Thesis (M.S. in Systems Technology)--Naval Postgraduate School, June 2005. / Thesis Advisor(s): Bill Kemple. Includes bibliographical references (p. 131-132). Also available online.

The fuel economy of Fuel Cell Vehicles (FCVs) is affected by various factors such as the fuel cell efficiency, the regenerative energy capturing, the power control strategy, the vehicle driving patterns, the degree of hybridization between fuel cells and energy storage systems, and so on. In this thesis, a new power control strategy is proposed to improve fuel economy for hybrid FCVs considering the fuel cell efficiency and battery energy management. In order to show the power flows due to the proposed power control strategy and analyze the fuel economy, an overall vehicle simulation for three types of FCVs is implemented. The results show that the fuel economy can be improved by operating the fuel cell system within the specified high efficiency region and managing the state of charge (SOC) of the battery for absorbing regeneration energy effectively.

This systematic review of the literature examined the effectiveness of the Power Card strategy to increase social skills in studies conducted with individuals with autism and other intellectual disabilities. Database searches conducted identified 12 studies that met the inclusion criteria with a total of 30 participants who had a diagnosis of autism spectrum disorder (ASD) or an intellectual disability (IDD). The majority of the Power Card studies (n= 7) targeted social skills, while other targeted skills include direction following, on-task behaviors, latency to teacher cues, executive functioning, and personal space. All 12 studies were reviewed and analyzed for their intervention procedures such as the use of a scenario card, access to the Power Card after reading, if a functional behavior assessment (FBA) was completed, and how the special interest item (SIA) was chosen. Results of the review highlight the need for more research to evaluate which steps of the Power Card strategy are most effective, the need for a greater variety of target behaviors, and the need to focus on the maintenance and generalization of skills learned via the Power Card strategy. Relevant suggestions for future research and practice are discussed. / Applied Behavioral Analysis

毛澤東是中共革命及建政中最具影響力的領導人物，他的政治思想在某種程度上改變了中共的發展路線。誠如斯圖爾特．施拉姆(Stuart．R．Schram)所說：「思想發源於歷史，思想也塑造歷史」，毛澤東的政治思想有其受中國歷史傳統影響的一面。但是，就身為一位革命者而言，毛的政治思想在開創歷史方面，更值得去研究與探討。 　　就中共的歷史來看，中共黨內歷次的鬥爭，所包含的意義除了領導者之間的權力鬥爭之外，它的深刻意含尚包括一套政治權力與策略的建立。從陳獨秀的「右傾」，瞿秋白、李立三的「盲動」，到王明國際派的倒台及毛澤東取得中共黨內的領導權，這一連串歷史過程不僅改變了中共的革命，也建立了毛澤東的政治思想與實踐，這就如同葛蘭西(Antonio Gra- msci)所稱的「建立領導階級同創造世界觀有同等的價值」，而「實現了的領導權意味著對哲學的實在評論，意味著它的實在的辯證法」，身為一位革命者，毛澤東既反傳統又從傳統文化吸取精華，他的革命改變了歷史，卻也造就出他獨特的政治思想，一種具體的社會實踐及辯證的政治思想。 　　傅柯(Michel Foucault)認為「當代哲學完全是政治的和歷史的。它是內在於歷史的政治，同政治不可分隔的歷史」，毛的政治思想，如同傅柯所言是「內在於歷史的政治」，毛熟讀中國歷史，懂得歷史的政治過程，他更瞭解歷史政治的權力與策略。當毛澤東以「中國人民站起來了」，總結中共革命及建政的時代意義時，這場革命似乎只關注道德及正義，革命所具有的權力爭奪被有意的忽略。如今是重新探討這場革命的權力與策略研究的時候了。

Thesis (M.S.)--Missouri State University, 2008. / "December 2008." Includes bibliographical references (leaves 92-97). Also available online.

abstract: The greenhouse gases in the atmosphere have reached a highest level due to high number of vehicles. A Fuel Cell Hybrid Electric Vehicle (FCHEV) has zero greenhouse gas emissions compared to conventional ICE vehicles or Hybrid Electric Vehicles and hence is a better alternative. All Electric Vehicle (AEVs) have longer charging time which is unfavorable. A fully charged battery gives less range compared to a FCHEV with a full hydrogen tank. So FCHEV has an advantage of a quick fuel up and more mileage than AEVs. A Proton Electron Membrane Fuel Cell (PEMFC) is the commonly used kind of fuel cell vehicles but it possesses slow current dynamics and hence not suitable to be the sole power source in a vehicle. Therefore, improving the transient power capabilities of fuel cell to satisfy the road load demand is critical. This research studies integration of Ultra-Capacitor (UC) to FCHEV. The objective is to analyze the effect of integrating UCs on the transient response of FCHEV powertrain. UCs has higher power density which can overcome slow dynamics of fuel cell. A power management strategy utilizing peak power shaving strategy is implemented. The goal is to decrease power load on batteries and operate fuel cell stack in it’s most efficient region. Complete model to simulate the physical behavior of UC-Integrated FCHEV (UC-FCHEV) is developed using Matlab/SIMULINK. The fuel cell polarization curve is utilized to devise operating points of the fuel cell to maintain its operation at most efficient region. Results show reduction of hydrogen consumption in aggressive US06 drive cycle from 0.29 kg per drive cycle to 0.12 kg. The maximum charge/discharge battery current was reduced from 286 amperes to 110 amperes in US06 drive cycle. Results for the FUDS drive cycle show a reduction in fuel consumption from 0.18 kg to 0.05 kg in one drive cycle. This reduction in current increases the life of the battery since its protected from overcurrent. The SOC profile of the battery also shows that the battery is not discharged to its minimum threshold which increasing the health of the battery based on number of charge/discharge cycles. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2017

