VeraGridEngine.Simulations.Rms.problems package

Submodules

VeraGridEngine.Simulations.Rms.problems.mti_hybrid_structure module

class VeraGridEngine.Simulations.Rms.problems.mti_hybrid_structure.MTISubProblemRow(var_idx: 'int', eq_idx: 'int', explicit: 'int', subset: 'int', subproblem: 'int')

Bases: object

eq_idx: int

explicit: int

subproblem: int

subset: int

var_idx: int

VeraGridEngine.Simulations.Rms.problems.mti_hybrid_structure.build_connected_subproblem_order(incidence: ndarray) → list[MTISubProblemRow]

Build a solving-order-like structure from equation-variable connectivity.

We build a bipartite graph [eq | var] and use connected components as subsets/subproblems. Rows are paired (eq_i, var_i) within each subset.

VeraGridEngine.Simulations.Rms.problems.mti_hybrid_structure.build_incidence_from_jacobian(jac: spmatrix, tol: float = 0.0) → ndarray

Build a boolean incidence matrix from a sparse Jacobian.

The matrix format follows the MTI-toolbox convention at a high level: rows are equations and columns are variables, with 1 where dependency exists.

VeraGridEngine.Simulations.Rms.problems.mti_hybrid_structure.build_single_subproblem_order(n_eq: int, n_vars: int) → list[MTISubProblemRow]

Create a deterministic fallback solving-order where all equations/variables belong to one subset and one subproblem.

This mirrors the toolbox row schema, while keeping the current VeraGrid implementation conservative until full incidence-based decomposition is ported.

VeraGridEngine.Simulations.Rms.problems.rms_problem_MTI module

class VeraGridEngine.Simulations.Rms.problems.rms_problem_MTI.RmsProblemMTI(*args, **kwargs)

Bases: RmsProblemPhasor

RMS problem exposing MTI-style equality/inequality evaluation.

Equalities are the same implicit residuals used by the DAE solver. Inequalities are compiled locally in this MTI class from Block.inequalities and evaluated at runtime via rhs_inequalities.

add_variables_to_compilation_dicts(elm, mdl)

add variables and parameters info to the system block

Parameters:

• elm (Union[VeraGridEngine.Devices.Substation.bus.Bus, VeraGridEngine.Devices.Injections.load.Load])

• mdl (VeraGridEngine.Utils.Symbolic.block.Block)

Returns:

Return type:

None

build_mti_incidence_and_order(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float) → None

compute_mti_equalities(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], h: float) → ndarray[tuple[Any, ...], dtype[float64]]

Compute equality residual vector F.

compute_mti_inequalities(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], h: float) → ndarray[tuple[Any, ...], dtype[float64]]

Compute inequality vector G (constraint convention: G <= 0).

enumerate_all_boolean_candidates() → list[ndarray]

evaluate_boolean_guard(bool_position: int, x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → float | None

evaluate_mti_step(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], h: float) → Tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]

Evaluate one MTI step and return (F, G, z).

get_continuous_equality_row_indices(row_idx: ndarray) → ndarray

Return equality rows that still involve continuous unknowns [xp;y].

get_equality_row_indices(row_idx: ndarray) → ndarray

Map MTI incidence row indices to equality residual row indices.

get_event_local_boolean_candidates(ineq_idx: int, z_prev: ndarray[tuple[Any, ...], dtype[float64]]) → list[ndarray]

Enumerate boolean candidates local to the subset touched by inequality.

Prefer direct guard/inequality variable overlap. A coarse solving order can collapse to one subset, and using that subset would enumerate every boolean in the model.

get_event_solving_stages(ineq_idx: int) → tuple[list[tuple[ndarray, ndarray]], list[tuple[ndarray, ndarray]], list[tuple[ndarray, ndarray]]]

Return (previous, event, following) stage groups from current solving order.

