https://github.com/ai4opt/ml4acopf_benchmark
Science Score: 13.0%
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Low similarity (7.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: AI4OPT
- Language: Python
- Default Branch: main
- Size: 7.68 MB
Statistics
- Stars: 3
- Watchers: 6
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Copyright © 2023, Georgia Institute of Technology. All rights reserved.
ml4acopf benchmark
Machine Learning for AC Optimal Power Flow(ACOPF) benchmark for VNNCOMP
Environment Setup
To set up the environment, follow these steps:
1. Create the environment using the command: conda env create -f env.yaml
2. Activate the environment using the command: conda activate onnx-vnnlib-env
The benchmark files are located in the onnx and vnnlib folders:
To reproduce vnnlib files, run: python generate_properties.py
Vnnlib description
Input:
+- a% perturbation of the reference active and reactive load + random noise between -b% and b%, where a and b are self-defined values.
Output:
Check the properties of the NN output
For example, we are interested in if power balance violation is within some threshold:
for each bus i
- |p_balance[i]| <= max(10^(-3), 10^(-2)*pd_i)
- |q_balance[i]| <= max(10^(-3), 10^(-2)*qd_i)
where pd_i and qd_i refer to the active and reactive load at each bus respectively.
Inference
The code to run inference is presented in the main.py file.
Example
Take 14-bus system as an example:
- onnx file: 14_ieee_ml4acopf.onnx
- vnnlib file: 14_ieee_prop1.vnnlib
Parameters
- N = 14 : number of buses
- G = 5 : number of generators
- L = 11 : number of loads
- E = 20 : number of lines
Onnx model description
Input: pd/qd
dim: (2L) = 22
- pd: Real power demand. (L)
- qd: Reactive power demand. (L)
Output: pg/qg/vm/va/pf/pt/qf/qt/thrm_1/thrm_2/p_balance/q_balance
dim: (2G + 4N + 6E) = 186
- pg: Real power generation. (G)
- qg: Reactive power generation. (G)
- vm: Voltage magnitude. (N)
- va: Voltage angle. (N)
- pf: Real power flow from. (E)
- pt: Real power flow to. (E)
- qf: Reactive power flow from. (E)
- qt: Reactive power flow to. (E)
- thrm_1: Thermal limit from residual. (E)
- thrm_2: Thermal limit to residual. (E)
- p_balance: Real power balance residual. (N)
- q_balance: Reactive power balance residual. (N)
Compute graph
Owner
- Name: AI4OPT
- Login: AI4OPT
- Kind: organization
- Website: https://www.ai4opt.org/
- Repositories: 1
- Profile: https://github.com/AI4OPT
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