No / This paper examines the effects of geopolitical risks on renewable energy demand in 20 Organization for Economic Co-operation and Development (OECD) member countries from 1970 to 2019. The renewable energy demand function includes carbon dioxide (CO2) emissions, economic globalisation, natural resources rents, and per capita income as control variables. It is found that geopolitical risks reduce the demand for renewable energy and threaten climate change mitigation policies. Degrading the environment in terms of rising CO2 emissions is detrimental to the renewable energy demand. Natural resource rents also decrease renewable energy consumption. However, higher per capita income and economic globalisation significantly increase renewable energy consumption. These findings bear crucial policy implications for the Russia-Ukraine War era, suggesting that geopolitical risks discourage renewable energy demand. Therefore, policymakers in the OECD countries should focus on geopolitical harmony among economic agents, groups, and regions.

The work presented in this thesis focuses on reducing the energy demand of a residential building by using a coupled ground-source heat pump/energy recovery ventilation (GSHP-ERV) system to present a novel approach to space condition and domestic hot water supply for a residence. The proposed system is capable of providing hot water on-demand with a high coefficient of performance (COP), thus eliminating the need for a hot water storage tank and circulation system while requiring little power consumption. The necessary size of the proposed system and the maximum and normal heating and cooling loads for the home were calculated based on the assumptions of an energy efficient home, the assumed construction specifications, and the climate characteristics of the Blacksburg, Virginia region. The results from the load analysis were used to predict energy consumption and costs associated with annual operations.The results for the predicted heating annual energy consumption and costs for the GSHP-ERV system were compared to an air-source heat pump and a natural gas furnace. On average, it was determined that the proposed system was capable of reducing annual energy consumption by 56-78% over air-source heat pumps and 85-88% over a natural gas furnace. The proposed GSHP-ERV system reduced costs by 45-61% over air-source heat pump systems and 52-58% over natural gas furnaces. The annual energy consumption and costs associated with cooling were not calculated as cooling accounts for a negligible portion (6%) of the total annual energy demand for a home in Blacksburg. / Master of Science

Energy forms a vital input and critical infrastructure for the economic development of countries and for improving the quality of life of people. Energy is utilized in society through the operation of large socio-technical systems called energy systems. In a growing world, as the focus shifts to better access and use of modern energy sources, there is a rising demand for energy. However, certain externalities result in this demand not being met adequately, especially in developing countries. This constitutes the energy demand – supply matching problem. Load shedding is a response used by distribution utilities in developing countries, to deal with the energy demand – supply problem in the short term and to secure the grid. This response impacts the activities of consumers and entails economic losses. Given this scenario, demand – supply matching becomes a crucial decision making activity. Traditionally demand – supply matching has been carried out by increasing supply centrally in the long term or reducing demand centrally in the short term. Literature shows that these options have not been very effective in solving the demand-supply problem. Gaps in literature also show that the need of the hour is the design of alternate solutions which are tailored to a nation's specific energy service needs in a sustainable way. Microgrids using renewable and clean energy resources and demand side management can be suitable decentralized alternatives to augment the centralized grid based systems and enable demand – supply matching at a local community level. The central research question posed by this thesis is: “How can we reduce the demand – supply gap existing in a community, due to grid insufficiency, using locally available resources and the grid in an optimal way; and thereby facilitate microgrid implementation?” The overall aim of this dissertation is to solve the energy demand – supply matching problem at the community level. It is known that decisions for the creation of energy systems are influenced by several factors. This study focuses on those factors which policy-makers and stakeholders can influence. It proposes an integrated decision framework for the creation of community microgrids. The study looks at several different dimensions of the existing demand – supply problem in a holistic way. The research objectives of this study are: 1. To develop an integrated decision framework that solves the demand – supply matching problem at a community level. 2. To decompose the consumption patterns of the community into end-uses. solar thermal, solar lighting and solar pumps and a combination of these at different capacities. The options feasible for medium income consumers are solar thermal, solar pumps, municipal waste based systems and a combination of these. The options for high income consumers are municipal waste based CHP systems, solar thermal and solar pumps. Residential consumers living in multi-storied buildings also have the options of CHP, micro wind and solar. For cooking, LPG is the single most effective alternative. 