Each group entry is (eq_indices0, var_indices0) with 0-based indices.

get_event_subset_ids(ineq_idx: int) → set[int]

Return solving-order subset ids touched by an inequality.

get_fixed_boolean_equality_row_indices(row_idx: ndarray) → ndarray

Return equality rows that only constrain fixed boolean columns.

get_group_subset_ids(eq_idx: ndarray, var_idx: ndarray) → set[int]

Return solving-order subset ids touched by a stage group.

get_inequality_row_indices(row_idx: ndarray) → ndarray

Map MTI incidence row indices to inequality residual row indices.

property get_mti_boolean_parameter_indices: list[int]

get_mti_boolean_parameters() → list[object]

get_mti_solving_order() → list[MTISubProblemRow]

get_subproblem_boolean_positions(eq_idx: ndarray, var_idx: ndarray) → list[int]

Return local boolean positions structurally coupled to a subproblem.

The solver works in continuous variable space, while the MTI incidence matrix is [diff vars; continuous vars; booleans]. This method maps a continuous subproblem back to boolean incidence columns so following subproblems can use toolbox-like variable-z propagation.

has_boolean_guard(bool_position: int) → bool

static inequalities_satisfied(g: ndarray[tuple[Any, ...], dtype[float64]], tol: float = 1e-09) → bool

Check if all inequalities satisfy G <= tol.

property non_bool_idx_params: ndarray

rhs_inequalities(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

set_events_group(rms_events_group)

Keep base phasor event-group behavior, then re-apply MTI boolean init.

The base implementation recompiles event parameters and rebuilds _variable_parameters_values, which can reset MTI boolean entries.

set_mti_boolean_state(z: ndarray[tuple[Any, ...], dtype[float64]]) → None

split_direct_and_coupled_booleans() → tuple[list[int], list[int]]

Return all booleans as coupled for inequality-driven MTI selection.

split_explicit_subproblem_pairs(eq_idx: ndarray, var_idx: ndarray) → tuple[list[tuple[int, int]], ndarray, ndarray]

Split a subproblem into toolbox-marked explicit pairs and implicit rest.

Returns explicit (eq0, var0) pairs in continuous variable space, followed by remaining equation and continuous-variable indices.

total_derivative_inequalities(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], xpp: ndarray[tuple[Any, ...], dtype[float64]] | None = None) → ndarray[tuple[Any, ...], dtype[float64]]

Jacobian-based approximation of dG/dt for active inequality checks.

Uses dG/dt = (dG/dx) * xdot + (dG/ddx) * xddot. When xpp is not available from the event-stage linearization, xpp=0 is used as a conservative fallback.

update_mti_boolean_state(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], h: float) → ndarray[tuple[Any, ...], dtype[float64]]

Update MTI boolean parameters using inequality feasibility only.

This mirrors MTI-style logic where region/mode selection is driven by inequality residuals (G <= 0), not by direct boolean guard evaluation.

update_variable_params(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]] | None = None)

Update the variable parameters.

VeraGridEngine.Simulations.Rms.problems.rms_problem_complex module

class VeraGridEngine.Simulations.Rms.problems.rms_problem_complex.RmsProblemComplex(grid: MultiCircuit, options: RmsOptions, pf_results: PowerFlowResults | None)

Bases: RmsProblemTemplate

Complex Phasor-based DAE (Differential-Algebraic Equation) class with MULTILINEAR equations.

This class uses complex phasor representation with a single complex voltage variable per bus. The key innovation is TRULY MULTILINEAR current equations:

I = Y * (Vf - Vt) [Linear in voltage!]

where Y = g + j*b is the complex admittance.

When expanded:

Ir = g*(Vrf - Vrt) - b*(Vif - Vit) Ii = g*(Vif - Vit) + b*(Vrf - Vrt)

These are truly linear - no products of voltage variables, no squares!

The current balance at each node (ΣI = 0) provides linear conservation equations.

Responsibilities:

• Store state and algebraic variables using complex phasor representation

• Store Jacobian matrices

• Store residual equations using multilinear current-based equations

• Store sparsity patterns

• Convert between polar (power flow) and complex phasor representations

CONSTANT_PARAMS_NAME = 'cprms'

DIFF_NAME = 'diff'

TIME_NAME = 'glob_time'

VARIABLE_PARAMS_NAME = 'vprms'

VARS_NAME = 'vars'

add_device_var(dev: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, var: Var)

Associate a variable with a device.

add_variables_to_compilation_dicts(elm: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, mdl: Block)

Add variables and parameters to the compilation dictionaries. :param elm: Device type to add variables to :param mdl: Block type to add variables to

property algebraic_vars

get_algebraic_var_number() → int

get_all_vars_number() → int

get_device_vars_dict() → Dict[Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, List[Var]]

