Approximating Nash Equilibrium in Day-ahead Electricity Market Bidding with Multi-agent Deep Reinforcement Learning

Approximating Nash Equilibrium in Day-ahead Electricity Market Bidding with Multi-agent Deep Reinforcement Learning

In this paper, a day-ahead electricity market bidding problem with multiple strategic generation company (GEN-CO) bidders is studied. The problem is formulated as a Markov game model, where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies. Considering unob...

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Bibliographic Details
Main Authors: Yan Du, Fangxing Li, Helia Zandi, Yaosuo Xue
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:Journal of Modern Power Systems and Clean Energy
Subjects:
Bidding strategy
day-ahead electricity market
deep reinforcement learning
Markov game
multi-agent deterministic policy gradient (MADDPG)
Nash equilibrium (NE)
Online Access:https://ieeexplore.ieee.org/document/9406572/

Internet

https://ieeexplore.ieee.org/document/9406572/

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