Design and Optimization of a Wind System Using a Genetic Algorithm
Issue: 2013 - Volume 3 [Issue 4]
Clarence Semassou *
Laboratory for Energy and Applied Mechanics, Technical University of Abomey, 01 BP 2009 Cotonou, Benin
Department of Physics, Faculty of Science and Technology, University of Abomey, Benin
Laboratoire de Caractérisation Thermophysique des Matériaux et d’Appropriation Energétique Ecole Polytechnique d’Abomey-Calavi, 01 BP 2009 Cotonou, Benin
*Author to whom correspondence should be addressed.
Aims: The aim sought is to design a wind energy system can meet the energy needs of a rural household in minimizing both the economic cost and the energy cost of the system over its life cycle while ensuring continuity in the provision of electrical energy.
Study Design: Design of a wind system study.
Place and Duration of Study: Department of Mechanical Engineering and Energy, Laboratory Energy and Applied Mechanics, between September 2012 and March 2013.
Methodology: We have adopted an approach that requires a combination of field work and scientific work. A survey has been conducted in the locality chosen to know the equipment used to determine the consumption profile; some players were involved in determining the weight we assigned to different criteria. The NSGA-II algorithm, evolutionary genetic type was used in the context of determining the set of optimal solutions of compromise. Design variables used are the wind turbines number, batteries number, wind turbine type, battery type and height of the mast of the wind. The various programs developed have been implemented in Matlab. The method proposed has been applied to a rural household locality of Benin, named Dekin to ensure its power supply.
Results: The design made it possible to generate several candidate solutions that are available to the user. There is also the implementation of solutions and promoting the shedding of solutions providing continuous coverage of consumer needs.
Conclusion: The multi-objective design of a wind system is not an easy task since antagonistic criteria are taken into account. To this end, we found a compromise by assigning different weight goals. Solutions to economic and energy low cost are found while improving the sevice delivered to the consumer.
Keywords: Optimization, photovoltaic autonomous, genetic algorithm, cost on life cycle, rural environment