Sizing optimization of hybrid photovoltaic-wind-battery system towards zero energy building using genetic algorithm

Abstract

A new topic of Zero Energy Building is getting famous in research area because of its goal of reaching zero carbon emission and low building cost. Renewable energy system is one of the ideas to achieve the objective of Zero Energy Building. Recently, Genetic Algorithm is widely used in many research area due to its capability to escape from a local minimal to obtain a better solution. In our study, Genetic Algorithm is chosen in sizing optimization of the number of photovoltaic, wind turbine and battery of a hybrid photovoltaic-wind-battery system. Besides, these numbers are used to minimize the total annual cost of the hybrid energy system towards the concept of Zero Energy Building. There are a few Genetic Algorithm parameters that need to be considered in the optimization process which is generation number, population size, crossover operator and mutation operator. Therefore, two Genetic Algorithm parameters will be analysed and optimized which is generation number and population size. All of the simulations are done by using Microsoft Visual Studio 2010. From the results of simulations, the best generation number and population size is 100 000 and 3 000 respectively. In summary, Genetic Algorithm is efficient in minimizing cost function of a hybrid photovoltaic-wind-battery system with its robustness property.