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University of Missouri--Kansas City

Pareto optimality based zonal allocation of distributed energy resources considering technical and economic constraints

Abstract

dc:description.abstract

Distributed Energy Resources (DERs) play a crucial role in enhancing the resilience of distribution networks during High-Impact Low-Frequency (HILF) events. Optimal allocation of DERs is essential to minimize capital costs while improving the operational performance of the distribution network. This paper introduces a Pareto Optimality Based Grey Wolf Optimization (GWO) approach for the strategic placement of DERs across various zones of a distribution network. We formulate a multi-objective optimization problem within the fuzzy domain, aiming to minimize DER installation costs, reduce power losses and enhance the voltage profile of the network. The proposed optimization algorithm is evaluated using the IEEE 123-bus test system through a co-simulation between MATLAB and OpenDSS. The results illustrate that the proposed approach effectively balances cost and power losses while ensuring the voltage profile remains within the NERC standards.

Degree

thesis:*
Name thesis:degree_name
M.S. (Master of Science)
Level thesis:degree_level
Masters
Discipline thesis:degree_discipline
Electrical Engineering (UMKC)
Grantor
University of Missouri--Kansas City
Year dc:date.issued
2024

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Alanazi, Waleed Mohammed
Advisor dc:contributor.advisor
  • Goli, Preetham

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/10355/107284
OAI identifier oai:identifier
oai:mospace.umsystem.edu:10355/107284

Chain of custody

source
Harvested from
University of Missouri - Kansas City
Base URL
mospace.umsystem.edu/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
related terms
citation

Alanazi, Waleed Mohammed. Pareto optimality based zonal allocation of distributed energy resources considering technical and economic constraints. Masters thesis, University of Missouri--Kansas City, 2024. https://hdl.handle.net/10355/107284