Get dictionary of device variables.

get_diff_var_number() → int

get_dt()

get_dt_value()

get_dx(x: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float) → ndarray[tuple[Any, ...], dtype[float64]]

get_init_guess_info() → DataFrame

Returns a df with uid, name, and initial value for the system variables.

get_j11(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j12(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j21(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j22(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_states_number() → int

get_var_idx(v: Var) → int

Get variable index.

get_variable_parameter_number() → int

get_x0() → ndarray[tuple[Any, ...], dtype[float64]]

Helper function to build the initial vector.

rhs_algebraic(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

rhs_state(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

set_init_guess(mdl: Block, reference_powerflow: VarPowerFlowReferenceType, val: float)

Add values from powerflow to initial guess.

property state_and_algebraic_vars: List[Var]

property state_vars

property uid2idx_vars

update_variable_params(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]] | None = None)

Update the variable parameters.

property vars_glob_name2uid

VeraGridEngine.Simulations.Rms.problems.rms_problem_complex.setP(P: ndarray[tuple[Any, ...], dtype[object_]], P_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Set or add to P value at index k.

VeraGridEngine.Simulations.Rms.problems.rms_problem_complex.setQ(Q: ndarray[tuple[Any, ...], dtype[object_]], Q_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Set or add to Q value at index k.

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae module

class VeraGridEngine.Simulations.Rms.problems.rms_problem_dae.RmsProblemDae(grid: MultiCircuit, options: RmsOptions, pf_results: PowerFlowResults, progress_signal: DummySignal | None = None, progress_text: DummySignal | None = None)

Bases: RmsProblemTemplate

DAE (Differential-Algebraic Equation) class to store and manage.

Responsibilities:

• Store state and algebraic variables (x, y)

• Store Jacobian matrices

• Store residual equations

• Store sparsity patterns

CONSTANT_PARAMS_NAME = 'cprms'

DIFF_NAME = 'diff'

TIME_NAME = 'glob_time'

VARIABLE_PARAMS_NAME = 'vprms'

VARS_NAME = 'vrs'

add_block_variables_to_compilation_dicts(elm: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, mdl: Block)

add variables and parameters info to the system block

Parameters:

• elm (Union[VeraGridEngine.Devices.Substation.bus.Bus, VeraGridEngine.Devices.Injections.load.Load])

• mdl (VeraGridEngine.Utils.Symbolic.block.Block)

Returns:

Return type:

None

add_device_var(dev: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, var: Var)

Associate a variable with a device :param dev: Device :param var: Variable

add_variables_to_compilation_dicts(elm: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, mdl: Block)

advance_fmu_cs_devices(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], h: float) → None

Advance imported FMU Co-Simulation devices for one RMS communication step.

Parameters:

• t – Current simulation time.

• x_snapshot – Current accepted state vector.

• h – RMS communication step.

Returns:

None.

advance_fmu_me_devices(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], h: float) → None

Advance imported FMU Model Exchange devices for one RMS communication step.

Parameters:

• t – Current simulation time.

• x_snapshot – Current accepted state vector.

• h – RMS communication step.

Returns:

None.

property algebraic_eqs

Returns:

property algebraic_vars

property boundary_update

close_fmu_cs_devices() → None

Release imported FMU Co-Simulation devices after the RMS simulation ends.

Returns:

None.

close_fmu_me_devices() → None

Release imported FMU Model Exchange devices after the RMS simulation ends.

Returns:

None.

def_event_params_fn(ev_param: ndarray[tuple[Any, ...], dtype[float64]], t: float) → ndarray[tuple[Any, ...], dtype[float64]]

Evaluate runtime event parameter expressions while preserving mode latches.

Parameters:

• ev_param – Current runtime parameter vector.

• t – Simulation time.

