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multi-objective optimization of a cogeneration system based on solar energy for clean hydrogen, cooling, and electricity production

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CASE STUDIES IN THERMAL ENGINEERING 2024年 54卷

作者： Bedakhanian, Ali Maleki, Akbar Haghighat, Siavash Shahrood Univ Technol Fac Mech Engn Shahrood Iran Univ Tehran Renewable Energy & Environm Engn Dept Tehran Iran

In an effort to encourage industries to switch from fossil fuels to renewable energy resources for supplying their energy demands, the exergy and financial aspects of a thermodynamic energy generation system were studied. The suggested system was modeled by MATLAB commercial software to assess the decision -making parameters affecting power generation, cooling capacity, and to produce hydrogen. The objective functions of this study were exergy efficiency and cost rate, while the temperatures at the inlet of the turbine and the evaporator, irradiated solar energy, mass flow rate, and surface area of the collector were the decision -making variables. The model was optimized via MOPSO and its results were compared with two widely utilized algorithms, namely NSGA-II and SPEA-II. The comparison results indicated that MOPSO surpassed other two optimization algorithm resulting in exergy efficiency and cost rate of 2.11 % and 21.14 $/h, respectively. According to this method, the optimum generated power was equal to 21.01 kW. Eventually, this system was utilized and evaluated in the city of Semnan, Iran. The performance results of the system in Semnan showed that the annual power output, taking into account the changes in radiation and ambient temperature, is between 316667.4 and 428080.5 kW. Also, the amount of hydrogen production is between 1503.66 and 1534.997 kg.

关键词： Solar energy Hydrogen multi-objective optimization algorithm Exergy efficiency Cost rate

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The optimization of Supply-Demand Balance Dispatching and Economic Benefit Improvement in a multi-Energy Virtual Power Plant within the Jiangxi Power Market

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ENERGIES 2024年第18期17卷 4691页

作者： Xinfa, Tang Jingjing, Wang Yonghua, Wang Youwei, Wan Jiangxi Sci & Technol Normal Univ Sch Econ Management & Law Nanchang 330038 Peoples R China State Grid Jiangxi Elect Power Co Ltd Nanchang 330032 Jiangxi Peoples R China

This paper presents an optimization method for scheduling a multi-energy VPP (Virtual Power Plant) supply-demand balance in the power market environment of Jiangxi Province. The primary objective of this method is to improve the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method takes into account the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Furthermore, it integrates real-time market price feedback to achieve the accurate allocation of power supply and demand. Through a case study of a multi-energy VPP in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply-demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

关键词： multi-energy VPP balance between supply and demand scheduling planning optimization multi-objective optimization algorithm energy efficiency of renewable energy

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multi-objective optimization of integrated energy systems in natural gas industrial zones: Enhancing performance through variable hydrogen blending ratios

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APPLIED THERMAL ENGINEERING 2025年 268卷

作者： Mao, Yudong Li, Xinwei Liu, Jiying Yu, Mingzhi Kim, Moon Keun Yang, Kaimin Shandong Jianzhu Univ Sch Thermal Engn Jinan 250101 Peoples R China Oslo Metropolitan Univ Dept Built Environm N-0130 Oslo Norway

In order to solve the problem of new energy consumption and reduce the use of fossil fuels, a multi-objective optimization and evaluation system is constructed for Integrated energy system (IES). The system takes into account factors including economy, environmental conservation, and hydrogen blending ratio. It establishes a mixed-integer linear programming model for multi-objective optimal scheduling. The conversion of renewable energy sources into electricity, followed by its conversion into hydrogen. By changing the hydrogen blending ratio of natural gas pipeline network, each objective function can reach the best. The optimal operation of each equipment of the heat and cold networks is obtained by changing the proportion of hydrogen blending into the natural gas pipeline network. Based on the multi-objective comprehensive analysis under conditions of constant heat consumption, the optimal solution is a hydrogen blending ratio of 10.67 %. This solution results in a cost of $305,426.29, carbon emissions of 19,709,221.40 kg, and a total natural gas consumption of 19,128,759.35 m3. Compared with the conventional energy system, this scheme not only offers better economy but also has characteristics of energy saving and emission reduction, making it superior in comprehensive evaluation compared to other schemes. In conclusion, this system enhances energy utilization efficiency, maximizes the integration of renewable energy sources, and reduces carbon emissions.

关键词： Integrated energy system multi-objective optimization algorithm Mixed-integer linear programming Hydrogen blending of natural gas Renewable energy

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A multi-objective optimization-Based Reflective Metasurface for Enhanced multi-Point Focusing With Diffraction Suppression

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RADIO SCIENCE 2024年第7期59卷 1-12页

作者： Xiao, Dongping Xu, Lanxin Su, Dongping Shi, Zhuxin Zhang, Huaiqing Chongqing Univ Sch Elect Engn Natl Key Lab Power Transmiss Equipment Technol Chongqing Peoples R China

Metasurface arrays can achieve beam control at low cost and high quality by providing different phase compensations for each unit, effectively focusing microwave energy on target locations. With the development of short-range communication technology or microwave power transmission technology, the demand for focusing has also increased. Using metasurface arrays to achieve multi-target focusing has wide application value. However, as the number of focal points increases, the superposition of electromagnetic wave propagation paths leads to significant interference phenomena, which can impact potential applications. Existing solutions are unable to solve such complex problems involving a large number of targets with conflicts between them. multi-objective algorithms, by iteratively obtaining a set of optimal solutions, provide decision support for designers in complex multi-objective problems. This paper alters the phase of cells in a reflective array, calculates the near-field electric field model using the Fresnel diffraction formula, and employs various solutions using the Non-dominated Sorting Genetic algorithm III (NSGA-III) combined with different constraints. Finally, we select the balanced solution to establish the array. After simulation, three adjacent focal points with normalized central values of 1, 0.86, and 0.88 were obtained, with the maximum electric field value outside the focal points being only 0.58, demonstrating the feasibility of multi-objective algorithms in solving complex multi-focal problems. Near field multi-objective focusing is the concentration of microwave energy on multiple target locations within the near-field range multiple-objective algorithms is a computational methods for multi-objective optimization problems and provides a set of optimal solutions

关键词： multi-point focusing reflective metasurface multi-objective optimization algorithm near field

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Homotypic clustering optimization algorithm for fitness centre geometric parameter configuration optimization study

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INDOOR AND BUILT ENVIRONMENT 2025年第1期34卷 97-115页

作者： Gao, Yue Huang, Yong Shenyang Jianzhu Univ Sch Architecture & Urban Planning Shenyang Peoples R China Key Lab Reginal Architecture & Human Settlements S Shenyang 110168 Peoples R China

Although many energy-saving technologies and sustainable building designs have successfully reduced building energy consumption and improved user satisfaction, little consideration has been given to the impact of building spatial configuration on the indoor environment. This study focused on a fitness centre and employed an optimization methodology based on computational fluid dynamics (CFD) simulation. The optimization aims of this study were to enhance interior ventilation efficiency and regulate temperature, resulting in the optimal building spatial characteristics. This work presents the homologous clustering optimization algorithm (HCO) as a solution to the limitations of existing optimization approaches. It also compares HCO with the multi-objective evolutionary mating algorithm (MOMA). HCO with a coefficient of eight performed better than MOMA. The Pareto frontier generated by HCO was more succinct, and the computational time was reduced by 52%. Moreover, an analysis of the CFD simulation results revealed that, in the context of this study, the influence of the interface width coefficient was greater than that of the interface height, and the impact of the cavity width was greater than that of the cavity height. The relationship between changes in air age and variations in geometric parameters was similar to that of indoor temperature.

