PowerGraph-Node

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PowerGraph-Node

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Benchmarking GNN datasets for PowerGrids - node level regression tasks

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# PowerGraph-node
# Benchmarking GNN datasets for PowerGrids- node level regression tasks
The increasing complexity of power systems, driven by electrification and the rise of intermittent energy sources, is posing new challenges. Transmission system operators (TSOs) require online tools for effective power systems monitoring, but the current methods for grid analyses, hindered by their computational speed, are unable to fully meet this need. 
With the InMemoryDatasets Class, we generate the GNN datasets for the UK, IEEE24, IEEE39, IEEE118 power grids. We use **InMemoryDataset** class of Pytorch Geometric.

## Installation

**Requirements**

- CPU or NVIDIA GPU, Linux, Python 3.7
- PyTorch >= 1.5.0, other packages

Load every additional packages:

```
pip install -r requirements.txt
```

## Prerequisites and data structure

To reproduce the results presented in the paper, download the following compressed data from [here](https://figshare.com/articles/dataset/PowerGraph/22820534?file=46619152) (~1.08GB, when uncompressed):

```bash
wget -O data.tar.gz "https://figshare.com/ndownloader/files/46619152"
tar -xf data.tar.gz
```

Each dataset folder contains the following files:

- `edge_attr.mat`: edge feature matrix for the power flow problem (branch conductance $G_{ij}$, branch susceptance $B_{ij}$.)
- `edge_attr_opf.mat`: edge feature matrix for the optimal power flow problem (branch conductance $G_{ij}$, branch susceptance $B_{ij}$.)
- `edge_index.mat`: branch list for the power flow problem
- `edge_index_opf.mat`: branch list for the optimal power flow problem
- `X.mat`: node feature matrix for the power flow problem (active power generation $P_{g}$ - active power demand $P_{d}$, reactive power generation $Q_{g}$ - reactive power demand $Q_{d}$, voltage magnitude $V$ , and voltage angle $\theta$, the number of loads $N_{loads}$, and number of generators $N_{gen}$). 
- `Xopf.mat`: node feature matrix for the optimal power flow problem  (active power generation $P_{g}$ - active power demand $P_{d}$, reactive power generation $Q_{g}$ - reactive power demand $Q_{d}$, voltage magnitude $V$ , and voltage angle $\theta$, the number of loads $N_{loads}$, and number of generators $N_{gen}$). 
- `Y_polar.mat`: node output matrix for the power flow problem (active power generation $P_{g}$ - active power demand $P_{d}$, reactive power generation $Q_{g}$ - reactive power demand $Q_{d}$, voltage magnitude $V$ , and voltage angle $\theta$). 
- `Y_polar_opf.mat`: node output matrix for the optimal power flow problem (active power generation $P_{g}$ - active power demand $P_{d}$, reactive power generation $Q_{g}$ - reactive power demand $Q_{d}$, voltage magnitude $V$ , and voltage angle $\theta$).

## Dataset description

| Dataset    |     Name     | Description                    |
| ---------- | :----------: | ------------------------------ |
| IEEE-24    |   `ieee24`   | IEEE-24 (Powergrid dataset)    |
| IEEE-39    |   `ieee39`   | IEEE-39 (Powergrid dataset)    |
| IEEE-118   |  `ieee118`   | IEEE-118 (Powergrid dataset)   |
| UK         |     `uk`     | UK (Powergrid dataset)         |

Graph dataset that models the power flow and optimal power flow problems in the power grid. To generate a comprehensive dataset for different power grids, we use MATPOWER. We obtain a reference solution for the optimal power flow and power flow problem starting from the loading conditions reported the folder 13_Power_system for each dataset. Bus and branches are the elements of a power grid, buses include loads and generators which represent the nodes of the graph, while branches include transmission lines and transformers which represent the edges of the graph. Each power grid loading is thus represented as a single graph, with a node-level labels assigned based on the results of the power flow and optimal power flow problem.  The node level features are assigned according to the type of bus, i.e., PQ, PV or Slack. While edge-level features are: line reactance and subsceptance.

## GNN Benchmarking

To test the datasets with different GNN architectures: GCN, GINe, GAT and Transformer, run:

```
python code/train_gnn.py
```

We have the main arguments to control namely

**--model_name**: transformer / gin / gat / gcn

**--datatype**: node / nodeopf (for power flow and optimal power flow problems, respectively)

**--dataset_name**: uk / ieee24 / ieee39 / ieee118

Make sure you have the dataset as per format. Models will be saved as per format (make sure you have the model folder)

Remove the for loop in train_gnn.py if running for a specific **--hidden_dim** and **num_layers**.

The models will be saved in **model** directory

## License

This work is licensed under a CC BY 4.0 license.

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