Returns:

Updated runtime parameter vector.

property event_parameters_eqs

Returns:

property event_parameters_eqs0

Returns:

get_E_matrix(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]])

get_algebraic_var_number() → int

property get_algebraic_vars

Returns:

get_alias_names_dict()

get_all_vars_number() → int

get_compiler_names_dict()

get_device_vars_dict() → Dict[Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, List[Var]]

Returns:

Return type:

get_diff_var_number() → int

Get the number of diff vars :return:

get_diff_vars()

get_dt()

get_dt_value()

get_dx(x: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float) → ndarray[tuple[Any, ...], dtype[float64]]

get_dx0() → ndarray[tuple[Any, ...], dtype[float64]]

Helper function to build the initial vector :return: array matching with the mapping, matching the solver ordering

get_equation_at(i: int) → Expr

Get the equation at a global position :param i: :return:

get_eventparams0() → ndarray[tuple[Any, ...], dtype[float64]]

Helper function to build the initial vector :return: array matching with the mapping, matching the solver ordering

get_init_guess_info() → DataFrame

returns a df with uid, name, and initial value for the system variables :return: :rtype:

get_j11(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j12(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j21(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j22(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_next_forced_event_time(t_prev: float, t_target: float) → float | None

get_parameters_values() → List[Const]

get_states_number() → int

get_static_state_matrix(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]])

get_var_idx(v: Var) → int

Parameters:

v

Returns:

get_variable_parameter_number() → int

get_x0() → ndarray[tuple[Any, ...], dtype[float64]]

Helper function to build the initial vector :return: array matching with the mapping, matching the solver ordering

property glob_time

initialize_fmu_cs_devices(x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], t: float = 0.0) → None

Initialize imported FMU Co-Simulation devices before the RMS time loop starts.

Parameters:

• x_snapshot – Initial accepted state vector.

• t – Initial simulation time.

Returns:

None.

initialize_fmu_me_devices(x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], t: float = 0.0) → None

Initialize imported FMU Model Exchange devices before the RMS time loop starts.

Parameters:

• x_snapshot – Initial accepted state vector.

• t – Initial simulation time.

Returns:

None.

reset_boundary_update_state(t0: float = 0.0) → None

rhs_algebraic(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

rhs_state(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

set_events_group(rms_events_group: RmsEventsGroup)

add events modifying values of event_parameters equations :param rms_events_group: :return:

set_init_guess(mdl: Block, reference_powerflow: VarPowerFlowReferenceType, val: float)

add values from powerflow to initial guess

Parameters:

• mdl

• reference_powerflow

• val

Returns:

:rtype:self._

property state_and_algebraic_vars: List[Var]

Returns:

property state_eqs

Returns:

property state_vars

Returns:

property uid2idx_event_params

property uid2idx_params

property uid2idx_vars

Returns:

update(t: float, x: ndarray[tuple[Any, ...], dtype[float64]], params: ndarray[tuple[Any, ...], dtype[float64]]) → None

update_variable_params(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]] | None = None, scheduled_t: float | None = None) → None

Update the variable parameters. Continuous runtime parameters are re-evaluated, while retained mode parameters are left untouched unless updated by boundary logic.

The RMS implicit solve needs two distinct time views. Continuous ramp-like parameters must be evaluated at the current local target time so the nonlinear solve sees the interpolated value inside the step. Historical scheduled step events, however, must keep the pre-event value on the sample aligned with the event instant and only latch afterwards. The optional scheduled_t argument therefore lets the caller decouple the continuous symbolic evaluation time from the scheduled event boundary time without duplicating the update logic.

Parameters:

• t – Continuous symbolic evaluation time.

• x_snapshot – Current state snapshot used by boundary logic.

• scheduled_t – Optional time used by scheduled step logic.

Returns:

None.

property variable_parameters

Returns:

property vars_glob_name2uid

Returns:

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae.assign_line_static_parameters(elm: Any, parameter_reference: ParamPowerFlowReferenceType) → Const

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae.setP(P: ndarray[tuple[Any, ...], dtype[object_]], P_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Parameters:

• P

• P_used

• k

• val

Returns:

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae.setQ(Q: ndarray[tuple[Any, ...], dtype[object_]], Q_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Parameters:

• Q

• Q_used

• k

• val

Returns:

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae_vectorized module

class VeraGridEngine.Simulations.Rms.problems.rms_problem_dae_vectorized.RmsProblemDaeVec(grid: MultiCircuit, options: RmsOptions, pf_results: PowerFlowResults, progress_signal: DummySignal | None = None, progress_text: DummySignal | None = None)

Bases: RmsProblemTemplate

DAE (Differential-Algebraic Equation) class to store and manage.

