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

页码：1849 / 1854

页数：6

共 50 条

[31] Reliability assessment of multi-agent reinforcement learning algorithms for hybrid local electricity market simulation
Zhang, Haoyang
Qiu, Dawei
Kok, Koen
Paterakis, Nikolaos G.
APPLIED ENERGY, 2025, 389
[32] Analysis of coordinated behavior structures with multi-agent deep reinforcement learning
Miyashita, Yuki
Sugawara, Toshiharu
APPLIED INTELLIGENCE, 2021, 51 (02) : 1069 - 1085
[33] Analysis of coordinated behavior structures with multi-agent deep reinforcement learning
Yuki Miyashita
Toshiharu Sugawara
Applied Intelligence, 2021, 51 : 1069 - 1085
[34] Multi-agent Collaborative Fire Rescue Based on Deep Reinforcement Learning
Feng, Yiming
2022 IEEE INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, BIG DATA AND ALGORITHMS (EEBDA), 2022, : 1317 - 1321
[35] Multi-Agent Deep Reinforcement Learning Based Pricing Strategy for Competing Cloud Platforms in the Evolutionary Market
Shi, Bing
Shi, Rongjian
Li, Bingzhen
2020 IEEE 13TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2020), 2020, : 159 - 167
[36] Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning
Harrold, Daniel J. B.
Cao, Jun
Fan, Zhong
APPLIED ENERGY, 2022, 318
[37] Distributed interference coordination based on multi-agent deep reinforcement learning
Liu T.
Luo Y.
Yang C.
Tongxin Xuebao/Journal on Communications, 2020, 41 (07): : 38 - 48
[38] Macro-Action-Based Deep Multi-Agent Reinforcement Learning
Xiao, Yuchen
Hoffman, Joshua
Amato, Christopher
CONFERENCE ON ROBOT LEARNING, VOL 100, 2019, 100
[39] Distributed Task Offloading based on Multi-Agent Deep Reinforcement Learning
Hu, Shucheng
Ren, Tao
Niu, Jianwei
Hu, Zheyuan
Xing, Guoliang
2021 17TH INTERNATIONAL CONFERENCE ON MOBILITY, SENSING AND NETWORKING (MSN 2021), 2021, : 575 - 583
[40] UAV Swarm Confrontation Based on Multi-agent Deep Reinforcement Learning
Wang, Zhi
Liu, Fan
Guo, Jing
Hong, Chen
Chen, Ming
Wang, Ershen
Zhao, Yunbo
2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 4996 - 5001

← 1 2 3 4 5 →