  In this paper, we present methodologies for optimal selection for renewable
energy sites under a different set of constraints and objectives. We consider
two different models for the site-selection problem - coarse-grained and
fine-grained, and analyze them to find solutions. We consider multiple
different ways to measure the benefits of setting up a site. We provide
approximation algorithms with a guaranteed performance bound for two different
benefit metrics with the coarse-grained model. For the fine-grained model, we
provide a technique utilizing Integer Linear Program to find the optimal
solution. We present the results of our extensive experimentation with
synthetic data generated from sparsely available real data from solar farms in
Arizona.
