Multi-objective RIME algorithm-based techno economic analysis for security constraints load dispatch and power flow including uncertainties model of hybrid power systems

Energy Reports, Год журнала: 2024, Номер 11, С. 4423 - 4451

Опубликована: Апрель 18, 2024

In recent times, the landscape of power systems has undergone significant evolution, particularly with integration diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance efficiency in modern grid, primarily by bolstering role stochastic RESs. The challenge lies optimal flow (OPF), a multifaceted and non-linear optimization that grows more complex inclusion RESs aims optimize allocation system resources minimize operational cost while maintaining stability security system. Addressing this, current study introduces innovative approach, Multi-Objective RIME (MORIME) algorithm. Drawing inspiration from physical phenomenon rime-ice, called RIME, MORIME seeks effectively tackle OPF issues. algorithm enhances solution accuracy smartly dividing non-dominated sorting crowding distance mechanism. proposed model incorporates three types RESs: solar photovoltaic, wind small-scale hydropower units. While uncertainties speed irradiation are managed through Monte Carlo simulations, small hydro unit is considered constant source. efficacy tested on IEEE 30 bus results indicate method identifies for multi-objective problem satisfying constraints, thereby proving its effectiveness superiority over MOWOA, MOGWO, MOALO, MOMRFO MOAGDE terms Hyper Volume (HV) reciprocal Pareto Sets Proximity (1/PSP) metrices. source code available at: https://github.com/kanak02/MORIME

Язык: Английский

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Love Evolution Algorithm: a stimulus–value–role theory-inspired evolutionary algorithm for global optimization

The Journal of Supercomputing, Год журнала: 2024, Номер 80(9), С. 12346 - 12407

Опубликована: Фев. 12, 2024

Язык: Английский

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Mirage search optimization: Application to path planning and engineering design problems

Advances in Engineering Software, Год журнала: 2025, Номер 203, С. 103883 - 103883

Опубликована: Фев. 18, 2025

Язык: Английский

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

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 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.

Язык: Английский

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Optimizing FACTS Device Placement Using the Fata Morgana Algorithm: A Cost and Power Loss Minimization Approach in Uncertain Load Scenario-Based Systems

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 20, 2025

Язык: Английский

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Improved snow geese algorithm for engineering applications and clustering optimization

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 6, 2025

The Snow Goose Algorithm (SGA) is a new meta-heuristic algorithm proposed in 2024, which has been proved to have good optimization effect, but there are still problems that easy fall into local optimal and premature convergence. In order further improve the performance of algorithm, this paper proposes an improved (ISGA) based on three strategies according real migration habits snow geese: (1) Lead goose rotation mechanism. (2) Honk-guiding (3) Outlier boundary strategy. Through above strategies, exploration development ability original comprehensively enhanced, convergence accuracy speed improved. paper, two standard test sets IEEE CEC2022 CEC2017 used verify excellent algorithm. practical application ISGA tested through 8 engineering problems, employed enhance effect clustering results show compared with comparison faster iteration can find better solutions, shows its great potential solving problems.

Язык: Английский

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Integration of optimal power flow with combined heat and power dispatch of renewable wind energy based power system using chaotic driving training based optimization

Renewable energy focus, Год журнала: 2024, Номер 49, С. 100573 - 100573

Опубликована: Апрель 27, 2024

Язык: Английский

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A novel artificial hummingbird algorithm improved by natural survivor method

Neural Computing and Applications, Год журнала: 2024, Номер 36(27), С. 16873 - 16897

Опубликована: Июнь 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.

Язык: Английский

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Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems

Applied Energy, Год журнала: 2024, Номер 365, С. 123297 - 123297

Опубликована: Апрель 27, 2024

Язык: Английский

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Multi-objective optimal power flow with wind–solar–tidal systems including UPFC using Adaptive Improved Flower Pollination Algorithm(AIFPA)

Smart Science, Год журнала: 2024, Номер 12(3), С. 495 - 518

Опубликована: Июнь 4, 2024

Among the most significant non-linear challenges for power network design and smooth functioning of current modern updated system networks is optimum flow (OPF) problem. Importance electrical modeling has recently come to light due incremental use energy from renewable sources in systems networks. The goal wind, solar tidal recreate issue OPF. In this work, Weibull, Lognormal, also Gumbel probability distribution functions were applied simulate uncertainties photovoltaic, system. Additionally, by adding test scenarios unpredictable involving minimization cost function, loss active power, voltage deviation, increase stability voltage. accordance with chosen thermal producing units, solutions evaluated using different locations IEEE 30-bus testing that incorporate sources. proposed planning problem was solved multi-objective function where unified controller are utilized as flexible AC transmission controllers via introduced optimization algorithms simulation outcomes aforementioned technique have been compared Multi Objective Adaptive Guided Differential Evolution algorithms. adaptive improved flower pollination algorithm (AIFPA) a strong reliable presented work. AIFPA can efficiently deal many kinds high-complexity objective regions situations. Utilizing an system, suggested approaches' performance examined range functions. results obtained effective finding optimal solution meta-heuristic reported literature.

Язык: Английский

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