Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm DOI Creative Commons
Hongli Liu, Hao Li, Li Ji

et al.

Energy Science & Engineering, Journal Year: 2024, Volume and Issue: 12(11), P. 4904 - 4917

Published: Nov. 1, 2024

Abstract Distributed power supply access to the distribution network, although it can effectively support band voltage, will also cause problems such as voltage overruns at point of grid connection and large network losses, so this paper establishes a reactive optimization model containing three objectives: loss, fluctuation rate, static generator (SVG) installation capacity in distributed photovoltaic generation scenarios by taking advantage characteristics SVG that both absorb send out power. A multiobjective particle swarm algorithm with an adaptive roulette mechanism is introduced ensure uniformity diversity Pareto boundaries under constraint output each device does not exceed constraints, obtain optimal set solutions capable coping stochastic fluctuations sources. When compared other algorithms, nondominated sorting genetic algorithm‐II, results show reduces loss about 25% significantly improves rate.

Language: Английский

An improved pelican optimization algorithm for solving stochastic optimal power flow problem of power systems considering uncertainty of renewable energy resources DOI Creative Commons
Raheela Jamal, Noor Habib Khan, Mohamed Ebeed

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104553 - 104553

Published: March 1, 2025

Language: Английский

Citations

2
An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty DOI Creative Commons

Deyaa Ahmed,

Mohamed Ebeed,

Salah Kamel

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 5, 2024

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, MMGs became complex strenuous with high penetration renewable resources due stochastic nature these along load fluctuations. In this regard, paper aims solve EM problem optimal inclusion photovoltaic (PV) systems, wind turbines (WTs), biomass systems. proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving 85-bus MMGS system minimize total cost, performance improvement concurrently. algorithm based on Weibull Flight Motion (WFM) Fitness Distance Balance (FDB) mechanisms tackle stagnation conventional JSO technique. EJSO tested standard CEC 2019 benchmark functions obtained results are compared optimization techniques. As per results, powerful method other like Sand Cat Swarm Optimization (SCSO), Dandelion (DO), Grey Wolf (GWO), Whale Algorithm (WOA), (JSO). reveal that by suggested can reduce cost 44.75% while voltage profile stability 40.8% 10.56%, respectively.

Language: Английский

Citations

13
Artificial hummingbird algorithm: Theory, variants, analysis, applications, and performance evaluation DOI
Buddhadev Sasmal, Arunita Das, Krishna Gopal Dhal

et al.

Computer Science Review, Journal Year: 2025, Volume and Issue: 56, P. 100727 - 100727

Published: Jan. 18, 2025

Language: Английский

Citations

1
Optimal Scheduling of Wind-Photovoltaic- Pumped Storage Joint Complementary Power Generation System Based on Improved Firefly Algorithm DOI
Liyuan Sun, Jing Bao, Nan Pan

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 70759 - 70772

Published: Jan. 1, 2024

Language: Английский

Citations

7
A novel artificial hummingbird algorithm improved by natural survivor method DOI Creative Commons
Hüseyin Bakır

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(27), P. 16873 - 16897

Published: June 2, 2024

Abstract The artificial hummingbird algorithm (AHA) has been applied in various fields of science and provided promising solutions. Although the demonstrated merits optimization area, it suffers from local optimum stagnation poor exploration search space. To overcome these drawbacks, this study redesigns update mechanism original AHA with natural survivor method (NSM) proposes a novel metaheuristic called NSM-AHA. strength developed is that performs population management not only according to fitness function value but also NSM score value. adopted strategy contributes NSM-AHA exhibiting powerful avoidance unique ability. ability proposed was compared 21 state-of-the-art algorithms over CEC 2017 2020 benchmark functions dimensions 30, 50, 100, respectively. Based on Friedman test results, observed ranked 1st out 22 competitive algorithms, while 8th. This result highlights provides remarkable evolution convergence performance algorithm. Furthermore, two constrained engineering problems including single-diode solar cell model (SDSCM) parameters design power system stabilizer (PSS) are solved better results other 9.86E − 04 root mean square error for SDSCM 1.43E 03 integral time PSS. experimental showed optimizer solving global problems.

