Optimal power flow of thermal-wind-solar power system using enhanced Kepler optimization algorithm: Case study of a large-scale practical power system

Wind Engineering, Journal Year: 2024, Volume and Issue: 48(5), P. 708 - 739

Published: Feb. 19, 2024

In the current century, electrical networks have witnessed great developments and continuous increases in demand for fossil fuels based energy, leading to an excessive rise total production cost (TPC), as well pollutant (toxic) gases emitted by thermal plants. Under this circumstances, energy supply from different resources became necessary, such renewable sources (RES) alternative solution. This latter, however, characterized with uncertainty nature its operation principle, especially when operator system wants define optimal contribution of each resource effort ensure economic enhanced reliability grid. paper presents Enhanced version Kepler optimization algorithm (EKOA) solve problem stochastic power flow (SOPF) a most efficient way incorporating wind generators solar photovoltaic objective functions, speed is modeled using Weibull lognormal probability density functions respectively. To prove effectiveness proposed EKOA, various case studies were carried out on two test systems IEEE 30-bus Algerian 114-bus, obtained results evaluated comparison those original KOA other methods published literatures. Thus, shows superiority EKOA over optimizers complex problem. The incorporation RES resulted significant 2.39% decrease cost, showcasing EKOA’s efficiency $780/h, compared KOA’s $781/h, system. For DZA 114-bus revealed even more substantial reductions, achieving impressive 12.6% reduction, following closely 12.4% cost.

Language: Английский

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A CNN-based model to count the leaves of rosette plants (LC-Net)

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

Published: Jan. 17, 2024

Abstract Plant image analysis is a significant tool for plant phenotyping. Image has been used to assess trails, forecast growth, and offer geographical information about images. The area segmentation counting of the leaf major component phenotyping, which can be measure growth plant. Therefore, this paper developed convolutional neural network-based model called LC-Net. original segmented parts are fed as input because part provides additional proposed well-known SegNet utilised obtain it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, Refine Net. LC-Net compared recent CNN-based models over combined Computer Vision Problems in Phenotyping (CVPPP) KOMATSUNA datasets. subjective numerical evaluations experimental results demonstrate superiority tested models.

Language: Английский

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Optimal Power Flow Analysis With Renewable Energy Resource Uncertainty: A Hybrid AEO-CGO Approach

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 122926 - 122961

Published: Jan. 1, 2023

Over the last decade, significant advancements have occurred in global electricity networks due to widespread adoption of renewable energy resources (RES). While these sources offer numerous benefits such as cost-effective operation solar photovoltaic and wind power stations reduction environmental hazards related traditional sources, they also introduced various challenges network scheduling operation. The optimal flow (OPF) problem, which is inherently complex, has become even more intricate with integration RES alongside thermal generators. This complexity arises from unpredictable intermittent nature those resources. To tackle intricacies incorporating into conventional electric systems, this study utilizes a pair probability distribution functions predict generation PV respectively. comprehensive OPF, includes components, expressed singular objective problem encompassing multiple goals including reducing fuel costs, emissions, real transmission losses, voltage deviations. challenge, novel hybrid metaheuristic optimization algorithm (ACGO) introduced. ACGO combines Chaos game (CGO) artificial ecosystem-based (AEO) method obtain optimum solution for OPF considering stochastic RES. technique aims enhance precision by increasing diversity through an process. modified optimizer's validation begins examining its performance using well-known benchmark functions, demonstrating superiority over CGO, AEO, other competitive algorithms. Subsequently, optimizer applied combined model PV-incorporated IEEE 30-bus system. proves be highly effective, yielding lowest fitness values 781.1675 $/h 808.4109 their respective scenarios Additionally, proposed achieves total cost 31623.5 31601.55 57-bus These results emphasize accuracy robustness effectively addressing instances problem. solving issue verified statistical boxplot comparisons, non-parametric tests, analyses. evaluations indicate that outperforms algorithms achieving involving systems. show offers faster convergence rates higher compared optimization, recent heuristic, metaheuristic, effectiveness been proven robust efficient, making it suitable multidisciplinary problems engineering challenges.

Language: Английский

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Improve coati optimization algorithm for solving constrained engineering optimization problems

Journal of Computational Design and Engineering, Journal Year: 2023, Volume and Issue: 10(6), P. 2223 - 2250

Published: Oct. 26, 2023

Abstract The coati optimization algorithm (COA) is a meta-heuristic proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (i) In group organization coatis, half coatis climb trees chase their prey away, while other wait beneath catch it (ii) Coatis avoidance predators behavior, which gives strong global exploration ability. However, over course our experiment, we uncovered opportunities for enhancing algorithm’s performance. When confronted with intricate problems, certain limitations surfaced. Much like long-nosed raccoon gradually narrowing its search range as approaches optimal solution, COA exhibited tendencies that could result reduced convergence speed risk becoming trapped local optima. this paper, propose an improved (ICOA) enhance efficiency. Through sound-based envelopment strategy, can capture more quickly accurately, allowing converge rapidly. By employing physical exertion have greater variety escape options when being chased, thereby exploratory capabilities ability Finally, lens opposition-based learning strategy added improve To validate performance ICOA, conducted tests using IEEE CEC2014 CEC2017 benchmark functions, well six engineering problems.

