Renewable energy sources have been growing worldwide, and solar energy is a significant part of such sources. An essential step in planning studies, including the problem of optimal location and sizing of photovoltaic (PV) generators, is estimating how much energy the panels will generate over time. For that, two aspects must be considered: the stochastic nature of the input variables and the way to calculate the generator's output power. A brief literature review identified twelve approaches to determine the PV output power. Therefore, this article compares such methods through actual meteorological data and generated energy over one year by a solar power plant located in a specific site in Brazilian Northeast. As expected, models that consider the influence of ambient temperature on output power performed better than those that do not. How the energy generated by the PV panel is estimated can influence the economic viability of a project since oversizing the PV system entails unnecessary additional costs. Monte Carlo simulations are used to determine the energy generated by each model, based on the integration of instantaneous powers. According to our findings, considering the correlation between meteorological variables reduces the error in estimating the generated energy by PV panels.
Faced with the growing demand for electricity, renewable energy sources are an increasingly attractive option. The optimal sizing of distributed generation problem can be considered through different objectives, but in this work the objective function is the total costs. Therefore, a Cuckoo Search Algorithm is used to determine the optimal size (model and number of modules) of a photovoltaic generator to be installed in a hotel so the total annual costs are minimal. For this, real data of solar irradiance, ambient temperature and demand are used by Monte Carlo simulation to consider the randomness of these variables. The economic viability of the project is determined by the calculation of the levelized cost of electricity and the payback time. Resumo: Diante da crescente demanda por energia elétrica, as fontes renováveis de energia são uma opção cada vez mais atrativa. O problema do dimensionamento ótimo de geração distribuída pode ser considerado por meio de diferentes objetivos, porém neste trabalho a função objetivo são os custos totais. Sendo assim, um algoritmo de busca Cuco é empregado para determinar o dimensionamento ótimo de um gerador fotovoltaico (modelo e quantidade de módulos) a ser instalado em um hotel a fim de que os custos totais anuais sejam mínimos. Para tanto, dados reais de irradiância solar, temperatura ambiente e demanda são utilizados mediante simulação de Monte Carlo para considerar a aleatoriedade dessas variáveis. A viabilidade econômica do projeto é determinada mediante o cálculo do custo nivelado de energia ou LCOE (do inglês Levelized Cost of Electricity) e do tempo de retorno do investimento.
Over the past years, the Brazilian electricity matrix has shown a tendency to increase the diversification with the insertion of renewable energy as solar and wind. However, the change in the electric power scenario leads to greater complexity in the planning and operation of the system due to its intermittency and the impossibility of storing both natural resources. In order to mitigate this problem, predictive systems have been sought to accurately forecast short-term irradiance and wind speed. The purpose of this paper was to predict irradiance 24 hours ahead from a method based on artificial intelligence, neural network and applying a data pre-processing stage which consisted of obtaining the stochastic components of the daytime data. Thus, the nighttime data were eliminated and the value of each daytime sample was subtracted from the calculated daily trend curve value at its corresponding hour, and finally, the data were normalized. In addition, statistical methods, such as integrated auto-regressive of moving average and integrated auto-regressive of seasonal moving average, were used in order to compare the results achieved, and the artificial neural network presented better results. Resumo: Nos últimos anos, a matriz elétrica brasileira tem apresentado uma tendência de maior diversificação com a inserção de fontes renováveis como solar e eólica. Porém, essa mudança do cenário elétrico tem resultado em uma maior complexidade no planejamento e operação do sistema em função da intermitência dessas fontes e a impossibilidade de armazenamento desses recursos naturais. Com o intuito de mitigar esse problema, tem-se buscado a melhor técnica de previsão dessas fontes a curto prazo. Para esse trabalho, realizou-se a predição da irradiância no horizonte de 24 horas, a partir do método baseado em inteligência artificial, a rede neural. Vale ressaltar que aplicou-se uma etapa de pré-processamento dos dados que consistiu na obtenção dos componentes estocásticos dos dados diurnos, com isso, dados noturnos foram eliminados, e o valor de cada amostra do período diurno é descontado o valor correspondente ao ponto da curva de tendência diária naquele instante, e por fim, os dados foram normalizados. Ademais, utilizou-se métodos estatísticos como auto-regressivo integrado de média móvel e auto-regressivo integrado de média móvel sazonal a fim de comparar os resultados alcançados, sendo que a rede neural apresentou melhor resultado.
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