3. To identify the ‗best fitting‘ distributed energy system (microgrid), based on the end-use consumption patterns of the community and locally available clean and renewable energy resources, for matching demand – supply at the community level. 4. To facilitate the implementation of microgrids by * Contextualizing the demand – supply matching problem to consider the local social and political environment or landscape, * Studying the economic impact of load shedding and incorporating it into the demand-supply matching problem, and * Presenting multiple decision scenarios, addressing the needs of different stakeholders, to enable dialogue and participative decision making. A multi-stage Integrated Decision Framework (IDF) is developed to solve the demand - supply matching problem in a sequential manner. The first stage in the IDF towards solving the problem is the identification and estimation of the energy needs / end-uses of consumers in a community. This process is called End-use Demand Decomposition (EUDD) and is accomplished by an empirical estimation of consumer electricity demand based on structural and socio-economic factors. An algorithm/ heuristic is also presented to decompose this demand into its constituent end-uses at the community level for the purpose of identifying suitable and optimal alternatives/ augments to grid based electricity. The second stage in the framework is Best Fit DES. This stage involves identifying the “best-fit‘ distributed energy system (microgrid) for the community that optimally matches the energy demand with available forms of supply and provides a schedule for the operation of these various supply options to maximize stakeholder utility. It provides the decision makers with a methodology for identifying the optimal distributed energy resource (DER) mix, capacity and annual operational schedule that “best fits” the given end-use demand profile of consumers in a community and under the constraints of that community such that it meets the needs of the stakeholders. The optimization technique developed is a Mixed Integer Linear Program and is a modification of the DER-CAM™ (Distributed Energy Resources Customer Adoption Model), which is developed by the Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory using the GAMS platform. The third stage is the Community Microgrid Implementation (CMI) stage. The CMI stage of IDF includes three steps. The first one is to contextualize the energy demand and supply for a specific region and the communities within it. This is done by the Energy Landscape Analysis (ELA). The energy landscape analysis attempts to understand the current scenario and develop a baseline for the study. It identifies the potential solutions for the demand - supply problem from a stakeholder perspective. The next step provides a rationale for the creation of community level decentralized energy systems and microgrids from a sustainability perspective. This is done by presenting a theoretical model for outage costs (or load shedding), empirically substantiating it and providing a simulation model to demonstrate the viability for distributed energy systems. Outage cost or the cost of non supply is a variable that can be used to determine the need for alternate systems in the absence/ unavailability of the grid. The final step in the CMI stage is to provide a scenario analysis for the implementation of community microgrids. The scenario analysis step in the framework enlightens decision makers about the baselines and thresholds for the solutions obtained in the “best fit‘ analysis. The first two stages of IDF, EUDD and Best Fit DES, address the problem from a bottom-up perspective. The solution obtained from these stages constitutes the optimal solution from a technical perspective. The third stage CMI is a top-down approach to the problem, which assesses the social and policy parameters. This stage provides a set of satisficing solutions/ scenarios to enable a dialogue between stakeholders to facilitate implementation of microgrids. Thus, IDF follows a hybrid approach to problem solving. The proposed IDF is then used to demonstrate the choice of microgrids for residential communities. In particular, the framework is demonstrated for a typical residential community, Vijayanagar, situated in Bangalore and the findings presented. The End-use Demand Decomposition (EUDD) stage provides the decision makers with a methodology for estimating consumer demand given their socio-economic status, fuel choice and appliance profiles. This is done by the means of a statistical analysis. For this a primary survey of 375 residential households belonging to the LT2a category of BESCOM (Bangalore Electricity Supply Company) was conducted in the Bangalore metropolitan area. The results of the current study show that consumer demand is a function of the variables family income, refrigeration, entertainment, water heating, family size, space cooling, gas use, wood use, kerosene use and space heating. The final regression model (with these variables) can effectively predict up to 60% of the variation in the electricity consumption of a household ln(ElecConsumption) = 0.2880.396*ln(Income)+0.2 66*Refri geration+ 0.708*Entertainment+0.334*WaterHeating+0.047*FamSize+ 0243*SpaceCooling.