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

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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MBHGA: A Matrix-Based hybrid Genetic Algorithm for Solving an Agent-Based Model of Controlled Trade Interactions

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IEEE ACCESS 2025年 13卷 26843-26863页

作者： Akopov, Andranik S. Russian Acad Sci Cent Econ & Math Inst Dept Dynam Models Econ & Optimizat Moscow 117418 Russia Moscow Inst Phys & Technol Dept Math Modeling Complex Syst & Optimizat Moscow 141701 Russia MIREA Russian Technol Univ Dept Instrumental & Appl Software Moscow 119454 Russia

The article discusses the development and study of a new matrix-based hybrid genetic algorithm (MBHGA) for solving an agent-based model of firms' behavior with controlled trade interactions. The proposed model employs symmetric strategies optimized using the MBHGA algorithm. This algorithm combines evolutionary search with real-coded crossover and matrix binary-coded crossover as genetic operators, creating a hybrid approach. The aim of the designed system is to assist decision-makers in selecting optimal strategies for their firms in situations where some companies impose restrictions on interactions. As demonstrated by the results of optimization experiments, the MBHGA algorithm significantly outperformed other methods, such as RCGA, PSO, GWO, SPEA2, and NSGA-II, in terms of both accuracy (measured by deviations from reference values) and the quality of the Pareto front approximation (measured by LHV, IGD, and CPF) when solving an agent-based model of firms' behavior. As demonstrated in this study, the performance of MBHGA significantly depends on the size of the model (i.e., the number of agents), and it can be enhanced by parallelizing the evolutionary search process, including matrix crossover operations for decision variables. optimization experiments using a genetic algorithm were conducted to maximize utility functions for agent companies under trade restrictions. The results showed that trade restrictions have a negative impact on the utility function values in both single- and multi-objective optimization, for selected countries and for all countries' firms. However, an optimization-based approach using the MBHGA algorithm can help minimize the negative consequences of trade restrictions by providing the ability to find nondominated solutions and the best possible trade-offs.

关键词： optimization Genetic algorithms Multi-agent systems Biological system modeling Decision making Companies Production Games Symmetric matrices Socioeconomics Multiagent systems genetic algorithms agent-based modeling controlled trade interactions matrix crossover hybrid optimization algorithms parallel computing multiobjective optimization Pareto front

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An enhanced method for intrusion detection systems in IoT environment

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-27页

作者： Alzubi, Qusay M. Sanjalawe, Yousef Makhadmeh, Sharif Naser Fakhouri, Hussam N. Yarmouk Univ Informat Technol & Comp Sci Fac Dept Informat Technol Cybersecur Program Irbid 21163 Jordan Univ Jordan UJ King Abdullah Sch Informat Technol 2 Dept Informat Technol Amman 11942 Jordan Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Univ Petra Fac Informat Technol Data Sci & Artificial Intelligence Dept Amman 1196 Jordan

The connectivity and integration of commodities have transformed many industries through the Internet of Things, enhancing both efficiency and functionality. However, this interconnection has also introduced critical security challenges, necessitating a robust Intrusion Detection System. This paper presents a novel, enhanced method for Intrusion Detection Systems in Internet of Things environments, utilizing advanced machine learning techniques and optimization algorithms. The proposed method integrates the Marine Predator Optimizer and Grey Wolf Optimizer for efficient feature selection, improving the detection and classification of anomalies. Two datasets, NFC-SECICIDS2018v2 and BotIoT2018, were used to evaluate the performance and effectiveness of the proposed Intrusion Detection System. The results demonstrate that the proposed method achieves an accuracy of 97.59% on the NFC-SECICIDS2018v2 dataset and 99.97% on the BotIoT2018 dataset. Additionally, the method shows a perfect sensitivity of 100% on the BotIoT2018 dataset and a sensitivity of 99.58% on the NFC-SECICIDS2018v2 dataset, significantly reducing the false alarm rate. The findings underscore the effectiveness of combining the Marine Predator Optimizer and Grey Wolf Optimizer to enhance the performance of Intrusion Detection Systems, offering a promising solution for securing the Internet of Things networks against evolving cyber threats.

关键词： Anomaly detection BotIoT2018 dataset Cybersecurity in smart devices Feature selection for IDS Grey wolf optimizer hybrid optimization algorithms Intrusion detection in IoT Internet of Things security Marine predator optimizer NFC-SECICIDS2018v2 dataset

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Multi-objectives transmission expansion planning considering energy storage systems and high penetration of renewables and electric vehicles under uncertain conditions

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ENERGY REPORTS 2024年 11卷 4143-4164页

作者： Al-Dhaifallah, Mujahed Refaat, Mohamed M. Alaas, Zuhair Aleem, Shady H. E. Abdel El-kholy, Elwy E. Ali, Ziad M. King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr lRC Renewable Energy & P KFUPM Box 120 Dhahran 31261 Saudi Arabia Elect Res Inst Photovolta Cells Dept Cairo 11843 Egypt Jazan Univ Coll 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 Aldawasir 11991 Saudi Arabia Aswan Univ Aswan Fac Engn Elect Engn Dept Aswan 81542 Egypt

