The optimal allocation problem for a stand-alone photovoltaic (SPV) generation can be achieved by good compromise between system objective and constraint requirements. The Lagrange technique (LGT) is a traditional method to solve such constrained optimization problem. To consider the nonlinear features of reliability constraints evolving from the consideration of different scenarios, including variations of component cost, load profile and installation location, the implementation of SPV generation planning is time-consuming and conventionally implemented by a probability method. Genetic Algorithm (GA) has been successfully applied to many optimization problems. For the optimal allocation of photovoltaic and battery devices, the cost function minimization is implemented by GA to attain global optimum with relative computation simplicity. Analytical comparisons between the results from LGT and GA were investigated and the performance of simulation was discussed. Different planning scenarios show that GA performs better than the Lagrange optimization technique.

The paper presents a feasibility computing approach to solve the optimal planning problem applied to Stand-alone Photovoltaic (SPV) system by considering the reliability requirement and economical performance. Evaluation technique based on genetic algorithm to get global optimum capacity of solar array and battery in a SPV system is more efficiently. Explicit strategy selects proper values of systems' parameters improving local exploration and avoiding trapped in local optimum. Different requirements of system reliability are investigated to achieve the optimal planning of a SPV system. Sensitivity analysis of components' cost and load profiles are conducted to demonstrate the impacts of system uncertainty. The solar radiation and temperature data from the Central Weather Bureau of Taiwan at four different locations were used.