Optimization of China's Electricity Market Allocation Under the "Paris Agreement"
- DOI
- 10.2991/jahp-19.2019.101How to use a DOI?
- Keywords
- carbon emission reduction; configuration optimization; optimal compromise solution
- Abstract
China's carbon emission of coal-fired thermal power generation has accounted for nearly 40% of the country's carbon emissions. According to the "Paris Agreement", reducing carbon emissions from power generation has become a thorny issue in China. In addition to power generation technology transformation, the optimization of China's electricity market configuration is an important means to solve this problem. Aiming at carbon emission reduction and economic dispatching, this paper constructs a cross-regional power generation optimization configuration model in China to find the optimal compromise solution that meets the technical conditions. Studies have shown that within the feasible domain, stringent target setting can increase the benefits of carbon emission reduction and scheduling optimization. When the carbon emission reduction limit is close to 9%, carbon emission reduction will reach 331 million tons, and the efficiency of dispatch optimization will reach 1.395 billion yuan. This paper has certain reference value for the scientific guidance and supervision of power generation by government officials, regulatory authorities and related enterprises.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kun Xiao AU - Yi Wang AU - Jingdong Zhang PY - 2019/09 DA - 2019/09 TI - Optimization of China's Electricity Market Allocation Under the "Paris Agreement" BT - Proceedings of the 4th International Conference on Economy, Judicature, Administration and Humanitarian Projects (JAHP 2019) PB - Atlantis Press SP - 484 EP - 492 SN - 2352-5428 UR - https://doi.org/10.2991/jahp-19.2019.101 DO - 10.2991/jahp-19.2019.101 ID - Xiao2019/09 ER -