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optimization of a hybrid solar/wind/storage system with bio-generator for a household by emerging metaheuristic optimization algorithm

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JOURNAL OF ENERGY STORAGE 2023年 73卷

作者： Fan, Jingya Zhou, Xiao Baoding Tech Coll Elect Power Baoding 071051 Hebei Peoples R China Hubei Univ Automot Technol Sch Math Phys & Optoelect Engn Shiyan 442000 Hubei Peoples R China

The ever-increasing need for electricity in off-grid areas requires a safe and effective energy supply system. Considering the development of a sustainable energy system and the reduction of environmental pollution and energy cost per unit, this study focuses on the techno-economic study and optimal sizing of the solar, wind, biodiesel generator, and energy storage structure. The emerging metaheuristic optimization method is used to estimate the techno-economic benefits of the suggested structure to provide the required electricity for a residential household. The optimal design is done with the harmony search (HS) method to validate the results obtained with the suggested algorithm. Minimizing the total annual cost (TAC) is a main objective for the design optimization of the system while satisfying the reliability and techno-enviro-economic constraints. The impact of the investment cost of biodiesel, biofuel cost, and reliability is investigated on the system sizing. The results show that the proposed algorithm has low simulation time (by 4.1 %) and the operating cost of the proposed algorithm is 1.12 % less than the existing HS algorithm based on the best TAC value. The TAC is received as $24,259 with the suggested method, which is the best optimal result compared to the result given by the HS algorithm. The proposed off-grid renewable energy system based on a bio-diesel generator is a viable and scalable solution to provide the required electricity for a residential household in all periods. The sensitivity analysis show that if the biofuel cost is reduced by 50.8 %, TAC will decrease by about 36.6 %. On the other hand, if the bio-diesel generator cost is reduced by 17 %, TAC will be decreased by 2.7 %. Finally, if the limit value of reliability is reduced from 2 to 0 %, TAC will increase by 13.2 %.

关键词： Energy storage system Solar & wind energy Bio-diesel generator metaheuristic optimization algorithm

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Improved metaheuristic optimization algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

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ENERGIES 2021年第3期14卷 619页

作者： Liu, En-Jui Hung, Yi-Hsuan Hong, Che-Wun Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 30013 Taiwan Natl Taiwan Normal Univ Undergrad Program Vehicle & Energy Engn Taipei 106 Taiwan

As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 center dot 10(-4)). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules.

关键词： metaheuristic optimization algorithm particle swarm algorithm photovoltaic cell proton exchange membrane fuel cell

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metaheuristic optimization algorithms for multi-area economic dispatch of power systems: Part I-a comprehensive survey

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第4期58卷 1-51页

作者： Wang, Yang Xiong, Guojiang Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Peoples R China

Multi-area economic dispatch (MAED) provides an indispensable component for the security and economic operation of contemporary power systems. Over recent years, numerous metaheuristic optimization algorithms (MOAs) have surfaced for addressing the MAED problem. However, none of the literature to date conducted a comprehensive statistical research work on the MAED problem. In part I of this series, we present a comprehensive survey on this problem. (1) We collect all eleven reported MAED cases studied over the years. These cases have different structures, scales, and constraints. We illustrate the structures of all cases and provide their corresponding system parameters. (2) We collect all the MOA solution algorithms. These algorithms are inspired by different ways, and we categorize them in detail and review them comprehensively. (3) We list the detailed applications of MOAs on different cases and count the percentage of studies on each case. (4) Finally, we summarize the current research progress and point out the future research directions in terms of MAED models and solution methods, respectively. This survey provides an extensive overview of the MAED cases and its solution methods. It can provide applicable and reference suggestions for future research on the MAED problem.

