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Integrating moving morphable components and plastic layout optimization: a two-stage approach for enhanced structural topology optimization

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2025年第2期68卷 1-19页

作者： Lotfalian, Amin Esmaeilpour, Peyman Yoon, Gil Ho Takalloozadeh, Meisam Shiraz Univ Sch Engn Dept Civil & Environm Engn Shiraz Iran Hanyang Univ Sch Mech Engn Seoul South Korea

The concept of Moving Morphable Components (MMC) represents a novel and effective technique within structural topology optimization, requiring fewer design variables and providing an explicit boundary definition. However, the optimal topology heavily depends on the initial shape and position of the components. Plastic layout optimization, on the other hand, quickly determines globally optimal layouts via linear programming but lacks detailed information about member connections, which are crucial for applications like additive manufacturing. This study introduces an innovative two-stage methodology that synergizes the strengths of MMC and plastic layout optimization, while addressing their individual limitations. The method builds on the concept that the topology optimization formulation for minimizing compliance under a single load case is mathematically equivalent to a minimum-weight plastic layout optimization formulation. In the first stage, the global optimal layout is obtained using plastic layout optimization, which serves as an advantageous starting point for the MMC approach in the second stage. This integration significantly reduces computational time, lowers compliance, and mitigates the risk of converging to local optima. The proposed methodology is adaptable to complex three-dimensional domains and provides a robust framework for efficient and precise structural topology optimization. Several examples demonstrate the efficacy, accuracy, and rapid convergence of this approach, validating its potential for advancing optimization techniques in engineering.

关键词： Topology optimization Layout optimization linear programming Moving morphable components Non-linear optimization

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Parameter Optimization Method for AC Filters in HVDC Considering Reactive Power Compensation Effectiveness

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IEEE TRANSACTIONS ON POWER DELIVERY 2025年第1期40卷 365-375页

作者： Lin, Sheng Mu, Dalin Xu, Lijuan He, Zhengyou Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

In high-voltage direct current (HVDC) systems, the parameters of the AC filter (ACF) have a significant impact on the performance of reactive power (RP) compensation and filtering, as well as the capital cost. However, the RP compensation effectiveness and economy of ACFs under different HVDC operation modes are difficult to be satisfied comprehensively. Therefore, a parameter optimization method for ACFs is proposed to balance the technical performance and economy in this paper. First, considering the variety of actual HVDC operation modes, the parameters of ACFs are analyzed in relation to the performance and capital cost, which leads to the key impact factors of parameter design. Then, considering both the RP exchange quantity and the capital cost of investment and land occupation, the objective function is defined. By considering the sub-bank numbers and filtering performance of ACFs, the constraints are proposed. Finally, genetic algorithm with the elitist strategy is used to obtain the optimized ACF parameters. The comparative results show that the performance of RP compensation and filtering of the optimized ACFs is effectively improved and guaranteed, and meanwhile the capital cost is reduced.

关键词： Costs HVDC transmission Investment Filters Power harmonic filters Harmonic analysis Capacitors Reactive power Optimization models linear programming HVDC AC filter reactive power compensation parameter optimization genetic algorithm

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Pricing filtering in Dantzig-Wolfe decomposition

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OPERATIONS RESEARCH LETTERS 2025年 58卷

作者： Mehamdi, Abdellah Bulaich Lacroix, Mathieu Martin, Sebastien Huawei Technol Ltd Paris Res Ctr Boulogne Billancourt France Univ Sorbonne Paris Nord Lab Informat Paris Nord LIPN CNRS Villetaneuse France EDF Lab Paris Saclay Paris France

Column generation is used alongside Dantzig-Wolfe Decomposition, especially for linear programs having a decomposable pricing step requiring to solve numerous independent pricing subproblems. We propose a filtering method to detect which pricing subproblems may have improving columns, and only those subproblems are solved during pricing. This filtering is done by providing light, computable bounds using dual information from previous iterations of the column generation. The experiments show a significant impact on different combinatorial optimization problems.

关键词： Dantzig-Wolfe decomposition linear programming Column generation Pricing subproblems Pricing filtering

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Demand response for renewable energy communities: Exploring coordination of prosumer-generated PV and flexible aggregated demand in the Italian framework

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ENERGY AND BUILDINGS 2025年 340卷

作者： Ercoli, Patricia Mugnini, Alice Arteconi, Alessia Univ Politecn Marche Dipartimento Ingn Industriale & Sci Matematiche Via Brecce Bianche 12 I-60131 Ancona Italy Univ E Campus CO Dipartimento Sci Teor & Applicate DiSTA Via Isimbardi 10 I-22060 Novedrate Italy Katholieke Univ Leuven Dept Mech Engn B-3000 Leuven Belgium Energyville BE-3600 Genk Belgium

Renewable Energy Communities (RECs) offer a decentralized approach to integrate Distributed Energy Resources (DER) and non-programmable Renewable Energy Sources (RESs), such as photovoltaics (PVs). However, achieving full self-sufficiency and maximizing the use of RESs remains a challenge due to seasonal variability and differences between supply and demand. This study explores energy flexibility in RECs using centralized Demand Response (DR) strategies adapted to the Italian context. A single prosumer with PV generation and multiple consumers with varying building characteristics are considered. Centralized linear programming (LP) is used to optimize energy management and coordinate the collective consumption of prosumer-generated PV. The results show that coordinated building response increases PV self-consumption and reduces electricity costs, but the potential for flexibility depends on factors such as community composition, PV availability, and energy sharing strategies. For example, aggregate displaceable energy for pre-cooling drops from 56.39% (one consumer) to 23.33% (six consumers), while larger PV systems improve flexibility (e.g., from 23.33% with 5.4 kWp to 50.56% with 10.8 kWp of energy shifted for pre-cooling strategies). In addition, DR strategies aligned with prosumer selfconsumption demands can allow up to 51.96% of displaceable energy for pre-cooling and 30.91% for preheating. Thus, tailored strategies in REC design and operation are essential to maximize energy and economic performance, emphasizing the importance of customized solutions for sustainable and resilient energy systems.

关键词： Renewable Energy Community Energy flexibility Demand Response Prosumer energy management PV sharing strategies linear programming

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Power allocation and communication resource scheduling for federated learning in wireless IoT networks

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ANNALS OF TELECOMMUNICATIONS 2025年 1-14页

作者： de Oliveira, Renan R. Silva, Rogerio S. e Freitas, Leandro A. Oliveira Jr, Antonio Univ Fed Goias BR-74690900 Goiania GO Brazil Fed Inst Goias BR-74055110 Goiania GO Brazil Fraunhofer Portugal AICOS P-4200135 Porto Portugal

Federated learning (FL) allows devices to train a machine learning model collaboratively without compromising data privacy. In wireless networks, FL presents challenges due to limited resources and the unstable nature of transmission channels that can cause delays and errors that compromise the consistency of global model updates. Furthermore, efficient allocation of communication resources is crucial in Internet of Things (IoT) environments, where devices often have limited energy capacity. This work introduces a novel FL algorithm called DFed-wOptDP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\text {Opt}}<^>{\text {DP}}$$\end{document}, designed for wireless networks within the IoT framework. This algorithm incorporates a device selection mechanism that evaluates the quality of device data distribution and connection quality with the aggregate server. By optimizing the power allocation for each device, DFed-wOptDP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\text {Opt}}<^>{\text {DP}}$$\end{document} minimizes overall energy consumption while enhancing the success rate of transmissions. The simulation results demonstrate that DFed-wOptDP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\text {Opt}}<^>{\text {DP}}$$\end{document} effectively operates with low transmission power while preserving the accuracy of the global model compared to other algorithms.

关键词： Federated learning Wireless IoT networks Dynamic power allocation Resource scheduling linear programming

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Enhanced Harmony Search Algorithm for Maximum Power Point Tracking Under Partial Shading in PV Systems

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IEEE ACCESS 2025年 13卷 51900-51910页

作者： Lee, Geon Hee Geem, Zong Woo Won, Jehyuk Gachon Univ Dept Next Generat Smart Energy Syst Convergence Seongnam 13120 South Korea Gachon Univ Coll IT Convergence Engn Seongnam 13120 South Korea Kangwon Natl Univ Coll IT Dept Elect & Elect Engn Chunchon 24341 South Korea

Multiple power peaks are introduced under nonuniform irradiation conditions in photovoltaic (PV) systems. However, conventional maximum power point tracking (MPPT) algorithms, such as the perturb-and-observe (P&O) algorithm, are only effective under homogeneous irradiation conditions. Therefore, there is a need for an efficient method for global maximum power point (GMPP) tracking under partial shading conditions (PSCs). This paper proposes a novel MPPT algorithm based on harmony search (HS). The proposed algorithm not only overcomes the power loss induced by a wide search but also avoids falling into local maximum power points under PSCs. A parameter-setting-free (PSF) HS algorithm is introduced, which is further improved for the MPPT problem. Furthermore, the algorithm integrates a reduced-range (RR) operator to reduce the convergence time. To validate the performance of the proposed algorithm, experiments are performed using a 250-W PV system with a PV emulator under different PSCs. The results show that the proposed algorithm outperforms conventional HS and particle swarm optimization algorithms in terms of the number of iterations and convergence time, while maintaining a similar level of accuracy.

关键词： Convergence Maximum power point trackers Bandwidth Voltage control Photovoltaic systems linear programming Accuracy Tuning Particle swarm optimization Metaheuristics Harmony search (HS) maximum power point tracking (MPPT) metaheuristic algorithms partial shading condition (PSC) photovoltaic (PV) system

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Optimized Renewable Energy Integration: Advanced Modeling, Control, and Design of a Standalone Microgrid Using Hybrid FA-PSO

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IEEE ACCESS 2025年 13卷 63486-63503页

作者： Talebi, Shiva Aly, Hamed H. Dalhousie Univ Dept Elect & Comp Engn Smart Grid & Green Power Res Lab Halifax NS B3H 4R2 Canada

The increasing environmental impacts and limited nature of fossil fuels have accelerated the growth of renewable energy sources (RESS). This study addresses the challenges associated with combining renewable energy sources, such as wind, solar, and tidal energy, into power systems, and it focuses on the design and optimization of a hybrid renewable microgrid that uses battery energy storage systems (BESS) to balance supply and demand while considering issues related to battery degradation. Battery degradation is a crucial constraint within the optimization framework. A hybrid optimization technique combining the Firefly Algorithm and Particle Swarm Optimization (FA-PSO) is proposed to enhance system reliability, known as loss of load probability (LPSP), and minimize the net present cost (NPC) of the system. The results and statistical analysis reveal that the proposed hybrid method outperforms the common algorithms used in the literature like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and firefly algorithm (FA). This work contributes to the literature by integrating tidal energy into renewable management and emphasizing realistic battery degradation considerations.

关键词： Optimization Batteries Costs Degradation Microgrids Wind turbines linear programming Reliability Wind speed Particle swarm optimization Battery degradation energy management system hybrid renewable energy firefly algorithm net present cost particle swarm optimization

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A dynamic optimization model for vaccine allocation with age considerations: A study inspired by the COVID-19 pandemic

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2025年 280卷

作者： Pirayesh, Amir Asadaraghi, Alireza Mohammadi, Mehrdad Siadat, Ali Battaia, Olga KEDGE Business Sch Ctr Excellence Supply Chain & Transportat CESIT Bordeaux France LCPI Arts & Metiers ParisTech Paris France Eindhoven Univ Technol Dept Ind Engn & Innovat Sci Eindhoven Netherlands Univ Lorraine LCFC Arts & Metiers ParisTech Metz France

In a pandemic situation, an effective vaccination campaign is seen as a powerful tool to prevent the spread of infectious diseases and reduce fatalities. However, its success highly depends on its organization and combination with other measures. To help the decision-makers in this endeavor, this paper proposes a Mixed-Integer linear programming - Vaccine Allocation (MILP-VA) model to plan the vaccination campaign to minimize the number of possible fatalities over a given period. To better integrate the pandemic dynamics, this model is coupled with a single-dose Susceptible-Vaccinated-Infected-Recovered (SVIR) model where the compartmentalization of the population allows for the adjustment of different demographic and epidemiological parameters based on age categories and their social interactions. This approach is proven to suit populations with heterogeneous age groups better. The applicability of the proposed SVIR-MILP-VA model is illustrated using a case study inspired by the COVID-19 pandemic. Accordingly, an extensive numerical analysis was conducted to test various managerial, epidemiological, and behavioral conditions, such as vaccine availability, transmission rates, and vaccine hesitancy. This approach facilitates robust discussions to address the uncertainties of an emerging pandemic and provides a solid foundation for informed vaccination decisions in real-world settings. The results are discussed, and the findings are formulated as insights for researchers and practitioners.

关键词： Vaccine allocation Age-based compartmentalization SVIR model Optimization linear programming COVID-19

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Multi-objective optimization of sustainable aviation fuel production pathways in the US Corn Belt

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BIOMASS & BIOENERGY 2025年 193卷

作者： Akdemir, Ece Ari Kern, Jordan Smith, Jack P. Limb, Braden J. Quinn, Jason C. Field, John L. Pack, Taylor NC State Univ Dept Civil Construct & Environm Engn Raleigh NC 27606 USA NC State Univ Dept Ind & Syst Engn Raleigh NC 27606 USA Colorado State Univ Dept Mech Engn Ft Collins CO 80521 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA NC State Univ Dept Forestry & Environm Resources Raleigh NC 27695 USA

As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield.

关键词： Biofuel supply chains Weather risk Multi-objective optimization linear programming

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Gradient-Based Search Algorithm for Selective Harmonic Elimination in Cascaded H-Bridge Multilevel Converters

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第5期72卷 4877-4888页

作者： Ghasemian, Amir Mohamadian, Sobhan Buccella, Concettina Cecati, Carlo Politecn Bari DEI I-70126 Bari Italy Univ LAquila DISIM I-67100 Laquila Italy Digi Power Srl I-67100 Laquila Italy Politecn Bari DIEEE I-70126 Bari Italy

This article develops a gradient-based search algorithm for selective harmonic elimination (SHE) to address the problems of high-computational cost, low-convergence speed, modulation index error, and slow-dynamic response often associated with SHE algorithms. First, the dimension of the search space is reduced by deriving intuitive equations to increase the search speed. Second, the desired modulation index is achieved by applying the proposed constraints. Third, gradient equations are modified to move and hold points in the reduced search space. Extensive comparative simulation studies show that, compared to conventional SHE methods, the proposed algorithm is highly efficient in producing smooth switching angle curves with minimal fluctuations over the entire modulation index range. The proposed method minimizes execution time and modulation index error, even with unbalanced dc input voltages. These superior characteristics are also experimentally verified on cascaded H-bridge inverters with different number of stages up to 25.

关键词： Switches Harmonic analysis Mathematical models linear programming Vectors Voltage Search problems Optimization Indexes Heuristic algorithms Cascaded H-bridge (CHB) gradient gradient-based optimization harmonic distortion multilevel converter search algorithm selective harmonic elimination (SHE) switching angle unbalanced dc input

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