Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
In this paper, we present methodologies for optimal site 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.
Recommended Citation
Sen, Arunabha; Sumnicht, Christopher; Choudhuri, Sandipan; Adeniye, Suli; and Sen, Amit B., "Methodologies for Selection of Optimal Sites for Renewable Energy Under a Diverse Set of Constraints and Objectives" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 9.
https://aisel.aisnet.org/hicss-57/es/renewable_resources/9
Methodologies for Selection of Optimal Sites for Renewable Energy Under a Diverse Set of Constraints and Objectives
Hilton Hawaiian Village, Honolulu, Hawaii
In this paper, we present methodologies for optimal site 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.
https://aisel.aisnet.org/hicss-57/es/renewable_resources/9