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A comprehensive review of microgrid control methods: Focus on AI, optimization, and predictive techniques

Computers and Electrical Engineering 2025年 125卷

作者： Limouni, Tariq Yaagoubi, Reda Bouziane, Khalid Guissi, Khalid Baali, El Houssain Energy and Agro-equipment Department Hassan II Institute of Agronomy and Veterinary Rabat 10112 Morocco School of Geomatics and Surveying Engineering Hassan II Institute of Agriculture and Veterinary Medicine Rabat 10112 Morocco LERMA Higher School of Energy Engineering International University of Rabat Campus de l'UIR Parc Technopolis Rocade de Rabat-Salé Sala Al Jadida 11100 Morocco

With the depletion of fossil fuels, the integration of renewable energy sources as distributed energy resources has become mandatory. However, the uncertainty and intermittent nature of these sources introduce significant challenges to their integration into microgrids. Effective control systems are essential for ensuring smooth integration, managing energy storage systems, and maintaining microgrid safety. In this study, a review of recent control methods applied in microgrid management was conducted with a focus on AI, optimization, and predictive techniques. These advanced and intelligent control methods were chosen for their potential to address current challenges. This study examined the benefits, limitations, and areas for future improvement. In addition, it explores the potential and the challenges of hybrid control techniques, which are less discussed in the literature, to further enhance control system efficiency and performance. © 2025 Elsevier Ltd

关键词： Artificial intelligence Fault detection Hybrid control technique Metaheuristics algorithms Microgrid control Model predictive control optimization algorithms Power and energy management

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optimization of the Stop-and-Go- and Dual-Mode-algorithms for Real-time Baseband Transmission 18

Optimization of the Stop-and-Go- and Dual-Mode-Algorithms fo...

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18th IEEE Conference on Signal Processing - algorithms, Architectures, Arrangements, and Applications (SPA)

作者： Meyer, Stephan Fraunhofer Inst Commun Informat Proc & Ergon Software Defined Radio Commun Syst Wachtberg Germany

ISBN: (纸本)9788362065189

This paper presents two optimized blind equalization algorithms, developed on a real-time digital signal processor (DSP) test bed. The most commonly used group of blind channel equalizers are those based on the Bussgang algorithm. By combining a joint algorithm (JA) with a dual-mode (DM) algorithm or a stop-and-go (SAG) algorithm exists the option for optimal matching to the channel behavior. The performance of a single algorithm like modified decision directed modulus (MDDMA), modified constant modulus (MCMA) or the modified decision directed (MDDA) algorithm can only be adapted by adjusting the step size for updating the equalizer tap vector or by changing the number of equalizer taps. Using SAG or DM algorithm introduces a flag (SAG) or a confidence zone (DM) to optimize the algorithm performance further. Combining the joint algorithm within the SAG or DM algorithm yields an optimized algorithm which offers the advantage of the different algorithms. The new algorithms lead to a decrease of the required transmit power by about 22 percent while achieving the same BER. Additional to this, a 6 percent reduction of the processing time is possible relative to MDDMA due to the possibility to reduce the equalizer length.

关键词： SAG SAG gene Equalizers step size algorithms Transmitter power Baseband transmission HELIOS DEVICES optimization algorithms Percent Optimal matching processing time

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Proposal and optimization of a fuzzy logic model for automobile injection diagnosis using evolutionary algorithms

Proposal and optimization of a fuzzy logic model for automob...

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IEEE International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Gonzalez Pinzon, Cesar Leonardo Espitia Cuchango, Helbert Eduardo Ponce Corral, Carlos Romero Gonzalez, Jaime Univ Autonoma Ciudad Juarez Ind Engn & Mfg Ciudad Juarez Mexico Univ Distrital Francisco Jose Caldas Syst Engn Program Bogota Colombia Univ Autonoma Ciudad Juarez Ciudad Juarez Mexico

ISBN: (纸本)9781479922536;9781479922529

This article presents results for the design of a fuel injection diagnostic strategy for motor vehicles in Ciudad Juarez, Chihuahua. The first part of this research develops a diagnosis model for the injector MD162524, implementing Fuzzy Logic. Characterization of the diagnostic model took five injector operation tests (spray pattern, injector opening cycles at 3 revolution levels, electrical resistance measurements in the injector, injector flow at a constant pressure and activation of two levels of pressure in the injector) and 3 possible outcomes of diagnostic status (good shape, repairable, useless). In the final part of the investigation, two evolutionary algorithms are used as a tool to optimize the response of the model designed, generating data sets with decreasing measurement error.