+580*GasUse+0.421*WoodUse–0.159*KeroseneUse+ 0.568*SpaceHeating ln(ElecConsumption) = 0.406*ln(Income)0.168*Ref rigeration+0.139*Entertainment+ 0.213*WaterHeating+0.114*FamSize+0.121*SpacCooling+0.171*GasUse+ 0.115*WoodUse–0.094*KeroseneUse+0.075*SpaceHeating   The next step of EUDD is to break up the demand into its constituent end-uses. The third step involves aggregating the end-uses at the community level. These two steps are to be performed using a heuristic. The Best Fit DES stage of IDF is demonstrated with data from an urban community in Bangalore. This community is located in an area called Vijayanagar in Bangalore city. Vijayanagar is a mainly a residential area with some pockets of mixed use. Since grid availability is the constraining parameter that yields varying energy availability, this constraint is taken as the criteria for evaluation of the model. The Best Fit DES model is run for different values of the grid availability parameter to study the changes in outputs obtained in DER mix, schedules and overall cost of the system and the results are tabulated. Sensitivity analysis is also performed to study the effect of changing load, price options, fuel costs and technology parameters. The results obtained from the BEST Fit DES model for Vijayanagar illustrate that microgrids and DERs can be a suitable alternative for meeting the demand – supply gap locally. The cost of implementing DERs is the optimal solution. The savings obtained from this option however is less than 1% than the base case due to the subsidized price of grid based electricity. The corresponding costs for different hours of grid availability are higher than the base case, but this is offset by the increased efficiency of the overall system and improved reliability that is obtained in the community due to availability of power 24/7 regardless of the availability of grid based power. If the price of grid power is changed to reflect the true price of electricity, it is shown that DERs continue to be the optimal solution. Also the combination of DERs chosen change with the different levels of non-supply from the grid. For the study community, Vijayanagar, Bangalore, the DERs chosen on the basis of resource availability are mainly discrete DERs. The DERs chosen are the LPG based CHP systems which run as base and intermediate generating systems. The capacity of the discrete DERs selected, depend on the end-use load of the community. Biomass based CHP systems are not chosen by the model as this technology has not reached maturity in an urban setup. Wind and hydro based systems are not selected as these resources are not available in Vijayanagar. The CMI stage of IDF demonstrates the top-down approach to the demand-supply matching problem. For the Energy Landscape Analysis (ELA), Bangalore metropolis was chosen in the study for the purpose of demonstration of the IDF framework. Bangalore consumes 25% of the state electricity supply and its per capita consumption at 1560kWh is higher than the state average of 1230kWh and is 250% more than the Indian average of 612kWh. A stakeholder workshop was conducted to ascertain the business value for clean and renewable energy technologies. From the workshop it was established that significant peak power savings could be obtained with even low penetrations of distributed energy technologies in Bangalore. The feasible options chosen by stakeholders for low income consumers are The second step of CMI is finding an economic rationale for the implementation of community microgrids. It is hypothesized that the ‘The cost of non-supply follows an s-shaped curve similar to a growth curve.’ It is moderated by the consumer income, consumer utility, and time duration of the load shedding. A pre and post event primary survey was conducted to analyze the difference in the pattern of consumer behaviour before and after the implementation of a severe load shedding program by BESCOM during 2009-10. Data was collected from 113 households during February 2009 and July 2010. The analysis proves that there is indeed a significant difference in the number of uninterrupted power systems (inverters) possessed by households. This could be attributed mainly to the power situation in Karnataka during the same period. The data also confirms the nature of the cost of non-supply curve. The third step in CMI is scenario analysis. Four categories of scenarios are developed based on potential interventions. These are business-as-usual, demand side, supply side and demand-supply side. About 21 scenarios are identified and their results compared. Comparing the four categories of scenarios, it is shown that business-as-usual scenarios may result in exacerbation of the demand-supply gap. Demand side interventions result in savings in the total costs for the community, but cannot aid communities with load shedding. Supply side interventions increase the reliability of the energy system for a small additional cost and communities have the opportunity to even meet their energy needs independent of the grid. The combination of both demand and supply side interventions are the best solution alternative for communities, as they enable communities to meet their energy needs 24/7 in a reliable manner and also do it at a lower cost. With an interactive microgrid implementation, communities have the added opportunity to sell back power to the grid for a profit. The thesis concludes with a discussion of the potential use of IDF in policy making, the potential barriers to implementation and minimization strategies. It presents policy recommendations based on the framework developed and the results obtained.