Transmission expansion planning (TEP) integrating electric vehicles (EVs) and renewable energy sources (RESs) is pivotal for the transition toward cleaner and more sustainable energy systems. One of the biggest challenges in TEP with EVs and RESs is the uncertainty inherent in their behaviors. Managing this uncertainty is critical for ensuring the grid's reliability and resilience, and for facilitating a seamless transition to EVs. A potent method for addressing uncertainties in power systems is the application of a scenario-based approach. However, the efficiency of this approach is contingent upon an increase in the number of representative scenarios, which, in turn, escalates computational time, making solving the TEP problem a crucial task. To tackle these challenges, this paper proposes a multi-objective resilience model for TEP, to advance towards more sustainable energy systems. The model seeks to strike a balance between economic, environmental, and technical considerations by minimizing planning costs, carbon dioxide emissions, and enhancing the voltage profile. Furthermore, the paper proposes a cascaded intelligent strategy to handle uncertainties and reduce computational complexity by reducing the number of representative scenarios without compromising system reliability. The adaptive neuroinference system is designed to manage long-term uncertainties, while two fuzzy systems are developed to address short-term uncertainties. The problem is formulated as a complex non-linear multi-objective optimization problem. To address this, a hybrid approach combining the mountain gazelle optimizer and the multiobjective salp swarm optimizer is developed to solve the problem. The efficacy of the proposed method is validated on two separate test systems, demonstrating its superiority in maintaining system reliability while reducing computing time by at least 92 % in comparison with the considered traditional methods. The results also highlight the superior performance

关键词： Transmission expansion planning Electric vehicles Renewable energy sources Fuzzy systems hybrid optimization algorithms

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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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An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function

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ENERGY 2023年 264卷

作者： Calasan, Martin Aleem, Shady H. E. Abdel Hasanien, Hany M. Alaas, Zuhair M. Ali, Ziad M. Univ Montenegro Fac Elect Engn Podgorica Montenegro Sci Valley Acad Valley High Inst Engn & Technol Dept Elect Engn Qalyubia 44971 Egypt Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Jazan Univ Coll Engn Elect Engn Dept Jazan 45142 Saudi Arabia 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

In the literature, identifying the parameters of proton exchange membrane fuel cells (PEMFCs) relies on minimizing the difference between measured and simulated voltage values for different current values. In this regard, expressions representing the PEMFC voltage as a current function are usually used. Unlike literature approaches, in this work, a new mathematical expression of PEMFC current as a voltage function is derived and solved using the iterative Lambert W function for the first time in literature. The root-mean-square error (RMSE) and sum-ofsquares error (SSE) between the measured and estimated current and voltage values are calculated, analyzed, and discussed using the parameters obtained from many optimization methods for two PEMFCs - Ballard-Mark-V 5 kW and BCS 500 W. An improved evaporation rate water cycle algorithm (ERWCA), named Chaotic ERWCA (ChERWCA), is employed to solve the new PEMFC parameter estimation problem based on minimizing SSE between the measured and estimated current values obtained using the new mathematical model. Moreover, SSE and RMSE of voltage and current measures were calculated for different recent methods presented in the literature. The results validate the effectiveness of the new mathematical model and show that the solutions obtained using ChERWCA outperformed those obtained by other methods presented for these fuel cells in the literature. The improvement of voltage SSE using the new mathematical formulation of the PEMFC model and the proposed algorithm is about 0.1% compared to the best result reported in the literature. Finally, this work lays the foundation for developing a novel mathematical model to investigate the operation of PEMFCs in modern power systems.

关键词： Energy storage Lambert W function hybrid optimization algorithms Proton exchange membrane fuel cells Special trans function theory

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Optimal Coordination of Directional Overcurrent Relays Using hybrid Firefly-Genetic Algorithm

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ENERGIES 2023年第14期16卷 5328页

作者： Foqha, Tareq Khammash, Maher Alsadi, Samer Omari, Osama Refaat, Shady S. Al-Qawasmi, Khaled Elrashidi, Ali Arab Amer Univ Fac Engn & Informat Technol Dept Elect Engn POB 240 Jenin Palestine Annajah Natl Univ Dept Elect Engn POB 7 Nablus Palestine Palestine Tech Univ Kadoorie Dept Elect Engn P305 Tulkarm Palestine Texas A&M Univ Qatar Elect & Comp Engn Dept POB 23874 Doha Qatar Univ Hertfordshire Sch Phys Engn & Comp Sci Hatfield AL10 9EU England Zarqa Univ Cybersecur Dept Zarqa 13133 Jordan Univ Business & Technol Coll Engn Elect Engn Dept Jeddah 21589 Saudi Arabia

