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multi-objective particle swarm optimization for optimal scheduling of household microgrids

FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Huang, Yu He, Gengsheng Pu, Zengxin Zhang, Ying Luo, Qing Ding, Chao Elect Power Res Inst Guizhou Power Grid Co Ltd Guiyang Peoples R China China Southern Power Grid Energy Dev Res Inst Guangzhou Peoples R China Guizhou Power Grid Co Ltd Duyun Power Supply Bur Duyun Peoples R China

Addressing the challenge of household loads and the concentrated power consumption of electric vehicles during periods of low electricity prices is critical to mitigate impacts on the utility grid. In this study, we propose a multi-objective particle swarm algorithm-based optimal scheduling method for household microgrids. A household microgrid optimization model is formulated, taking into account time-sharing tariffs and users' travel patterns with electric vehicles. The model focuses on optimizing daily household electricity costs and minimizing grid-side energy supply variances. Specifically, the mathematical model incorporates the actual input and output power of each distributed energy source within the microgrid as optimization variables. Furthermore, it integrates an analysis of capacity variations for energy storage batteries and electric vehicle batteries. Through arithmetic simulation within the Pareto optimal solution set, the model identifies the optimal solution that effectively mitigates fluctuations in energy input and output on the utility side. Simulation results confirm the effectiveness of this strategy in reducing daily household electricity costs. The proposed optimization approach not only improves the overall quality of electricity consumption but also demonstrates its economic and practical feasibility, highlighting its potential for broader application and impact.

关键词： multi-objective particle swarm algorithm household microgrid optimization distributed energy economic effectiveness

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Conductor Arrangement and Phase Sequence Optimization Scheme for 500 kV Four-Circuit Transmission Lines on Same Tower

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Energy Engineering 2023年第10期120卷 2287-2306页

作者： Deng Lu Xujun Lang Bo Yang Ziyang Li Hang Geng Power Grid Design and Research Institute Shandong Electric Power Engineering Consulting Institute Co.Ltd.Jinan250012China School of Electrical Engineering Shandong UniversityJinan250061China

The four-circuit parallel line on the same tower effectively solves the problems faced by the line reconstruction and construction under the condition of the increasing shortage of transmission *** the conductor and phase sequence arrangement of multiple transmission lines is conducive to improving electromagnetic and electrostatic coupling caused by electromagnetic *** paper uses the ATP-EMTP simulation software to build a 500 kV multi-circuit transmission line on the same *** stimulates the induced voltage and current values of different line lengths,tower spacing,vertical and horizontal spacing between different circuits,phase sequence arrangement,and nominal tower ***,use the BP neural network optimized by a genetic algorithm to predict the induced voltage and current under the unknown conductor and phase sequence ***,based on multi-objective particle swarm algorithm to construct the optimization model of conductor arrangement scheme of overhead transmission line,combined with electromagnetic environment control index,determine the optimal conductor arrangement scheme by the size of particle fitness function,a significant reduction in induced voltages and currents between transmission lines and the four-circuit conductor layout scheme meeting the requirements of the electromagnetic environment is obtained,which provides a reference for the tower design of the transmission station project.

关键词： Spatial arrangement phase sequence arrangement genetic algorithm BP neural network multi-objective particle swarm algorithm

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multi-objective Optimal Dispatch of multi-energy Complementary Systems Considering Environmental Factors 43

Multi-objective Optimal Dispatch of Multi-energy Complementa...

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43rd Chinese Control Conference (CCC)

作者： Chen, Rui Yao, Yubin Du, Jianwei Yang, Fan Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

In this paper, a day-ahead economic and environmental dispatching model for multi-energy complementary system composed of thermal power, wind and photovoltaic power, and pumped storage is established. The objective functions are the smallest net load fluctuation and the lowest comprehensive cost. In this model, the environmental cost is considered in comprehensive cost which includes the cost of SO2, NOx emission, the operation cost of desulfurization and denitrification, and carbon trading. An improved multi-objective particle swarm algorithm which can accelerate the convergence speed and also avoid the emergence of local optimums is presented to solve the dispatch model, and finally, by analyzing the dispatching results, it can effectively reduce the comprehensive cost of the system and decrease the net load fluctuation while reducing the pollutant emission, which verifies the effectiveness of the dispatch strategy and the improved algorithm.

