LAPSE:2023.12599
Published Article
LAPSE:2023.12599
Optimizing Low-Carbon Pathway of China’s Power Supply Structure Using Model Predictive Control
Yue Ma, Xiaodong Chu
February 28, 2023
With the increasing severity of climate change, the power industry, as one of the main sources of carbon emissions, is playing an extremely important role in the process of low-carbon energy transformation. The purpose of this paper is to try to find a general method to solve the optimal path for the low-carbon evolution of the power supply structure so as to meet the challenges faced by the low-carbon transformation of the power industry in the future. This paper first uses the capacity coefficient index (CCI) to represent the power generation ability of different technologies and proposes a forecasting method for the CCI of renewable energy generation. In this paper, a two-layer optimization model considering multiple constraints is established and solved using the MPC method. The results show that China’s installed capacity of renewable power could account for more than 50% in 2030, while the carbon emissions will decrease after reaching a peak in 2023. On the premise of ensuring sufficient reserve adjustment capacity of thermal power units, increasing the proportion of renewable energy generation is an important way to realize emission reduction in the power industry.
Keywords
low-carbon transformation, Model Predictive Control, power industry, power supply structure
Suggested Citation
Ma Y, Chu X. Optimizing Low-Carbon Pathway of China’s Power Supply Structure Using Model Predictive Control. (2023). LAPSE:2023.12599
Author Affiliations
Ma Y: School of Electrical Engineering, Shandong University, 17923 Jingshi Road, Jinan 250061, China
Chu X: School of Electrical Engineering, Shandong University, 17923 Jingshi Road, Jinan 250061, China [ORCID]
Journal Name
Energies
Volume
15
Issue
12
First Page
4450
Year
2022
Publication Date
2022-06-18
Published Version
ISSN
1996-1073
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Original Submission
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PII: en15124450, Publication Type: Journal Article
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doi:10.3390/en15124450
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