Update README.md

README.md

@@ -1,3 +1,269 @@
 ---
 license: mit
 ---
+
+# PowerDiT Dataset
+
+This repository contains processed power-system graph data for machine learning experiments on power-flow- and optimal-power-flow-related tasks.  
+The data is generated with [**gridfm-datakit**](https://github.com/gridfm/gridfm-datakit) based on benchmark grid cases from [**PGLib**](https://github.com/power-grid-lib/pglib-opf), and then preprocessed into graph-structured samples.
+It is intended to be stored in the folders examples/data_processed/pf and examples/data_processed/opf. 
+
+## Overview
+
+The dataset consists of graph representations of electric power systems under a range of operating conditions and perturbations.
+
+Each sample corresponds to a power-grid state derived from a benchmark network case, with:
+
+- **nodes** representing buses
+- **edges** representing transmission lines
+- **node features** encoding electrical state, bus metadata, and generator properties.
+- **edge features** encoding electrical line parameters.
+
+The data is split into two different datasets:
+
+- **PF mode (Power Flow)**  
+  The samples are merely physically (power-flow) consistent with non-cost-optimal generations.
+
+- **OPF mode (Optimal Power Flow)**  
+  The samples are creating using an opf-solver such that generator setpoints are cost-optimal.
+
+
+## Available data
+
+The processed dataset currently includes the following grid cases and sample counts:
+
+| grid | #buses | #gens | #lines | pf samples | opf samples |
+|---|---:|---:|---:|---:|---:|
+| case3_lmbd | 3 | 3 | 6 | 1,224,988 | 934,045 |
+| case5_pjm | 5 | 5 | 12 | 1,287,400 | 1,214,688 |
+| case14_ieee | 14 | 5 | 40 | 1,290,095 | 1,300,186 |
+| case24_ieee_rts | 24 | 33 | 76 | 1,290,126 | 1,301,982 |
+| case30_ieee | 30 | 6 | 82 | 1,300,680 | 1,296,767 |
+| case30_as | 30 | 6 | 82 | 1,300,200 | 1,284,562 |
+| case39_epri | 39 | 10 | 92 | 1,300,211 | 1,301,635 |
+| case57_ieee | 57 | 7 | 160 | 1,301,222 | 1,273,026 |
+| case60_c | 60 | 23 | 176 | 1,302,069 | 1,300,858 |
+| case73_ieee_rts | 73 | 99 | 240 | 1,300,405 | 1,301,531 |
+| case89_pegase | 89 | 12 | 420 | 654,591 | 1,483,225 |
+| case118_ieee | 118 | 54 | 372 | 1,300,742 | 1,301,781 |
+| case162_ieee_dtc | 162 | 12 | 568 | 1,046,318 | 181,038 |
+| case179_goc | 179 | 29 | 526 | 1,293,617 | 739,821 |
+| case197_snem | 197 | 35 | 572 | 1,301,323 | 1,391,101 |
+| case200_activ | 200 | 49 | 490 | 1,301,480 | 1,320,560 |
+| case240_pserc | 240 | 143 | 896 | 1,011,541 | 260,102 |
+| case300_ieee | 300 | 69 | 822 | 1,067,772 | 625,028 |
+| case500_goc | 500 | 224 | 1,466 | 1,018,707 | 543,710 |
+| case588_sdet | 588 | 167 | 1,372 | 1,427,568 | 622,209 |
+| case793_goc | 793 | 214 | 1,826 | 1,414,878 | 405,521 |
+
+
+## What the data consists of
+
+At a high level, the dataset contains:
+
+- benchmark power-grid topologies
+- sampled and perturbed operating conditions
+- simulated physical solutions
+- graph-structured representations for ML
+  
+### Node features
+
+| # | Feature | Name | PF-PQ | PF-PV | PF-REF | OPF-PQ | OPF-PV | OPF-REF | SE-PQ | SE-PV | SE-REF |
+|---:|---|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| 0 | P_d | Active load demand | I | I | I | I | I | I | M | M | M |
+| 1 | Q_d | Reactive load demand | I | I | I | I | I | I | M | M | M |
+| 2 | Q_g | Reactive generation | I | O | O | I | O | O | M | M | M |
+| 3 | V_m | Voltage magnitude | O | I | O | O | O | O | M | M | M |
