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Networked rural electrification – optimal network design under complex topography

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dc.contributor.authorLi, Jerry Chun-Fung, author
dc.contributor.authorYoung, Peter, advisor
dc.contributor.authorZimmerle, Daniel, advisor
dc.contributor.authorCale, Jim, committee member
dc.contributor.authorCross, Jeni Eileen, committee member
dc.date.accessioned2022-05-30T10:22:34Z
dc.date.available2023-05-24T10:22:34Z
dc.date.issued2022
dc.description.abstractThe 7th of United Nations' Sustainable Development Goals (SDG7) aims to "ensure access to affordable, reliable, sustainable and modern energy for all" by 2030. While substantial progresses have been made in the last few years, 759 million people in rural areas still have no or limited access to electricity. Due to the distances and geographical complexity of rural areas, providing electricity to this unserved population is very costly. As IEA recently pointed out, rural electrification is increasingly costly. With the current electrification approach, it is expected that 660 million people will remain without electricity access by 2030. In addition, accurate planning for small rural power system is difficult as both demand and energy resource forecasts are highly uncertain. Thus, achieving SDG7 is very challenging. In this research, a Networked Rural Electrification framework has been proposed. This approach can potentially accelerate SDG7 by reducing system cost, enhancing reliability, and offering installation flexibility for small communities in remote areas. In this framework, villages and generation facilities are connected via an optimal, low voltage network that can be built with inexpensive poles and cables. To make this approach economically feasible, cost for building the network is crucial. A specific difficulty associated with this approach is the anisotropicity of search space for optimal design of the power distribution network, which results from complex topographical variations in these rural areas. Traditional optimization methods are not suitable for designing this network because of computational complexity, accuracy requirement, and practical implementation considerations. To address the issues, new computation methods and tools have been developed. These include (i) Multiplier-accelerated A* (MAA*) and (ii) Adaptive Multiplier-accelerated A* (AMAA*) algorithms, which resolve the computational complexity problem by significantly reduce computation time while maintaining good optimality, and (iii) Levelized Interpolative Genetic Algorithm (LIGA) which, when used in conjunction with A*, MAA*, or AMAA*, provides viable alternative plans to tackle unexpected route change problem right before or even during project implementation, and (iv) a fuzzy rule-based system for further network topology optimization.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierLi_colostate_0053A_17067.pdf
dc.identifier.urihttps://hdl.handle.net/10217/235290
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectgenetic algorithm
dc.subjectoptimal path-finding
dc.subjectSDG7
dc.subjectoptimal network
dc.subjectfuzzy inference
dc.subjectrural electrification
dc.titleNetworked rural electrification – optimal network design under complex topography
dc.typeText
dcterms.embargo.expires2023-05-24
dcterms.embargo.terms2023-05-24
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineSystems Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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