关键词： Building spatial structure building optimization multi-objective optimization algorithm ventilation performance thermal comfort

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Active-Isolation Improvement of Shared-Aperture Beam-Scanning Array Based on Excitation optimization Techniques

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第2期23卷 808-812页

作者： Hou, Hui Ying Cheng, Yu Jian Univ Elect Sci & Technol China Sch Elect Sci & Engn EHF Key Lab Fundamental Sci Chengdu 611731 Peoples R China

In this letter, a methodology based on the excitation optimization technique is proposed to increase the array isolation in a shared-aperture beam-scanning array. A multiple-constraint active isolation model is proposed to comprehensively reflect the isolation between the transmitting and receiving arrays. The first constraint condition in such a model ensures that the receiving channels are not this letter, a oversaturated by the energy coupling from the transmitting array. The second constraint condition ensures that the receiving signal is large enough to suppress noise interference. By only optimizing the excitation of array elements while preserving the radiation performance, the active cancellation can be implemented to improve the hybrid active isolation. To validate this method, a K/Ka dual-band dual-circular-polarized shared-aperture beam-scanning array is designed to achieve the active isolation better than 50 dB throughout the scanning range of +/- 50(degrees).

关键词： High isolation multi-objective optimization algorithm transmitting and receiving shared-aperture beam-scanning array

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An area autonomous routing protocol based on multi-objective optimization methods for field observation instrument network

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COMPUTER COMMUNICATIONS 2023年 197卷 71-86页

作者： Huo, Jiuyuan Lu, Shubin Yang, Jiguang Wang, Lei AL-Neshmi, Hamzah Murad Mohammed Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Lanzhou Ruizhiyuan Informat Technol co LTD Lanzhou 730070 Peoples R China Natl Cryosphere Desert Data Ctr NCDC Lanzhou 730070 Peoples R China

The cold and arid regions of China occupy a large proportion of the total land area, are rich in resources and have a prominent strategic position, but their fragile ecological environment seriously affects the information collection and lifecycle of the field observation instrument network (FOIN), which affects the in-depth research for cold and arid regions. To balance the energy consumption and improve performance of the FOIN, an area autonomous routing protocol based on multi-objective optimization methods for FOIN (FOI-MOC) was proposed Firstly, in the network preparation stage, the FOI-MOC algorithm calculates the number of optimal cluster heads, evenly partitions for FOIN, and allocates the number of regional cluster heads. Then, in the cluster establishment stage, the different objective functions are constructed based on the residual energy, distance, and density of nodes in their respective regions. multi-objective optimization algorithms, NSGAII and PSO are utilized to address the Pareto optimal solution set The Pareto optimal solution set is scored by dynamically assigning weights to each objective function through the entropy method, and final cluster heads are elected for each region. Finally, in the data transmission stage, single-hop transmission is adopted within clusters, and single-hop or multi-hop transmission is employed between clusters according to the distance between cluster head and base station. The experimental results indicate that the developed protocol based on multi-objective optimization methods can efficiently balance network energy consumption and prolong network lifecycle.

关键词： Field observation instrument network (FOIN) Routing protocol multi-objective optimization algorithm Pareto optimal solution set Entropy method

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Design and optimization of a body weight support system for lower-limb rehabilitation robots considering vibration characteristics

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STRUCTURAL AND multiDISCIPLINARY optimization 2023年第12期66卷 1-21页

作者： Li, Shuoyu Huang, Shuangyuan Huang, Li Shen, Hongyi Liu, Youwei Xie, Longhan South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 511442 Peoples R China Sun Yat Sem Univ Lingnan Hosp Affiliated Hosp 3 Guangzhou 510000 Peoples R China

The structural characteristics of a Body Weight Support System (BWSS) significantly influence the rehabilitation effect and control system of a lower-limb robot. This study considers vibration characteristics to design and optimize a BWSS. First, the design principles of the BWSS are formulated, and the force of the system during the rehabilitation training process is analyzed. Second, the BWSS optimization model is established by considering the vibration characteristics, including the design object, selected materials, design variables, optimization objective functions and constraints in static and dynamic analyses. Third, a hybrid multi-objective optimization method combining the finite element method, Kriging metamodel and multi-objective Genetic algorithm (MOGA) is proposed. After performing optimization, the Kriging metamodel is discussed, and the final design parameters of a single-arm support structure are obtained by the MOGA. Finally, different MOGA parameters and the screening algorithm are compared and analyzed. Simultaneously, the experimental vibration acceleration data and the shock data of the actual original rehabilitation robot are compared with the numerical results of the optimized design. The numerical results indicate that the proposed hybrid multi-objective optimization method is reliable in terms of designing the BWSS, and the vibration characteristics of the designed BWSS are improved over those of the actual original BWSS.

