Evolutionary algorithms for the design of grid-connected PV-systems

Authors:
Daniel Gómez-Lorente;Isaac Triguero;Consolación Gil;A. Espín Estrella
Affiliations:
Dept. of Civil Engineering, Electrical Engineering Section, ETSICCP, University of Granada, Campus Fuentenueva, Granada 18071, Spain;Dept. of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, 18071 Granada, Spain;Dept. of Computer Arquitecture and Electronics, CITE III, University of Almería, La Cañada de San Urbano s/n, Almería 04120, Spain;Dept. of Civil Engineering, Electrical Engineering Section, ETSICCP, University of Granada, Campus Fuentenueva, Granada 18071, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

The sale of electric energy generated by photovoltaic (PV) plants has attracted much attention in recent years. The installation of PV plants aims to obtain the maximum benefit of captured solar energy. The current methodologies for planning the design of the different components of a PV plant are not completely efficient. This paper addresses the optimization of the design of PV plants with solar tracking, which consists of the optimization of the variables that make up the PV plant to obtain the minimum electric (Joule) losses possible. These variables are the size and distribution of solar modules in the solar tracker, the distribution of the solar trackers in the field and the choice of inverter. Evolutionary algorithms (EAs) are adaptive methods based on natural evolution that may be used for searching and optimization. Four different EAs have been used for optimizing the design of PV plants: steady-state genetic algorithm, generational genetic algorithm, CHC algorithm and DE algorithm. In order to test the performance of these algorithms we have used different proposed fields to mount PV plants. The results obtained show that EAs, and specifically DE with rand mutation schemes, are promising techniques to optimize design of PV plants. Furthermore, the results are contrasted with nonparametric statistical tests to support our conclusions.