Modeling and sizing optimization of hybrid photovoltaic/wind power generation system
محورهای موضوعی : Mathematical Optimization
1 - Department of Mechanical Engineering, University of Sistan and Baluchestan, Zahedan, Iran
کلید واژه: Sizing study Match evaluation method, (MEM) Electricity match rate (EMR) Hybrid renewable, energy systems,
چکیده مقاله :
The rapid industrialization and growth of world’s human population have resulted in the unprecedented increase in the demand for energy and in particular electricity. Depletion of fossil fuels and impacts of global warming caused widespread attention using renewable energy sources, especially wind and solar energies. Energy security under varying weather conditions and the corresponding system cost are the two major issues in designing hybrid power generation systems. In this paper, the match evaluation method (MEM) is developed based on renewable energy supply/demand match evaluation criteria to size the proposed system in lowest cost. This work is undertaken with triple objective function: inequality coefficient, correlation coefficient, and annualized cost of system. It provides optimum capacity of as many numbers of supplies as required to match with a load demand in lowest investment, so it can handle large-scale design problems. Meteorological data were collected from the city of Zabol, located in south-east of Iran, as a case study. Six types of wind turbine and also six types of PV modules, with different output powers and costs, are considered for this optimization procedure. A battery storage system is used to even out irregularities in meteorological data. A multi-objective particle swarm optimization algorithm has been used for the prediction of an optimized set of design based on the MEM technique. The results of this study are valuable for evaluating the performance of future stand-alone hybrid power system. It is worth mentioning that the proposed methodology can be effectively employed for any composition of hybrid energy systems in any locations taking into account the meteorological data and the consumer’s demand.
