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25 January 2026

Power Grid Electrification Through Grid Extension and Microgrid Deployment: A Case Study of the Navajo Nation

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1
Advanced Energy Systems Program, Colorado School of Mines, Golden, CO 80401, USA
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Electrical and Electronic Engineering Department, Jazan University, Jazan 45142, Saudi Arabia
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Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA
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School of Engineering, Math, and Technology, Navajo Technical University, Crownpoint, NM 87313, USA
Appl. Sci.2026, 16(3), 1227;https://doi.org/10.3390/app16031227 
(registering DOI)
This article belongs to the Special Issue Recent Advances in Smart Microgrids

Abstract

Ensuring affordable and reliable electricity access to areas with low population density is challenging, as network sparsity and lower connectivity rates can make it nearly impossible for electric utilities to cover the cost of interconnection without raising electricity tariffs. Utility providers that consider extending their networks to remote households must balance multiple and often conflicting objectives, including investment cost, grid resilience, geographical coverage, and environmental impacts. In this paper, a multi-objective decision-making framework is proposed for the electrification of rural households, considering traditional distribution network extension as well as microgrid deployment. In order to condense a wide range of spatial inputs into a tractable problem, a multi-criteria decision-making approach is adopted to identify and rank candidate sites for microgrid deployment that offer superior performance over a variety of technical, environmental, and economic criteria. A novel optimization model is then proposed using multi-objective Chebyshev goal programming, in which project costs, environmental impacts, and energy justice criteria are jointly optimized. The applicability of this framework is demonstrated through a case study of the Shiprock region within the Navajo Nation. The results indicate that the proposed methodology provides a balanced trade-off among conflicting objectives and identifies a priority order of loads to energize first under marginally increasing budgets.

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