Responsibilities:

• Store state and algebraic variables (x, y)

• Store Jacobian matrices

• Store residual equations

• Store sparsity patterns

CONSTANT_PARAMS_NAME = 'cprms'

DIFF_NAME = 'diff'

TIME_NAME = 'glob_time'

VARIABLE_PARAMS_NAME = 'vprms'

VARS_BUS_VAF_NAME = 'varsbusvaf'

VARS_BUS_VAT_NAME = 'varsbusvat'

VARS_BUS_VA_NAME = 'varsbusva'

VARS_BUS_VMF_NAME = 'varsbusvmf'

VARS_BUS_VMT_NAME = 'varsbusvmt'

VARS_BUS_VM_NAME = 'varsbusvm'

VARS_NAME = 'vrs'

add_block_variables_to_compilation_dicts(elm: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, mdl: Block)

add variables and parameters info to the system block

Parameters:

• elm (Union[VeraGridEngine.Devices.Substation.bus.Bus, VeraGridEngine.Devices.Injections.load.Load])

• mdl (VeraGridEngine.Utils.Symbolic.block.Block)

Returns:

Return type:

None

add_device_var(dev: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, var: Var)

Associate a variable with a device :param dev: Device :param var: Variable

add_variables_to_compilation_dicts(elm: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, mdl: Block)

advance_fmu_cs_devices(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], h: float) → None

Advance imported FMU Co-Simulation devices for one RMS communication step.

Parameters:

• t – Current simulation time.

• x_snapshot – Current accepted state vector.

• h – RMS communication step.

Returns:

None.

advance_fmu_me_devices(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], h: float) → None

Advance imported FMU Model Exchange devices for one RMS communication step.

Parameters:

• t – Current simulation time.

• x_snapshot – Current accepted state vector.

• h – RMS communication step.

Returns:

None.

property algebraic_eqs

Returns:

property algebraic_vars

property boundary_update

close_fmu_cs_devices() → None

Release imported FMU Co-Simulation devices after the RMS simulation ends.

Returns:

None.

close_fmu_me_devices() → None

Release imported FMU Model Exchange devices after the RMS simulation ends.

Returns:

None.

def_event_params_fn(ev_param: ndarray[tuple[Any, ...], dtype[float64]], t: float) → ndarray[tuple[Any, ...], dtype[float64]]

Evaluate runtime event parameter expressions while preserving mode latches.

Parameters:

• ev_param – Current runtime parameter vector.

• t – Simulation time.

Returns:

Updated runtime parameter vector.

property event_parameters_eqs

Returns:

property event_parameters_eqs0

Returns:

get_E_matrix(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]])

get_algebraic_var_number() → int

property get_algebraic_vars

Returns:

get_alias_names_dict()

get_all_vars_number() → int

get_compiler_names_dict()

get_device_vars_dict() → Dict[Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, List[Var]]

Returns:

Return type:

get_diff_var_number() → int

Get the number of diff vars :return:

get_diff_vars()

get_dt()

get_dt_value()

get_dx(x: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float) → ndarray[tuple[Any, ...], dtype[float64]]

get_dx0() → ndarray[tuple[Any, ...], dtype[float64]]

Helper function to build the initial vector :return: array matching with the mapping, matching the solver ordering

get_equation_at(i: int) → Expr

Get the equation at a global position :param i: :return:

get_eventparams0() → ndarray[tuple[Any, ...], dtype[float64]]

Helper function to build the initial vector :return: array matching with the mapping, matching the solver ordering

get_init_guess_info() → DataFrame

returns a df with uid, name, and initial value for the system variables :return: :rtype:

get_j11(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j11_vec(h: float) → csc_matrix

get_j12(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j12_vec(h: float) → csc_matrix

get_j21(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j21_vec(h: float) → csc_matrix

get_j22(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j22_vec(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float) → csc_matrix

get_next_forced_event_time(t_prev: float, t_target: float) → float | None

get_parameters_values() → List[Const]

get_states_number() → int

get_var_idx(v: Var) → int

Parameters:

v

Returns:

get_variable_parameter_number() → int

get_x0() → ndarray[tuple[Any, ...], dtype[float64]]

Helper function to build the initial vector :return: array matching with the mapping, matching the solver ordering

property glob_time

initialize_fmu_cs_devices(x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], t: float = 0.0) → None

Initialize imported FMU Co-Simulation devices before the RMS time loop starts.

