To implement demand response in residential sector and facilitate the integration of renewable resources and plug-in electric vehicles in future smart grid, this paper proposes a framework of home energy management sy...
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ISBN:
(纸本)9781479987306
To implement demand response in residential sector and facilitate the integration of renewable resources and plug-in electric vehicles in future smart grid, this paper proposes a framework of home energy management system (HEMS) and a optimization algorithm for it based on improved artificial bee colony. The algorithm schedules the operations of schedulable home appliances according to electricity price, forecasted outdoor temperature and renewable power output, and user preferences to minimize user's electricity cost. The effectiveness of the algorithm is verified by simulations, and the electricity cost can be reduced by 47.76%.
After the reference of straight line stability control strategy of four-wheel drive vehicle, this paper proposes a control algorithm combining the sliding mode variable structure and optimization control method. The c...
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After the reference of straight line stability control strategy of four-wheel drive vehicle, this paper proposes a control algorithm combining the sliding mode variable structure and optimization control method. The control algorithm is mainly divided into the upper generalized moment calculation based on the sliding mode variable structure controller and the lower torque distribution controller based on the optimization algorithm, also including the slip rates controller based on PID algorithm to ensure the straight line stability control. This paper establishes the combined model based on the CarSim and MATLAB, and tests to verify the validation of the control strategy through the four-wheel drive vehicle test-bed based on RT_LAB. The simulation and experimental results show that when the tire-road friction coefficient is low, the control strategy can not only make the vehicle tire slip rates stay near the optimal slip ratio, at the same time through the yawing moment adjustment, ensure the yaw angle of vehicle not beyond 0.5 deg/s, so the method can effectively ensure the straight line stability of four wheel drive vehicle.
In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied...
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In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.
High-entropy alloys (HEAs) have emerged as promising candidates for laser cladding applications, sparking considerable interest in their design. Phase prediction of HEA coatings presents a formidable challenge due to ...
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High-entropy alloys (HEAs) have emerged as promising candidates for laser cladding applications, sparking considerable interest in their design. Phase prediction of HEA coatings presents a formidable challenge due to the non-equilibrium solidification characteristics inherent in laser cladding and the intricate influence of substrate variables. This study employs machine learning (ML), leveraging a dataset of 513 HEAs, to explore phase formation and stability in coatings. The dilution rate and carbon equivalent are introduced as input features that encapsulate the processing nuances of laser cladding. Simulated annealing algorithm and over-sampling method are used to optimize the ML model. The predicted outcomes for 10 unexplored HEA phases closely align with experimental results from laser cladding experiments. Utilizing the SHAP (Shapley Additional Explanations) model, the importance of input features is elucidated, identifying valence electron concentration (VEC) as the most influential. Data analysis further probes the solid solution formation capability of HEA coatings, establishing the following criteria: for single BCC phase, VEC is less than 7.4 and delta is less than 7.63;for single FCC phase, VEC is greater than 7.5 and delta is less than 7.46;and for dual-phase BCC + FCC structure, VEC ranges from 7.2 to 7.8 and delta spans 2.5 to 8.24.
The offshore wind power sector has witnessed exponential growth over the past decade, with large-scale offshore wind farms grappling with the challenge of elevated construction and maintenance expenses. Given that the...
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The offshore wind power sector has witnessed exponential growth over the past decade, with large-scale offshore wind farms grappling with the challenge of elevated construction and maintenance expenses. Given that the collector system constitutes a substantial part of the investment cost in wind farms, the design and optimization of this system are pivotal to enhancing the economic viability of offshore wind farms. A thorough examination of collector system design and optimization methodologies is essential to elucidate the critical aspects of collector system design and to assess the comparative merits and drawbacks of various optimization techniques, thereby facilitating the development of collector systems that offer superior economic performance and heightened reliability. This paper conducts a review of the evolving trends in collector system research, with a particular emphasis on topology optimization models and algorithms. It juxtaposes the economic and reliability aspects of collector systems with varying topologies and voltage levels. Building on this foundation, the paper delves into the optimization objectives and variables within optimization models. Furthermore, it provides a comprehensive overview and synthesis of AI-driven optimization algorithms employed to address the optimization challenges inherent in offshore wind farm collector systems. The paper concludes by summarizing the existing research limitations pertaining to offshore wind farm collector systems and proposes innovative directions for future investigative endeavors. The overarching goal of this paper is to enhance the comprehension of offshore wind farm collector system design and optimization through a systematic analysis, thereby fostering the continued advancement of offshore wind power technology.
Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorit...
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Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm. (C) 2015 Elsevier B.V. All rights reserved.
Currently, the design of the cold chain system for the HPR1000 nuclear island does not take a unified approach and largely relies on the design experience of the respective designers. The various subsystems contain nu...
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Currently, the design of the cold chain system for the HPR1000 nuclear island does not take a unified approach and largely relies on the design experience of the respective designers. The various subsystems contain numerous devices, and the cumulative design margins often result in many unreasonable parameters in the original design scheme of the cold chain system. To address this issue, this paper establishes a mathematical model for the cold chain system to assist in its design. Based on genetic algorithms and the simplex algorithm, adaptive relaxation constraint dominance relations and two improved NSGA-II multi-objective handling methods are introduced, leading to the development of a new hybrid multi-objective genetic algorithm. The performance of this algorithm is verified using optimized benchmark testing functions, thus allowing for the scientific optimization of the cold chain system design scheme. A sensitivity analysis is conducted on the design parameters of the ventilation system, refrigeration system, component cooling system, and seawater system within the cold chain system to explore the impact of these parameters on performance indicators. optimization design calculations for the cold chain system are performed under safe and feasible conditions, resulting in an optimization scheme. The results indicate that the developed algorithm is effective in addressing the complex optimization problems of the cold chain system, and the optimized cold chain system can reduce weight by up to 18.4 %, volume by up to 18.6 %, investment costs by up to 5.7%, and system energy consumption by up to 7.5 %.
Influence Maximization (IM) is a fundamental problem in social networks, aiming to identify a small set of seed nodes that maximize the spread of influence. Fair Influence Maximization (FIM) extends this concept by in...
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Influence Maximization (IM) is a fundamental problem in social networks, aiming to identify a small set of seed nodes that maximize the spread of influence. Fair Influence Maximization (FIM) extends this concept by incorporating fairness criteria, ensuring equitable influence distribution among diverse groups. Welfare fairness, a prominent fairness measure, strives to balance influence spread and fairness levels. However, optimizing welfare fairness objectives is computationally challenging and often limited to small-scale networks. While community structures are widely used to improve FIM performance, the overlapping nature of communities has been largely overlooked in the literature. This oversight can significantly impact the trade-off between computational complexity and fairness. To address these gaps, this study introduces Scalable FIM based on overlapping Communities and optimization algorithms (SFIMCO). SFIMCO considers the influence potential of nodes within communities by distinguishing between overlapping and non-overlapping nodes. It employs an efficient optimization algorithm to select the seed nodes, ensuring a scalable solution. Meanwhile, the objective function for welfare fairness in SFIMCO is formulated as maximizing the weighted exponential welfare across all overlapping communities, ensuring scalable approximation of fair influence. Experimental evaluations on real- world datasets demonstrate that SFIMCO outperforms state-of-the-art methods in both scalability and fairness. Notably, it achieves a 2.54% improvement in average influence spread compared to the best existing approach, highlighting its effectiveness in addressing the trade-offs between influence spread, fairness, and computational efficiency.
An algorithmic strategy to determine the minimal fusion area of a tibia pseudarthrosis to achieve mechanical stability is presented. For this purpose, a workflow capable for implementation into clinical routine workup...
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An algorithmic strategy to determine the minimal fusion area of a tibia pseudarthrosis to achieve mechanical stability is presented. For this purpose, a workflow capable for implementation into clinical routine workup of tibia pseudarthrosis was developed using visual computing algorithms for image segmentation, that is a coarsening protocol to reduce computational effort resulting in an individualized volume-mesh based on computed tomography data. An algorithm detecting the minimal amount of fracture union necessary to allow physiological loading without subjecting the implant to stresses and strains that might result in implant failure is developed. The feasibility of the algorithm in terms of computational effort is demonstrated. Numerical finite element simulations show that the minimal fusion area of a tibia pseudarthrosis can be less than 90% of the full circumferential area given a defined maximal von Mises stress in the implant of 80% of the total stress arising in a complete pseudarthrosis of the tibia. (C) 2015 Elsevier Ltd. All rights reserved.
The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sou...
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The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal
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