关键词： fuzzy logic automotive diagnostic electronic injection optimization algorithms

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optimization of thermal conductivity in coir fibre-reinforced PVC composites using advanced computational techniques

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Scientific Reports 2025年第1期15卷 1-21页

作者： Anand, Saksham Gopalan, Venkatachalam Pitchumani, Shenbaga Velu School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai 600127 India Centre for Advanced Materials and Innovative Technologies Vellore Institute of Technology Tamilnadu Chennai 600127 India

This research focuses on enhancing the thermal conductivity of coir fibre-reinforced polyvinyl chloride (PVC) composites using advanced optimization techniques. While coir fibre adds sustainability and biodegradability, it poses challenges in achieving optimal thermal performance when integrated into PVC. To address these challenges, the study uses Response Surface Methodology (RSM) and three nature-inspired optimization methods viz. Particle Swarm optimization (PSO), Dragonfly optimization (DFO) and Cuckoo Search Algorithm (CSA) to improve factors like fibre content, particle size and chemical treatment. A Box-Behnken experimental design helps to create composite samples using hydraulic injection moulding and thermal conductivity is measured with a two-slab guarded hot plate device. Among the optimization methods, CSA emerges as the most effective, achieving a maximum thermal conductivity of 0.801 W/mK with minimal error deviation (0.01–5.5%) by the process parameters such as potassium hydroxide treatment, coir content of 2 wt% and powder diameter of 75 (µm). DFO delivers consistent results with slightly higher error rates, while PSO demonstrates rapid convergence but greater variability. The comparison shows that CSA performs better, providing a dependable and long-lasting way to create high-quality coir-reinforced PVC composites that are good for industrial use. This work is among the first to compare multiple bio-inspired optimization algorithms for enhancing the thermal properties of coir-reinforced PVC composites, offering a new pathway for developing high-performance, eco-friendly materials for industrial applications. © The Author(s) 2025.

关键词： Biodegradable materials Coir fibre Cuckoo search algorithm (CSA) Dragonfly optimization (DFO) optimization algorithms Particle swarm optimization (PSO) PVC composites Response surface methodology (RSM) Sustainable composites Thermal conductivity

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Accelerated algorithms for Constrained Nonconvex-Nonconcave Min-Max optimization and Comonotone Inclusion 41

Accelerated Algorithms for Constrained Nonconvex-Nonconcave ...

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41st International Conference on Machine Learning (ICML)

作者： Cai, Yang Oikonomou, Argyris Zheng, Weiqiang Yale Univ Dept Comp Sci New Haven CT 06520 USA

We study constrained comonotone min-max optimization, a structured class of nonconvexnonconcave min-max optimization problems, and their generalization to comonotone inclusion. In our first contribution, we extend the Extra Anchored Gradient (EAG) algorithm, originally proposed by Yoon & Ryu (2021) for unconstrained min-max optimization, to constrained comonotone min-max optimization and comonotone inclusion, achieving an optimal convergence rate of O (1/T) among all first-order methods. Additionally, we prove that the algorithm's iterations converge to a point in the solution set. In our second contribution, we extend the Fast Extra Gradient (FEG) algorithm, as developed by Lee & Kim (2021), to constrained comonotone min-max optimization and comonotone inclusion, achieving the same O (1/T) convergence rate. This rate is applicable to the broadest set of comonotone inclusion problems yet studied in the literature. Our analyses are based on simple potential function arguments, which might be useful for analyzing other accelerated algorithms.

关键词： optimization algorithms

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Parallelization of computational algorithms for optimization problems with high time consumption 22

Parallelization of computational algorithms for optimization...

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22nd Slovak-Polish Scientific Conference on Machine Modelling and Simulations (MMS)

作者： Vasko, Milan Handrik, Marian Sapietova, Alzbeta Handrikova, Jana Univ Zilina Fac Mech Engn Dept Appl Mech Univ 8215-1 Zilina Slovakia Armed Forces Acad Gen MR Stefanik Dept Informat Liptovsky Mikulas Slovakia

The paper presents an analysis of the use of optimization algorithms in parallel solutions and distributed computing systems. The primary goal is to use evolutionary algorithms and their implementation into parallel calculations. Parallelization of computational algorithms is suitable for the following cases computational models with a large number of design variables or cases where the objective function evaluation is time consuming (FE analysis). As the software platform for application of distributed optimization algorithms is using MATLAB and BOINC software package.

关键词： ADINA MATLAB BOINC optimization algorithms parallelization distributed computing systems evolutionary algorithms FE analysis

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Methods for Solving optimization Problems with a Multimodal Objective Function Based on Hybrid algorithms

Methods for Solving Optimization Problems with a Multimodal ...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Evich, L. N. Ostroukh, E. N. Panasenko, P. A. Don State Tech Univ Dept Informat Technol Dept Media Commun & Multimedia Technol Rostov Na Donu Russia Krasnodar Higher Mil Sch Dept Sci Work Krasnodar Russia

ISBN: (纸本)9781538643075

Popular algorithms of global optimization are reviewed. Comparison of two hybrid algorithms is presented. In particular, consecutive and conveyor hybridization of Artificial bee colony (ABC) and Gravitational Search Algorithm (GSA) is introduced. The analysis of two algorithms work productivity based on spherical function, Rosenbrak, Girvonk, Rastrigin, Shevfel functions is performed. Efficiency of algorithm with conveyor hybridization compared to algorithm with consecutive hybridization is set for determined parameters.