关键词： Optimal Dispatch Desulfurization and Denitrification Carbon Trading multi-objective particle swarm algorithm

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Operation optimization of ventilated floor heating system in nearly-zero-energy building

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JOURNAL OF BUILDING ENGINEERING 2024年 86卷

作者： Guo, Jiwei Wang, Fenggang Jiang, Yiqiang Sun, Chen Harbin Inst Technol Sch Architecture & Design Harbin Peoples R China Minist Ind & Informat Technol Key Lab Cold Reg Urban & Rural Human Settlement En Harbin Peoples R China Imperial Coll London London England Univ Bath Bath England

As the share of renewable energy sources increases and building energy systems become more complex, optimizing system operation is necessary to fully improve economic, environmental, renewable penetration and thermal comfort performance. Most current studies focus on singleobjective or multi-objective optimization of building systems, but the impact of different objectives and their combinations on operation is unclear. In this study, optimal schedules for a ventilated heating floor in a nearly-zero-energy building were determined using an improved particle swarm optimization algorithm coupled with prior knowledge-based search accelerating strategy. System performance was investigated under different combinations of optimization objectives, including reducing operational costs and carbon emissions, increasing wind power penetration, and improving thermal comfort. The results show that under the influence of prior knowledge, stochastic algorithm converges quickly with reasonable results even when the solution space is large. The performance of some single-objective optimization cases is unsatisfactory due to the exclusion of some control variables. In contrast, multi-objective optimization yields optimization problems better, which finds better dynamic operation status and leads to better system performance. Specifically, co-optimization of operational cost, wind power penetration and environmental cost together produces the most reasonable solution.

关键词： Dynamic optimization multi-objective particle swarm algorithm Prior knowledge-based search accelerating strategy Energy flexible building

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Nanobiosensing from an In Vivo multi-objective Optimization Perspective 22

Nanobiosensing from an In Vivo Multi-objective Optimization ...

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IEEE 22nd International Conference on Nanotechnology (NANO)

作者： Jiang, Zhaoyang Shi, Shaolong Chen, Yifan Yao, Shuaiting Univ Elect Sci & Technol China Sch Life Sci & Technol Chengdu Peoples R China Univ Elect Sci & Technol China Yangtze Delta Region Inst Quzhou Quzhou Peoples R China

ISBN: (数字)9781665452250

ISBN: (纸本)9781665452250

In our study, due to the inefficiency of conventional medical imaging techniques for early tumor detection, we use an external magnetic field to guide nanoswimmers in high-risk tissue region to move through the biological gradient field (BGF) induced by tumor tissue and search for the tumor location. Since a tumor can generate multiple BGFs, we propose a novel framework named in vivo multi-objective optimization by viewing the BGFs as optimizable objective functions. Furthermore, we propose two computing techniques based on the traditional particle swarm optimization to solve this in vivo computational problem. Some simulation experiments are carried out by comparing the proposed techniques to the traditional brute-force search. The results show that both of the proposed techniques perform better than the brute-force search method for nanobiosensing.

关键词： cancer detection nanoswimmer swarm intelligence multi-objective particle swarm algorithm

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Research on Bionic Hierarchical Optimization of Wing Based on PLSR and PSO

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MECHANIKA 2023年第5期29卷 405-413页

作者： Zhang, Xiaoxin Wang, Qi Nanchang Hangkong Univ Coll Aircraft Engn Nanchang 330063 Peoples R China

The layout of the wing's internal structure not only dramatically influences the wing's strength stiffness but also directly affects the aerodynamic characteristics of the aircraft. Based on the original wing structure, a more flexible spatial design layout to achieve improved overall structural load-bearing performance, and a reasonable structural lightweight design are the research priorities to be considered for the development of future aircraft. Therefore, this paper attempted to design and analyze a lightweight airfoil that meets the performance requirements. Combining the strategy of hierarchical optimization design with the advantages of engineering bionics, the diatom Arachnoidiscus bionic structure, topological optimization, partial least squares regression (PLSR), and multi-objective particle swarm algorithm (PSO) are applied to optimize the placement and size of wing's internal components. The simulation results show that the weight of the optimized wing structure is reduced by 6% while satisfying the requirements of maximum stress and maximum deformation.

关键词： hierarchical optimization engineering bionics wing partial least squares regression multi-objective particle swarm algorithm

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multi-objective Optimal Dispatch of multi-energy Complementary Systems Considering Environmental Factors

Multi-objective Optimal Dispatch of Multi-energy Complementa...

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第43届中国控制会议

作者： Rui Chen Yubin Yao Jianwei Du Fan Yang School of Marine Electrical Engineering Dalian Maritime University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In this paper, a day-ahead economic and environmental dispatching model for multi-energy complementary system composed of thermal power, wind and photovoltaic power, and pumped storage is established. The objective functions are the smallest net load fluctuation and the lowest comprehensive cost. In this model, the environmental cost is considered in comprehensive cost which includes the cost of SO, NO emission, the operation cost of desulfurization and denitrification, and carbon trading. An improved multi-objective particle swarm algorithm which can accelerate the convergence speed and also avoid the emergence of local optimums is presented to solve the dispatch model, and finally, by analyzing the dispatching results,it can effectively reduce the comprehensive cost of the system and decrease the net load fluctuation while reducing the pollutant emission, which verifies the effectiveness of the dispatch strategy and the improved algorithm.

