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20th IEEE International Conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power Systems Europe Conference (I and CPS Europe)

作者： Khasanov, Mansur Kamel, Salah Tostado-Veliz, Marcos Jurado, Francisco Chongqing Univ Coll Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400030 Peoples R China Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Univ Jaen EPS Linares Dept Elect Engn Jaen 23700 Spain

ISBN: (纸本)9781728174556

This paper presents a combined approach based on Real Power Loss Sensitivity Index (RPLSI) and artificial ecosystem-based optimization (AEO) to determine the optimal allocation of Photovoltaic (PV) and Wind Turbine (WT) based distributed generation (DG) units to minimize power loss of the distribution network (DN). RPLSI is used to determine most suitable buses for integrating DG units in RDN. AEO is recently developed optimizer, inspired from the flow of energy in an ecosystem on the earth. The effectiveness of the developed approach is validated on the standard 33-bus RDN. The results obtained by AOE is compared with other meta-heuristic algorithms such as Harris Hawks Optimizer (HHO) and Henry gas solubility optimization (HGSO). The results indicate the better performance of AEO compared with other methods. Accordingly, AEO can be a very favorable method to cope with the optimal DG allocation problem.

关键词： Distributed Generation Power Loss Sensitivity Factor artificial ecosystem-based optimization Power Loss

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Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm

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ENERGY 2023年第1期280卷

作者： Wang, Yuhan Zhang, Chu Fu, Yongyan Suo, Leiming Song, Shihao Peng, Tian Nazir, Muhammad Shahzad Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

Solar energy is highly economical and widespread in new energy applications, and analyzing solar radiation information is an important part of solar photovoltaic power applications. However, because of its data complexity and difficulty to measure, solar radiation data needs to be predicted. Temporal Convolutional Network (TCN) model is used to extract features and artificial ecosystem-based optimization (AEO) algorithm is used to optimize the parameters of TCN. Out of consideration for the phenomenon of strong fluctuations and complex features of solar radiation data, the optimal variational mode decomposition (OVMD) method is incorporated into the model. First, the signal decomposition is performed on the original data to obtain several subsequences, and then aggregated by fuzzy entropy to reduce the number of sequences, after which the data are fed into the TCN model and the model parameters are optimized using the improved AEO algorithm, and finally the results of the model prediction are the output. Four months of solar radiation data are selected for testing, it is finally concluded that the OVMD-IAEO-TCN model can be used for solar radiation prediction with higher accuracy and reliability than the other nine comparison models.

关键词： Solar radiation prediction Optimal variational mode decomposition artificial ecosystem-based optimization Temporal convolutional network

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An Enhanced artificial ecosystem-based optimization for Optimal Allocation of Multiple Distributed Generations

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IEEE ACCESS 2020年 8卷 178493-178513页

作者： Eid, Ahmad Kamel, Salah Korashy, Ahmed Khurshaid, Tahir Aswan Univ Dept Elect Engn Fac Engn Aswan 81542 Egypt Qassim Univ Dept Elect Engn Coll Engn Unaizah 56452 Saudi Arabia Yeungnam Univ Dept Elect Engn Gyongsan 38541 South Korea

Optimal allocation of distributed generations (DGs) is vital to the proper operation of the distribution systems, which leads to power loss minimization and acceptable voltage regulation. In this paper, an Enhanced artificial ecosystem-based optimization (EAEO) algorithm is proposed and used to solve the optimization problem of DG allocations to minimize the power loss in distribution systems. In the suggested algorithm, the search space is reduced using operator G and sine-cosine function. The G-operator affects the balance between explorative and exploitative phases. At the same time, it gradually decreases during the iterative process in order to converge to the optimal global solutions. On the other hand, the sine-cosine function creates different and random solutions. The EAEO algorithm is applied for solving the standard 33-bus 69-bus, and 119-bus distribution systems with the aim of minimizing the total power losses. Multiple DG units operating at various power factors, including unity-, fixed-, and optimal-power factors, are considered. Both single and multiple objectives are considered to minimize the total voltage deviation (TVD), maximize the system stability, and reduce the total power losses. The obtained results are compared with those obtained by the AEO and other algorithms. The results demonstrate a significant reduction of total power losses and improvement of the voltage profile of the network, especially for the DGs operating at their optimal power factors. Comparisons show the dominance of the proposed EAEO algorithm against other analytical, metaheuristic, or hybrid algorithms. Moreover, the EAEO outperforms the original AEO algorithm with a faster convergence speed and better system performance.

关键词： optimization Resource management Reactive power Minimization Mathematical model Stability analysis Hybrid power systems artificial ecosystem-based optimization distributed generation (DG) multi-objective optimization optimal DG allocation power loss minimization voltage profile

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A novel objective function with artificial ecosystem-based optimization for relieving the mismatching power loss of large-scale photovoltaic array

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ENERGY CONVERSION AND MANAGEMENT 2020年 225卷 113385-113385页

作者： Yousri, Dalia Babu, Thanikanti Sudhakar Mirjalili, Seyedali Rajasekar, N. Abd Elaziz, Mohamed Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum Egypt Univ Tenaga Nas Inst Power Engn Dept Elect Power Engn Jalan IKRAM UNITEN Kajang 43000 Selangor Malaysia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Vellore Inst Technol VIT Vellore Sch Elect Engn SELECT Solar Energy Res Cell SERC Vellore Tamil Nadu India Zagazig Univ Fac Sci Dept Math Zagazig Egypt

Harvesting maximum power from a partially shaded photovoltaic array is a critical issue that attracts the attention of several researchers. As per the literature, it is found that providing an optimal reconfigured pattern of the shaded photovoltaic array is an optimal solution for this issue. Therefore, in this paper, an innovative fitness function has been considered with the artificial ecosystem-based optimization for an electrical photovoltaic array reconfiguration approach. The proposed approach has been applied for the large scale photovoltaic arrays including 9 x9, 6 x 20, 16x16, and 25 x 25 photovoltaic array with different shade patterns. The new fitness function has been validated via a comparison with the regular used weighted function in literature. The quality of the solutions of the proposed artificial ecosystem-based optimization-reconfiguration approach has been assessed and demonstrated via performing several measures namely fill factor, percentage of power loss, mismatch power loss, and power enhancement in comparison with a total cross-tied, particle swarm optimizer approaches, and harris hawks optimizer. Furthermore, the Wilcoxon signed-rank test has been performed to illustrate the applicability, robustness, and consistency of the proposed algorithm results across several independent runs. The analysis reveals the quality of the innovative fitness function while integrating with the optimization algorithms in comparison to the weighted fitness function in producing higher power values via attaining a more efficient photovoltaic array design. Furthermore, the results confirmed the efficiency of the artificial ecosystem-based optimization-photovoltaic reconfiguration approach in boosting the generated photovoltaic power by a percentage of 28.688%, 7.0197 %, 29.2565%, 8.3811% and 5.3884 % across the considered systems with an uniform dispersion of the shadow on the photovoltaic surface and providing highest consistent in the maximum power values acr

关键词： artificial ecosystem-based optimization Particle Swarm optimization Photovoltaic reconfiguration Large photovoltaic array Mitigation techniques Partial shading optimization heuristic particle swarm optimizer

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Energy Management and Improved Metaheuristic optimization-based Control of Photovoltaic/Hybrid Energy Storage System-based Microgrid

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-19页

作者： Firah, Abdelkader Birane, Mouhoub Degla, Aicha Hadroug, Nadji Amar Telidji Univ Laghouat Lab Mat Energet Syst Renewable Energies & Energy M Laghouat Algeria Ctr Dev Energies Renouvelables CDER Algiers Algeria Univ Djelfa Fac Sci & Technol Appl Automat & Ind Diagnost Lab Djelfa Algeria

As global energy demand escalates and fossil fuel reserves dwindle, the associated rise in greenhouse gas emissions and environmental concerns becomes increasingly urgent. This research addresses these challenges by focusing on the integration of photovoltaic (PV) systems with energy storage systems, particularly hybrid energy storage systems (HESS), to enhance sustainability and environmental friendliness. Despite the advantages of PV systems, their power generation is significantly influenced by weather and diurnal variations. This study introduces a novel control mechanism designed to bolster the stability of PV-HESS microgrids. The proposed microgrid utilizes both a lead-acid battery and a supercapacitor as part of the HESS, aiming at storing energy from PV system and balancing the variance between load power demand and the generated PV power. The microgrid's direct current bus voltage is meticulously regulated using a proportional-integral-based controller. This controller is optimized using three cutting-edge metaheuristic algorithms: particle swarm optimization, artificial ecosystem-based optimization (AEO), and wild horse optimizer. Additionally, an energy management strategy is proposed to protect microgrid components, efficiently distribute energy between the battery and supercapacitor, and manage the connection of microgrid components. Simulation results demonstrate the robustness of the proposed approach under various operating conditions. The AEO-based controller achieves lower discharging rate and both lower rise time and settling time compared to the other two controllers, highlighting a notable increase in energy preservation by 0.15% and higher response time advantages to the AEO-based controller.

关键词： Photovoltaic systems Hybrid energy storage system PI-based controller Energy management strategy artificial ecosystem-based optimization Microgrid stability Particle swarm optimization Sustainable energy solutions Wild horse optimizer

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

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IEEE ACCESS 2023年 11卷 122926-122961页

作者： Hassan, Mohamed H. Kamel, Salah Alateeq, Ayoob Alassaf, Abdullah Alsaleh, Ibrahim Minist Elect & Renewable Energy Cairo 11517 Egypt Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Univ Hail Coll Engn Dept Elect Engn Hail 55211 Saudi Arabia

Over the last decade, significant advancements have occurred in global electricity networks due to the widespread adoption of renewable energy resources (RES). While these sources offer numerous benefits such as cost-effective operation of solar photovoltaic and wind power stations and reduction of environmental hazards related to traditional power sources, they have also introduced various challenges to power network scheduling and operation. The traditional optimal power flow (OPF) problem, which is inherently complex, has become even more intricate with the integration of RES alongside traditional thermal power generators. This complexity arises from the unpredictable and intermittent nature of those resources. To tackle the intricacies of incorporating RES into conventional electric power systems, this study utilizes a pair of probability distribution functions to predict the power generation of wind and solar PV systems, respectively. The comprehensive OPF, which includes RES components, is expressed as a singular objective problem encompassing multiple goals including reducing fuel costs, emissions, real transmission losses, and voltage deviations. To tackle this challenge, a novel hybrid metaheuristic optimization algorithm (ACGO) is introduced. The ACGO algorithm combines Chaos game optimization (CGO) with the artificial ecosystem-based optimization (AEO) method to obtain the optimum solution for the OPF problem considering stochastic RES. This technique aims to enhance solution precision by increasing solution diversity through an optimization process. The modified optimizer's validation begins by examining its performance using well-known benchmark optimization functions, demonstrating its superiority over CGO, AEO, and other competitive algorithms. Subsequently, the modified optimizer is applied to a combined model of a wind and PV-incorporated IEEE 30-bus system. The ACGO technique proves to be highly effective, yielding the lowest fitness values of 781.

关键词： Prediction algorithms Metaheuristics Costs Uncertainty Renewable energy sources Chaos Task analysis Flow production systems ecosystems Optimal power flow stochastic renewable energy sources artificial ecosystem-based optimization chaos game optimization

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Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 124卷 142-154页

作者： Abd Elaziz, Mohamed Abualigah, Laith Attiya, Ibrahim Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Acad Sci Res & Technol ASRT 101 Qasr Al Aini StPOB 11516 Cairo Egypt Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk Russia

Cloud-fog computing frameworks are emerging paradigms developed to add benefits to the current Internet of Things (IoT) architectures. In such frameworks, task scheduling plays a key role, and the optimized schedule of IoT task requests can improve system performance and productivity. In this paper, we developed an alternative task scheduling technique for IoT requests in a cloud-fog environment based on a modified artificial ecosystem-based optimization (AEO), called AEOSSA. This modification is developed using the operators of the Salp Swarm Algorithm (SSA) in an attempt to enhance the exploitation ability of AEO during the process of finding the optimal solution for the problem under consideration. The performance of the designed AEOSSA approach to tackling the task scheduling problem is evaluated using different synthetic and real-world datasets of different sizes. In addition, a comparison is conducted between AEOSSA and other well-known metaheuristic methods for performance investigation. The experimental results demonstrate the high ability of AEOSSA to tackle the task scheduling problem and perform better than other methods according to the performance metrics such as makespan time and throughput. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Internet of Things (IoT) Cloud computing Fog computing Task scheduling Makespan artificial ecosystem-based optimization Salp Swarm Algorithm

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Parameter estimation of photovoltaic cell and module models relied on metaheuristic algorithms including artificial ecosystem optimization

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NEURAL COMPUTING & APPLICATIONS 2022年第15期34卷 12819-12844页

作者： Thuan Thanh Nguyen Thang Trung Nguyen Thanh Ngoc Tran Ind Univ Ho Chi Minh City Fac Elect Engn Technol Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Power Syst Optimizat Res Grp Ho Chi Minh City Vietnam