This thesis focuses on the relationship between the Organisation of Petroleum Exporting Countries (OPEC) economies and global climate change mitigation policies with a view to determining the energy exports demand security risks of OPEC member states. The successful implementation of a universally adopted climate regime has been marred with controversies as different interest groups have raised their concerns about all the options presented so far. OPEC as the major crude oil exporting group in the world has been in the forefront of these debates and negotiations. OPEC’s major concern is the envisaged adverse impacts of the industrialised countries carbon reductions on its members' economies. Several studies have shown that when industrialised countries adopt carbon dioxide emissions reduction policies in line with the United Nations Framework Convention on Climate Change, such as carbon taxes and energy efficiency strategies, OPEC’s net price of crude oil decreases at the same time as a reduction in the quantity of crude oil products sold. OPEC believes that such climate change policy-induced fall in crude oil exports revenues would have a significant negative effect on its members' economies. With the limitations related to the assumptions of the existing energy economy models on the impacts of climate change mitigation policies on OPEC’s economies (Barnett et al, 2004), this study opts for a risk based model. This model quantifies the energy exports demand security risks of OPEC members with special interest on crude oil. This study also investigates the effects of carbon reduction policies on crude oil prices vis-à-vis the impacts of crude oil prices on OPEC’s economies. To address these three main issues, this thesis adopts a three-prong approach. The first paper addresses the impacts of climate change mitigation on crude oil prices using a dynamic panel model. Results from the estimated dynamic panel model show that the relationship between crude oil prices and climate change mitigation is positive. The results also indicate that a 1% change in carbon intensity causes a 1.6% and 8.4% changes in crude oil prices in the short run and long run, respectively. The second paper focuses on the impacts of crude oil prices on OPEC economies using a panel vector auto regression (VAR) approach, highlighting the exposure of OPEC members to the volatile crude oil prices. The findings from the panel VAR model show that the relationship between OPEC members’ economic growth and crude oil prices is positive and economic growth in OPEC member states respond positively and significantly to a 10% deviation in crude oil prices by 1.4% in the short run and 1.7% in the long run. The third paper creates an index of the risks OPEC members face when there is a decline in the demand for their crude oil exports. To show these risks, this study develops two indexes to show the country level risks and the contributions to the OPEC-wide risks exposure. The results from the indexes show that OPEC members that are more dependent on crude oil exports are faced with more energy exports demand risks. The findings from this thesis are relevant for the development of a new OPEC energy policy that should accommodate the realities of a sustainable global climate regime. They are also useful to the respective governments of the countries that are members of OPEC and non-OPEC crude oil exporting countries. Finally, the outcomes of this thesis also contribute to the climate change and energy economics literature, especially for academic and subsequent research purposes.

Pour répondre aux enjeux énergétiques et climatiques, une des échelles d’action pertinentes est désormais celle du quartier ou de la ville. Des besoins de connaissance, d’outils d’aide à la décision et d’évaluation à cette échelle se manifestent de plus en plus. Un des volets concerne la modélisation de la demande d’énergie des bâtiments résidentiels, préalable à la mise en place d’actions de rénovation de l’existant ou à la valorisation de sources d’énergie locales. La diversité de situations de terrains, d’objectifs d’acteurs et de contextes de disponibilité de données incitent à rechercher des modèles flexibles, aptes à produire de l’information pour différentes applications, à partir de jeux alternatifs de données d’entrée, combinant des modèles de natures diverses (notamment physiques et statistiques) selon les besoins. Dans cet esprit, le présent travail cherche à explorer le potentiel de méthodes dites ascendantes, s’appuyant sur des modèles développés à l’origine pour la simulation à l’échelle d’un bâtiment isolé, mais extrapolés ici pour le parc de bâtiments d’une zone urbaine sur la base de bâtiments types. Les deux questions clés abordées sont celles de la sélection des bâtiments types et de la reconstitution des données d’entrée pertinentes sur le plan statistique pour la zone étudiée. Des techniques d’analyse de sensibilité, en particulier la méthode des effets élémentaires de Morris, ont été appliquées à un code de calcul thermique de bâtiment (ESP-r). Elles ont mis en évidence une réponse non linéaire du modèle, notamment du fait des interactions entre paramètres et de la dispersion des paramètres d’entrée. Elles ont permis d’identifier les paramètres les plus sensibles et les plus en interaction (concernant les bâtiments eux-mêmes, leur environnement ou leurs habitants), sur lesquels doit être concentré le travail de collecte ou de reconstitution statistique. Un modèle, dénommé MEDUS, de reconstitution de la distribution des besoins de chaleur sur un quartier à partir de trois typologies de bâtiments, a été développé et testé sur le secteur St-Félix à Nantes. Il est alimenté par des données INSEE à l’échelle d’un IRIS. Ses résultats sont analysés, à la fois sous l’angle de la pertinence des typologies choisies et dans une perspective d’application à l’échelle du quartier. / Urban scale is now considered as one of the most relevant scales to face energy and climate challenges. Specific needs for knowledge, decision making tools and evaluation are identified at urban scale. Modelling energy demand from residential buildings is one key aspect, priorto energy retrofitting of existing building asset or to valorisation of local energy sources. Diversity of local contexts, stake holder goals and data availability lead to search flexible models, with ability to produce information for different applications, from alternative input data sets, combining different types of basic models (namely both physical and statistical ones), according to user needs. The present work is exploring the potential of bottom-up approaches, based on engineering models, developed originally for isolated buildings. These models are extrapolated for the complete set of buildings in a city or neighbourhood, based on building archetypes. Two key questions tackled are the selection of suitable archetypes and the reconstitution of relevant input data, statistically representative for the area of interest Sensitivity analysis techniques have been applied to a thermal simulation programme (ESP-r), particularly the Morris elementary effects method. A non-linear response of the model has been emphasized, caused by scattering of input parameters and interaction effects. The most influencing and interacting parameters have been identified. They concern the buildings themselves, their environment and the inhabitants. Data collection or statistical reconstitution must be concentrated in priority to these main parameters. A model of the heat demand at a neighbourhood scale has been developed and tested on the sector St-Félix in Nantes. It is called MEDUS (Modelling Energy Demand at Urban Scale). Application is based on three building archetypes. Census data (INSEE) available at the sector scale are the main input data. Results are analyzed both to check archetype relevancy and to study a possible application for evaluating actions at sector scale, such as energy retrofitting.

