the penalty functions used to implement the interior methodis exhibited in this thesis. It is shown that itallows us to approach the solution of the non linear optimal controlproblem using a sequence of unconstrained problems, whose solutionsare readily characterized by the simple calculus of variations.Two representatives study of energy management in buildings are conducted using the provided algorithm. The first study consists in quantifying the maximal duration of daily complete load shiftings achievable by several buildings ranging from poorly to well insulated. The second study focuses on low consumption buildings and aim at quantifying the ability of these buildings to perform complete load shiftings of the heating electrical consumption from the day (6 a.m. to 10 p.m.) to the night period over the whole heating season.

Orientador: Carlos Alberto de Castro Junior / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-06T19:37:05Z (GMT). No. of bitstreams: 1 Gomes_RicardoBorges_M.pdf: 725231 bytes, checksum: e24b9811f14e33910092b2f639a89050 (MD5) Previous issue date: 2006 / Resumo: O método desacoplado rápido (MDR) [2] é uma variante do tradicional método de Newton [1] para a resolução do problema de fluxo de carga (obtenção do estado de operação de redes elétricas de potência). Sabe-se que o MDR apresenta desempenho insatisfatório quando aplicado a redes de distribuição, devido à desfavorável relação r/x dos ramos, resultando num processo de cálculo que pode apresentar divergência ou convergência lenta (grande número de iterações). Há algum tempo foi proposta uma alteração no MDR, chamada de rotação de eixos[4], que melhora as características de convergência do método. A idéia consiste em obter uma rede fictícia para a qual o MDR funcione bem e cujo estado de operação (magnitudes e ângulos de tensão) seja o mesmo da rede original. O valor do ângulo de rotação de eixos, único para toda a rede, é determinado empiricamente. Recentemente uma outra proposta de rotação ótima de eixos[5] foi apresentada, sugerindo modificações ao método que trouxeram maior automação aos cálculos, apesar de efeitos desfavoráveis em relação à manipulação de matrizes e ao significado físico da rede elétrica durante o processo iterativo. O presente trabalho traz um novo algoritmo de rotação de eixos que supera algumas desvantagens dos métodos apresentados em [4, 5], com bom desempenho. Além disso, traz uma interessante contribuição sobre a rotação de barras do tipo PV, não abordado anteriormente / Abstract: The fast decoupled loadflow (FDLF) [2] is a variant of the traditional Newton method [1] for solving the loadflow problem (find the operational state of electrical power networks). It is well-known that FDLF presents unsatisfactory performance when applied to distribution systems. Their unfavourable r/x branch ratios may lead to divergence or slow convergence (large number of iterations). A modification to the FDLF, called axesrotation[4], was proposed some time ago, which improves convergency of the method. The idea is to obtain a fictitious network for which the FDLF performs better and which operational state (voltage magnitudes and angles) is the same as the original network. However, the rotation angle is determined empirically. Recently the optimal axes rotation[5] was presented, suggesting some modifications that led to more automated calculations, despite of some undesirable effects on matrices handling and also to the physical meaning of networks during the iterative process. This research work presents a new algorithm for axes rotation that overcomes some disadvantages found in [4, 5], with good performance. Moreover, it brings an interesting contribution on the rotation of PV buses, not previously considered. / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica

Orientador: Takaaki Ohishi / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-08T10:06:35Z (GMT). No. of bitstreams: 1 LeoneFilho_MarcosdeAlmeida_M.pdf: 1557959 bytes, checksum: 92dc63d4e3140cc61ba7900961c0e9fb (MD5) Previous issue date: 2006 / Resumo: Neste trabalho é proposta uma metodologia para previsão de séries temporais de carga de energia elétrica de curto prazo. Esta metodologia vem sendo muito utilizada no contexto da previsão de séries temporais e do reconhecimento de padrões. Os autores que propuseram esta metodologia a chamaram de "Ensembles". Este nome tenta explicar o é este modelo: uma combinação de partes que juntas formam um só modelo. Neste sentido, este nome expressa com relativa clareza qual é o principal aspecto desta metodologia, que no caso específico deste trabalho, é o de fazer várias previsões de uma mesma série temporal utilizando diferentes ferramentas que sozinhas são suficientemente competentes para prever a série temporal em questão, e em seguida combinar as soluções para, deste modo, tentar obter uma solução melhor do que quando é usada somente uma ferramenta. As ferramentas usadas para compor a previsão dos "Ensembles" finais são Redes Neurais Artificiais (RNAs) e Redes Neurais Nebulosas. Atualmente, estas redes são largamente utilizadas em problemas de previsão de séries temporais, principalmente quando o fator gerador destas séries é um sistema não-linear. Desta forma, isto as tornou candidatas potenciais para prever valores de uma série de cargas de energia elétrica, pois este tipo de série tem características essencialmente não-lineares. Sendo assim, foram utilizados quatro tipos de redes: RNAs MLPs, RNAs Recorrentes, RNAs de Base Radial e Redes Neurais Nebulosas tipo ANFIS. Com os modelos básicos de redes foram, utilizados Algoritmos Genéticos para evoluir os parâmetros destas redes e, assim, chegar a uma população de redes suficientemente competentes para fazer as previsões da série de cargas. Na próxima etapa, com os resultados das previsões da população de redes evoluídas foi feita a seleção dos melhores agrupamentos destas redes evoluídas e, como este processo requer a avaliação de diferentes configurações de modelos, esta seleção é baseada em Algoritmos Genéticos.Os resultados obtidos ao se utilizar "ensembles" mostraram que este modelo foi capaz de alcançar uma grande robustez na previsão, reduzindo os erros de previsão, suavizando os resultados de previsão e deixando o modelo menos suscetível a grandes erros quando surgem "outliers" no conjunto de dados / Abstract: This work proposes a methodology for short-term electric power load forecasting. This methodology is being widely used under the context of time series prediction and pattern recognition. It was named "ensembles" by the authors who developed it. This name carries the meaning of an assemblage of parts considered as forming a whole. Therefore, this name expresses rather clearly the main characteristic of this methodology, which under the framework of this study is to make several predictions of the same time series using various different tools in which every single one alone is sufficiently competent to predict the above mentioned time series. After that, the predictions are combined in order to achieve a better prediction compared to the one that is obtained if a single predictor is used. The tools implemented to form the final "ensembles" prediction are Artificial Neural Networks (ANNs) and Neuro-fuzzy Networks. Nowadays, these networks are being widely used in time series predictions problems, mainly when the factor that generates these series is a non-linear system. Hence, this fact has elected them as potential candidates to predict future values of an electric power load series because this series has essentially non-linear characteristics. As a result, four types of networks were utilized in this work: MLPs ANNs, Recurrent ANNs, Radial Basis ANNs and ANFIS type Neuro-fuzzy networks. So, with the basic networks models, Genetic Algorithms were applied to evolve the parameters of these networks and, as a consequence, a population of networks sufficiently capable of predicting future values of the load time series was built. On the next step, with the results obtained from the evolved population of networks, a selection of the most suitable results of the individual networks were made and, as soon as this process implies the evaluation of multiple different combinations of models, this methodology was based on Genetic Algorithms. Then, this selected networks were combined. The results when using "ensembles" revealed that this model was able to reach a great robustness in prediction tasks. In that sense, it was possible to reduce the level of prediction error, to smooth the resulting predictions and to make the model more stable reducing the possibilities of presenting high levels of errors when the used data set contains "outliers" / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica