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Optimized Two-Stage Planning Model for Integrating Compressed Air Energy Storage With Uncertain Correlated Wind Farms in Power Systems

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ENERGY CONVERSION AND MANAGEMENT-X 2025年 25卷

作者： Alahmad, Ahmad K. Verayiah, Renuga Ba-swaimi, Saleh Shareef, Hussain Ramasam, Agileswari Abu-Rayash, Azzam Univ Tenaga Nasl UNITEN Inst Power Engn Putrajaya CampusJalan IKRAM UNITEN Kajang 43000 Selangor Malaysia Hadramout Univ Coll Engn & Petr Dept Elect & Commun Engn Mukalla Yemen United Arab Emirates Univ Coll Engn Dept Elect & Commun Engn POB 15551 Al Ain U Arab Emirates Hamad Bin Khalifa Univ Coll Sci & Engn Div Sustainable Dev Doha Qatar

As energy demands grow, integrating renewable energy sources (RESs) and energy storage systems (ESSs) has become essential for reducing carbon emissions and reliance on conventional power plants while maintaining grid security. Among ESS technologies, Compressed Air Energy Storage (CAES) stands out as a promising solution but remains underexplored in grid applications. This study introduces a two-stage mixed-integer non-linear programming (MINLP) model for optimizing CAES integration with hybrid power systems. The first stage addresses planning variables, including site selection, capacity, and power ratings, minimizing total costs and voltage deviations. The second stage optimizes the operation of CAES units and conventional generators, focusing on reducing operational costs, energy losses, carbon emissions, and voltage deviations. The model employs probabilistic approaches with the five-point estimation method (5-PEM) and Clayton copulas to handle wind power variability. optimization is achieved using MOPSO-TOPSIS and a hybrid NSGAII-MOPSO-TOPSIS, with Tabu Search Algorithm (TSA) for local refinement. The model's effectiveness is validated on a modified IEEE-57 bus system with wind farms. Results demonstrate significant economic, environmental, and technical benefits of CAES integration. The hybrid approach (Case 3) outperforms MOPSO-TOPSIS (Case 2), achieving a 3.68% reduction in daily fuel costs and a 6.05% reduction in carbon emissions, compared to 2.92% and 4.23% in Case 2. Case 3 also shows superior reductions in power losses (5.04% vs. 4.84%) and voltage deviations (0.81% vs. 0.60%).

关键词： Compressed air energy storage system (CAES) hybrid optimization algorithms Integrated energy system Energy storage Wind farm modeling Sustainable energy

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Bandwidth enhancement of rectangular microstrip antenna with a rectangular slot by using a novel hybrid optimization method based on the ABC and DE algorithms

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2018年第5期31卷 e2345-e2345页

作者： Akdagli, Ali Ustun, Deniz Mersin Univ Dept Elect Elect Engn Fac Engn TR-33343 Ciftlikkoy Mersin Turkey Mersin Univ Dept Software Engn Fac Tarsus Technol TR-33400 Tarsus Mersin Turkey

In this study, the bandwidth of the rectangular microstrip antenna is improved by using a new integrated technique consisting of a novel hybrid optimization algorithm (HOA) and HyperLynx (R) 3D electromagnetic platform based on the method of moments (MoM). The novel HOA is developed by integrating the powerful mutation and crossover strategies of the differential evolution algorithm with onlooker bee stage of the artificial bee colony algorithm. The main objective of the design is to enhance bandwidth of the rectangular microstrip antenna by the etching rectangular slot on the antenna ground plane. The optimal position and sizes of the slot are determined by using the integrated optimization technique. The simulation and measurement results indicated a high improvement in the bandwidth of the rectangular microstrip antenna over 45% by using the proposed slot etching method on the ground plane. Additionally, in order to evaluate performance of the HOA, eight benchmark functions having multi or unimodal characteristics were used. The experimental results show that the performance of the HOA was better than both artificial bee colony and differential evolution algorithms.

关键词： artificial bee colony bandwidth differential evolution hybrid optimization algorithms rectangular microstrip patch antenna slot antennas

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A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: hybrid meta-heuristic algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 203卷

作者： Seydanlou, Pourya Jolai, Fariborz Tavakkoli-Moghaddam, Reza Fathollahi-Fard, Amir M. Univ Tehran Dept Ind Engn Alborz Campus Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Univ Quebec Ecole Technol Super Dept Elect Engn Montreal PQ Canada

A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable ClosedLoop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO2) emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran.

关键词： Sustainable closed-loop supply chain Multi-objective Perishable products Olive industry hybrid optimization algorithms

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Topological Active Models optimization with Differential Evolution

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第15期39卷 12165-12176页

作者： Novo, J. Santos, J. Penedo, M. G. Univ A Coruna Dept Comp Sci La Coruna Spain

The Topological Active Model is an active model focused on segmentation tasks. It provides information about the surfaces and the inside of the detected objects in the scene. The segmentation process turns into a minimization task of the energy functions which control the model deformation. In this work we propose a new optimization method of the segmentation model that uses Differential Evolution as an alternative evolutionary method that minimizes the decisions of the designer with respect to others such as genetic algorithms. Moreover, we hybridized Differential Evolution with a greedy search to integrate the advantages of global and local searches at the same time that the segmentation speed is improved. We also included in the local search the possibility of topological changes to perform a better adjustment in complex surfaces, topological changes that introduce the necessary mechanism to divide the mesh in the case of the presence of several objects in the scene. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Active models Topological Active Nets Topological active volumes Differential Evolution hybrid optimization algorithms

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Multi-Objective hybrid optimization Algorithm for Design a Printed MIMO Antenna With n78-5G NR Frequency Band Applications

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IEEE ACCESS 2023年 11卷 68231-68242页

作者： Hasbestan, Vahid Hosseini Farhang, Yousef Majidzadeh, Kambiz Ghobadi, Changiz Islamic Azad Univ Comp Engn Dept Urmia Branch Orumiyeh *** Iran Islamic Azad Univ Dept Comp Engn Khoy Branch Khoy *** Iran Urmia Univ Dept Elect Engn Orumiyeh *** Iran

This study introduces a novel multi-objective optimization algorithm integrating Customized Mutated Particle Swarm optimization (CM-PSO) and an innovative modified Genetic Algorithm (GA) using an unexplored merged chaotic map. The hybrid algorithm converges to desired results faster than CM-PSO and modified GA without trapping in local minima. Validation is conducted by designing a single-element and simple-structure dipole antenna so that its optimized S11 is better than-30 dB at the resonance frequency and covers the 3.3 to 3.8 GHz frequency band with S-11 < -10 dB. Certainly, the-30 dB and covering frequency band criteria can be modified in the proposed algorithm. In the algorithm, the isolation between elements of a quad-Multiple-Input/Multiple-Output antenna, constructed using optimized dipole antennas, is set to be less than-20 dB (changeable criteria) so that the smallest size can be achieved. Computer Simulation Technology (CST) Studio Suite carries out electromagnetic and high-frequency simulations, and the novel developed optimization algorithm in MATLAB determines what and how much parameter values need to be changed by CM-PSO or an innovative modified GA in order to enhance the antenna's S-11 result and its Impedance Bandwidth (IBW). The input parameters of the algorithm are the dimensions of the proposed antenna's elements, which significantly influence its performance.

关键词： Chaotic map CM-PSO algorithm dipole antennas genetic algorithm hybrid optimization algorithms MIMO antennas printed antennas

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Optimal allocation method of multi-energy system based on hybrid optimization algorithm

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ENERGY REPORTS 2023年 9卷 1415-1423页

作者： Li, Ji Xu, Wei Feng, Xiaomei Qiao, Biao Xing, Lu Liu, Chao Xue, Huiyu China Acad Bldg Res Inst Bldg Environm & Energy Beijing 100013 Peoples R China Shandong Jianzhu Univ Sch Thermal Engn Jinan 250101 Peoples R China Northumbria Univ Engn & Environm Fac Newcastle Upon Tyne NE1 8ST England Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China

With the rapid development of industry, the research of energy storage technology and renewable energy continues to be hot, and the energy industry opens the era of diversification. Multi-energy complementary has become a new trend in sustainable energy development, leading the energy industry to a new energy system of deep integration and integration of multiple energy sources. This paper proposes a hybrid optimization algorithm that combines particle swarm algorithms and Hooke-Jeeves (HJ) with a comprehensive evaluation index as the optimization objective, aiming to improve the speed of solving the capacity optimization of integrated energy systems. The multi-energy system configuration optimization platform that covers the index system, optimization model, and system analysis module was established to systematically solve the integrated energy system optimization configuration problem, moreover provide an important reference for integrated energy system design and implementation. Besides, the influence of optimization algorithms on the configuration results was analyzed. Taking the combination of soil source heat pump system and combined cooling, heating and power system as an example, this study quantifies and compares the optimization results and solution speeds of the hybrid algorithm and the traditional single optimization calculation. It is shown that the hybrid optimization algorithm reduces the amount of iteration steps by approximately 31% compared with the particle swarm algorithm and by approximately 48% compared with the HJ algorithm. This significantly improves the speed of the optimization computation while ensuring the accuracy of the computation results. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Multi -energy systems Optimal allocation Multi -objective optimization hybrid optimization algorithms

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Design of two-dimensional IIR digital filters by using a novel hybrid optimization algorithm

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2017年第4期28卷 1267-1281页

作者： Lv, Chao Yan, Shi Cheng, Gang Xu, Li Tian, Xiaoyong Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Akita Prefectural Univ Dept Elect & Informat Syst Akita 0150055 Japan

This paper proposes a hybrid optimization algorithm named as BBO-PSO, which is a combination of biogeography-based optimization (BBO) and particle swarm optimization (PSO). In BBO-PSO, the whole population will be split into several subgroups and BBO is employed for local search in each subgroup independently to achieve the different local optima while PSO is employed for global search based on the local optima to achieve the global optimum. The test results on the benchmark functions show that BBO-PSO has powerful search ability with great robustness. Furthermore, the proposed algorithm is applied to the design of the 2-D IIR digital filters and the simulation results show that it outperforms the existing methods on this problem.

关键词： hybrid optimization algorithms Biogeography-based optimization Particle swarm optimization 2-D IIR digital filters

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hybrid optimization algorithm for thermal analysis in a solar parabolic trough collector based on nanofluid

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ENERGY 2015年 82卷 857-864页

作者： Zadeh, P. Mohammad Sokhansefat, T. Kasaeian, A. B. Kowsary, F. Akbarzadeh, A. Univ Tehran Dept New Sci & Technol Tehran Iran Univ Tehran Sch Mech Engn Tehran Iran RMIT Univ Sch Aerosp Mech & Mfg Engn Melbourne Australia

In recent years, many research works focused on improving and reducing the cost of solar collectors. This paper focuses upon the development of an efficient modeling and optimization of solar collector. The approach adopted in modeling utilizes a parabolic trough collector absorber tube with non-uniform heat flux, fully developed mixed convection flow and Al2O3/synthetic oil as a base fluid. optimization of thermal analysis in a solar trough collector using nanofluid is non-convex, non-linear and computationally intensive process. In order to overcome these difficulties, a hybrid optimization method involving GA (genetic algorithm) and SQP (sequential quadratic programming) is introduced in the optimization process. The optimization problem used in this study involves maximization of a non-dimensional correlation consisting of Nusselt number and pressure drop with Reynolds and Richardson number which are used as design constraints. The methodology implemented within an integrated environment involving Matlab, Gambit and Fluent. The results obtained show that heat transfer enhancement has a direct relationship with the nanoparticle concentration ratio whereas it has inverse relationship with the operational temperature. In addition, the results show that the proposed methodology provides an effective way of solving thermal analysis in a solar parabolic trough collectors based on simulation models. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： hybrid optimization algorithms GA (genetic algorithm) SQP (sequential quadratic programming Parabolic trough collector Nanofluid

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Genetic approaches for topological active nets optimization

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PATTERN RECOGNITION 2009年第5期42卷 907-917页

作者： Ibanez, O. Barreira, N. Santos, J. Penedo, M. G. European Ctr Soft Comp Mieres 33600 Asturias Spain Univ A Coruna Dept Comp Sci La Coruna 15071 Spain

The topological active nets (TANs) model is a deformable model used for image segmentation. It integrates features of region-based and edge-based segmentation techniques so it is able to Fit the contours of the objects and model their inner topology. Also, topological changes in its structure allow the detection of concave and convex contours, holes, and several objects in the scene. Since the model deformation is based on the minimization of an energy functional, the adjustment depends on the minimization algorithm. This paper presents two evolutionary approaches to the energy minimization problem in the TAN model. The first proposal is a genetic algorithm with ad hoc operators whereas the second approach is a hybrid model that combines genetic and greedy algorithms. Both evolutionary approaches improve the accuracy of the segmentation even though only the hybrid model allows topological changes in the model structure. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Active nets Topological active nets Genetic algorithms hybrid optimization algorithms Lamarckian strategy

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Enhancing hosting capacity for electric vehicles in modern power networks using improved hybrid optimization approaches with environmental sustainability considerations

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SCIENTIFIC REPORTS 2024年第1期14卷 1-40页

作者： Al-Dhaifallah, Mujahed Refaat, Mohamed M. Alaas, Zuhair Abdel Aleem, Shady H. E. Ali, Ziad M. King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals IRC Sustainable Energy Syst IRC SES Dhahran 31261 Saudi Arabia Elect Res Inst Photovolta Cells Dept Cairo 11843 Egypt Jazan Univ Fac Engn Dept Elect Engn Jizan 45142 Saudi Arabia Inst Aviat Engn & Technol Dept Elect Engn Giza 25152 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Elect Engn Dept Wadi Addawaser 11991 Saudi Arabia Aswan Univ Aswan Fac Engn Elect Engn Dept Aswan 81542 Egypt

The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes-Series hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme-are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model's effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed

关键词： Hosting capacity Electric vehicles hybrid optimization algorithms Transmission expansion planning

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