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
相关论文
共 50 条
  • [21] HALFTONING WITH MULTI-AGENT DEEP REINFORCEMENT LEARNING
    Jiang, Haitian
    Xiong, Dongliang
    Jiang, Xiaowen
    Yin, Aiguo
    Ding, Li
    Huang, Kai
    2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, 2022, : 641 - 645
  • [22] Deep reinforcement learning for multi-agent interaction
    Ahmed, Ibrahim H.
    Brewitt, Cillian
    Carlucho, Ignacio
    Christianos, Filippos
    Dunion, Mhairi
    Fosong, Elliot
    Garcin, Samuel
    Guo, Shangmin
    Gyevnar, Balint
    McInroe, Trevor
    Papoudakis, Georgios
    Rahman, Arrasy
    Schafer, Lukas
    Tamborski, Massimiliano
    Vecchio, Giuseppe
    Wang, Cheng
    Albrecht, Stefano, V
    AI COMMUNICATIONS, 2022, 35 (04) : 357 - 368
  • [23] Multi-agent deep reinforcement learning: a survey
    Sven Gronauer
    Klaus Diepold
    Artificial Intelligence Review, 2022, 55 : 895 - 943
  • [24] Deep Multi-Agent Reinforcement Learning: A Survey
    Liang X.-X.
    Feng Y.-H.
    Ma Y.
    Cheng G.-Q.
    Huang J.-C.
    Wang Q.
    Zhou Y.-Z.
    Liu Z.
    Zidonghua Xuebao/Acta Automatica Sinica, 2020, 46 (12): : 2537 - 2557
  • [25] Lenient Multi-Agent Deep Reinforcement Learning
    Palmer, Gregory
    Tuyls, Karl
    Bloembergen, Daan
    Savani, Rahul
    PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18), 2018, : 443 - 451
  • [26] Multi-agent deep reinforcement learning: a survey
    Gronauer, Sven
    Diepold, Klaus
    ARTIFICIAL INTELLIGENCE REVIEW, 2022, 55 (02) : 895 - 943
  • [27] Learning to Communicate with Deep Multi-Agent Reinforcement Learning
    Foerster, Jakob N.
    Assael, Yannis M.
    de Freitas, Nando
    Whiteson, Shimon
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), 2016, 29
  • [28] Heterogeneous multi-agent deep reinforcement learning based low carbon emission task offloading in mobile edge computing
    Zhou, Xiongjie
    Guan, Xin
    Sun, Di
    Zhang, Xiaoguang
    Zhang, Zhaogong
    Ohtsuki, Tomoaki
    COMPUTER COMMUNICATIONS, 2025, 234
  • [29] MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
    Malysheva, Aleksandra
    Kudenko, Daniel
    Shpilman, Aleksei
    2019 XVI INTERNATIONAL SYMPOSIUM PROBLEMS OF REDUNDANCY IN INFORMATION AND CONTROL SYSTEMS (REDUNDANCY), 2019, : 171 - 176
  • [30] Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning
    Wu, Jiahui
    Wang, Jidong
    Kong, Xiangyu
    ENERGY, 2022, 256
12345