Location Optimization of Solar Plants by an Integrated Multivariable DEA-PCA Method

Unique features of renewable energies such as solar energy has caused increasing demands for such resources. In order to use solar energy as a natural resource, environmental circumstances and geographical location related to solar intensity must be considered. Different factors may affect efficiency of solar energy. Because of special structure of solar radiation models, they have a high degree of uncertainly and may be used for limited locations. This article presents a technical and economical research for allocation of solar plants by using multivariable methods namely, Data Envelopment Analysis (DEA) and Principle Component Analysis (PCA). A hybrid model for allocation of solar plants is presented by utilization of most related parameters to solar plants and an integrated DEA-PCA approach. The prescribed approach is tested for twenty different geographical locations in Iran. This is the first study that considers a DEA approach for geographical location optimization of solar plants. Moreover, PCA is used to validate the results of DEA. Furthermore implementation of the approach of this study in other countries would reduce costs associated with location and allocation of solar plants when compared with conventional solar measurement systems.