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Abstract(s)
Este trabalho abordarĆ” a importĆ¢ncia das energias renovĆ”veis, onde se destacam paĆses em
que as matrizes elĆ©tricas possuem um alto nĆvel de dependĆŖncia dos recursos eĆ³licos para
garantir o atendimento a demanda por energia. SerĆ” contextualizado a tecnologia Airborne
Wind System, seus tipos de geraĆ§Ć£o, formas aerodinĆ¢micas, a potĆŖncia produzida e aspetos
importantes a se considerar quando buscamos otimizĆ”-lo.
Como ferramentas de anƔlise deste trabalho, serƔ utilizado algoritmos genƩticos como forma
de se otimizar uma unidade geradora, e entĆ£o evoluĆmos para a utilizaĆ§Ć£o de Biased Random
Key Genetic Algorithm para otimizaĆ§Ć£o de um parque eĆ³lico com kites de pequeno porte, de
forma a possibilitar a estimativa de produĆ§Ć£o de energia anual. Como ferramenta de anĆ”lise
financeira, serĆ£o utilizados modelos de custos com base nas diversas referĆŖncias
bibliogrĆ”ficas, com intuito de melhor e mais prĆ³ximo ao cenĆ”rio real estimar o custo nivelado
da energia.
Como forma de fiabilizar os resultados, serĆ£o feitas simulaƧƵes utilizando os algoritmos
genĆ©ticos como ferramenta de otimizaĆ§Ć£o, desde a unidade atĆ© o parque. Posteriormente,
com as estimativas obtidas e a partir de um novo modelo de custos, com base na literatura e
aproximando ao mƔximo de um cenƔrio real, podemos estimar o custo nivelado da energia
para um parque eĆ³lico de pequeno porte de kites. Desta forma, podemos comparar esta
tecnologia com as demais presentes na Ć”rea de geraĆ§Ć£o de energia elĆ©trica.
Ao final, compararemos os resultados obtidos com valores atuais de custo nivelado de
energia das eĆ³licas convencionais, onshore e offshore. Faremos uma comparaĆ§Ć£o utilizando
nĆveis diferentes de capacidade instalada do parque com kites, de forma a saber o impacto
sobre o custo nivelado da energia e a partir de que momento esta tecnologia fica competitiva
perante a eĆ³lica convencional.
This work will approach the matter of renewable energy sources in countries highly dependent on wind sources to ensure the energy demand. It will contextualize the Airborne Wind Energy System, the kinds of generation, aerodynamics shapes, power produced and important aspects to be considered aiming the optimization of the system. As analysing tools, this work will use genetic algorithms to optimize generating unit, then will escalate to the use of Biased Random Key Genetic Algorithm to optimize a small proportion kite farm, aiming to estimate the annual energy production. This work will also use cost models based on several bibliographic references to make a financial analysis in order to be as close as possible of the real scenario. As way to entrust the results, simulations using genetic algorithms will be made to optimize the process from the generating unit to the wind farm. Posteriorly, based on the results and from a new cost model based on literature it will be possible to estimate the cost producing energy on a small kite farm. In this way it will be possible to compare this technology to others present in the power generation market. Finally, we will compare the results with the current cost of conventional ways of generating power by wind, onshore and offshore. We will also compare different levels of installed power capacity in the kite farms as a way to know the impact upon the levelized cost of energy and from which point kite farms start being competitive compared to conventional wind farms.
This work will approach the matter of renewable energy sources in countries highly dependent on wind sources to ensure the energy demand. It will contextualize the Airborne Wind Energy System, the kinds of generation, aerodynamics shapes, power produced and important aspects to be considered aiming the optimization of the system. As analysing tools, this work will use genetic algorithms to optimize generating unit, then will escalate to the use of Biased Random Key Genetic Algorithm to optimize a small proportion kite farm, aiming to estimate the annual energy production. This work will also use cost models based on several bibliographic references to make a financial analysis in order to be as close as possible of the real scenario. As way to entrust the results, simulations using genetic algorithms will be made to optimize the process from the generating unit to the wind farm. Posteriorly, based on the results and from a new cost model based on literature it will be possible to estimate the cost producing energy on a small kite farm. In this way it will be possible to compare this technology to others present in the power generation market. Finally, we will compare the results with the current cost of conventional ways of generating power by wind, onshore and offshore. We will also compare different levels of installed power capacity in the kite farms as a way to know the impact upon the levelized cost of energy and from which point kite farms start being competitive compared to conventional wind farms.
Description
Keywords
Airborne wind energy system Genetic Algorithm Renewable energy Wind energy Levelized cost of energy