With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, th...
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With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.
Wind power is one of the widely used renewable resources and it is connected to power system steadily. In recent years, wind power is developed in the form of large-scale wind farm at offshore, which is composed of do...
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Wind power is one of the widely used renewable resources and it is connected to power system steadily. In recent years, wind power is developed in the form of large-scale wind farm at offshore, which is composed of dozens or hundreds of wind turbines. In inner grid of wind farm, wind turbines are connected to each other through cable, and there are a wide variety of configurations depending on how to connect wind turbines. Due to difficult and expensive construction activity at sea, the problem to connect optimally wind turbines is very important. In order to solve the problem, this paper introduces a methodology based on the k-clustering algorithm, minimum spanning tree(MSP) algorithm and local search method. k-clustering is applied to divide wind turbines into k-groups, and MSP algorithm is used to link wind turbines in each group with the objective that total length of cables is minimized. Optimal configuration is determined by local search method which explores diverse combinations depending on the number of groups and the number of wind turbines in each group. The case studies show that the proposed methodology can be utilized usefully for designing inner grid of offshore wind farm.
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