grid_feedback_optimizer.models package

Submodules

grid_feedback_optimizer.models.loader module

grid_feedback_optimizer.models.loader.load_network(file_path: str | Path) → Network

Load a network from a JSON file and validate it using Pydantic.

Parameters:: file_path – Path to the JSON network file.
Returns:: Validated Network object.
Return type:: Network

grid_feedback_optimizer.models.loader.load_network_from_excel(file_path: str | Path) → Network

Load a Network object from an Excel file where each sheet corresponds to a component type (e.g., ‘buses’, ‘lines’, etc.).

Missing sheets are treated as empty lists.

grid_feedback_optimizer.models.network module

class grid_feedback_optimizer.models.network.Bus(*, index: int, u_rated: float, u_pu_max: float, u_pu_min: float)

Bases: BaseModel

index: int

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

u_pu_max: float

u_pu_min: float

u_rated: float

class grid_feedback_optimizer.models.network.Line(*, index: int, from_bus: int, to_bus: int, r1: float, x1: float, c1: float, tan1: float, i_n: float)

Bases: BaseModel

c1: float

from_bus: int

i_n: float

index: int

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

r1: float

tan1: float

to_bus: int

x1: float

class grid_feedback_optimizer.models.network.Load(*, index: int, bus: int, p_norm: float, q_norm: float)

Bases: BaseModel

Represents a non-controllable unit: either a load or a generator.

bus: int

index: int

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

p_norm: float

q_norm: float

class grid_feedback_optimizer.models.network.Network(*, buses: List[Bus], lines: List[Line], transformers: List[Transformer] = [], sources: List[Source], renew_gens: List[RenewGen], loads: List[Load])

Bases: BaseModel

buses: List[Bus]

lines: List[Line]

loads: List[Load]

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

renew_gens: List[RenewGen]

sources: List[Source]

transformers: List[Transformer]

class grid_feedback_optimizer.models.network.RenewGen(*, index: int, bus: int, p_max: float, s_inv: float | None = None, q_min: float | None = None, q_max: float | None = None, pf_min: float | None = None, p_min: float | None = 0.0, q_norm: float | None = 0.0, p_norm: float | None = None, c1_p: float | None = 0.0, c2_p: Annotated[float, Ge(ge=0)] = 1.0, c1_q: float | None = 0.0, c2_q: Annotated[float, Ge(ge=0)] = 0.1)

Bases: BaseModel

Represents a renewable generator or power-consuming device.

Notes

If p_max > 0 and p_min >= 0 → behaves as a generator.
If p_max <= 0 and p_min <= 0 → behaves as a load/consuming device.
Mixed cases (p_min < 0 < p_max) → flexible device (can consume or generate).

bus: int

c1_p: float | None

c1_q: float | None

c2_p: float

c2_q: float

compute_p_norm(): Compute p_norm only if not provided by user.

index: int

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

p_max: float

p_min: float | None

p_norm: float | None

pf_min: float | None

q_max: float | None

q_min: float | None

q_norm: float | None

s_inv: float | None

class grid_feedback_optimizer.models.network.Source(*, index: int, bus: int, u_ref_pu: float)

Bases: BaseModel

bus: int

index: int

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

u_ref_pu: float

class grid_feedback_optimizer.models.network.Transformer(*, index: int, from_bus: int, to_bus: int, u1: float, u2: float, sn: float, uk: float, pk: float, i0: float, p0: float, winding_from: WindingType, winding_to: WindingType, clock: int, tap_side: BranchSide, tap_min: int, tap_max: int, tap_size: float, tap_pos: int | None = 0)

Bases: BaseModel

clock: int

from_bus: int

i0: float

index: int

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

p0: float

pk: float

sn: float

tap_max: int

tap_min: int

tap_pos: int | None

tap_side: BranchSide

tap_size: float

to_bus: int

u1: float

u2: float

uk: float

winding_from: WindingType

winding_to: WindingType

grid_feedback_optimizer.models.solve_data module

class grid_feedback_optimizer.models.solve_data.OptimizationInputs(*, u_pu_meas: ndarray, P_line_meas: ndarray, Q_line_meas: ndarray, p_gen_last: ndarray, q_gen_last: ndarray, P_transformer_meas: ndarray | None = None, Q_transformer_meas: ndarray | None = None)

Bases: BaseModel

Structured container for optimization inputs at each iteration.

P_line_meas: ndarray

P_transformer_meas: ndarray | None

Q_line_meas: ndarray

Q_transformer_meas: ndarray | None

check_lengths(): Ensure measurement and generator arrays have consistent lengths.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

p_gen_last: ndarray

q_gen_last: ndarray

to_dict(): Convert to a plain dict (for compatibility with existing code).

u_pu_meas: ndarray

class grid_feedback_optimizer.models.solve_data.OptimizationModelData(*, p_min: ndarray, p_max: ndarray, q_min: ndarray | None = None, q_max: ndarray | None = None, s_inv: ndarray | None = None, pf_min: ndarray | None = None, c1_p: ndarray, c2_p: ndarray, c1_q: ndarray, c2_q: ndarray, p_norm: ndarray, q_norm: ndarray, u_pu_max: ndarray, u_pu_min: ndarray, s_line: ndarray, s_transformer: ndarray | None = None)

Bases: BaseModel

Minimal structure needed for the optimizer.

c1_p: ndarray

c1_q: ndarray

c2_p: ndarray

c2_q: ndarray

check_fill_optional_arrays(): Ensure optional arrays are initialized as arrays of None if missing.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

p_max: ndarray

p_min: ndarray

p_norm: ndarray

pf_min: ndarray | None

q_max: ndarray | None

q_min: ndarray | None

q_norm: ndarray

s_inv: ndarray | None

s_line: ndarray

s_transformer: ndarray | None

u_pu_max: ndarray

u_pu_min: ndarray

class grid_feedback_optimizer.models.solve_data.SolveResults(*, final_output: Dict[Any, Any], final_gen_update: ndarray, iterations: List[Dict[str, Any]])

Bases: BaseModel

Container for optimization and power flow results. Provides structured access and convenient save/load utilities.

final_gen_update: ndarray

final_output: Dict[Any, Any]

iterations: List[Dict[str, Any]]

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

plot_iterations() → None

Plot evolution of voltages, line loadings, transformer loadings, and generator active/reactive powers over optimization iterations.

Layout: 5 rows * 1 column (shared x-axis: iteration number)

print_summary() → None: Print a concise summary of the optimization results.

save(output_file: str = 'optimization_results.json') → None: Save the results of the optimization and power flow to a JSON file.

grid_feedback_optimizer.models package

Submodules

grid_feedback_optimizer.models.loader module

grid_feedback_optimizer.models.network module

grid_feedback_optimizer.models.solve_data module

Module contents