Parameters:

• x_snapshot – Initial accepted state vector.

• t – Initial simulation time.

Returns:

None.

initialize_fmu_me_devices(x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], t: float = 0.0) → None

Initialize imported FMU Model Exchange devices before the RMS time loop starts.

Parameters:

• x_snapshot – Initial accepted state vector.

• t – Initial simulation time.

Returns:

None.

reset_boundary_update_state(t0: float = 0.0) → None

rhs_algebraic(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

rhs_algebraic_vec(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

rhs_state(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

rhs_state_vec() → ndarray[tuple[Any, ...], dtype[float64]]

set_events_group(rms_events_group: RmsEventsGroup)

add events modifying values of event_parameters equations :param rms_events_group: :return:

set_init_guess(mdl: Block, reference_powerflow: VarPowerFlowReferenceType, val: float)

add values from powerflow to initial guess

Parameters:

• mdl

• reference_powerflow

• val

Returns:

Return type:

property state_and_algebraic_vars: List[Var]

Returns:

property state_eqs

Returns:

property state_vars

Returns:

property uid2idx_event_params

property uid2idx_params

property uid2idx_vars

Returns:

update(t: float, x: ndarray[tuple[Any, ...], dtype[float64]], params: ndarray[tuple[Any, ...], dtype[float64]]) → None

update_input_matrices_by_model(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]])

update_variable_params(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]] | None = None, scheduled_t: float | None = None) → None

Update the variable parameters. Continuous runtime parameters are re-evaluated, while retained mode parameters are left untouched unless updated by boundary logic.

The RMS implicit solve needs two distinct time views. Continuous ramp-like parameters must be evaluated at the current local target time so the nonlinear solve sees the interpolated value inside the step. Historical scheduled step events, however, must keep the pre-event value on the sample aligned with the event instant and only latch afterwards. The optional scheduled_t argument therefore lets the caller decouple the continuous symbolic evaluation time from the scheduled event boundary time without duplicating the update logic.

Parameters:

• t – Continuous symbolic evaluation time.

• x_snapshot – Current state snapshot used by boundary logic.

• scheduled_t – Optional time used by scheduled step logic.

Returns:

None.

property variable_parameters

Returns:

property vars_glob_name2uid

Returns:

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae_vectorized.assign_line_static_parameters(elm: Any, parameter_reference: ParamPowerFlowReferenceType) → Const

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae_vectorized.assign_static_parameters(elm: Any, parameter_reference: ParamPowerFlowReferenceType) → Const

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae_vectorized.get_all_uids_from_block_composition_dict(block_composition_dict: Dict[int, List[int]]) → List[int]

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae_vectorized.setP(P: ndarray[tuple[Any, ...], dtype[object_]], P_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Parameters:

• P

• P_used

• k

• val

Returns:

VeraGridEngine.Simulations.Rms.problems.rms_problem_dae_vectorized.setQ(Q: ndarray[tuple[Any, ...], dtype[object_]], Q_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Parameters:

• Q

• Q_used

• k

• val

Returns:

VeraGridEngine.Simulations.Rms.problems.rms_problem_phasor module

class VeraGridEngine.Simulations.Rms.problems.rms_problem_phasor.RmsProblemPhasor(grid: MultiCircuit, options: RmsOptions, pf_results: PowerFlowResults | None, progress_signal: DummySignal | None = None)

Bases: RmsProblemTemplate

Phasor-based DAE (Differential-Algebraic Equation) class.

This class uses phasor representation (Vr, Vi) for voltages instead of polar coordinates (Vm, Va). The phasor representation makes current equations linear and is more suitable for certain analysis.

