Computing tools applied to renewable energy analysis
Mentor: Abraham Duarte Muñoz
Phone: (+34) 916655073
University: Universidad Rey Juan Carlos
Partner Host Institution: N.A
Keywords: power generation and supply, optimization, modeling, renewable energy, climate change, electricity mix

Computing tools applied to renewable energy analysis

Energy production directly impacts on climate change. Fossil fuels combustion means close to 50% of the Spanish electricity mix. Spain is the European country with most kWh/m2 average yearly irradiance received, with also high wind resources. The promotion of the renewable energies would potentially save important amount of emissions. Developing an algorithm for energy production optimisation could save tons of carbon dioxide emissions, between other highly toxic gasses. Therefore, we aim to build an optimisation algorithm to calculate the best option for the greenest development of the energy sector which would define a new Spanish electricity mix, by proposing the alternative scenario where most of the produced electricity would come from renewable generators. The algorithm will utilise well-defined data such as yearly average irradiance, wind-speed and other social and physical factors to distribute the most reliable and profitable renewable generators in different areas of the country.

Departament: Computing Science, Computer Architecture, Programming Languages and Systems and Statistics and Operative Investigation
Research Group: Group for Research in Algorithms For Optimization (GRAFO)
More Information:
Relevants projects on the area:
Relevants publications on the area: 1.- Serrano-Luján, L., Espinosa, N., Abad, J., & Urbina, A. (2017). The greenest decision on photovoltaic system allocation. Renewable Energy, 101, 1348–1356.
2.- Serrano-Luján, L., García-Valverde, R., Espinosa, N., García-Cascales, M. S., Sánchez-Lozano, J. M., & Urbina, A. (2015). Environmental benefits of parking-integrated photovoltaics: a 222 kWp experience. Progress in Photovoltaics: Research and Applications, 23(2), 253–264.
3.- Serrano-Luján, L., Cadenas, J. M., Faxas-Guzmán, J., & Urbina, A. (2016). Case of study: Photovoltaic faults recognition method based on data mining techniques. Journal of Renewable and Sustainable Energy, 8(4), 043506.