基于云存储和物联网基础设施优化网络物理能源系统的资源分配

Zhiqing Bai, Caizhong Li, Javad Pourzamani, Xuan Yang, Dejuan Li
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引用次数: 0

摘要

鉴于电力系统中存在禁止运行区、损耗和阀点效应,此类系统中的能源优化分析包括大量非凸和非平滑参数,如经济调度问题。此外,本文还考虑了多燃料发电机和输电损耗,以便将所有可能的情况都纳入经济调度问题。然而,从非凸的角度来看,这些特征使得经济调度问题变得更加复杂。为了解决作为电力系统重要考虑因素的经济调度问题,本文提出了一种改进的鲁棒、有效的优化算法。考虑到多种燃料、阀点效应、大规模系统、禁止运行区和输电损耗等因素,本文对算法进行了一些修改,以解决此类复杂问题并找到最佳解决方案。此外,提出的优化算法还分析了一些复杂的电力系统,包括 6 台、13 台和 40 台使用一种燃料的发电机、10 台使用多种燃料的发电机,以及由 80 台和 120 台发电机组成的两个大型系统。本文还评估了所提方法在准确性、鲁棒性和收敛速度方面的有效性。此外,本文还探讨了云存储和物联网(IoT)的整合,以增强所提方法在处理各种发电机数量和约束条件下的非凸能源资源管理和分配问题时的监控能力的适应性。结果表明,无论发电机数量和约束条件如何,所提出的算法都能解决非凸能源资源管理和分配问题。根据所获得的结果,无论是小型系统还是大型系统,所提出的方法都能提供良好的结果。例如,对于有损耗和无损耗的 6 个发电厂系统,所提出的方法总能获得最佳结果,分别为 15 276.894 美元和 15 443.7967 美元。此外,该方法的改进使得多燃料发电厂的经济调度问题不仅能以最优结果(623.83 美元)求解,而且迭代次数少于 35 次。最后,在由 40 个发电厂组成的系统中,最佳结果(121,412 美元)与最差结果(121,316.1992 美元)之间的差额仅为 4 美元左右,这是可以接受的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure
Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features make economic dispatch problems more complex from a non-convexity standpoint. In order to solve economic dispatch problems as an important consideration in power systems, this paper presents a modified robust, and effective optimization algorithm. Here, some modifications are carried out to tackle such a sophisticated problem and find the best solution, considering multiple fuels, valve point effect, large-scale systems, prohibited operating zones, and transmission losses. Moreover, a few complicated power systems including 6, 13, and 40 generators which are fed by one type of fuel, 10 generators with multiple fuels, and two large-scale cases comprised of 80 and 120 generators are analyzed by the proposed optimization algorithm. The effectiveness of the proposed method, in terms of accuracy, robustness, and convergence speed is evaluated, as well. Furthermore, this paper explores the integration of cloud storage and internet of things (IoT) to augment the adaptability of monitoring capabilities of the proposed method in handling non-convex energy resource management and allocation problems across various generator quantities and constraints. The results show the capability of the proposed algorithm for solving non-convex energy resource management and allocation problems irrespective of the number of generators and constraints. Based on the obtained results, the proposed method provides good results for both small and large systems. The proposed method, for example, always yields the best results for the system of 6 power plants with and without losses, which are $15,276.894 and $15,443.7967. Moreover, the improvements made in the proposed method have allowed the economic dispatch problem regarding multi-fuel power plants to be solved not only with optimal results ($623.83) but also in less than 35 iterations. Lastly, the difference between the best-obtained results ($121,412) and the worst-obtained results ($121,316.1992) for the system of 40 power plants is only about $4 which is quite acceptable.
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