As regras definidas pelo Decreto 5.163/2004 trazem incentivos e penalidades aos Distribuidores no processo de apresentação de suas declarações de necessidades de compra de energia ao Ministério de Minas e Energia. Nesse sentido, é importante para os Geradores estabelecer uma metodologia robusta para prever o comportamento dos agentes de distribuição com confiabilidade razoável, de forma a permitir uma preparação adequada para os leilões de que pretendem participar e, adicionalmente, simular os cenários pós-leilões de modo a compreender os efeitos dos preços e volumes contratados no ambiente regulado sobre as condições de contratação no ambiente livre. Este trabalho propõe-se a analisar as referidas regras, apresentando um modelo de otimização utilizando Algoritmos Genéticos que simula o comportamento das distribuidoras, obtendo-se uma importante ferramenta de apoio à definição de estratégias de comercialização de uma empresa geradora. / The rules defined by the Decree 5.163/2004 bring incentives and penalties for Distribution companies to present their power purchase necessity declaration for the Ministry of Mines and Energy. In this sense, it is important for the Generation companies to establish a robust methodology for predicting Distribution companies behavior with enough accountability in order to allow an adequate preparation for the auctions in which those agents intend to participate and, additionally, simulate post auctions scenarios in order to understand the effects of prices and contracted volumes in the regulated environment over the free market contracting conditions. This work is supposed to analyze those rules, presenting an optimization model using Genetic Algorithms, which simulates Distribution companies behavior, getting an important power trading strategy decision support tool for a Generation Company.

La nature intermittente et aléatoire des sources renouvelables, telles que le photovoltaïque et l’éolien, nécessite un complément de stockage, tel une batterie et un système de secours énergétique, tel un générateur diesel, en particulier dans un système autonome. En ce qui concerne le générateur diesel, il a besoin d'un certain temps pour démarrer et il ne peut pas donner immédiatement la puissance nécessaire, en raison de son comportement dynamique. Alors, la qualité de l'énergie est abaissée pendant cette période en raison du manque de puissance. Par conséquent, pendant la période de démarrage du générateur diesel, un supercondensateur est suggéré pour équilibrer la puissance en raison de sa réponse rapide et de sa densité de puissance élevée. Une stratégie de contrôle de puissance est proposée pour réaliser la coordination entre le générateur diesel et le supercondensateur. La simulation et les résultats expérimentaux montrent que la stratégie de contrôle proposée est capable de réguler la tension du bus continu dans des limites acceptables et d’alimenter la charge pendant la sous production d'énergie renouvelable ou lors d'augmentation de la demande de la charge. De plus, le supercondensateur peut également être utilisé pour surmonter les limites de stockage électrochimique telles que son état de charge et son courant maximal. Ainsi, cette thèse propose le contrôle de puissance en temps réel pour un micro réseau continu avec un système hybride photovoltaïque-batterie-supercondensateur-diesel, visant à répondre à la demande de puissance de charge avec fiabilité et à stabiliser de la tension du bus continu. La simulation et les résultats expérimentaux montrent également que la stratégie de contrôle améliore les performances dynamiques et statiques du micro réseau continu pour différentes conditions de fonctionnement. De plus, afin de minimiser le coût énergétique du groupe diesel, le coût du carburant et la consommation de carburant sont analysés à travers plusieurs tests expérimentaux. Par conséquent, la valeur optimale de sa production d'énergie est déduite et appliquée dans une nouvelle stratégie de gestion de la puissance est proposée. Cette stratégie peut atteindre l'objectif de maximiser l'utilisation de l'énergie photovoltaïque et de prendre en compte la caractéristique de démarrage lent et le coût énergétique du générateur diesel. Les simulations et expérimentations sont réalisées en utilisant des données photovoltaïques réelles pour illustrer les performances et le comportement du système hybride. Les résultats obtenus vérifient l'efficacité de cette stratégie. De plus la comparaison avec la stratégie de gestion de la puissance précédente, dans laquelle le coût d’énergie du générateur diesel n'est pas pris en compte, démontre que la nouvelle stratégie de gestion peut réduire le coût total du système de puissance à courant continu hybride. / The intermittent and random nature of renewable sources, such as photovoltaic and wind turbine, asks for the complement of storage, such as battery and back-up energy, such as diesel generator, especially in a standalone power system. Concerning the diesel generator, it needs some time to start up and cannot immediately offer the needed power, due to its dynamic behavior. Hence, the power quality is lowered down during this period because of the shortage of power. Therefore, during the period of the diesel generator starting up, a supercapacitor is suggested to compensate the power balance because of its fast response and high power density. A power control strategy is proposed to achieve the coordination between diesel generator and supercapacitor. Both simulation and experimental results show that the proposed control strategy is able to regulate the DC bus voltage within the acceptable limits and supplying the load during the renewable power under generation or load step-increase situations. In addition, the supercapacitor can be also used to overcome the electrochemical storage limits like its state of charge and maximum current. So, this thesis proposes the real time power control for a hybrid photovoltaic-battery-supercapacitor-diesel generator DC microgrid system, aiming to meet the load power demand with reliability and stabilizing the DC bus voltage. Both simulation and experimental results show that the designed control strategy improves the DC microgrid dynamic and static performances under different operating conditions. Furthermore, in order to minimize the diesel generator energy cost, the fuel cost and fuel consumption are analysed through several experimental tests. Therefore, the optimal value of its power generation is deduced and applied in a newly proposed energy management strategy. This strategy can achieve the goal of maximizing the utilization of photovoltaic energy and taking into account the slow start-up characteristic and energy cost of diesel generator. Both simulation and experimental studies are carried out by using the real photovoltaic data to illustrate the performance and the behavior of the hybrid system. The obtained results verify the effectiveness of this strategy. Furthermore, the comparison with the previous energy management strategy, in which the diesel generator energy cost is not considered, demonstrates that the newly proposed energy management strategy can reduce the total cost of the hybrid DC power system.

