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--- |
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license: mit |
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language: |
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- en |
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pretty_name: MAPDN |
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--- |
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This dataset is for simulating the multi-agent reinforcement learning (MARL) environment for distributed active voltage control problem, a key research area to guarantee the safety of voltage in the future power grid. The GitHub repository for the environment is [MAPDN](https://github.com/Future-Power-Networks/MAPDN). If you would like to run the MARL environment, you require to first download the dataset held in this dataset storage. |
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Briefly speaking, the user load data is modified based on the real-time Portuguese electricity consumption accounting for 232 consumers for 3 years, while the PV data is collected from Elia group, i.e. a Belgiums power network operator for 3 years. To distinguish among different solar radiation levels in various regions, the 3-year PV generations from 10 cites/regions are collected and PVs in the same control region possess the same generation profiles. |
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One motivation for us to upload the data to Hugging Face is that we are curious about whether LLMs are capable of dealing with the time-series forecasting for the real-world user electricity consumption which are beneficial for us to understand users behaviours, and the real-world PV generation which is beneficial for us to estimate intermittent renewable power generation influenced by weather. Success of both directions can help develop more convincing control/AI algorithms for regulating future power grids. |
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The `voltage_control_data.zip` include the PV (solar panel) generator data and user load data for MARL simulation. |
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The `traditional_control_data.zip` with the same data is for replicating the droop control and OPF methods in MATLAB. |
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If you either use the dataset or run the MARL environment, please cite the following paper published in NeurIPS 2021: |
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``` |
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@inproceedings{NEURIPS2021_1a672771, |
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author = {Wang, Jianhong and Xu, Wangkun and Gu, Yunjie and Song, Wenbin and Green, Tim C}, |
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booktitle = {Advances in Neural Information Processing Systems}, |
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editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan}, |
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pages = {3271--3284}, |
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publisher = {Curran Associates, Inc.}, |
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title = {Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks}, |
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url = {https://proceedings.neurips.cc/paper/2021/file/1a6727711b84fd1efbb87fc565199d13-Paper.pdf}, |
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volume = {34}, |
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year = {2021} |
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} |
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``` |
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If you have any question, please feel free to contact `jianhong.wang@bristol.ac.uk`. |
