Module ml4opf.functional

Functional interface

Sub-modules

ml4opf.functional.ac

ACOPF Functional interface

ml4opf.functional.dc

DCOPF Functional interface

ml4opf.functional.ed

EconomicDispatch Functional interface

ml4opf.functional.incidence

Incidence matrices and related functions.

ml4opf.functional.soc

SOCOPF Functional interface

Functions

def adjacency(fbus: torch.Tensor, tbus: torch.Tensor, n_bus: int, n_branch: int) ‑> torch.Tensor

Browse git

Sparse adjacency matrix.

Each row corresponds to a bus and each column corresponds to a bus. The value is 1 if there is a branch between the buses, and 0 otherwise.

Args

fbus : Tensor

From bus indices. (nbranch,)

tbus : Tensor

To bus indices. (nbranch,)

n_bus : int

Number of buses.

n_branch : int

Number of branches.

Returns

Tensor

Sparse adjacency matrix. (nbus, nbus)

def angle_difference(va: torch.Tensor, i: torch.Tensor, j: torch.Tensor) ‑> torch.Tensor

Browse git

def bound_residual(x: torch.Tensor, xmin: torch.Tensor, xmax: torch.Tensor) ‑> tuple[torch.Tensor, torch.Tensor]

Browse git

Calculate the bound residuals for a tensor x.

$$g_\text{lower} = x_\text{min} - x$$ $$g_\text{upper} = x - x_\text{max}$$

Args

x : Tensor

Input tensor. (batch, n)

xmin : Tensor

Lower bounds. (batch, n)

xmax : Tensor

Upper bounds. (batch, n)

Returns

tuple[Tensor, Tensor]

Lower and upper bound residuals. (batch, n)

def branch_from_incidence(fbus: torch.Tensor, n_bus: int, n_branch: int) ‑> torch.Tensor

Browse git

Sparse branch from incidence matrix.

Each row corresponds to a bus and each column corresponds to a branch. The value is 1 if the branch is from the bus, and 0 otherwise.

Args

fbus : Tensor

From bus indices. (nbranch,)

n_bus : int

Number of buses.

n_branch : int

Number of branches.

Returns

Tensor

Sparse branch from incidence matrix. (nbus, nbranch)

def branch_incidence(fbus: torch.Tensor, tbus: torch.Tensor, n_bus: int, n_branch: int) ‑> torch.Tensor

Browse git

Sparse branch incidence matrix.

Each row corresponds to a bus and each column corresponds to a branch. The value is 1 if the branch is from the bus, -1 if the branch is to the bus, and 0 otherwise.

Args

fbus : Tensor

From bus indices. (nbranch,)

tbus : Tensor

To bus indices. (nbranch,)

n_bus : int

Number of buses.

n_branch : int

Number of branches.

Returns

Tensor

Sparse branch incidence matrix. (nbus, nbranch)

def branch_to_incidence(tbus: torch.Tensor, n_bus: int, n_branch: int) ‑> torch.Tensor

Browse git

Sparse branch to incidence matrix.

Each row corresponds to a bus and each column corresponds to a branch. The value is 1 if the branch is to the bus, and 0 otherwise.

Args

tbus : Tensor

To bus indices. (nbranch,)

n_bus : int

Number of buses.

n_branch : int

Number of branches.

Returns

Tensor

Sparse branch to incidence matrix. (nbus, nbranch)

def equality_violation(residual: torch.Tensor, clamp: bool = False) ‑> torch.Tensor

Browse git

Return the absolute violation (always positive) if clamp=True. Otherwise, pass-through the residual.

def generator_incidence(gen_bus: torch.Tensor, n_bus: int, n_gen: int) ‑> torch.Tensor

Browse git

Sparse generator incidence matrix.

Each row corresponds to a bus and each column corresponds to a generator. The value is 1 if the generator is at the bus, and 0 otherwise.

Args

gen_bus : Tensor

Generator bus indices. (ngen,)

n_bus : int

Number of buses.

n_gen : int

Number of generators.

Returns

Tensor

Sparse generator incidence matrix. (nbus, ngen)

def inequality_violation(residual: torch.Tensor, clamp: bool = False) ‑> torch.Tensor

Browse git

Return only the violation (always positive) if clamp=True. Otherwise, pass-through the residual.

def load_incidence(load_bus: torch.Tensor, n_bus: int, n_load: int) ‑> torch.Tensor

Browse git

Sparse load incidence matrix.

Each row corresponds to a bus and each column corresponds to a load. The value is 1 if the load is at the bus, and 0 otherwise.

Args

load_bus : Tensor

Load bus indices. (nload,)

n_bus : int

Number of buses.

n_load : int

Number of loads.

Returns

Tensor

Sparse load incidence matrix. (nbus, nload)

def map_to_bus_matrix(x: torch.Tensor, x_matrix: torch.Tensor)

Browse git

Map component-wise values to bus-wise values using matrix multiplication.

def map_to_bus_pad(x: torch.Tensor, x_per_bus: torch.Tensor)

Browse git

Map component-wise values to bus-wise values using zero-padding.