Equilibrium Analysis for Electricity Market Considering Carbon Emission Trading Based on Multi-agent Deep Reinforcement Learning

被引：0
|
作者：
Liu, Qiyuan [1 ]
Feng, Donghan [1 ]
Zhou, Yun [1 ]
Li, Hengjie [2 ]
Zhang, Kaiyu [3 ]
Shi, Shanshan [3 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai, Peoples R China
[2] Lanzhou Univ Technol, Sch Elect & Informat Engn, Lanzhou, Peoples R China
[3] State Grid Shanghai Municipal, Elect Power Res Inst, Shanghai, Peoples R China
来源：
2023 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA, I&CPS ASIA | 2023年
基金：
中国国家自然科学基金;
关键词：
electricity market equilibrium; carbon emission trading; multi-agent deep reinforcement learning; carbon price; carbon quota;
D O I：
10.1109/ICPSASIA58343.2023.10294544
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
With the proposal of China's carbon peaking and carbon neutrality goals, carbon emission trading (CET) is gradually participating in the electricity market to accelerate carbon emission reduction and the improvement of power supply structure. In this study, we analyze the impacts of CET on the electricity market based on the electricity market equilibrium model and multi-agent deep reinforcement learning (MADL) method. We firstly establish the electricity market clearing process involved CET and develop the bi-level problem to model the electricity market equilibrium with strategic generation company (GENCO) bidders. Then, a multi-agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm is applied to solve the market equilibrium described above. Finally, we simulate multiple cases based on a modified IEEE 30-bus system. The result shows that an excessive carbon price can raise the nodal electricity price and have a negative influence on reducing carbon emission, and an appropriately low carbon emission quota setting can help for carbon emission reduction.
引用
收藏
页码：1849 / 1854
页数：6
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