A Low-Carbon Generation Expansion Planning Model Considering Carbon Trading and Green Certificate Transaction Mechanisms

被引:12
|
作者
Huang, Yuansheng [1 ]
Hu, Jianjun [1 ]
Yang, Yingqi [1 ]
Yang, Lei [1 ]
Liu, Shijian [1 ]
机构
[1] North China Elect Power Univ, Dept Econ & Management, Baoding, Peoples R China
来源
关键词
generation expansion planning; carbon trading mechanism; green certificate transaction mechanism; fuzzy chance constraints; brain storm optimization; PARTICLE SWARM OPTIMIZATION; SYSTEM DYNAMICS; POWER-SYSTEM; PLACEMENT; MARKET; COST;
D O I
10.15244/pjoes/106025
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Generation expansion planning for more renewable energy is of great significance to the implementation of low-carbon economy and energy transition in the power sector. This paper introduces two widely used renewable energy incentives (such as carbon trading mechanism and green certificate transaction mechanism) into traditional generation expansion planning, and establishes a low-carbon generation expansion planning model. Then the brain storm optimization algorithm was employed to solve the model. Finally, for the comparison between the two mechanisms, this paper sets four scenarios for case simulation. The results show that both carbon trading mechanisms and green certificate transaction mechanisms can increase the installed capacity of renewable energy and reduce carbon emissions, and the optimization effect of green certificate transaction mechanism on planning results is better than that of the carbon trading mechanism. When both mechanisms are introduced, the installed proportion of renewable energy will be the highest and carbon emissions will achieve the minimum. Moreover, with the increase of carbon price or green certificate price, and the strengthening of carbon emission constraint or renewable energy quota constraint, the proportion of coal-fired units in the power supply structure is gradually decreasing, and the carbon emissions of the system are gradually reduced.
引用
收藏
页码:1169 / 1183
页数:15
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