Mathematics and Computers in Simulation
A methodology to design a fuzzy logic based supervision of Hybrid Renewable Energy Systems
Mathematics and Computers in Simulation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Stand-alone hybrid renewable energy systems are more reliable than one-energy source systems. However, their design is crucial. For this reason, a new methodology with the aim to design an autonomous hybrid PV-wind-battery system is proposed here. Based on a triple multi-objective optimization (MOP), this methodology combines life cycle cost (LCC), embodied energy (EE) and loss of power supply probability (LPSP). For a location, meteorological and load data have been collected and assessed. Then, components of the system and optimization objectives have been modelled. Finally, an optimal configuration has been carried out using a dynamic model and applying a controlled elitist genetic algorithm for multi-objective optimization. This methodology has been applied successfully for the sizing of a PV-wind-battery system to supply at least 95% of yearly total electric demand of a residential house. Results indicate that such a method, through its multitude Pareto front solutions, will help designers to take into consideration both economic and environmental aspects.