Responsibilities:

• Store state and algebraic variables (x, y) using phasor representation

• Store Jacobian matrices

• Store residual equations using phasor-based power flow equations

• Store sparsity patterns

• Convert between polar (power flow) and phasor (RMS) representations

CONSTANT_PARAMS_NAME = 'cprms'

DIFF_NAME = 'diff'

TIME_NAME = 'glob_time'

VARIABLE_PARAMS_NAME = 'vprms'

VARS_NAME = 'vars'

add_device_var(dev: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, var: Var)

Associate a variable with a device.

add_variables_to_compilation_dicts(elm: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, mdl: Block)

add variables and parameters info to the system block

Parameters:

• elm (Union[VeraGridEngine.Devices.Substation.bus.Bus, VeraGridEngine.Devices.Injections.load.Load])

• mdl (VeraGridEngine.Utils.Symbolic.block.Block)

Returns:

Return type:

None

advance_fmu_cs_devices(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], h: float) → None

Advance imported FMU Co-Simulation devices for one RMS communication step.

Parameters:

• t – Current simulation time.

• x_snapshot – Current accepted state vector.

• h – RMS communication step.

Returns:

None.

advance_fmu_me_devices(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], h: float) → None

Advance imported FMU Model Exchange devices for one RMS communication step.

Parameters:

• t – Current simulation time.

• x_snapshot – Current accepted state vector.

• h – RMS communication step.

Returns:

None.

property algebraic_vars

close_fmu_cs_devices() → None

Release imported FMU Co-Simulation devices after the RMS simulation ends.

Returns:

None.

close_fmu_me_devices() → None

Release imported FMU Model Exchange devices after the RMS simulation ends.

Returns:

None.

get_E_matrix(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]])

property get_algebraic_eqs

get_algebraic_var_number() → int

get_all_vars_number() → int

get_device_vars_dict() → Dict[Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, List[Var]]

Get dictionary of device variables.

get_diff_var_number() → int

get_dt()

get_dt_value()

get_dx(x: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float) → ndarray[tuple[Any, ...], dtype[float64]]

get_init_guess_info() → DataFrame

Returns a df with uid, name, and initial value for the system variables.

get_j11(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j12(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j21(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j22(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_next_forced_event_time(t_prev: float, t_target: float)

Phasor RMS currently has no forced sub-step events.

property get_state_eqs

get_states_number() → int

get_static_state_matrix(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]])

get_var_idx(v: Var) → int

Get variable index.

get_variable_parameter_number() → int

get_x0() → ndarray[tuple[Any, ...], dtype[float64]]

Helper function to build the initial vector.

initialize_fmu_cs_devices(x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], t: float = 0.0) → None

Initialize imported FMU Co-Simulation devices before the RMS time loop starts.

Parameters:

• x_snapshot – Initial accepted state vector.

• t – Initial simulation time.

Returns:

None.

initialize_fmu_me_devices(x_snapshot: ndarray[tuple[Any, ...], dtype[float64]], t: float = 0.0) → None

Initialize imported FMU Model Exchange devices before the RMS time loop starts.

Parameters:

• x_snapshot – Initial accepted state vector.

• t – Initial simulation time.

Returns:

None.

reset_boundary_update_state(t0: float = 0.0) → None

Reset runtime event-parameter state to the initial simulation time.

rhs_algebraic(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

rhs_state(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

set_events_group(rms_events_group: RmsEventsGroup)

Set the events group to use for this simulation. This filters events by group and recompiles the event parameters function.

Parameters:

rms_events_group – The RmsEventsGroup to use

set_init_guess(mdl: Block, reference_powerflow: VarPowerFlowReferenceType, val: float)

Add values from powerflow to initial guess.

property state_and_algebraic_vars: List[Var]

property state_vars

property uid2idx_vars

update(t: float, x: ndarray[tuple[Any, ...], dtype[float64]], params: ndarray[tuple[Any, ...], dtype[float64]]) → None

Store runtime event parameters for the current Newton step.

update_variable_params(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]] | None = None, scheduled_t: float | None = None)

Update the variable parameters.

property vars_glob_name2uid

VeraGridEngine.Simulations.Rms.problems.rms_problem_phasor.setIi(Ii: ndarray[tuple[Any, ...], dtype[object_]], Ii_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Set or add to Ii (imaginary current) value at index k.

VeraGridEngine.Simulations.Rms.problems.rms_problem_phasor.setIr(Ir: ndarray[tuple[Any, ...], dtype[object_]], Ir_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Set or add to Ir (real current) value at index k.

VeraGridEngine.Simulations.Rms.problems.rms_problem_phasor.setP(P: ndarray[tuple[Any, ...], dtype[object_]], P_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Set or add to P value at index k.