Due to the nonlinear characteristic of the power-voltage (P-V) and current-voltage (I-V) relationship of the photovoltaic systems, building accurate mathematical models of photovoltaic cell and module is essential for validation and optimization performance of photovoltaic systems. However, determination of the unknown parameters of photovoltaic cell and module models is a complex nonlinear optimization problem that requires effective solving methods. This paper proposes an approach of parameter estimation of photovoltaic cell and module models based on artificial ecosystem-based optimization (AEO). The AEO is a new developed metaheuristic algorithm taken idea of the mechanisms of ecosystem consisting of production, consumption and decomposition. The advanced feature of AEO is the diversity of exploration and exploitation mechanisms without special control parameters. The performance of AEO is discussed in two photovoltaic cell models and two photovoltaic module models. Its performances compared to five algorithms consisting of backtracking search algorithm (BSA), cuckoo search algorithm (CSA), equilibrium optimizer (EO), genetic algorithm (GA) and particle swarm optimization (PSO) as well as the previous methods. The numerical results show that AEO has given the better performance than BSA, CSA, EO, GA and PSO for the problem of parameter estimation of photovoltaic models in terms of aspects such as reaching the lower error between the experimental and estimated values and having the better statistical results in several runs than the above methods. Thus, AEO can be an effective method for estimating the unknown parameters of photovoltaic cell and module models.

关键词： artificial ecosystem-based optimization Photovoltaic cell Parameter estimation Equilibrium optimizer

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Optimal identification of model parameters for PEMFCs using neoteric metaheuristic methods

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IET RENEWABLE POWER GENERATION 2023年第3期17卷 659-680页

作者： Diab, Ahmed A. Zaki Abdelhamid, Ashraf M. Minia Univ Fac Engn Elect Engn Dept Al Minya Egypt Umm Al Qura Univ Coll Engn Commun & Elect Engn Dept Al Lith Branch Mecca Saudi Arabia

In this paper, the neoteric metaheuristic methods of artificial ecosystem-based optimization (AEO), Coot Bird-based optimization (COOT), Equilibrium optimizer (EQO), Runge-Kutta Method (RUN), Hunger games search (HGS), and Weighted Mean of Vectors (INFO) have been applied and evaluated to discover a preferable estimation for the Proton Exchange Membrane Fuel Cells (PEMFCs) model. The validation of the applied methods has been done for valuing the model parameters of BCS 500W-PEM, 500W-SR-12PEM, and 250W-stack. The objective function has been formulated as the sum of square errors (SSEs) between the measured and estimated data. MATLAB has been used for the verification of the optimization techniques. The results show that for BCS 500W-PEM: (1) the six algorithms can accurately solve the problem of the FC parameter assessment;(2) there are small distinguished between the six algorithms concerning their best value of the objective function;this distinction between the best and worst techniques is 2.4 x 10(-9);(3) the best algorithm is INFO with 0.011556306 while the worst algorithm is RUN with 0.011556308;(4) statistical results prove that the six algorithms have the tracking efficiencies of 99.99961329%, 99.97114568%, 99.83529736%, 100%, 99.89195368%, and 99.90200833% for AEO, COOT, EQO, INFO, HGS, and RUN, respectively based on 30 individual runs.

关键词： Equilibrium optimizer Optimisation techniques weighted mean of vectors Runge-Kutta Method BCS 500W-PEM metaheuristics measured estimated data RUN INFO Coot Bird-based optimization HGS 500W-SR-12PEM model parameters Numerical approximation and analysis Runge-Kutta methods search problems optimization techniques Hunger games search EQO Fuel cells preferable estimation AEO Proton Exchange Membrane Fuel Cells model applied methods Differential equations (numerical analysis) worst algorithm FC parameter assessment artificial ecosystem-based optimization proton exchange membrane fuel cells neoteric metaheuristic methods objective function optimal identification

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Wind integrated optimal power flow considering power losses, voltage deviation, and emission using equilibrium optimization algorithm

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ENERGY ECOLOGY AND ENVIRONMENT 2022年第4期7卷 369-392页

作者： Amroune, Mohammed Univ Ferhat Abbas Setif 1 Dept Elect Engn Setif 19000 Algeria

In this paper, the recently developed optimization algorithm, namely equilibrium optimization (EO), will be utilized to solve the optimal power flow problem (OPF), combining stochastic wind power with conventional thermal power generators in the system. The objectives are to minimize generation costs, including those incurred in thermal and stochastic wind power generation, active power loss, voltage deviation, and emission. To evaluate the performance of the EO algorithm in the OPF problem, modified IEEE 30-bus and IEEE 57-bus test systems with stochastic wind power generators will be used. A comparative study will be performed to show the efficiency of the EO algorithm compared with other recently developed metaheuristic algorithms such as the marine predators algorithm (MPA), artificial ecosystem-based optimization (AEO), and slime mould algorithm (SMA), as well as with other well-known algorithms. based on the obtained results, the EO algorithm offered the best results.

关键词： Optimal power flow Wind power Equilibrium optimization Marine predators algorithm artificial ecosystem-based optimization Slime mould algorithm

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