An ultra-fast power flow based on Laurent series expansion. The power flow is intended for applications where massive amounts of power flow computations are required. e.g., electrical load time series, metaheuristics, electrical grid environments for reinforcement learning.
The package can be installed via pip using:
pip install tensorpowerflowRun the load base case as:
from tensorpowerflow import GridTensor
import numpy as np
#%% Solve base case (34 node bus)
network = GridTensor()
solution = network.run_pf_sequential()
print(solution["v"])
#%% Solve 10_000 power flows on the 34 node bus case.
network_size = network.nb - 1 # Remove slack node
active_ns = np.random.normal(50, scale=1, size=(10_000, network_size)) # Power in kW
reactive_ns = active_ns * 0.1 # kVAr
solution_tensor = network.run_pf_tensor(active_power=active_ns, reactive_power=reactive_ns)
print(solution_tensor["v"])
#%% Generate random radial network of 100 nodes and a maximum of 1 to 3 branches per node.
network_rnd = GridTensor.generate_from_graph(nodes=100, child=3, plot_graph=True)
solution_rnd = network_rnd.run_pf_sequential()
print(solution_rnd["v"])More examples can be found in the examples folder (under development). Also, you can try the package via jupyter lab clicking in the binder icon:
The mathematical formulation of the power flow can be found at:
"A Fixed-Point Current Injection Power Flow for Electric Distribution Systems using Laurent Series." J.S. Giraldo, O.D. Montoya, P.P. Vergara, F. Milano. Power Systems Computational Conference (PSCC) 2022. link
Any questions, suggestions or collaborations contact Juan S. Giraldo at <jnse@ieee.org>