关键词： optimization algorithms hybrid algorithms genetic algorithms bee colony gravitational algorithm

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Parametric algorithms for Global optimization of Mixed-Integer Fractional Programming Problems in Process Engineering

Parametric Algorithms for Global Optimization of Mixed-Integ...

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American Control Conference

作者： Zhong, Zhixia You, Fengqi Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA

ISBN: (纸本)9781479932740

In this work, we proposed novel parametric algorithms for solving large-scale mixed-integer linear and nonlinear fractional programming problems, and illustrate their application in process systems engineering. By developing an equivalent parametric formulation of the general mixed-integer fractional program (MIFP), we propose four exact parametric algorithms based on the root-finding methods, including bisection method, Newton's method, secant method and false position method, respectively, for the global optimization of MIFPs. We also propose an inexact parametric algorithm that can potentially outperform the exact parametric algorithms for some types of MIFPs. Extensive computational studies are performed to demonstrate the efficiency of these parametric algorithms and to compare them with the general-purpose mixed-integer nonlinear programming methods. The applications of the proposed algorithms are illustrated through a case study on process scheduling. Computational results show that the proposed exact and inexact parametric algorithms are more computationally efficient than several general-purpose solvers for solving MIFPs.

关键词： Algorithm design and analysis Computational efficiency Linear programming optimization Productivity Programming Testing Computational methods Numerical algorithms optimization algorithms

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Multi-objective optimization in mobile robot path planning: a joint strategy of A* and simulated annealing algorithms 11

Multi-objective optimization in mobile robot path planning: ...

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11th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) / 11th IEEE International Conference on Robotics, Automation and Mechatronics (RAM)

作者： Jiang, Zihan Li, Fumin Yang, Rui Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou 215000 Peoples R China Univ Liverpool Sch Elect Engn Elect & Comp Sci Liverpool L69 3BX Merseyside England

ISBN: (纸本)9798350364200;9798350364194

This paper proposes a novel optimization method for the A* algorithm to address the search efficiency problem in multi-objective path planning. The approach combines the A* algorithm with an annealing algorithm to enhance the quality and efficiency of path planning. Specifically, the traditional A* algorithm is improved by reducing the search direction, thereby decreasing the search space complexity and improving search efficiency. Additionally, an annealing algorithm is embedded within the A* framework. The annealing algorithm, based on the principle of simulated annealing, can avoid local optima by accepting inferior solutions with a certain probability. A series of experiments were conducted in a simulated environment, comparing the improved A* algorithm with other standard multiobjective path planning algorithms. The experimental results demonstrate the effectiveness of the proposed method in enhancing the quality of path planning.

关键词： optimization algorithms

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Airport Ground Optimizer (AGO): A decision support system initiative for air traffic controllers with optimization and decision-aid algorithms

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JOURNAL OF AIR TRANSPORT MANAGEMENT 2024年 119卷

作者： Donmez, Kadir Samsun Univ Aircraft Maintenance Dept Samsun Turkiye

The growing global air traffic has necessitated more efficient ground management in airports, especially during peak hours. Current approaches mainly consist of radar-based systems and radio communications, which provide limited visual assistance. Hence, Decision Support Systems (DSS) are designed to assist human Air Traffic Controllers (ATCos) by providing reliable recommendations based on current data and situations. The Airport Ground Optimizer (AGO) is a DSS designed for ATCos to manage the complexities of ground traffic in airports. It is based on advanced optimization algorithms and intuitive user interfaces, offering an enhanced operational experience. AGO uses mixed-integer programming (AGO-MIP) and stochastic programming (AGO-STC) to detect conflicts based on expected gate release and taxi times, and then optimizes the entire schedule to minimize hold fuels, resolve conflicts, and reduce fuel consumption, emissions, and delay times. Its key strength lies in translating complex mathematical solutions into user-friendly visual representations. AGO's core functionality includes comprehensive visualizations such as position charts, delay graphs, and potential conflict maps, providing a clear, real-time picture of ground operations for informed decision-making. AGO's queue and gate occupancy analysis calculates and visualizes the maximum queue length and concurrent number of aircraft at gates, enhancing airport capacity, reducing aircraft waiting times, and streamlining ground traffic flow. Simulated in a high-traffic Turkish airport layout, AGO-MIP demonstrated significant improvement in operational efficiency compared to traditional First Come First Served (FCFS) approach. AGO outperformed the traditional FCFS approach, reducing hold fuel by 27.9%, cutting delay time by 21.6%, lowering HC, CO, and NOx emissions by 16.4%, 22.5%, and 29.3% respectively, and decreasing the maximum queue length and its duration by 15.3% and 26.6%, as well as decreasing the number

关键词： Air traffic management Decision support system optimization algorithms Fuel and emissions reduction Conflict detection and resolution

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