Optimal power flow solution incorporating hybrid conventional and renewable resources using electric eel foraging optimization algorithm DOI Creative Commons

Anwar Fellahi,

Souhil Mouassa, Hacène Mellah

et al.

STUDIES IN ENGINEERING AND EXACT SCIENCES, Journal Year: 2024, Volume and Issue: 5(2), P. e11612 - e11612

Published: Dec. 5, 2024

In recent years, metaheuristic algorithms have become the main tool in solving Optimal Power Flow (OPF) problem due to their effectiveness addressing complicated modern power systems. This complexity is fueled by rise of Renewable Energy Resources (RERs) and need decrease greenhouse emissions. research presents a comprehensive approach that aims optimize performance networks presence thermal, wind, Solar Photovoltaic (SPV) units. The algorithm implemented named Electrical Eel Foraging Optimization (EEFO). It carried out using modified IEEE 30-bus test system. EEFO compared alongside Kepler Algorithm (KOA) Self-adaptive Bonobo Optimizer (SaBO). Two cases were taken into consideration. first one minimizing Total Generation Cost (TGC); second generation cost, including emission effects. results show reduction TGC at 781.1981 $/h 792.6531 for cases, respectively; emissions also decreased with previous studies. findings obtained this validity proposed algorithm.

Language: Английский

Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique DOI Creative Commons
Shubhranshu Mohan Parida, Vivekananda Pattanaik, Subhasis Panda

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 16, 2025

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling critical due to many factors, such as inherent nonlinearity, existing complexity, wide range parameters. Although different researchers have recently proposed several effective techniques system parameter identification, it still an interesting challenge enhance accuracy modelling. With above motivation, this article suggests stage-specific mutation strategy enhanced differential evolution (EDE) that adopts better search process arrive at optimal solutions by adaptively varying factor crossover rate stages. The identification systems formulated single objective function. It appears in form Root Mean Square Error (RMSE) between current from experimental data calculated using identified considering constraints (limits). I-V (current-voltage) characteristics/data with are validated justify approach's efficacy cells modules. Extensive simulation has been demonstrated two (RTC France & PVM-752-GaAs) three modules (ND-R250A5, STM6 40/36 STP6 120/36). results obtained EDE technique show Errors 7.730062e-4, 7.419648e-4, 7.33228e-4 respectively, RTC models based on single, double, triple diodes. Also, RMSE involved PVM-752-GaAs diodes 1.59256e-4, 1.408989e-4, 1.30181e-4, respectively. ND-R250A5, 120/36 involve values 7.697716e-3, 1.772095e-3, 1.224258e-2, All these least compared other well-accepted algorithms, thereby justifying its higher accuracy.

Language: Английский

Citations

1
Nonlinear controller design for automotive engine speed regulation utilizing electric eel foraging optimization DOI
Serdar Ekinci

International Journal of Dynamics and Control, Journal Year: 2025, Volume and Issue: 13(2)

Published: Feb. 1, 2025

Language: Английский

Citations

0
Accurate parameters extraction of photovoltaic models using Lambert W-function collaborated with AI-based Puma optimization method DOI Creative Commons
Rabeh Abbassi, Salem Saidi, Houssem Jerbi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104268 - 104268

Published: Feb. 1, 2025

Language: Английский

Citations

0
Parameters estimation of PV models using a novel hybrid equilibrium optimization algorithm DOI Creative Commons

Fude Duan,

Ali B. M. Ali,

Dheyaa J. Jasim

et al.

Energy Exploration & Exploitation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

This study presents a novel algorithm, termed equilibrium optimizer-single candidate optimizer (EO-SCO), which combines the EO and SCO techniques. The objective of this approach is to achieve accurate reliable parameter estimates for photovoltaic (PV) solar cells modules. EO-SCO, as outlined, functions through two-phase approach. first phase uses an pool elite particles traverse search space find interesting places using EO, retaining solution diversity. second integrates lead searching toward better vicinities high-quality by employing its detecting pattern movements increase proposed method's exploitation potential in last steps. described EO-SCO technique accurately determines PV model unknown parameters. identification these parameters denoted function that must be reduced reducing disparities between estimated experimental data. extensive findings evaluations have confirmed exhibits comparable performance other cutting-edge technologies, particularly relation quality dependability solution. from simulation demonstrate newly optimization methodology yields optimal solutions outperform earlier techniques terms across diverse cell types, while also achieving lowest root mean square error.