The application of directional overcurrent relays (DOCRs) plays an important role in protecting power systems and ensuring their safe, reliable, and efficient operation. However, coordinating DOCRs involves solving a highly constrained and nonlinear optimization problem. The primary objective of optimization is to minimize the total operating time of DOCRs by determining the optimal values for decision variables such as the time multiplier setting (TMS) and plug setting (PS). This article presents an efficient hybrid optimization algorithm that combines the modified firefly algorithm and genetic algorithm to achieve improved solutions. First, this study modifies the firefly algorithm to obtain a global solution by updating the firefly's brightness and to prevent the distance between the individual fireflies from being too far. Additionally, the randomized movements are controlled to produce a high convergence rate. Second, the optimization problem is solved using the genetic algorithm. Finally, the solution obtained from the modified firefly algorithm is used as the initial population for the genetic algorithm. The proposed algorithms have been tested on the IEEE 3-bus, 8-bus, 9-bus and 15-bus networks. The results indicate the effectiveness and superiority of the proposed algorithms in minimizing the total operating time of DOCRs compared with other optimization methods presented in the literature.

关键词： directional overcurrent relay coordination genetic algorithm firefly algorithm hybrid optimization algorithms power system protection

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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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Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach

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ENERGY 2024年 308卷

作者： Hasanien, Hany M. Alsaleh, Ibrahim Alassaf, Abdullah Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Univ Hail Coll Engn Dept Elect Engn Hail 55211 Saudi Arabia

In this article, a novel optimization algorithm named hybrid Osprey optimization and Particle Swarm optimization (OOPSO) is proposed to solve the Probabilistic Optimal Power Flow (POPF) problem in power systems with high penetration of renewable energy resources (RERs), such as photovoltaic, wind, and wave energy and integration of electric vehicles (EVs). The algorithm's results are compared with established optimization algorithms like Osprey optimization Algorithm (OOA), Particle Swarm optimization (PSO), and Heap optimization. Applied to the 30 and 118-bus IEEE test systems, the OOPSO algorithm efficiently deals with the uncertainties produced by RERs generation. This research study includes EV charging profiles for weekdays and weekends. It considers home and work-level charging scenarios. The RERs and EVs penetration ensure system reliability while minimizing generation costs and satisfying power flow constraints. Compared to existing algorithms, the OOPSO algorithm achieves superior performance, faster convergence, and higher solution accuracy. The simulation results show the effectiveness of OOPSO. It appears to be a robust tool for POPF, integrating RERs in power systems with EVs. This work highlights the potential of OOPSO as a feasible approach for power system optimization in the context of RERs and EV integration, paving the way for further research into unconventional grid optimization techniques.

关键词： Electric-vehicle integration hybrid optimization algorithms Probabilistic optimal power flow Wave energy integration

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Efficient Model Points Selection in Insurance by Parallel Global optimization Using Multi CPU and Multi GPU

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BUSINESS & INFORMATION SYSTEMS ENGINEERING 2020年第1期62卷 5-20页

作者： Ferreiro-Ferreiro, Ana Maria Garcia-Rodriguez, Jose Antonio Souto, Luis A. Vazquez, Carlos Univ A Coruna Fac Informat Dept Math La Coruna 15071 Spain Univ A Coruna Fac Informat CITIC La Coruna 15071 Spain

In the insurance sector, Asset Liability Management refers to the joint management of the assets and liabilities of a company. The liabilities mainly consist of the policies portfolios of the insurance company, which usually contain a large amount of policies. In the article, the authors mainly develop a highly efficient automatic generation of model points portfolios to represent much larger real policies portfolios. The obtained model points portfolio must retain the market risk properties of the initial portfolio. For this purpose, the authors propose a risk measure that incorporates the uncertain evolution of interest rates to the portfolios of life insurance policies, following Ferri (Optimal model points portfolio in life, 2019, ). This problem can be formulated as a minimization problem that has to be solved using global numerical optimization algorithms. The cost functional measures an appropriate distance between the original and the model point portfolios. In order to solve this problem in a reasonable computing time, sequential implementations become prohibitive, so that the authors speed up the computations by developing a high performance computing framework that uses hybrid architectures, which consist of multi CPUs together with accelerators (multi GPUs). Thus, in graphic processor units (GPUs) the evaluation of the cost function is parallelized, which requires a Monte Carlo method. For the optimization problem, the authors compare a metaheuristic stochastic differential evolution algorithm with a multi path variant of hybrid global optimization Basin Hopping algorithms, which combines Simulated Annealing with gradient local searchers (Ferreiro et al. in Appl Math Comput 356:282-298, 2019a). Both global optimizers are parallelized in a multi CPU together with a multi GPU setting.

关键词： Model points portfolio Risk functional hybrid optimization algorithms Differential evolution Basin hopping Monte Carlo simulation HPC Multi CPU Multi GPU

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