Nature-inspired swarm intelligence algorithms for optimal distributed generation allocation: A comprehensive review for minimizing power losses in distribution networks
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10257051" target="_blank" >RIV/61989100:27240/24:10257051 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.sciencedirect.com/science/article/pii/S1110016824009207?pes=vor&utm_source=scopus&getft_integrator=scopus" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1110016824009207?pes=vor&utm_source=scopus&getft_integrator=scopus</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.aej.2024.08.033" target="_blank" >10.1016/j.aej.2024.08.033</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Nature-inspired swarm intelligence algorithms for optimal distributed generation allocation: A comprehensive review for minimizing power losses in distribution networks
Popis výsledku v původním jazyce
The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field.
Název v anglickém jazyce
Nature-inspired swarm intelligence algorithms for optimal distributed generation allocation: A comprehensive review for minimizing power losses in distribution networks
Popis výsledku anglicky
The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20200 - Electrical engineering, Electronic engineering, Information engineering
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Alexandria Engineering Journal
ISSN
1110-0168
e-ISSN
—
Svazek periodika
105
Číslo periodika v rámci svazku
105
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
32
Strana od-do
692-723
Kód UT WoS článku
001300387900001
EID výsledku v databázi Scopus
2-s2.0-85201625124