The basic objective in bulk electrical system planning is to determine the necessary generating facilities required to ensure an adequate and economic supply of electrical energy and the development of an adequate transmission network to transport the generated energy to the customers. Quantitative adequacy assessment is a basic task in achieving this objective. An important requirement in this task is the ability to forecast the system load requirements at specific times in the future. These forecasts must also recognize the inherent uncertainty in predicting the future load demands.<p> The primary focus of the research described in this thesis is to examine the effects and implications of load forecast uncertainty on the load point and system adequacy indices of a composite generation and transmission system. This thesis considers two techniques to incorporate the inherent uncertainty associated with future load forecasts in the adequacy assessment of bulk electrical systems. Base case and factor analyses are performed on a number of power system configurations to identify and address the relative contributions to the load point and system indices due to load forecast uncertainty. A transmission reinforcement option and a number of generation system expansion options are presented to examine the system reliability response due to load forecast uncertainty.<p> The actual magnitudes of the changes due to load forecast uncertainty in the load bus and system risk indices and in the percentage change values are different for each generation expansion scenario. The topology and parameters of the system are different in each of the studied power system configurations. The effect of load forecast uncertainty on the system and load point adequacy can be quantified and utilized in the decision-making process associated with system generation and transmission planning. Load forecast uncertainty has important impacts on the system and load point indices that can only be appreciated by conducting comprehensive bulk system adequacy assessment. The actual effects are a complicated function of the system topology and parameters, and the system load curtailment philosophy.

In this paper we use the long-term empirical relationship among primary energy consumption, real income, physical capital, population and technology, obtained by averaged panel error correction models, to project the long-term primary energy consumption of 56 countries up to 2100. In forecasting long-term primary energy consumption, we work with four different Shared Socioeconomic Pathway Scenarios (SSPs) developed for the Intergovernmental Panel on Climate Change (IPCC) framework, assuming different challenges to adaptation and mitigation. We find that in all scenarios, China, the United States and India will be the largest energy consumers, while highly growing countries will also significantly contribute to energy use. We observe for most scenarios a sharp increase in global energy consumption, followed by a levelling-out and a decrease towards the second half of the century. The reasons behind this pattern are not only slower population growth, but also infrastructure saturation and increased total factor productivity. This means, as countries move towards more knowledge based societies, and higher energy efficiency, their primary energy usage is likely to decrease as a result. Global primary energy consumption is expected however to increase significantly in the coming decades, thus increasing the pressure on policy makers to cope with the questions of energy security and greenhouse gas mitigation at the same time. (authors' abstract) / Series: Department of Economics Working Paper Series

The basic objective in bulk electrical system planning is to determine the necessary generating facilities required to ensure an adequate and economic supply of electrical energy and the development of an adequate transmission network to transport the generated energy to the customers. Quantitative adequacy assessment is a basic task in achieving this objective. An important requirement in this task is the ability to forecast the system load requirements at specific times in the future. These forecasts must also recognize the inherent uncertainty in predicting the future load demands.<p> The primary focus of the research described in this thesis is to examine the effects and implications of load forecast uncertainty on the load point and system adequacy indices of a composite generation and transmission system. This thesis considers two techniques to incorporate the inherent uncertainty associated with future load forecasts in the adequacy assessment of bulk electrical systems. Base case and factor analyses are performed on a number of power system configurations to identify and address the relative contributions to the load point and system indices due to load forecast uncertainty. A transmission reinforcement option and a number of generation system expansion options are presented to examine the system reliability response due to load forecast uncertainty.<p> The actual magnitudes of the changes due to load forecast uncertainty in the load bus and system risk indices and in the percentage change values are different for each generation expansion scenario. The topology and parameters of the system are different in each of the studied power system configurations. The effect of load forecast uncertainty on the system and load point adequacy can be quantified and utilized in the decision-making process associated with system generation and transmission planning. Load forecast uncertainty has important impacts on the system and load point indices that can only be appreciated by conducting comprehensive bulk system adequacy assessment. The actual effects are a complicated function of the system topology and parameters, and the system load curtailment philosophy.