Language: Английский

Citations

5
AEOWOA: hybridizing whale optimization algorithm with artificial ecosystem-based optimization for optimal feature selection and global optimization DOI
Reham R. Mostafa, Abdelazim G. Hussien,

Marwa A. Gaheen

et al.

Evolving Systems, Journal Year: 2024, Volume and Issue: 15(5), P. 1753 - 1785

Published: May 15, 2024

Language: Английский

Citations

4
Modified effective butterfly optimizer for solving optimal power flow problem DOI Creative Commons
Kadír Abaci, Zekí Yetgín, Volkan Yamaçlı

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(12), P. e32862 - e32862

Published: June 1, 2024

The optimal power flow (OPF) problem remains a popular and challenging work in optimizing systems. Although researchers have suggested many optimization algorithms to solve this the literature, their comparison studies lack fairness transparency. As these increase number, they deviate from standard test system, considering common security technical constraints., there is growing trend away system. Different used different search ranges for same decision constraint parameters, than by IEEE This caused unfair comparisons literature. Furthermore, are generally not transparent enough so that results cannot be verified. has resulted numerous infeasible solutions violating limits of parameters. recent incorporating renewable energy sources OPF made situation more complicated. Sorting through literature identifying those applications having exactly conditions process. main contribution paper adapts modified effective butterfly algorithm (MEBO) under parameter constraints sufficient focus on with works values. compares performance proposed other state-of-the-art focusing wind without 30-bus 57-bus systems most commonly constraints. demonstrate efficiency superiority algorithm. For instance, compared initial case, fuel cost been reduced 11.42 %, emission 14.33 L-index 45.10 active losses 51.60 voltage deviation 92.70 %.

Language: Английский

Citations

4
Solving optimal power flow frameworks using modified artificial rabbit optimizer DOI Creative Commons
Noor Habib Khan, Yong Wang, Raheela Jamal

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 3883 - 3903

Published: Oct. 3, 2024

Language: Английский

Citations

4
Single and Multi-Objective Optimal Power Flow Based on JAYA Algorithm with Teaching-Learning Based Optimization DOI Creative Commons
Oğuz Taşdemır, Salih Ermis

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract This paper deals with the Optimal Power Flow (OPF) in an IEEE standard bus (30-bus) power system and presents a multi-objective optimization approach to minimize generation costs, active losses voltage deviations. The OPF problem is of critical importance for reliable, efficient economical operation systems. However, solution this complex due its nonlinear nature large number constraints. Conventional methods are often insufficient overcome challenges inherent OPF. In addressing these challenges, study employs metaheuristic algorithms, namely Teaching-Learning Based Optimisation (TLBO), JAYA hybrid TLBO-JAYA, enhance efficiency convergence speed process. To manage problem, Pareto optimisation utilised identify set that balances conflicting objectives. outcomes demonstrate TLBO-JAYA algorithm offers balanced enhancement terms cost, loss stability, thereby providing versatile framework contemporary These findings underscore potential algorithms problems

Language: Английский

Citations

0
Sturnus vulgaris escape algorithm and its application to mechanical design DOI Creative Commons

Y. G. Liu,

Yaping Fan,

Jiaxing Ma

et al.

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

Published: Feb. 24, 2025

Practical engineering optimization problems are characterized by high dimensionality, non-convexity, and non-linearity, the use of optimizers to provide better quality solutions target problem in an acceptable time is a hot research topic field optimal design. In this paper, inspired Sturnus vulgaris escape behavior, Vulgaris Escape Algorithm (SVEA) proposed high-performance optimizer for complex problems. The algorithm composed exploration exploitation strategies, controlled fixed parameters. strategies include High-Altitude Strategy Wave 1, while consist Cordon Line 2. enhances capabilities reorganizing subgroups, preventing leader individuals from overlapping, avoiding collisions between individuals. conducts refined searches around high-value regions, further improving precision. Strategies 1 2 help population local optima prevent over-spreading. performance SVEA evaluated through employment 23 benchmark test functions CEC2017 set, with subsequent comparison undertaken nine statE − of-thE art meta-heuristic algorithms. outcomes evaluation demonstrate that attains top ranking identified as best-performing across all sets. A statistical analysis reveals solution set exhibits superior other algorithms, discrepancy being deemed be statistically significant. Finally, applied five real-world problems, providing satisfying constraints.

Language: Английский

Citations

0