Evolutionary algorithms for the design of grid-connected PV-systems |
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| Authors: | Daniel Gómez-Lorente Isaac Triguero Consolación Gil A. Espín Estrella |
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| Affiliation: | 1. Dept. of Civil Engineering, Electrical Engineering Section, ETSICCP, University of Granada, Campus Fuentenueva, Granada 18071, Spain;2. Dept. of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, 18071 Granada, Spain;3. Dept. of Computer Arquitecture and Electronics, CITE III, University of Almería, La Cañada de San Urbano s/n, Almería 04120, Spain;1. Electrical engineering department, University of Skikda, BP26 Route Elhadaik, Skikda 21000, Algeria;2. Department of physics, University of Constantine1, Route Ain Elbey, Constantine 25000, Algeria;3. IREENA-Université de Nantes, PRES-L’UNAM, IUT, Saint-Nazaire, France;1. Electrical Engineering Dept., Faculty of Engineering, Fayoum University, Fayoum, Egypt;2. Electrical Engineering Dept., Faculty of Engineering, Jouf University, Al-Jouf, Saudi Arabia;3. Electrical Power & Machine Dept., Faculty of Engineering, Zagazig University, Zagazig, Egypt;4. College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Saudi Arabia;5. Electrical Engineering Department, Faculty of Engineering, Minia University, Egypt;6. Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India;7. Chemistry department, College of science, Jouf University, Al-Jouf, Saudi Arabia;1. Zurich University of Applied Sciences, Institute for Natural Resource Science, CH-8820 Wadenswil, Switzerland;2. Future Cities Laboratory, Singapore-ETH Centre, CREATE Tower, Singapore;3. Architecture and Building Systems, Institute of Technology in Architecture, ETH Zurich, CH-8093 Zurich, Switzerland;1. Laboratory of Theoretical Physics, University of Science and Technology H. Boumedienne, BP 32 Bab Ezzouar, 16111, Algiers, Algeria;2. Laboratory of Coating Materials and Environment, University of M. Bougara, Avenue de L''indépendance, 35000, Boumerdès, Algeria |
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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. |
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