Frequency and voltage regulation enhancement for microgrids with electric vehicles based on red panda optimizer

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022171" target="_blank" >RIV/62690094:18450/25:50022171 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2590174525000042?pes=vor&utm_source=scopus&getft_integrator=scopus" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590174525000042?pes=vor&utm_source=scopus&getft_integrator=scopus</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecmx.2025.100872" target="_blank" >10.1016/j.ecmx.2025.100872</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Frequency and voltage regulation enhancement for microgrids with electric vehicles based on red panda optimizer

Popis výsledku v původním jazyce

Vehicle-to-Grid (V2G) technology uses electric vehicle (EV) batteries for two-way energy flow, enhancing grid stability and reliability. It reduces supply–demand mismatches by utilizing renewable sources, making it especially beneficial for developing nations. This paper persents a control method for controlling frequency and voltage variation in microgrids that maximizes the benefits of the PI controller. However obtaining the optimal parameters of the PI controller is not a straightforward as it depends on the trial-and-error method for tuning a PI controller and observing the system&apos;s response to find a suitable set of parameters, particularly for systems with complex dynamics. This paper utilizes the Red Panda Optimizer (RPO) for the optimal identification of the controller&apos;s parameters under various system conditions comparing the results with Ant Lion Optimizer (ALO) and Whale Optimizer (WOA). The main objective is minimizing the performance indices including integral of absolute error (IAE), integral of squared error (ISE), integral of time weighted squared error (ITSE), and integral of time multiplied absolute error (ITAE) considering set of constraints such as frequency limits, voltage limits, state-of-charge (SOC) limits and PI controller paramerters’ limits. A detailed simulation study was conducted in MATLAB/Simulink to evaluate the performance of the proposed control strategy. The microgrid model incorporates various components, including distributed generation units, loads, and EV charging/discharging stations. Different load scenarios were simulated to assess the controller&apos;s robustness. The system includes four PI controllers to manage the charging and discharging of two EV batteries. Two controllers regulate the current and two regulate the voltage for each battery. The current controllers adjust the charging/discharging current by modifying the PWM signals for the DC-DC converters, while the voltage controllers ensure the batteries are charged to the desired voltage level. These PI controllers help in maintaining of efficient and stable operation and optimizing energy transfer between the grid, the batteries, and the load. Performance metrics such as settling time, peak overshoot, and rise time were used to evaluate the system&apos;s dynamic response. Compared to ALO and WOA, the ITAE value with RPO is 1.52, which is somewhat lower. Furthermore, RPO has settling time and peak overshoot values of 1.57 and 0.0474, respectively for the frequency waveform, which are lower than those of the other algorithms. The findings show that, in comparison to alternative techniques, the suggested RPO finds the globally optimal values with robustness and convergence efficiency. This proposal ensures the optimal utilization of EVs, where V2G can serve as mobile battery storage devices and minimizes supply and demand imbalances.

Název v anglickém jazyce

Frequency and voltage regulation enhancement for microgrids with electric vehicles based on red panda optimizer

Popis výsledku anglicky

Vehicle-to-Grid (V2G) technology uses electric vehicle (EV) batteries for two-way energy flow, enhancing grid stability and reliability. It reduces supply–demand mismatches by utilizing renewable sources, making it especially beneficial for developing nations. This paper persents a control method for controlling frequency and voltage variation in microgrids that maximizes the benefits of the PI controller. However obtaining the optimal parameters of the PI controller is not a straightforward as it depends on the trial-and-error method for tuning a PI controller and observing the system&apos;s response to find a suitable set of parameters, particularly for systems with complex dynamics. This paper utilizes the Red Panda Optimizer (RPO) for the optimal identification of the controller&apos;s parameters under various system conditions comparing the results with Ant Lion Optimizer (ALO) and Whale Optimizer (WOA). The main objective is minimizing the performance indices including integral of absolute error (IAE), integral of squared error (ISE), integral of time weighted squared error (ITSE), and integral of time multiplied absolute error (ITAE) considering set of constraints such as frequency limits, voltage limits, state-of-charge (SOC) limits and PI controller paramerters’ limits. A detailed simulation study was conducted in MATLAB/Simulink to evaluate the performance of the proposed control strategy. The microgrid model incorporates various components, including distributed generation units, loads, and EV charging/discharging stations. Different load scenarios were simulated to assess the controller&apos;s robustness. The system includes four PI controllers to manage the charging and discharging of two EV batteries. Two controllers regulate the current and two regulate the voltage for each battery. The current controllers adjust the charging/discharging current by modifying the PWM signals for the DC-DC converters, while the voltage controllers ensure the batteries are charged to the desired voltage level. These PI controllers help in maintaining of efficient and stable operation and optimizing energy transfer between the grid, the batteries, and the load. Performance metrics such as settling time, peak overshoot, and rise time were used to evaluate the system&apos;s dynamic response. Compared to ALO and WOA, the ITAE value with RPO is 1.52, which is somewhat lower. Furthermore, RPO has settling time and peak overshoot values of 1.57 and 0.0474, respectively for the frequency waveform, which are lower than those of the other algorithms. The findings show that, in comparison to alternative techniques, the suggested RPO finds the globally optimal values with robustness and convergence efficiency. This proposal ensures the optimal utilization of EVs, where V2G can serve as mobile battery storage devices and minimizes supply and demand imbalances.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

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

2590-1745

Svazek periodika

25

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

19

Strana od-do

"Article Number: 100872"

Kód UT WoS článku

001401490400001

EID výsledku v databázi Scopus

2-s2.0-85214108780