Sizing Optimization Of The Hybrid Power System Based On The Fruit Fly Optimization Algorithm

With the rapid development of the global economy, human life is inseparable from power energy. However, power supply is difficult for some remote areas far away form the power grid. Fortunately, these areas typically have rich wind, solar and other natural resources. Besides, wind and solar energy are clean, renewable, environmental and economical. Therefore, setting up hybrid power system based on the local renewable energy resources can solve the power supply problem effectively. This paper proposes a hybrid PV-wind-diesel-battery power system according to the unique geographical environment and load demand condition of Dongao Island. The main research and design contents for the system in this paper are as follows.At the beginning, according to the system's structural characteristics, some key components'mathematical model and their operating characteristics are introduced. Then, the wind and solar resources of Dongao Island are evaluated and analyzed based on the region's natural resources distribution condition and climate features.Sizing optimization for the system is a prerequisite for its operational control. According to the system features, the objective function, constraints and energy control strategies of the system are established. Then, this paper uses a fruit fly optimization algorithm (FOA)-based method to optimize the system's capacity. Simulation experiments are conducted under two kinds of energy control strategies. After analyzing the simulation results and the system energy balance problems, it is shown that the different choice of the energy control strategy has great influence on the system sizing optimization results.Secondly, on the basis of the basic FOA algorithm, an improved fruit fly optimization algorithm (IFOA) is proposed. The IFOA-based method is used to optimize the capacity of the hybrid power system. In order to highlight the advantages of IFOA, its optimization results are compared with those of the basic FOA, particle swarm optimization algorithm (PSO) and artificial bee colony (ABC) algorithm, respectively. At the same time, simulation experiments are conducted for different types of hybrid power system to show the impact of the renewable power on the system cost. Besides, two kinds of typical weather conditions are selected to test the performance of the obtained system and the affectiveness of the designed method.Finally, the IFOA-based multi-objective optimization method is proposed for the sizing optimization design of the hybrid power system. Here, the objectives to be minimized are the annual total cost and the pollutant emission of the system. Power supply reliability is one of the constraints of the optimization problem. After establishing the objective optimization functions model, we present some methods for solving multi-objective problems. Simulation experiments are carried out in MATLAB simulation tool, and the simulation results are analyzed in detail. What's more, we have studied the influences of different equipment selection and different project region selection on the system optimization results, respectively. Multi-objective optimization results are obtained by considering various factors. Therefore, the obtained solutions of the multi-objective problems can provide more intuitive system configuration options for decision-makers.