Language: Английский

Citations

0
A novel pressure control method for nonlinear shell-and-tube steam condenser system via electric eel foraging optimizer DOI Creative Commons
Serdar Ekinci, Cebrail Turkeri, Davut İzci

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 4, 2025

Precise pressure control in shell-and-tube steam condensers is crucial for ensuring efficiency thermal power plants. However, traditional controllers (PI, PD, PID) struggle with nonlinearities and external disturbances, while classical tuning methods (Ziegler-Nichols, Cohen-Coon) fail to provide optimal parameter selection. These challenges lead slow response, high overshoot, poor steady-state performance. To address these limitations, this study proposes a cascaded PI-PDN strategy optimized using the electric eel foraging optimizer (EEFO). EEFO, inspired by prey-seeking behavior of eels, efficiently tunes controller parameters, improved stability precision. A comparative analysis against recent metaheuristic algorithms (SMA, GEO, KMA, QIO) demonstrates superior performance EEFO regulating condenser pressure. Additionally, validation documented studies (CSA-based FOPID, RIME-based GWO-based PI, GA-based PI) highlights its advantages over existing methods. Simulation results confirm that reduces settling time 22.7%, overshoot 78.7%, error three orders magnitude, ITAE 81.2% compared based The EEFO-based achieves faster convergence, enhanced robustness precise tracking, making it highly effective solution real-world applications. findings contribute optimization-based strategies plants open pathways further bio-inspired innovations.

Language: Английский

Citations

0
Towards enhanced photovoltaic Modeling: New single diode Model variants with nonlinear ideality factor dependence DOI
Martin Ćalasan,

Snežana Vujošević,

Gojko Krunić

et al.

Engineering Science and Technology an International Journal, Journal Year: 2025, Volume and Issue: 65, P. 102037 - 102037

Published: March 19, 2025

Language: Английский

Citations

0
Nonlinear Marine Predator Algorithm for Robust Identification of Fractional Hammerstein Nonlinear Model under Impulsive Noise with Application to Heat Exchanger System DOI
Zeshan Aslam Khan, Taimoor Ali Khan,

Muhammad Waqar

et al.

Communications in Nonlinear Science and Numerical Simulation, Journal Year: 2025, Volume and Issue: unknown, P. 108809 - 108809

Published: March 1, 2025

Language: Английский

Citations

0
Optimal power flow solution incorporating hybrid conventional and renewable resources using electric eel foraging optimization algorithm DOI Creative Commons

Anwar Fellahi,

Souhil Mouassa, Hacène Mellah

et al.

STUDIES IN ENGINEERING AND EXACT SCIENCES, Journal Year: 2024, Volume and Issue: 5(2), P. e11612 - e11612

Published: Dec. 5, 2024

In recent years, metaheuristic algorithms have become the main tool in solving Optimal Power Flow (OPF) problem due to their effectiveness addressing complicated modern power systems. This complexity is fueled by rise of Renewable Energy Resources (RERs) and need decrease greenhouse emissions. research presents a comprehensive approach that aims optimize performance networks presence thermal, wind, Solar Photovoltaic (SPV) units. The algorithm implemented named Electrical Eel Foraging Optimization (EEFO). It carried out using modified IEEE 30-bus test system. EEFO compared alongside Kepler Algorithm (KOA) Self-adaptive Bonobo Optimizer (SaBO). Two cases were taken into consideration. first one minimizing Total Generation Cost (TGC); second generation cost, including emission effects. results show reduction TGC at 781.1981 $/h 792.6531 for cases, respectively; emissions also decreased with previous studies. findings obtained this validity proposed algorithm.

Language: Английский

Citations

0