Open AccessRenewable Energy Generation and Load Classification based on H-K compound clustering algorithm
Author(s) -
Yang Sun,
Zhenyuan Li,
Dexin Li,
Chang Liu,
Shengyan Wang,
Fengkai Qiu,
Ming Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/5/052005
Subject(s) - cluster analysis , computer science , renewable energy , generator (circuit theory) , diesel generator , photovoltaic system , wind power , electricity generation , process (computing) , energy demand , power (physics) , algorithm , data mining , artificial intelligence , engineering , diesel fuel , automotive engineering , electrical engineering , physics , quantum mechanics , natural resource economics , economics , operating system
In this paper, the uncertainty of diesel generator (hereinafter referred to as DG) output and load demand is studied by multi scenario analysis. Firstly, this paper studies the timing characteristics of DG power generation and load demand, and describes the annual and daily distribution of photovoltaic power generation, wind power generation and load demand. Then it introduces the method of multi scene analysis, describes the process of dealing with the uncertainty in multi scene in detail, and generates enough scenes through the density function of classification probability to get the probability of each scene. Finally, H-K compound clustering algorithm is used to solve the above problems. The scale scene is compressed to get a typical “planning scene”.