[IEEE Transactions on Power Systems] Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map

Official codebase for paper Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map. This codebase is based on the open-source Tianshou and PandaPower framework and please refer to those repo for more documentation.

A novel approach named as FSA is recently proposed to solve the same task as an enhancement to MAM.

Overview

TLDR: This work is the first dedicated attempt towards learning multiple transmission interface power flow adjustment tasks jointly, a highly practical problem yet largely overlooked by existing literature in the field of the power system. We design a novel deep reinforcement learning (DRL) method based on multi-task attribution map (MAM) to handle multiple adjustment tasks jointly, where MAM enables the DRL agent to selectively integrate the node features into a compact task-adaptive representation for the final adjustment policy. Simulations are conducted on the IEEE 118-bus system, a realistic 300-bus system in China, and a very large European 9241-bus system, demonstrating that the proposed method brings remarkable improvements to the existing methods. Moreover, we verify the interpretability of the learnable MAM in different operation scenarios.

Prerequisites

Install dependencies

• python==3.8.13
• dgl==1.1
• torch==1.13
• pandapower==2.11
• gym==0.23
• tianshou==0.4.11
• numpy==1.22.4
• numba==0.55.2
• pandas==1.4.2

Usage

Please follow the instructions below to replicate the results in the paper. Note that the model of the realistic 300-bus system in China is not available due to confidentiality policies of SGCC.

• Unzip the data for training and testing under the same working directory:

tar -Jxvf data.tar.xz

• Train the DRL agent with MAM:

# IEEE 118-bus System under the multi-task setting (10 single-interface tasks)
python train.py --case='case118' --task='S10' --method='MAM' --model='Attention'

# IEEE 9241-bus System under the multi-task setting (10 single-interface tasks)
python train.py --case='case9241' --task='S10' --method='MAM' --model='Attention'

# IEEE 118-bus System under the multi-task setting (different 5-interface tasks)
python train.py --case='case118' --task='M5' --method='MAM' --model='Attention'

# IEEE 9241-bus System under the multi-task setting (different 3-interface tasks)
python train.py --case='case9241' --task='M3' --method='MAM' --model='Attention'

Citation

If you find this work useful for your research, please cite our paper:

@article{liu2024MAM,
  author={Liu, Shunyu and Luo, Wei and Zhou, Yanzhen and Chen, Kaixuan and Zhang, Quan and Xu, Huating and Guo, Qinglai and Song, Mingli},
  journal={IEEE Transactions on Power Systems}, 
  title={Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach Based on Multi-Task Attribution Map}, 
  year={2024},
  volume={39},
  number={2},
  pages={3324--3335},
  doi={10.1109/TPWRS.2023.3298007}
}

Contact

Please feel free to contact me via email ([email protected], [email protected]) if you are interested in my research :)