ORIGINAL RESEARCH
Research on the Evaluation and Prediction of the Prefabricated Cabin Substations Carbon Footprint Based on Life Cycle Theory and an Extreme Learning Machine
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1
Guangzhou Power Supply Bureau of Guangdong Grid Co, GuangZhou 510600, China
 
2
Central Southern China Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Wuhan 430064, China
 
 
Submission date: 2024-08-19
 
 
Final revision date: 2024-09-23
 
 
Acceptance date: 2024-10-13
 
 
Online publication date: 2025-03-05
 
 
Corresponding author
Junjian Zhang   

Central Southern China Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Wuhan 430064, China
 
 
 
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ABSTRACT
Prefabricated cabin substations, as a new type of substation, have advantages such as saving investment, a short construction period, and low-carbon environmental protection. They are the mainstream development trend of low-carbon substations in the future and play an important role in the construction of new power systems, especially in the development of urban power grids. This article systematically conducted carbon footprint tracing, analysis, calculation, and evaluation of prefabricated substations during the planning, construction, operation, and scrapping stages, forming a carbon footprint accounting method for the entire life cycle of prefabricated substations. The research results show that the carbon footprint of the construction and operation stages accounts for more than 90% of the carbon footprint of a prefabricated substation throughout its life cycle. Material carbon footprint, SF6 carbon footprint, and station electricity carbon footprint are important components of the substation’s carbon footprint. Green plants and lawns can effectively reduce the carbon emissions of prefabricated substations and have a positive effect on controlling carbon footprints. Suggestions were put forward to reduce the carbon footprint of prefabricated cabin substations in the areas of equipment replacement, energy conservation and consumption reduction, operation monitoring, and optimization. Simultaneously, carbon footprint prediction models for substations based on the bat algorithm (BA) and extreme learning machine (ELM) were constructed to accurately predict the carbon footprint of substations at various stages, providing a reference for the design selection, scheme optimization, energy-saving, and carbon reduction transformation of prefabricated cabin substations.
eISSN:2083-5906
ISSN:1230-1485
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