Open AccessAn energy internet efficient regional optimal allocation method based on block data
Author(s) -
Lixin Hou,
Li Jin,
Yusen Zhang,
Furong Huang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/546/2/022008
Subject(s) - computer science , block (permutation group theory) , the internet , energy (signal processing) , mathematical optimization , tree traversal , stability (learning theory) , efficient energy use , distributed computing , algorithm , engineering , mathematics , statistics , geometry , machine learning , world wide web , electrical engineering
In the operation of energy internet, the problem of uneven regional energy distribution which caused by the different social, economic, geographical and environmental factors has existed for a long time, and has become the most complex and intractable problem to solve at the Multi-space energy allocation. To alleviate the problems of uneven regional energy distribution, combined with the rapid development of block data, in this paper, an energy internet efficient regional optimal allocation method based on block data is explicitly proposed. Especially, by means of considering the difference of social, economic, geographical and environmental factors in each region, a concept of dynamic spatial energy allocation adjustment is proposed in this paper. Energy utilization rates and a new objective functional is proposed to support regional energy allocation strategy which adjusting dynamically to optimize the energy utilization ratio when the regional energy allocation is found to be unreasonable. The traversal algorithm method is applied in the strategy to ensure the comprehensiveness and stability of adjustment scheme selection in practical application. A fractional steps computing solution that simultaneously emphasizes the insufficient computational capabilities of hardware and out of memory error is implemented to deal with the huge amount of block data caused by complex energy internet. As a result, a promising approach is introduced for the energy internet efficient regional optimal allocation adjustment.