Renewable Energy Analysis in Indigenous Communities Using Bottom-Up Demand Prediction

Mohammad Fathollahzadeh, Andrew Speake, Paulo Tabares-Velasco, Zoheir Khademian, Lisa Lone Fight

Research output: Contribution to journalArticlepeer-review

11 Scopus Citations

Abstract

This paper provides a methodology for the holistic analysis of hybrid renewable energy systems in rural communities. Electric demand is an important component for modeling and analysis of renewable energy systems. Typically, electric demand data is not available due to the internal privacy policies of utility providers. Therefore, this study proposes the use of bottom-up approaches for the development of the electric demand profile, considering the general homogeneity of residential and commercial buildings in rural communities. As a test case, this study develops the electric demand profile and investigates the technical and environmental feasibility of a hybrid renewable energy system for the New Town community on the Fort Berthold Indian Reservation (FBIR) in North Dakota. This study conducts the hybrid renewable energy system's analysis by developing scripts in the LK scripting language and integrating System Advisor Model software's open-source modules for modeling of renewable energy systems. The results for the validation testbed of this study show that hybrid renewable resources have higher ratios of energy used for self-consumption to the total energy generated compared to stand-alone wind and PV farms.

Original languageAmerican English
Article numberArticle No. 102932
Number of pages15
JournalSustainable Cities and Society
Volume71
DOIs
StatePublished - Aug 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5500-79824

Keywords

  • EnergyPlus
  • Fort Berthold Indian Reservation
  • Hybrid renewable energy systems
  • Rural communities
  • System Advisor Model (SAM)

Fingerprint

Dive into the research topics of 'Renewable Energy Analysis in Indigenous Communities Using Bottom-Up Demand Prediction'. Together they form a unique fingerprint.

Cite this