As regras definidas pelo Decreto 5.163/2004 trazem incentivos e penalidades aos Distribuidores no processo de apresentação de suas declarações de necessidades de compra de energia ao Ministério de Minas e Energia. Nesse sentido, é importante para os Geradores estabelecer uma metodologia robusta para prever o comportamento dos agentes de distribuição com confiabilidade razoável, de forma a permitir uma preparação adequada para os leilões de que pretendem participar e, adicionalmente, simular os cenários pós-leilões de modo a compreender os efeitos dos preços e volumes contratados no ambiente regulado sobre as condições de contratação no ambiente livre. Este trabalho propõe-se a analisar as referidas regras, apresentando um modelo de otimização utilizando Algoritmos Genéticos que simula o comportamento das distribuidoras, obtendo-se uma importante ferramenta de apoio à definição de estratégias de comercialização de uma empresa geradora. / The rules defined by the Decree 5.163/2004 bring incentives and penalties for Distribution companies to present their power purchase necessity declaration for the Ministry of Mines and Energy. In this sense, it is important for the Generation companies to establish a robust methodology for predicting Distribution companies behavior with enough accountability in order to allow an adequate preparation for the auctions in which those agents intend to participate and, additionally, simulate post auctions scenarios in order to understand the effects of prices and contracted volumes in the regulated environment over the free market contracting conditions. This work is supposed to analyze those rules, presenting an optimization model using Genetic Algorithms, which simulates Distribution companies behavior, getting an important power trading strategy decision support tool for a Generation Company.

The world is right now in a global transition from a fossil fuel dependency towards an electrified society based on green and renewable energy. Investments in power grid capacity are therefore needed to meet the increased future demand which this transition implicates. One part of this is the expansion of intermittent energy sources, such as wind and solar power. Even though these sources have benefits in form of cheap and green energy, they have other characteristics that need to be addressed. Per definition, intermittent power sources cannot produce energy on demand since they are dependent on weather conditions such as wind and sun. This induces a second problem which is that it can be hard to predict the production from intermittent power sources, especially wind, which increases the volatility in the power market. Because of these characteristics, the expansion of wind power has increased the volume traded on the intraday power market. The intermittent energy surge, emphasizes the need of a good trading strategy for balance responsible parties to handle the increased trading volume and volatility. The prupose of this report is to introduce the elements which affect intraday power trading, formulate the fundamentals of a power trading strategy and thereafter explore how predictive models can be used in such a strategy. This includes predicting regulating and intraday market prices using linear regression models, neural networks and LSTM-models. Furthermore, the report highlights underlying properties which affects the predictive power of a prediction model used to forecast wind power production. Regulating prices can be predicted well using both linear regression models and more complex deep learning models based on weather and market data. Both approaches are better than using a simple model based on the latest regulating and market price, since the simple model tends to fall short in a volatile market. Overall, the deep learning models performs the best.  The difference in result when predicting the volume weighted average price on the intraday market, using linear regression and machine learning, are not as substantial. In fact, the linear models tends to outperform the machine learning models in some instaces. The conclusion when analyzing how underlying properties affect wind power prediction models is that how far ahead the model predicts is not the key factor affecting predictive power. Instead, the production volume predicted has a larger effect.