关键词： Multi-area economic dispatch metaheuristic optimization algorithm Literature review Survey

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Applications of metaheuristic optimization algorithms in model predictive control for chemical engineering processes: A systematic review

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ANNUAL REVIEWS IN CONTROL 2024年 58卷

作者： Al Bannoud, Mohamad da Silva, Carlos Alexandre Moreira Martins, Tiago Dias Univ Estadual Campinas Sch Chem Engn Lab Optimizat Design & Adv Control Campinas SP Brazil Univ Fed Sao Paulo Dept Chem Engn Inst Ciencias Ambientais Quim & Farmaceut Diadema SP Brazil

The growing competitiveness of the chemical industry, along with sustainability demands and regulatory requirements, calls for optimized and well-controlled operations. Chemical engineering processes are often characterized by non-linearity, strong variable coupling, dead times, multiple inputs and outputs, and operational constraints, making control strategies challenging. Model predictive control is widely used for its advantages in optimal control, flexibility, robustness, and ability to handle multi-objective tasks. However, precise tuning and optimization are essential for implementing this strategy in real-time applications. metaheuristic optimization algorithms offer an alternative to traditional optimization methods, as they can quickly reach near-optimal solutions and avoid local minima, making them well-suited for use with model predictive control. This study aims to analyze the application of metaheuristic optimization algorithms in conjunction with model predictive control in chemical engineering processes through a systematic review. The review considers three eligibility criteria: applying model predictive control for process control, utilizing metaheuristic optimization algorithm, and chemical engineering-related processes. A total of 46 studies were analyzed, revealing three main application areas for metaheuristic optimization algorithms in model predictive control: improving dynamic models used in the receding horizon, tuning model predictive control parameters, and serving as optimizers in the model predictive control formulation. Over 20 different metaheuristic optimization algorithms and various process models were identified, with typical applications including continuous stirred tank reactors, tank-level control, and column distillation. Genetic algorithms and particle swarm optimization were the most frequently used algorithms. This review concludes that metaheuristic optimization algorithms have been successfully applied to enhance model pre

关键词： Model predictive control metaheuristic optimization algorithm optimization Tuning Process modeling

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Nature-inspired metaheuristic optimization algorithms for FDTD dispersion

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2024年 187卷

作者： Park, Jaesun Cho, Jeahoon Jung, Kyung-Young Hanyang Univ Dept Elect Engn Seoul 04763 South Korea

optimization algorithms have been employed for a variety of applications such as engineering design optimization, machine learning, control systems, computer science and software engineering. Among various optimization approaches, nature-inspired metaheuristic optimization algorithms excel in addressing complex optimization problems by considering various constraints and optimizing a wide array of variables and target functions. In finite-difference time-domain (FDTD) methods for complex dispersive media, it is crucial to derive accurate dispersion model parameters that satisfy the numerical stability conditions by applying an optimization algorithm. In this work, we apply five representative nature-inspired metaheuristic optimization algorithms to extract accurate and numerically stable dispersion modeling parameters: continuous genetic algorithm, particle swarm optimization (PSO), artificial bee colony, grey wolf optimization, and coyote optimization algorithm. To achieve a comprehensive analysis, this study examines the FDTD dispersion modeling for various materials across different frequency ranges. The numerical examples illustrate that PSO excels at extracting numerically stable and highly accurate parameters for the FDTD dispersion model.

关键词： Dispersion model Finite-Difference Time-Domain (FDTD) metaheuristic optimization algorithm Numerical stability condition

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Predicting Porosity in Grain Compression Experiments Using Random Forest and metaheuristic optimization algorithms

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FOOD SCIENCE & NUTRITION 2025年第4期13卷 e70107页

作者： Chen, Jiahao Li, Jiaxin Zheng, Deqian Zhang, Yan Jing, Hang Han, Jianjun Wang, Manxing Zhao, Runmei Henan Univ Technol Coll Civil Engn Zhengzhou Peoples R China Henan Key Lab Gain Storage Facil & Safety Zhengzhou Peoples R China Henan Int Joint Lab Modern Green Ecol Storage Syst Zhengzhou Peoples R China

