Abstract:

Renewable energy contains embedded uncertainties, such as wind energy, solar energy, and load demand. This uncertainty adds ambiguity to smart grid planning. The existing research only focuses on intelligent optimization algorithms or the uncertain output of the source load to solve the optimal configuration problem of the independent microgrid; it fails to combine the intelligent optimization algorithm with the uncertainty processing method. Monte Carlo simulation （MCS） can successfully simulate the system output in a certain timescale to effectively deal with the uncertainty factors associated with the system. Furthermore, the gravitational search algorithm （GSA） is fast and accurate when dealing with this kind of optimal allocation. An MCS-embedded universal gravitation search algorithm （GSA-MCS） is proposed for solving the optimization model to effectively improve the economy of independent microgrids （by fully considering the reliability of microgrid power supply and renewable energy waste） and aiming at the optimization of system levelized energy cost （LCOE）. The main objective of GSA-MCS is to simulate the uncertainty of wind load through an MCS and substitute the simulated load data into the universal gravitation search algorithm to solve the capacity optimization configuration model. First, an MCS is applied to deal with the uncertainty of wind, light, and load, and the global optimization of wind, light, and storage is conducted using a gravitational search algorithm. The allocation scheme determines the allocation capacity at different cumulative probability levels. This study considers an island microgrid as an example to conduct simulation analysis and verify the effectiveness of the method used herein. The simulation results reveal that under different cumulative probability levels, the number of microgrid fans, photovoltaic panels, and energy storage cells reduce when compared with those obtained using traditional methods, and the LCOE is also reduced. When compared with the traditional optimization method, the proposed model can provide a compromise and flexible scheme for the capacity allocation of microgrid systems.

Key words: standalone micro-grid, Monte Carlo Simulation, GSA, optimizing configuration