Language: Английский

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A New-Fangled Approach for Optimal Placement of Facts Controllers in a Hybridized System

Electric Power Components and Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 17

Published: May 6, 2024

This paper proposes a novel approach for Optimal Power Flow (OPF) problems in power systems by integrating the optimal placement of Flexible Alternating Current Transmission System (FACTS) devices with hybrid renewable energy system comprising wind and photovoltaic (PV). The objective is to optimize FACTS simultaneously minimize loss, voltage deviation reduce gross cost. To achieve this goal, Enhanced Multi – Strategies Sparrow Search Algorithm (EMSSA) employed, which combines multiple optimization strategies inspired collaborative foraging behavior sparrows. By considering objectives, including loss reduction, cost minimization, proposed methodoloty aims identify locations installing such as SVC (Static Var Compensator), TCSC (Thyristor Controlled Seris TCPS Phase Shifter). results showcase capability methodology significant improvements performance, thereby contributing efficient sustainable operation modern grids. research also provides valuable insights operators planners make decisions regarding deployment integration sources.

Language: Английский

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Improving power system performance by integrating TCSC and SVC devices in the presence of stochastic renewable energy generators

Wind Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

This paper investigates the application of advanced metaheuristic algorithms Blood-Sucking Leech Optimizer (BSLO), Bonobo (BO), and Electric Eel Foraging Optimization (EEFO) to solve optimal power flow (OPF) problem with stochastic renewable energy generators (REGs), specifically photovoltaic (PVGs) wind (WGs). Two scenarios are examined: Scenario 1 evaluates proposed performance without Flexible AC Transmission Systems (FACTS), focusing on minimizing Total Generation Cost (TGC), Active Power Loss (APL), a combined objective TGC Emissions (TGCE). The including both thermal REG costs, in which cost related PV generation encompasses direct, reserve, penalty costs due overestimation underestimation available power. 2 introduces Thyristor-Controlled Series Capacitor (TCSC) Static Var Compensator (SVC) evaluate their impact three functions. is evaluated modified IEEE 30-bus system. results show that BSLO algorithm consistently achieves best TGC, APL, TGCE values at 781.1209 $/h, 1.9960 MW, 810.7376 respectively. These outcomes highlight its effectiveness competitive first scenario. integration FACTS devices second scenario 6.73% reduction APL insertion TCSC, 1.86% SVC, 6.10% TCSC compared value case (1.9960 MW). study comprehensively analyzes how different optimization techniques enhance system integration.

Language: Английский

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Optimal planning for distribution networks considering system uncertainties using pseudo-inspired gravitational search algorithm

Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: April 18, 2024

Language: Английский

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The Quick Crisscross Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario based Power Systems

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103703 - 103703

Published: Dec. 1, 2024

Language: Английский

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Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

Sustainability, Journal Year: 2024, Volume and Issue: 16(21), P. 9599 - 9599

Published: Nov. 4, 2024

In the rapidly evolving landscape of electrical power systems, optimal flow (OPF) has become a key factor for efficient energy management, especially with expanding integration renewable sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF grids. Their inherent variability complexity demand advanced optimization methods to determine settings that maintain stable system operation. This paper introduces multi-objective version Kepler algorithm (KOA) based on non-dominated sorting (NS) principle referred as NSKOA deal IEEE 57-bus system. The methodology incorporates RES alongside multiple types FACTS model offers flexibility determining size location static var compensator (SVC) thyristor-controlled series capacitor (TCSC), considering associated investment costs. Further enhancements were observed when combining devices RESs network, achieving reduction 6.49% production cost 1.31% from total their cost. Moreover, there is 9.05% real losses (RPLs) 69.5% voltage deviations (TVD), while enhancing stability index (VSI) by approximately 26.80%. addition network performance improvement, emissions are reduced 22.76%. Through extensive simulations comparative analyses, findings illustrate proposed approach effectively enhances across variety operational conditions. results underscore significance employing techniques modern systems enhance overall grid resilience stability.

Language: Английский

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An enhanced dynamic differential annealed algorithm for global optimization and feature selection

Journal of Computational Design and Engineering, Journal Year: 2023, Volume and Issue: 11(1), P. 49 - 72

Published: Dec. 28, 2023

Abstract Dynamic differential annealed optimization (DDAO) is a recently developed physics-based metaheuristic technique that mimics the classical simulated annealing mechanism. However, DDAO has limited search abilities, especially when solving complicated and complex problems. A unique variation of DDAO, dubbed as mDDAO, in this study, which opposition-based learning novel updating equation are combined with DDAO. mDDAO tested on 10 different functions from CEC2020 compared original nine other algorithms. The proposed algorithm performance evaluated using numerical constrained released CEC 2020 benchmark suite, includes variety dimensionally challenging optimisation tasks. Furthermore, to measure its viability, employed solve feature selection problems fourteen UCI datasets real-life Lymphoma diagnosis problem. Results prove superior consistently outperforms counterparts across benchmarks, achieving fitness improvements ranging 1% 99.99%. In selection, excels by reducing count 23% 79% methods, enhancing computational efficiency maintaining classification accuracy. Moreover, lymphoma diagnosis, demonstrates up 54% higher average fitness, 18% accuracy improvement, 86% faster computation times.

Language: Английский

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