The wind speed characteristics are analyzed statistically based on a long-term hourly data record to evaluate the proper wind energy potential. The annual average wind speed and wind power density are investigated and compared by some significant indices, wind energy output and capacity factor, to show the variations of proper wind turbine specifications of installation in different locations of Taiwan. The minimum cost of wind energy is used to assess the economical feasibility for turbine installation in Taiwan. Great variations occur in the simulation results in both of the cost of energy and capacity factor. The detailed statistical analysis should be conducted to ensure the successful operation after wind turbine installations.

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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.

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