关键词： Optimal Dispatch Desulfurization and Denitrification Carbon Trading multi-objective particle swarm algorithm

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Optimization of energy ratio, benefit to cost and greenhouses gasses using metaheuristic techniques (genetic and particular swarm algorithms) and data envelopment analysis: Recommendations for mitigation of inputs consumption (a case crop: edible onion)

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2022年第6期41卷

作者： Elhami, Behzad Raeini, Mahmoud Ghasemi Nejad Taki, Morteza Marzban, Afshin Heidarisoltanabadi, Mohsen Agr Sci & Nat Resources Univ Khuzestan Dept Agr Machinery & Mechanizat Engn Mollasani Iran AREEO Agr Engn Res Dept Isfahan Agr & Nat Resources Res & Educ Ctr Esfahan Iran

The present study aimed to optimize the energy, economic, and greenhouse effects indices for edible onion production in Fereydan (85 growers), and Buin (15 growers) regions in Isfahan. Required data were collected through direct measurement, interview, and questionnaire. Total energy input was obtained before and after optimization using data envelopment analysis (DEA), multi-objective genetic algorithm (MOGA), and multi-objective particle swarm algorithm (MOPSA), and was determined as 236,335, 213,068 (8.75% saving), 125,663 (48.63% saving), and 320,657 MJ ha(-1) (35.67%), respectively. In addition, energy ratio (ER) was improved from 0.79 to 0.84, 1.81, and 0.8 using DEA, MOGA, and MOPSA, respectively. By reducing the production costs by 10.2%, 63.12%, and 29.4% using DEA, MOGA, and MOPSA, respectively, benefit-to-cost ratio (BCR) index was improved from 1.44 to 1.88, 5.21, and 1.94, respectively. Also results showed that GHG emissions were mitigated by 18.39%, 47.56%, and 27.94%, respectively. Based on the findings, MOGA showed better performance compared to DEA, and MOPSA in terms of ER, BCR, and GHG factors. Despite applying metaheuristic, and artificial intelligence methods by MOPSA, this algorithm was not able to optimize the target factors, and applied the energy, and production costs in negative status (in reverse).

关键词： data envelopment analysis multi-objective genetic algorithm multi-objective particle swarm algorithm transplanting onion

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multi-objective Ecological Operation Model for Reservoirs and Its Application 2

Multi-Objective Ecological Operation Model for Reservoirs an...

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2nd International Conference on Renewable Energy and Environmental Technology (REET)

作者： Liu, Lina Liu, Weilin Nanchang Inst Technol Jiangxi Prov Key Lab Hydrol Water Resources & Wat Nanchang 330099 Jiangxi Peoples R China

ISBN: (纸本)9783038352877

Traditional reservoir operation is mainly for flood control and promoting benefits, which ecological factors are not fully considered in. This would probably lead to degradation of river ecosystem. In order to alleviate the influence of reservoirs on river ecosystem, a multi-objective ecological operation model for reservoirs was established. Then, a novel approach was proposed to solve the multi-objective model, which combined a multi-objective particle swarm optimization (MOPSO) algorithm with the non-dominated solutions evaluation approach. MOPSO algorithm was introduced to generate a set of Pareto-optimal solutions, while the non-dominated solutions evaluation approach, based on k-means clustering and the information entropy method, was adopted to provide an alternative sequence for a set of Pareto-optimal solutions. Finally, the proposed approach was applied to the ecological operation of the reservoirs at the main stream of Xiuhe river in Poyang Lake basin in China. The results show that the proposed approach is able to offer the quantifiable benefits or costs among different objectives for the reservoir operators, and can provide a useful tool for decision makers to solve multi-objective water resources and hydrology problems.

关键词： Reservoirs Ecological operation multi-objective particle swarm algorithm Information entropy

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Inductive Reactive Power Compensation Optimization for 10kV Distribution Network With Distributed Generations

Inductive Reactive Power Compensation Optimization for 10kV ...

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Chinese Automation Congress (CAC)

作者： Lin, Xiyi Yao, Jiangang Mao, Tian Gong, Lei Hunan Univ Elect & Informat Engn Dept Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9781479903337

Massive distributed generations (DGs), e. g., the small hydropower stations, accessing to the regional power network would result in the shortage of inductive reactive power, which in turn would cause the high voltage issue. An inductive reactive power optimization configuration method towards the 10kV distribution network with DGs is proposed. The methodology was conducted with the minimum of the power loss and the minimum of the voltage deviation as its objectives, and the two objectives were computed by utilizing the multi-objective particle swarm algorithm to obtain the Pareto optimal solutions. Then the fuzzy decision-making approaches are used to get the compromise solution so as to determine the reactive power compensation capacity. An actual regional network is simulated through the Matlab software, and the compared simulation results certify that the proposed method not only has a good voltage regulation effect, but also has the economy superiority in terms of reducing the total amount of the inductive reactive power compensation.

关键词： distributed generations distribution network inductive reactive power compensation multi-objective particle swarm algorithm voltage quality

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