<p>This thesis discusses novel developments in Bayesian analytics for high-dimensional multivariate time series. The focus is on the class of multiregression dynamic models (MDMs), which can be decomposed into sets of univariate models processed in parallel yet coupled for forecasting and decision making. Parallel processing greatly speeds up the computations and vastly expands the range of time series to which the analysis can be applied. </p><p>I begin by defining a new sparse representation of the dependence between the components of a multivariate time series. Using this representation, innovations involve sparse dynamic dependence networks, idiosyncrasies in time-varying auto-regressive lag structures, and flexibility of discounting methods for stochastic volatilities.</p><p>For exploration of the model space, I define a variant of the Shotgun Stochastic Search (SSS) algorithm. Under the parallelizable framework, this new SSS algorithm allows the stochastic search to move in each dimension simultaneously at each iteration, and thus it moves much faster to high probability regions of model space than does traditional SSS. </p><p>For the assessment of model uncertainty in MDMs, I propose an innovative method that converts model uncertainties from the multivariate context to the univariate context using Bayesian Model Averaging and power discounting techniques. I show that this approach can succeed in effectively capturing time-varying model uncertainties on various model parameters, while also identifying practically superior predictive and lucrative models in financial studies. </p><p>Finally I introduce common state coupled DLMs/MDMs (CSCDLMs/CSCMDMs), a new class of models for multivariate time series. These models are related to the established class of dynamic linear models, but include both common and series-specific state vectors and incorporate multivariate stochastic volatility. Bayesian analytics are developed including sequential updating, using a novel forward-filtering-backward-sampling scheme. Online and analytic learning of observation variances is achieved by an approximation method using variance discounting. This method results in faster computation for sequential step-ahead forecasting than MCMC, satisfying the requirement of speed for real-world applications. </p><p>A motivating example is the problem of short-term prediction of electricity demand in a "Smart Grid" scenario. Previous models do not enable either time-varying, correlated structure or online learning of the covariance structure of the state and observational evolution noise vectors. I address these issues by using a CSCMDM and applying a variance discounting method for learning correlation structure. Experimental results on a real data set, including comparisons with previous models, validate the effectiveness of the new framework.</p> / Dissertation

Submitted by VALTER AKIRA BRAVO (valterovarb@hotmail.com) on 2018-08-10T21:38:41Z No. of bitstreams: 1 dissertação final - valter com ficha catalográfica e aprovação.pdf: 2060934 bytes, checksum: afdb81670103de47e094031d7f61ba46 (MD5) / Approved for entry into archive by Ana Paula Santulo Custódio de Medeiros null (asantulo@rc.unesp.br) on 2018-08-13T11:39:36Z (GMT) No. of bitstreams: 1 bravo_va_me_rcla.pdf: 1991723 bytes, checksum: 1af34f48af2fad362dcac01855af8018 (MD5) / Made available in DSpace on 2018-08-13T11:39:37Z (GMT). No. of bitstreams: 1 bravo_va_me_rcla.pdf: 1991723 bytes, checksum: 1af34f48af2fad362dcac01855af8018 (MD5) Previous issue date: 2018-07-27 / Está bem estabelecido que a contribuição glicolítica anaeróbia é predominante nos eventos próximos a 50 e 100 metros, e que a contribuição aláctica alcança sua maior capacidade de contribuição próximos aos 50 metros. Todavia, pode-se questionar se a taxa de ajuste do metabolismo anaeróbio não seria mais apropriada para analisar os desempenhos de curta duração (50 e 100 metros), ao invés da demanda total. Bem como, pouco se sabe se AOD e as demandas de cada metabolismo (láctico vs. aláctico vs aeróbio) tendem a apresentar respostas diferentes entre sexos. Assim, o objetivo do presente estudo foi analisar o déficit acumulado