Cheetah optimization-based smart energy management for appliance scheduling and DER integration in residential and commercial-industrial grids

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258560" target="_blank" >RIV/61989100:27240/25:10258560 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/25:10258560

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2590174525003241" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590174525003241</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecmx.2025.101192" target="_blank" >10.1016/j.ecmx.2025.101192</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cheetah optimization-based smart energy management for appliance scheduling and DER integration in residential and commercial-industrial grids

Popis výsledku v původním jazyce

This study proposes a smart energy management strategy for the IEEE 15-bus radial distribution system (RDS) to minimize electricity costs, power losses, and grid dependency, while maximizing renewable energy usage and system stability. The system includes solar (PV) and wind (WT) generation, modeled using Monte Carlo (MC) simulations, along with electric vehicles (EVs) operating in vehicle-to-grid (V2G) mode and battery energy storage systems (BESS), addressing both residential and commercial-industrial loads. These normalized power outputs serve as inputs to a multi-objective optimization framework that balances technical (power loss, voltage stability, fault detection), economic (total electricity cost), and operational criteria. The cheetah optimization algorithm (COA), implemented in MATLAB, handles this complex optimization task. COA offers an adaptive exploration-exploitation balance through dynamic coefficients and stochastic movements, delivering better solution diversity and faster convergence than traditional algorithms such as GA, PSO, and WOA. Simulation results verify COA&apos;s superior performance. In the residential sector, COA achieves the lowest multi-objective function value (0.7064), with a 73.37 % increase in renewable energy usage and a 64.49 % drop in grid dependency. Similar improvements appear in commercial-industrial scenarios. These outcomes demonstrate COA&apos;s effectiveness in solving smart energy management challenges and establish the proposed framework as a scalable solution for modern smart grids.

Název v anglickém jazyce

Cheetah optimization-based smart energy management for appliance scheduling and DER integration in residential and commercial-industrial grids

Popis výsledku anglicky

This study proposes a smart energy management strategy for the IEEE 15-bus radial distribution system (RDS) to minimize electricity costs, power losses, and grid dependency, while maximizing renewable energy usage and system stability. The system includes solar (PV) and wind (WT) generation, modeled using Monte Carlo (MC) simulations, along with electric vehicles (EVs) operating in vehicle-to-grid (V2G) mode and battery energy storage systems (BESS), addressing both residential and commercial-industrial loads. These normalized power outputs serve as inputs to a multi-objective optimization framework that balances technical (power loss, voltage stability, fault detection), economic (total electricity cost), and operational criteria. The cheetah optimization algorithm (COA), implemented in MATLAB, handles this complex optimization task. COA offers an adaptive exploration-exploitation balance through dynamic coefficients and stochastic movements, delivering better solution diversity and faster convergence than traditional algorithms such as GA, PSO, and WOA. Simulation results verify COA&apos;s superior performance. In the residential sector, COA achieves the lowest multi-objective function value (0.7064), with a 73.37 % increase in renewable energy usage and a 64.49 % drop in grid dependency. Similar improvements appear in commercial-industrial scenarios. These outcomes demonstrate COA&apos;s effectiveness in solving smart energy management challenges and establish the proposed framework as a scalable solution for modern smart grids.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy Conversion and Management-X

ISSN

2590-1745

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

August 2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

23

Strana od-do

1-23

Kód UT WoS článku

001582822800001

EID výsledku v databázi Scopus

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