Imperial College London
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Professor Yujian Ye

Faculty of Engineering, Department of Electrical and Electronic Engineering

Honorary Lecturer
 
 
 
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Contact

 

yujian.ye11

 
 
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Location

 

1105Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ye:2020:10.1109/TSG.2019.2936142,
author = {Ye, Y and Qiu, D and Sun, M and Papadaskalopoulos, D and Strbac, G},
doi = {10.1109/TSG.2019.2936142},
journal = {IEEE Transactions on Smart Grid},
pages = {1343--1355},
title = {Deep reinforcement learning for strategic bidding in electricity markets},
url = {http://dx.doi.org/10.1109/TSG.2019.2936142},
volume = {11},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants' physical non-convex operating characteristics, while conventional RL methods require discretization of state and / or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy.
AU  - Ye,Y
AU  - Qiu,D
AU  - Sun,M
AU  - Papadaskalopoulos,D
AU  - Strbac,G
DO  - 10.1109/TSG.2019.2936142
EP  - 1355
PY  - 2020///
SN  - 1949-3053
SP  - 1343
TI  - Deep reinforcement learning for strategic bidding in electricity markets
T2  - IEEE Transactions on Smart Grid
UR  - http://dx.doi.org/10.1109/TSG.2019.2936142
UR  - http://hdl.handle.net/10044/1/82270
VL  - 11
ER  -
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