Grain stored for long periods is highly susceptible to localized condensation, mold growth, and insect infestations, leading to significant storage losses. These issues are particularly acute in large-capacity bungalow warehouses, where food security concerns are even more pronounced. The porosity of grain piles is a critical parameter that influences heat and moisture transfer within the grain mass, as well as the ventilation of grain storage. To investigate the distribution pattern of bulk grain pile porosity in bungalow warehouses, this study employs machine learning (ML) techniques to predict grain pile porosity based on compression experiments. Four metaheuristic optimization algorithms-particle swarm optimization (PSO), gray wolf optimizer (GWO), sine cosine algorithm (SCA), and tunicate swarm algorithm (TSA)-were introduced to enhance the random forest (RF) algorithm, and five ML-based models (RF, PSO-RF, GWO-RF, SCA-RF, and TSA-RF) for predicting grain porosity were developed. The predictive performance of the five models was analyzed using error analysis, Taylor diagrams, evaluation metrics, and multi-criteria assessments to identify the optimal ML prediction model. The results indicate that the predictive performance of the four RF-based hybrid models surpasses that of the single RF model. Among these hybrid models, the TSA-RF model demonstrated the best predictive performance, achieving R2 values of 0.9923 in the training set and 0.9723 in the test set. The TSA-RF model was employed to conduct a hierarchical prediction of bulk grain pile porosity in the bungalow warehouse. The results indicate that the porosity of the grain pile exhibits a pattern of being higher in the middle and smaller at the edges as the depth of the grain pile increases. The TSA-RF model developed in this study offers a novel and efficient method for predicting grain porosity, enabling rapid assessments of porosity in bulk grain piles within the bungalow warehouse.

关键词： bungalow warehouse compression experiment grain porosity metaheuristic optimization algorithm random forest

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Comparison of popular metaheuristic optimization algorithms for the optimal design of DC-DC converters

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第1期16卷 199-233页

作者： Saharia, Barnam Jyoti Sarmah, Nabin Tezpur Univ Dept Elect Engn Tezpur 784028 Assam India Tezpur Univ Dept Energy Tezpur 784028 Assam India

DC-DC converters are an important area of power electronics. They have been used as the power converter interface for power point tracking in photovoltaic systems. The design of the optimized DC-DC converter thus is an important area for the research community. Design optimization of a DC-DC Buck, Boost, Synchronous Buck and Double Buck converters to reduce overall operational losses is the subject of investigation in this study. The ideal design requires selecting the most suitable values for circuit inductance, capacitance, and switching frequency to guarantee functioning in continuous conduction mode (CCM) and continuous voltage mode. The selected design constraints are the ripple content in voltage and current, and bandwidth for operation in CCM. A total of twenty eight (28) recently developed and popular existing metaheuristic optimization algorithms are utilized to select the optimized DC-DC converter's design. For identifying the best algorithm and to carry out a performance analysis established optimization algorithms like the Grey Wolf Optimizer (GWO), Moth Flame optimization algorithm , Particle Swarm optimization, Whale optimization algorithm (WOA) and Firefly algorithm are selected. The simulated results indicate that majority of algorithms are able to select the best design for the converter topologies within the selected constraint criterion's. The efficacy of an algorithm is determined based on statistical studies, convergence characteristics, computational time and robustness. It is noted that the algorithm that most effectively solves the current optimization problem is the WOA.

关键词： DC-DC converters metaheuristic optimization algorithm Boost converter Buck converter Synchronous buck converter Whale optimization algorithm

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Comparison of recent metaheuristic optimization algorithms to solve the SHE optimization problem in MLI

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NEURAL COMPUTING & APPLICATIONS 2023年第10期35卷 7369-7388页

作者： Yigit, Halil Urgun, Satilmis Mirjalili, Seyedali Kocaeli Univ Dept Informat Syst Engn TR-41001 Kocaeli Turkey Kocaeli Univ Fac Aeronaut & Astronaut TR-41380 Kocaeli Turkey Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

