Methodologies for Selection of Optimal Sites for Renewable Energy Under a Diverse Set of Constraints and Objectives

TL;DR

This paper develops methodologies for selecting optimal renewable energy sites considering diverse constraints and objectives, using approximation algorithms and integer linear programming, validated through extensive experiments on synthetic data.

Contribution

It introduces two models for site selection, providing approximation algorithms and an ILP approach, advancing the methodology for renewable energy site optimization.

Findings

Approximation algorithms with performance guarantees for coarse-grained model.

Optimal solutions via Integer Linear Programming for fine-grained model.

Extensive experimental validation using synthetic data from solar farms.

Abstract

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.