关键词： Rehabilitation robot Body weight support system Design and optimization multi-objective optimization algorithm Kriging metamodel Vibration characteristics

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Fuzzy clustering optimal k selection method based on multi-objective optimization

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SOFT COMPUTING 2023年第3期27卷 1289-1301页

作者： Wang, Lisong Cui, Guonan Cai, Xinye Nanjing Univ Aeronaut & Astronaut 29Th Jiangjun Ave Nanjing Jiangsu Peoples R China

Because of the complexity of data sets from the real world, it is difficult to classify the data sets clearly and effectively, thus we prefer to adopt fuzzy clustering approaches to analyze the data sets. However, due to the variety of fuzzy clustering algorithms, the different number of clusters will lead to different clustering results. The number of clusters is closely related to the clustering division, so how to determine the number of fuzzy clustering (k ) has become a problem. Until now, many researchers have proposed utilizing fuzzy clustering validity indexes to deal with this kind of problem. However, the effectiveness index of fuzzy clustering can only be evaluated on the basis of the fuzzy clustering algorithm FCM to divide the clusters. When the range of k value is too large, FCM's clustering for different k values is quite time-consuming. From this perspective, this paper proposes a fuzzy clustering optimal k selection method based on multi-objective optimization (FMOEA-K). Different from the traditional methods, this method combines the fuzzy clustering effectiveness index with multi-objective optimization algorithm (MOEA), and uses multi-objective optimization algorithm to search the appropriate cluster center concurrently. Because of the concurrency of the multi-objective optimization algorithm, the calculation time is shortened. The experimental results show that compared with the traditional method, the FMOEA-K can shorten the calculation time and improve the accuracy of calculating the optimal k value.

关键词： Clustering validity index Fuzzy clustering multi-objective optimization algorithm The number of clustering

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Artificial intelligence for sustainable development of smart cities and urban land-use management

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地球空间信息科学学报（英文版） 2024年第4期27卷 1212-1236页

作者： Zohreh Masoumi John van Genderen Department of Earth Sciences Institute for Advanced Studies in Basic Sciences(IASBS)ZanjanIran Center for Research in Climate Change and Global Warming(CRCC) Institute for Advanced Studies in Basic Sciences(IASBS)ZanjanIran Department of Earth Observation Science Faculty of Geoinformation Science and Earth Observation(ITC)University of TwenteEnschedeThe Netherlands

The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban *** problem is an NP(nondeterministic polynomial time)-hard problem because of involving many objective functions,many constraints,and complex search ***,this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of *** types ofmulti-objective optimization algorithms(MOOAs)based on Artificial Intelligence(AI)have been frequently employed,but their ability and performance have not been evaluated and compared *** paper aims to employ and compare three commonly used MOOAs ***-Ⅱ,MOPSO,and MOEA/D in urban land-use allocation *** algorithms belong to different categories of MOOAs family to investigate their advantage and *** objective functions of this study are compatibility,dependency,suitability,and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban *** of results is based on the dispersion of the solutions,diversity of the solutions'space,and comparing the number of dominant solutions in *** results showed that all three algorithms improved the objective functions related to the current arrangement of the ***,the run time of NSGA-Ⅱ is the worst,related to the Diversity Metric(DM)which represents the regularity of the distance between solutions at the highest ***,MOPSO provides the best Scattering Diversity Metric(SDM)which shows the diversity of solutions in the solution ***,In terms of algorithm execution time,MOEA/D performed better than the other ***,Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use manage-ment problems.

关键词： Urban land-use management geo-spatial information sciences multi-objective optimization algorithm smart cities artificial intelligence

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