Multilevel inverters (MLIs) are one of the most popular topics of power electronics. Selective harmonic elimination (SHE) method is used to eliminate low-order harmonics in the MLI output voltage by determining the optimum switching angles. It includes the solution of nonlinear sets of transcendental equations. The optimization becomes more difficult as the number of levels in MLIs increases. Therefore, various metaheuristic algorithms have emerged toward obtaining optimal solutions to find the switching angles in the SHE problem in the last decade. In this study, a number of recent metaheuristics, such as ant lion optimization (ALO), hummingbird algorithm (AHA), dragonfly algorithm (DA), harris hawk optimization, moth flame optimizer (MFO), sine cosine algorithm (SCA), flow direction algorithm (FDA), equilibrium optimizer (EO), atom search optimization, artificial electric field algorithm and arithmetic optimization algorithm (AOA), are employed as an attempt to find the best optimization framework to identify switching moments in 11-level MLI. Marine predator algorithm (MPA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), particle swarm optimization (PSO), multiverse optimizer (MVO), teaching-learning-based optimization (TLBO), and genetic algorithm (GA), which are widely used in solving this problem, are selected for performance analysis. AHA, ALO, AOA, DA, EO, FDA, GA, GWO, MFO, MPA, MVO, PSO, SCA, SSA, TLBO and WOA methods meet maximum 8% THD requirement specified in IEEE 519 standard in the range of 0.4-0.9 modulation index. Simulation results show that MFO is superior other algorithms in terms of THD minimization, convergence rate, a single iteration time and robustness.

关键词： metaheuristic optimization algorithm Multilevel inverter (MLI) Selective harmonic elimination (SHE) Total harmonic distortion (THD)

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metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds

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RENEWABLE ENERGY 2020年第0期149卷 664-681页

作者： Alrashidi, Musaed Rahman, Saifur Pipattanasomporn, Manisa Virginia Tech Bradley Dept Elect & Comp Engn Adv Res Inst Blacksburg VA 24061 USA Chulalongkorn Univ Dept Elect Engn Smart Grid Res Unit Bangkok Thailand Qassim Univ Coll Engn Dept Elect Engn Buraydah Saudi Arabia

An accurate analysis of wind speeds is vital to justify wind energy projects. Statistical distributions can be used to characterize wind speeds through considering uncertainty in wind resources. However, the selection of the most suitable probability density function (PDF) is still a challenging task. Therefore, this study aims at developing a framework to accurately evaluate the performance of different PDFs to fit wind speeds, as well as presenting a new metaheuristic optimization algorithm method, called Social Spider optimization (SSO), for wind characterization purposes. Seven sites in Saudi Arabia are used as case studies. Results indicate that combined PDFs outperform single PDFs in representing the observed wind speeds frequencies at all considered sites. Weibull distribution appears to be the most prevalent single distribution while no combined PDF dominates the others. In addition, the proposed SSO method is found to be the most efficient method for estimating PDFs parameters in Saudi Arabia. Overall, this proposed framework can be used to evaluate different wind PDFs in other countries. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Wind speed Probability density function Combined density function metaheuristic optimization algorithm Social spider optimization

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Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization

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ADVANCES IN ENGINEERING SOFTWARE 2024年 195卷

作者： Wang, Wen-chuan Tian, Wei-can Xu, Dong-mei Zang, Hong-fei North China Univ Water Resources & Elect Power Coll Water Resources Zhengzhou 450046 Peoples R China

In this paper, we innovatively propose the Arctic Puffin optimization (APO), a metaheuristic optimization algorithm inspired by the survival and predation behaviors of the Arctic puffin. The APO consists of an aerial flight (exploration) and an underwater foraging (exploitation) phase. In the exploration phase, the Levy flight and velocity factor mechanisms are introduced to enhance the algorithm's ability to jump out of local optima and improve the convergence speed. In the exploitation phase, strategies such as the synergy and adaptive change factors are used to ensure that the algorithm can effectively utilize the current best solution and guide the search direction. In addition, the dynamic transition between the exploration and development phases is realized through the behavioral conversion factor, which effectively balances global search and local development. In order to verify the advancement and applicability of the APO algorithm, it is compared with nine advanced optimization algorithms. In the three test sets of CEC2017, CEC2019, and CEC2022, the APO algorithm outperforms the other compared algorithms in 72%, 70%, and 75% of the cases, respectively. Meanwhile, the Wilcoxon signed-rank test results and Friedman rank-mean statistically prove the superiority of the APO algorithm. Furthermore, on thirteen real-world engineering problems, APO outperforms the other compared algorithms in 85% of the test cases, demonstrating its potential in solving complex real-world optimization problems. In summary, APO proves its practical value and advantages in solving various complex optimization problems by its excellent performance.

关键词： Arctic puffin optimization metaheuristic optimization algorithm Engineering design Constrained optimization Swarm intelligence

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