Module ml4opf.formulations.dc.violation

Class interface for DCOPF constraints, objective, etc.

Classes

class DCViolation (data: dict[str, torch.Tensor])

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OPFViolation for DCPPowerModel/DCOPF

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

• OPFViolation
• torch.nn.modules.module.Module
• abc.ABC

Methods

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

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Compute the angle differences per branch given the voltage angles per bus.

The branch indices are assumed to be constant for the batch, matching the reference case.

$$\text{dva} = \boldsymbol{\theta}_{f} - \boldsymbol{\theta}_{t}$$

Args

va : Tensor

Voltage angles per bus ( $\boldsymbol{\theta}$ ). (batch_size, nbus)

Returns

Tensor

Angle differences per branch. (batch_size, nbranch)

def balance_residual(self,
pd: torch.Tensor,
pg: torch.Tensor,
pf: torch.Tensor,
embed_method: str = 'pad',
clamp: bool = True) ‑> torch.Tensor

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Compute power balance residual.

$$\text{g_balance} = \text{pg_bus} - \text{pd_bus} - \text{gs_bus} - \text{pf_bus} - \text{pt_bus}$$

Args

pd : Tensor

Power demand per bus. (batch_size, nbus)

pg : Tensor

Power generation per generator. (batch_size, ngen)

pf : Tensor

Power flow per branch. (batch_size, nbranch)

embed_method : str, optional

Embedding method to convert component-wise values to bus-wise – one of "pad", "dense_matrix", or "matrix". Defaults to "pad".

clamp : bool, optional

Clamp to extract only violations. Defaults to True.

Returns

Tensor

Power balance residual. (batch_size, nbus)

def calc_violations(self,
pd: torch.Tensor,
pg: torch.Tensor,
va: torch.Tensor,
pf: torch.Tensor | None = None,
reduction: str = 'mean',
clamp: bool = True,
embed_method: str = 'pad') ‑> dict[str, torch.Tensor]

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Compute all DCOPF violations.

Args

pd : Tensor

Power demand per bus. (batch_size, nbus)

pg : Tensor

Power generation per generator. (batch_size, ngen)

va : Tensor

Voltage angles per bus. (batch_size, nbus)

pf : Tensor, optional

Power flow per branch. Defaults to None.

reduction : str, optional

Reduction method. Defaults to "mean".

clamp : bool, optional

Clamp to extract only violations. Defaults to True.

embed_method : str, optional

Method to convert component-wise values to bus-wise – one of "pad", "dense_matrix", or "matrix". Defaults to "pad".

Returns

dict[str, Tensor]

Dictionary of all violations:

• "pg_lower": Lower bound violation of power generation. (batch_size, ngen)

• "pg_upper": Upper bound violation of power generation. (batch_size, ngen)

• "dva_lower": Lower bound violation of voltage angle difference. (batch_size, nbranch)

• "dva_upper": Upper bound violation of voltage angle difference. (batch_size, nbranch)

• "pf_lower": Lower bound violation of power flow. (batch_size, nbranch)

• "pf_upper": Upper bound violation of power flow. (batch_size, nbranch)

• "p_balance": Power balance violation. (batch_size, nbus)

• "ohm": Ohm's law violation. (batch_size, nbranch)

def dva_bound_residual(self, dva: torch.Tensor, clamp: bool = False) ‑> torch.Tensor

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Calculate the voltage angle difference bound residual.

$$g_{\text{lower}} = \text{angmin} - \text{dva}$$ $$g_{\text{upper}} = \text{dva} - \text{angmax}$$

Args

dva : Tensor

Voltage angle differences per branch. (batch_size, nbranch)

clamp : bool, optional

Clamp the residual to be non-negative (extract violations). Defaults to False.

Returns

Tensor

Lower bound residual. (batch_size, nbranch)

Tensor

Upper bound residual. (batch_size, nbranch)

def objective(self, pg: torch.Tensor) ‑> torch.Tensor

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Compute DCOPF objective function.

Cost is assumed to be constant for the batch, matching the reference case.

$$\text{objective} = \sum_i^n \text{cost}_{2,i} + \text{cost}_{1,i} \cdot \text{pg}_i$$

Args

pg : Tensor

Power generation per generator. (batch_size, ngen)

Returns

Tensor

Objective function value. (batch_size, 1)

def ohm_residual(self, pf: torch.Tensor, dva: torch.Tensor, clamp: bool = False) ‑> torch.Tensor

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Compute Ohm's law violation.

$$\text{g_ohm} = - b \cdot \text{dva} - \text{pf}$$

Args

pf : Tensor

Power flow per branch. (batch_size, nbranch)

dva : Tensor

Voltage angle differences per branch. (batch_size, nbranch)

clamp : bool, optional

Clamp to extract only violations. Defaults to False.

Returns

Tensor

Ohm's law violation. (batch_size, nbranch)

def pf_bound_residual(self, pf: torch.Tensor, clamp: bool = False) ‑> torch.Tensor

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Calculate the power flow bound residual.

$$g_{\text{lower}} = -\text{rate_a} - \text{pf}$$ $$g_{\text{upper}} = \text{pf} - \text{rate_a}$$

Args

pf : Tensor

Power flow per branch. (batch_size, nbranch)

clamp : bool, optional

Clamp the residual to be non-negative (extract violations). Defaults to False.

Returns

Tensor

Lower bound residual. (batch_size, nbranch)

Tensor

Upper bound residual. (batch_size, nbranch)

def pf_from_va(self, va: torch.Tensor) ‑> torch.Tensor

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Compute power flow given voltage angles.

$$\mathbf{p}_f = -\text{b} \cdot (\boldsymbol{\theta}_{f} - \boldsymbol{\theta}_{t})$$

Args

va : Tensor

Voltage angles per bus ( $\boldsymbol{\theta}$ ). (batch_size, nbus)

Returns

Tensor

Power flow per branch ( $\mathbf{p}_f$ ). (batch_size, nbranch)

def pg_bound_residual(self, pg: torch.Tensor, clamp: bool = False) ‑> torch.Tensor

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Calculate the power generation bound residual.

$$g_{\text{lower}} = \text{pmin} - \text{pg}$$ $$g_{\text{upper}} = \text{pg} - \text{pmax}$$

Args

pg : Tensor

Active power generation per generator. (batch_size, ngen)

clamp : bool, optional

Clamp the residual to be non-negative (extract violations). Defaults to False.

Returns

Tensor

Lower bound residual. (batch_size, ngen)

Tensor

Upper bound residual. (batch_size, ngen)

Inherited members

• OPFViolation:
  • adjacency_matrix
  • branch_from_incidence
  • branch_from_to_bus
  • branch_incidence
  • branch_to_incidence
  • branch_to_to_bus
  • forward
  • gen_to_bus
  • generator_incidence
  • load_incidence
  • load_to_bus
  • reduce_violation
  • reduce_violations
  • violation_shapes