Distributed generation parameter optimization method based on fuzzy C-means clustering under the Internet of Things architecture
Distributed generation parameter optimization method based on fuzzy C-means clustering under the Internet of Things architecture
复制标题
物联网架构下基于模糊C均值聚类的分布式发电参数优化方法
DOI：
10.1016/j.egyr.2021.10.049
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发表时间：
2021-11
期刊：
影响因子：
5.2
通讯作者：
Ping Xin
中科院分区：
文献类型：
--
作者：
Xin Yao;Liyun Xing;Ping Xin
针对不同类型分布式电源在电网中协同工作时难以实现市场利润最大化的问题，提出了一种基于物联网和模糊C均值聚类的分布式电源参数优化方法。首先，建立了物联网体系结构下的网格分布式发电模型，并确定了其内部参数关系。其次，提出了以市场效益最大化为目标的分布式电源综合评价准则和约束条件。最后，采用基于物联网和模糊C均值聚类的分布式通信模型，实现了电网分布式发电参数的自适应优化。在IEEE标准测试系统上进行了实验仿真。结果表明，与传统的考虑发电侧收益的优化算法和仅考虑电能损耗的优化算法相比，该算法的总负荷和总收益分别提高了8.41%和14.66%。实验结果充分证明了该算法的有效性。
Aiming at the problem that it is difficult to maximize market profits when various types of distributed generation work together in the power grid, this paper proposes a distributed generation parameter optimization method based on Internet of Things and fuzzy C-means clustering. Firstly, a grid distributed generation model is established under the Internet of Things architecture, and its internal parameter relationships are determined correspondingly. Secondly, the comprehensive evaluation criteria and constraints of distributed generation are proposed for maximizing market benefits. Finally, the distributed communication model based on Internet of Things and fuzzy C-means clustering are adopted to achieve the adaptive optimization of grid distributed generation parameters. The experiment simulation is carried out with IEEE standard test system. The results show that the total load and total revenue of the proposed algorithm are increased by 8.41% and 14.66% respectively compared with the traditional optimization algorithm considering generation side benefits and the algorithm considering energy loss only. Experimental results fully demonstrate the effectiveness of proposed algorithm.
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