Generating solar irradiance data series with 1-minute time resolution based on hourly observational data

Authors

  • Fernando Ramos Martins Universidade Federal de São Paulo https://orcid.org/0000-0002-7618-4462
  • Thaiane Gambarra Soares Universidade Federal de São Paulo
  • Francisco José Lopes Lima Universidade Federal de São Paulo

Keywords:

Markov processes, numerical simulation, remote s, Renewable Energy, Solar energy

Abstract

The large-scale integration of intermittent renewable energy sources into electricity distribution is one of the significant challenges in meeting energy demand in the near future. The solar energy resource has a fluctuating spatial and temporal character intrinsic to the radiative processes with the atmosphere and the Earth’s surface. Efficient use of solar energy for power generation and electricity distribution requires technologies that consider such variability and its impacts on the electrical system. In this context, a reliable computational model to estimate the surface solar irradiance is beneficial for operational issues. This work presents a methodology based on Markov chains for the generation of a global irradiance time series with one-minute temporal resolution using observational database acquired by a basic weather station. The statistical model was developed using irradiance data acquired in São Martinho da Serra/RS and Petrolina/PE to verify their performance in two different climates. The transition probability matrices were determined for the following cloud conditions: cloudy, partly cloudy or clear sky using two years of observational data. Next, Markov’swalking technique was used to generate a data series with one-minute temporal resolution taking into account the cloudiness classification. Model validation was performed using solar irradiance data not considered to obtain MPTs. The mean squared deviation of the relative distribution frequencies indicated a deviation of only 1.5%, and the Kolmogorov-Smirnov test indicated that the synthetic and observational series presented similar cumulative frequency distributions. Thus, the proposed methodology showed high reliability in reproducing the temporal variability due to the stochastic nature of the incoming solar energy in both sites.

Author Biographies

Fernando Ramos Martins, Universidade Federal de São Paulo

I am a researcher and professor working at the Federal University of São Paulo – Campus Baixada Santista since 2013. I was the coordinator of the undergraduate course Interdisciplinary in Marine Science and Technology (2014-2015 and the head of Department for the Marine Sciences (2016-2018). Now, I am the coordinator of the Graduate Interdisciplinary Program in Marine Science and Technology approved by CAPES in December 2018. CNPq fellowship (level2), my research activities focus on numerical modeling applied to interdisciplinary areas involving renewable resources, atmospheric remote sensing, and geographic information systems. In recent years, I participated in several research collaborations with national and international institutions such as INPE, UNIFEI, UFAL, IAE, USP, Catholic University of Chile, University of Oldenburg (Germany), University of Hannover (Germany) and UNEP. The major projects developed recently or in progress are the INCT-Climate Change, Brazilian Atlas for Solar Energy, Project SONDA, NOPA (New Partners) program. Currently, I am a reviewer for national and international journals such as Solar Energy, Renewable Energy, Energy Policy, Applied Energy, Brazilian J. of Meteorology, and others. I have large experience in undergraduate and graduate education.

Thaiane Gambarra Soares, Universidade Federal de São Paulo

BacharelemCieˆncia e Tecnologia do Mar e Bacharel em Engenharia de Petro ́leopelaUniversidadeFederaldeSa ̃oPaulo,campus Baixada Santista. Bolsista TT FAPESP em Projeto PIPE desenvolvido por Virtux S.A. ao longo de 2019. Membro da equipe de pesquisadores do La- borato ́rio Interdisciplinar de Computac ̧a ̃o Aplicada (LaICA/Unifesp).

Francisco José Lopes Lima, Universidade Federal de São Paulo

Doutor em Meteorologia pelo Instituto Nacional de Pesquisas Espaciais (INPE). Realizou pós-doutorado na Universidade Federal de São Paulo e na Universidade de Oldenburgo (Alemanha) em modelagem numérica e previsão de recursos energe ́ticos renováveis. Atuamente ocupa uma posição de pós-doutor com atividades em modelagem computacional, física, meteorologia dinâmica.

Published

2020-10-03
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