+| 4 | V_a | Voltage angle | O | O | I | O | O | I | O | O | O |
+| 5 | PQ flag | PQ bus indicator | I | I | I | I | I | I | I | I | I |
+| 6 | PV flag | PV bus indicator | I | I | I | I | I | I | I | I | I |
+| 7 | REF flag | Slack bus indicator | I | I | I | I | I | I | I | I | I |
+| 8 | V_m^min | Min. voltage magnitude | -- | -- | -- | I | I | I | I | I | I |
+| 9 | V_m^max | Max. voltage magnitude | -- | -- | -- | I | I | I | I | I | I |
+| 10 | Q_g^min | Min. reactive generation | -- | -- | -- | I | I | I | I | I | I |
+| 11 | Q_g^max | Max. reactive generation | -- | -- | -- | I | I | I | I | I | I |
+| 12 | G_s | Shunt conductance | -- | -- | -- | I | I | I | I | I | I |
+| 13 | B_s | Shunt susceptance | -- | -- | -- | I | I | I | I | I | I |
+| 14 | V_n (kV) | Nominal voltage | -- | -- | -- | I | I | I | I | I | I |
+
+### Generator features
+
+| # | Feature | Name | PF-PQ/PV | PF-REF | OPF | State Est. |
+|---:|---|---|:---:|:---:|:---:|:---:|
+| 0 | P_g | Active generation | I | O | O | M |
+| 1 | P_g^min | Min. active generation | -- | -- | I | I |
+| 2 | P_g^max | Max. active generation | -- | -- | I | I |
+| 3 | c_0 | Cost constant term | -- | -- | I | I |
+| 4 | c_1 | Cost linear coefficient | -- | -- | I | I |
+| 5 | c_2 | Cost quadratic coefficient | -- | -- | I | I |
+
+### Edge features
+
+| # | Feature | Name | Power Flow | OPF | State Est. |
+|---:|---|---|:---:|:---:|:---:|
+| 0 | P_e | Active power flow | O | O | M |
+| 1 | Q_e | Reactive power flow | O | O | M |
+| 2 | Y_ff,r | From-from admittance (real) | I | I | I |
+| 3 | Y_ff,i | From-from admittance (imag.) | I | I | I |
+| 4 | Y_ft,r | From-to admittance (real) | I | I | I |
+| 5 | Y_ft,i | From-to admittance (imag.) | I | I | I |
+| 6 | tap | Transformer tap ratio | I | I | I |
+| 7 | theta_min | Min. angle difference | -- | I | I |
+| 8 | theta_max | Max. angle difference | -- | I | I |
+| 9 | rate_A | Thermal rating (MVA) | -- | I | I |
+
+- I = input feature
+- O = output / target feature
+- M = measured feature
+- -- = not used
+
+
+## Data source
+
+The samples have been generated using **Gridfm-datakit** on the [**Future Technologies Partition**](https://www.nhr.kit.edu/userdocs/ftp/) on four intel sapphire rapids nodes. 
+The underlying network cases (topologies and initial load and generation profiles) are based on **PGLib** benchmark systems, the perturbation framework is **Gridfm-datakit**.
+The preprocessing from **Gridfm-graphkit** has been adapted to allow for larger dataset sizes.
+
+## Perturbations applied during data generation
+
+The data generation process in **gridfm-datakit** applies perturbations to create diverse operating conditions and topologies.
+
+The perturbation types include:
+
+### 1. Load perturbations
+Load levels are randomly varied using a global scaling factor and local noise.
+
+### 2. Topology and admittance perturbations
+N-k perturbations are used to randomly cut off buses or edges. We used k=1.
+Further, the resistance and reactance parameters are randomly scaled during the perturbation process.
+
+### 3. Generation perturbations
+The generators are perturbed by changing their cost coefficients. In OPF-mode, the generator setpoints are recalculated with an opf-solver
+after topology and load perturbations have been applied. In PF-mode, an OPF-solver is only used BEFORE the perturbations. 
+
+## Preprocessing
+
+The raw power-system scenarios are preprocessed into a disk-backed heterogeneous graph format for efficient training and random access.