de oxigênio (AOD) nos desempenhos do crawl em distâncias de 50, 100 e 200 metros, comparando-o pelo método da variação da resposta lactacidêmica e perfil aláctico da curva de débito oxigênio. Participaram deste estudo 12 (doze) nadadores homens (16,8 ± 2,2 anos, 179,3 ± 7,0 cm e 69,4 ± 7,8 kg) e 10 (dez) nadadoras (15,5 ± 3 anos, 161,8 ± 6,2 cm e 55,5 ± 6,8 kg). Todos realizaram o desempenho máximo para as distâncias de 50, 100 e 200 metros para a determinação do O2 acumulado. Após 24 horas, os nadadores desempenharam um teste incremental escalonado máximo e descontínuo (TIE: 6x250m e 1x200m, 50 a 100% da v200m) para a avaliação do V̇O2max e obtenção da relação V̇O2 vs. velocidade de nado em intensidades submáximas. A partir desta relação, projetou-se a demanda de O2 nas velocidades correspondentes ao 50, 100 e 200m foi estimada. Em seguida, a permuta gasosa pulmonar também foi analisada durante os desempenhos de 50, 100 e 200m para se obter a oferta de O2 durante e após cada distância. A estimativa de AOD foi realizada pela comparação entre demanda e oferta de O2. A fase rápida de decaimento exponencial da curva de recuperação do O2, após cada distância de nado, estimou a contribuição aláctica. Enquanto que o equivalente de O2 para a variação da resposta do lactato sanguíneo foi utilizada para reconstruir a demanda anaeróbia láctica. Em todos os testes, o V̇O2 foi obtido respiração-a-respiração por uma unidade metabólica automatizada e portátil (CPET K4b2), que esteve acoplada a um snorkel específico e validado na natação (new-AquaTrainer®). O teste ANOVA (uma entrada, com Sidak como post-hoc) comparou as médias do perfil metabólico entre homens e mulheres para cada distância de desempenho (50, 100 e 200 metros). O nível de significância foi estabelecido em ρ≤0,05. Os resultados preliminares indicam que o V̇O2max correspondeu à 4012,3 ± 453,3 ml×min-1 entre homens e à 3043 ± 335,6 ml×min-1 entre as mulheres. a demanda anaeróbia (AOD) é maior entre as mulheres (50m: 26,2  5,6 mlO2×kg-1; 100m: 43,3  10,2 mlO2×kg-1; 200m: 62,8  15,4 mlO2×kg-1) quando comparadas aos homens (50m: 19,7  3,9 mlO2×kg-1; 100m: 33,6  10,6 mlO2×kg-1; 200m: 48,3  15,7 mlO2×kg-1). Todavia, essas diferenças, quando analisada pela relação percentual entre Déficit/Demanda Acumulada de O2, mostram-se significativas em 50m (P=0,05), mas não em 100m (P=0,32) e 200m ( P=0,47). Ao ponderar pela distância de nado, o AOD (em mlO2×kg-1×m-1) evidenciou tendência decrescente entre as distâncias de nado (significativa apenas ao comparar 50m e 100m vs. 200m, P<0,01 e P<0,05, respectivamente), bem como as diferenças entre sexos não foram significativas ao compará-los em 50m (H: 0,39 e M: 0,52 mlO2×kg-1×m-1, P=0,122), em 100m (H: 0,33 e M: 0,43 mlO2×kg-1×m-1, P=0,741) e em 200m (H: 0,24 e M: 0,31 mlO2×kg-1×m-1, P=0,948). Durante o desempenho nas distâncias de 50, 100 e 200 metros, os perfis de ativação e parcela de contribuição de cada metabolismo energético apresenta a taxa absoluta (mlO2×kg-1) aláctica mais elevada em homens, quando comparada à ativação em mulheres, que demonstram desempenho com predomínio láctico nas distâncias. As diferenças na produção energética entre os sexos mostram valores distintos apenas para a produção aláctica em 50m (P=0,039). Assim, mulheres não apresentam restrições para o suprimento energético anaeróbio, em velocidades supra-máximas de nado, quando essa demanda é estimada pelo método AOD tradicional. Porém, os valores relativos de contribuição dos metabolismos tendem a não serem similares entre homens e mulheres na construção da demanda anaeróbia total (29,2% (50 metros), 15,8% (100 metros) e 29,3% (em 200 metros) superior em homens), causada pela maior ativação do metabolismo aláctico (64,7%, 47,4% e 60,9%) em cada uma destas distâncias. Essa maior contribuição anaeróbia aláctica em homens pode ser um efeito produzido pelas diferenças de massa muscular, sugerindo o desenvolvimento da massa magra para melhorar o desempenho em tarefas com demanda anaeróbia elevada. / It is well established that the anaerobic glycolytic contribution is predominant in events close to 50 and 100 meters, and that the alactic contribution reaches