A smart electricity markets for a decarbonized microgrid system
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50021847" target="_blank" >RIV/62690094:18450/25:50021847 - isvavai.cz</a>
Výsledek na webu
<a href="https://link.springer.com/article/10.1007/s00202-024-02699-9?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot&getft_integrator=clarivate" target="_blank" >https://link.springer.com/article/10.1007/s00202-024-02699-9?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot&getft_integrator=clarivate</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00202-024-02699-9" target="_blank" >10.1007/s00202-024-02699-9</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A smart electricity markets for a decarbonized microgrid system
Popis výsledku v původním jazyce
Demand response (DR) programs are potentially powerful tools to support renewable energy integration, ensure power balance and update electricity market mechanism. Based on the existing work, in this paper propose a day-ahead a smart electricity markets for a decarbonized microgrid system with the DR program. The proposed system aims to minimize the operating cost, and carbon emission. An IEEE 33-bus system is used as an illustrative example to validate the application of the proposed smart electricity market model in the real large system. The proposed unit utilizes the African Vultures Optimization Algorithm (AVOA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Artificial Rabbits Optimization Algorithm (AROA), and Grasshopper Optimization Algorithm (GOA). The simulation results reveal that energy costs and PAR can be reduced energy cost, and carbon emission, whereas the Discomfort Index (DI) is maintained at a minimum value.
Název v anglickém jazyce
A smart electricity markets for a decarbonized microgrid system
Popis výsledku anglicky
Demand response (DR) programs are potentially powerful tools to support renewable energy integration, ensure power balance and update electricity market mechanism. Based on the existing work, in this paper propose a day-ahead a smart electricity markets for a decarbonized microgrid system with the DR program. The proposed system aims to minimize the operating cost, and carbon emission. An IEEE 33-bus system is used as an illustrative example to validate the application of the proposed smart electricity market model in the real large system. The proposed unit utilizes the African Vultures Optimization Algorithm (AVOA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Artificial Rabbits Optimization Algorithm (AROA), and Grasshopper Optimization Algorithm (GOA). The simulation results reveal that energy costs and PAR can be reduced energy cost, and carbon emission, whereas the Discomfort Index (DI) is maintained at a minimum value.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20201 - Electrical and electronic engineering
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
ELECTRICAL ENGINEERING
ISSN
0948-7921
e-ISSN
1432-0487
Svazek periodika
107
Číslo periodika v rámci svazku
5
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
21
Strana od-do
5405-5425
Kód UT WoS článku
001329810600001
EID výsledku v databázi Scopus
2-s2.0-85206603209