+
+### Raw inputs
+
+The preprocessing pipeline starts from three parquet tables per dataset split:
+
+- `bus_data.parquet`
+- `gen_data.parquet`
+- `branch_data.parquet`
+
+Each row belongs to a specific `scenario`, and each scenario corresponds to one graph sample.
+
+### Scenario validation and filtering
+
+Before graph construction, scenarios are validated for structural consistency.
+
+In particular, scenarios are removed if their bus indices are invalid, i.e. if:
+
+- bus IDs are duplicated, or
+- bus IDs are not consecutive from `0` to `N-1`
+
+This ensures that each scenario can be converted into a well-formed graph with a consistent node indexing scheme.
+
+### Feature extraction
+
+The preprocessing selects fixed feature subsets for buses, generators, and branches.
+
+#### Bus features
+Bus node features include electrical state, bus type indicators, and operating constraints:
+
+- `Pd`, `Qd`
+- `Qg`, `Vm`, `Va`
+- `PQ`, `PV`, `REF` flags
+- `min_vm_pu`, `max_vm_pu`
+- `min_q_mvar`, `max_q_mvar`
+- `GS`, `BS`
+- `vn_kv`
+
+#### Generator features
+Generator node features include operating quantities and cost parameters:
+
+- `p_mw`
+- `min_p_mw`, `max_p_mw`
+- `cp0_eur`, `cp1_eur_per_mw`, `cp2_eur_per_mw2`
+- `in_service`
+
+#### Branch features
+Branch edge features include power-flow quantities, admittance terms, and branch constraints.
+
+For each physical branch, the preprocessing creates **two directed edges**:
+- one forward edge (`from_bus -> to_bus`)
+- one reverse edge (`to_bus -> from_bus`)
+
+The forward and reverse edges receive direction-specific attributes, e.g.:
+- forward: `pf`, `qf`, `Yff_*`, `Yft_*`
+- reverse: `pt`, `qt`, `Ytt_*`, `Ytf_*`
+
+Shared branch attributes include:
+- `tap`
+- `ang_min`, `ang_max`
+- `rate_a`
+- `br_status`
+
+### Bus-level aggregation of generator limits
+
+Reactive power limits from generators are aggregated onto buses before graph construction.
+
+For each `(scenario, bus)`, the preprocessing sums:
+- `min_q_mvar`
+- `max_q_mvar`
+
+across all generators connected to that bus, and merges these totals into the bus feature table.
+
+This enriches bus nodes with bus-level reactive capability information.
+
+### Target construction
+
+Task labels are derived from subsets of the selected features.
+
+- **Bus targets** are taken from the leading subset of bus features
+- **Generator targets** are taken from the leading subset of generator features
+- **Branch targets** are stored separately as edge-level flow quantities
+
+The exact interpretation of inputs and outputs depends on the downstream task (e.g. PF, OPF, state estimation, reconstruction).
+
+### Heterogeneous graph construction
+
+Each scenario is converted into a `HeteroData` graph with:
+
+- **bus nodes**
+- **generator nodes**
+- **bus-to-bus edges** for physical branches
+- **generator-to-bus edges**
+- **bus-to-generator edges**
+
+This yields a heterogeneous graph representation that preserves both the electrical network structure and generator attachment structure.
+
+### Storage format
+
+Instead of storing one file per scenario, the processed dataset is written into a small number of large NumPy memory-mapped arrays (`memmap`).
+
+This avoids metadata overhead on large cluster file systems and enables efficient random access.
+
+The processed directory contains arrays such as:
+
+- `bus_x.npy`, `bus_y.npy`
+- `gen_x.npy`, `gen_y.npy`
+- `edge_index.npy`, `edge_attr.npy`, `edge_y.npy`
+- `gen_bus_ei.npy`, `bus_gen_ei.npy`
+
+along with an `index.pt` file containing per-scenario offsets.
+
+Using these offsets, a single scenario can be loaded without materializing the full dataset in memory.
+
+### Runtime loading and normalization
+
+When a sample is accessed:
+
+1. the corresponding slices are read from the memmap arrays,
+2. a PyTorch Geometric `HeteroData` object is reconstructed,
+3. edge indices are converted into PyG format,
+4. feature normalization is applied at access time.
+
+Normalization is therefore **not baked into the stored arrays**, but applied dynamically during dataset loading.