its greatest capacity of contribution close to 50 meters. However, one question that needs to be asked is whether a rate of anaerobic metabolism adjustment is no longer useful for analyzing short-term performance (50 and 100 meters), as opposed to total demand. As well as, little is known if AOD and the demands of each metabolism (lactic vs. alactic vs aerobic) tend to present different responses between genders. Thus, the objective of the present study was to analyze the accumulated oxygen deficit (AOD) in crawl performance at distances of 50, 100 and 200 meters, comparing it by the lactacidemic response and alactic profile of the recovery curve Twelve (12) male swimmers (16.8 ± 2.2 years, 179.3 ± 7.0 cm and 69.4 ± 7.8 kg) and 10 (ten) female swimmers (15.5 ± 3 years old, 161.8 ± 6.2 cm and 55.5 ± 6.8 kg)) participated in this study. All participants were performed a maximal effort at 3 distances (50, 100 and 200 meters) for the determination of accumulated O2 After 24 hours, swimmers performed a maximum and discontinuous step incremental test (TIE: 6x250m and 1x200m, 50 to 100% of v200m) for an evaluation of V̇O2max and obtaining the VO2 vs. swimming speed relationship. From this relation, the demand for O2 at the velocities corresponding to 50, 100 and 200m was estimated. Then, gas exchange was also analyzed during the exercises of 50, 100 and 200m for an O2 supply during and after each distance. The AOD estimate was made by comparing demand and supply of O2. The energy produced from anaerobic alactic metabolism was estimated from the fast component of the post V̇O2. The net energy produced from anaerobic lactic acid metabolism was determined from [La-]net. In all tests, VO2 was obtained breath-by-breath by an automated and portable metabolic unit (CPET K4b2), which was coupled to a specific and validated swimming snorkel (new-AquaTrainer®). The ANOVA test (an entry, with Sidak as a post-hoc) compared the medias of the metabolic profile between men and women for each distance of performance (50, 100 and 200 m). The level of significance was set at ρ≤0.05. Preliminary results indicate that VO2max corresponded to 4012.3 ± 453.3 ml×min-1 for men and 3043 ± 335.6 ml×min-1 for women. Anaerobic demand (AOD) is higher among women (50m: 26,2  5,6 mlO2×kg-1; 100m: 43,3  10,2 mlO2×kg-1; 200m: 62,8  15,4 mlO2×kg-1) than in men (50m: 19,7  3,9 mlO2×kg-1; 100m: 33,6  10,6 mlO2×kg-1; 200m: 48,3  15,7 mlO2×kg-1). However, these differences, when analyzed by the percentage ratio between O2 Accumulated Deficit / Demand, are significant in 50m (P=0,05, but not in 100m (P = 0.32) and 200m (P = 0.47). When considering swimming distance, the AOD (in mlO2×kg-1×m-1) showed a decreasing trend between swimming distances (significant only when comparing 50m and 100m vs. 200m, P <0.01 and P <0.05, respectively), as well as the differences between sexes were not significant when comparing them in 50m (H: 0,39 e M: 0,52 mlO2×kg-1×m-1, P=0,122), in 100m (H: 0,33 e M: 0,43 mlO2×kg-1×m-1, P=0,741) and in 200m (H: 0,24 e M: 0,31 mlO2×kg-1×m-1, P=0,948). During performance at distances of 50, 100 and 200 meters, the activation and contribution profiles of each energy metabolism has the highest absolute (mlO2×kg-1) alactic rate in men, when compared to activation in women, which demonstrate performance with lactic predominance at distances. Differences in energy production between the sexes show different values only for the production in 50 m (P = 0.039). Thus, women do not present restrictions for the anaerobic energetic supply, at supramaximum swimming speeds, when this demand is estimated by the traditional AOD method. However, the relative values of metabolic contribution tend not to be similar between men and women in the construction of the total anaerobic demand total (29,2% (50 m), 15,8% (100 m) and 29,3% (200 m) superior in men), caused by the greater activation of the alactic metabolism (64,7%, 47,4% e 60,9%) at each of these distances. This greater anaerobic alactic contribution in men may be an effect produced by muscle mass differences, suggesting the development of lean mass to improve performance in tasks with high anaerobic demand.