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 p...
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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.
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 p...
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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.
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 fu...
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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.
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 ...
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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 particleswarm 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.
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 biolog...
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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 particleswarm 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.
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 ...
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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.
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 fu...
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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.
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 thr...
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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).
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 allevia...
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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-objectiveparticleswarm 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.
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...
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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.
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