main.py

from tensorpowerflow import GridTensor
import numpy as np
from time import perf_counter

# %% Example 1: Solve base case (34 node bus)
network = GridTensor(gpu_mode=False)
solution = network.run_pf()
print(solution["v"])

# %% Example 2: Solve 10_000 power flows on the 34 node bus case.
network_size = network.nb - 1  # Size of network without slack bus.
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(active_power=active_ns, reactive_power=reactive_ns)
print(solution_tensor["v"])

# %% Example 3: 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()
print(solution_rnd["v"])

# %% Example 4: Solve a tensor power flow. For 10 scenarios, 8_760 time steps (one year - 1 hr res), for the 33 PQ nodes.
# Meaning that the dimensions of the tensor is (10, 8_760, 33)
network = GridTensor(numba=True)  # Loads the basic 34 bus node network.
active_ns = np.random.normal(50, scale=10, size=(10, 8_760, 33)).round(3)  # Assume 1 slack variable (Power in kW)
reactive_ns = (active_ns * 0.1).round(3)  # Constant PF of 0.1

start_tensor_dense = perf_counter()
solution = network.run_pf(
    active_power=active_ns, reactive_power=reactive_ns, algorithm="tensor"
)
t_tensor_dense = perf_counter() - start_tensor_dense
assert solution["convergence"], "Algorithm did not converge."
assert solution["v"].shape == active_ns.shape
print(f"Time tensor dense: {t_tensor_dense:.3f} sec.")