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.

翻译：本文提出了在多样化约束与目标下可再生能源场址最优选择的方法论。我们针对场址选择问题构建了粗粒度与细粒度两种模型，并通过分析寻求解决方案。研究采用多种方式评估场址建设的效益指标。针对粗粒度模型，我们提出了两种不同效益指标下具有保证性能边界的近似算法；针对细粒度模型，则采用整数线性规划技术求得最优解。基于亚利桑那州太阳能电站稀疏真实数据生成的合成数据，我们开展了大量实验并呈现了实验结论。