VeraGridEngine.Simulations.Rms.problems.rms_problem_phasor.setQ(Q: ndarray[tuple[Any, ...], dtype[object_]], Q_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Set or add to Q value at index k.

VeraGridEngine.Simulations.Rms.problems.rms_problem_template module

class VeraGridEngine.Simulations.Rms.problems.rms_problem_template.RmsProblemTemplate(progress_signal: DummySignal | None = None, progress_text: DummySignal | None = None)

Bases: ABC

algebraic_vars() → List

get_E_matrix(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]])

get_algebraic_var_number() → int

get_algebraic_vars() → List

get_all_vars_number() → int

get_diff_var_number() → int

get_dt()

get_dt_value()

get_dx(x: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float) → ndarray[tuple[Any, ...], dtype[float64]]

get_j11(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j12(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j21(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j22(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_states_number() → int

get_static_state_matrix(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]])

get_variable_parameter_number() → int

get_vars_info() → Dict[Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, List[Var]]

get_x0() → ndarray[tuple[Any, ...], dtype[float64]]

is_initialized() → bool

report_progress(val: float)

Report progress :param val: float value

report_progress2(current: int, total: int)

Report progress :param current: current value (zero based) :param total: total value

report_text(val: str)

Report text :param val: text value

rhs_algebraic(values: ndarray[tuple[Any, ...], dtype[float64]], diff_values: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

rhs_state(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

set_initialize_flag()

state_vars()

update_variable_params(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]] | None = None)

VeraGridEngine.Simulations.Rms.problems.rms_problem_tensygrid module

class VeraGridEngine.Simulations.Rms.problems.rms_problem_tensygrid.RmsProblemTensygrid(grid: MultiCircuit, options: RmsOptions, pf_results: PowerFlowResults | None, progress_signal: DummySignal | None = None)

Bases: RmsProblemTemplate

DAE (Differential-Algebraic Equation) class to store and manage.

Responsibilities:

• Store state and algebraic variables (x, y)

• Store Jacobian matrices

• Store residual equations

• Store sparsity patterns

CONSTANT_PARAMS_NAME = 'cprms'

DIFF_NAME = 'diff'

TIME_NAME = 'glob_time'

VARIABLE_PARAMS_NAME = 'vprms'

VARS_NAME = 'vars'

add_device_var(dev: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, var: Var)

Associate a variable with a device :param dev: Device :param var: Variable

add_variables_to_compilation_dicts(elm: Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, mdl: Block)

add variables and parameters info to the system block

Parameters:

• elm (Union[VeraGridEngine.Devices.Substation.bus.Bus, VeraGridEngine.Devices.Injections.load.Load])

• mdl (VeraGridEngine.Utils.Symbolic.block.Block)

Returns:

Return type:

None

property algebraic_vars

Returns:

get_algebraic_var_number() → int

get_all_vars_number() → int

get_device_vars_dict() → Dict[Generator | Battery | Load | ExternalGrid | StaticGenerator | Shunt | ControllableShunt | CurrentInjection | Line | DcLine | Transformer2W | HvdcLine | VSC | UPFC | Winding | Switch | SeriesReactance | FluidNode | FluidPath | FluidP2x | FluidTurbine | FluidPump | Substation | Bus | BusBar | VoltageLevel | Country | Region | Community | Municipality | Area | Zone | Transformer3W | TransformerNW | OverheadLineType | Wire | TransformerType | EmissionGas | BranchGroup | LineLocations | LineLocation | ModellingAuthority | Facility | Fuel | Investment | InvestmentsGroup | Contingency | ContingencyGroup | RemedialAction | RemedialActionGroup | Technology | Owner | UndergroundLineType | SequenceLineType | RmsModelTemplate | EmtModelTemplate | FmuTemplate | RmsEvent | RmsEventsGroup | ShortCircuitEvent | IfMeasurement | ItMeasurement | QfMeasurement | PfMeasurement | QtMeasurement | PtMeasurement | QiMeasurement | PiMeasurement | VmMeasurement | VaMeasurement | PgMeasurement | QgMeasurement, List[Var]]

Returns:

Return type:

get_diff_var_number() → int

Get the number of diff vars :return:

get_dt()

get_dt_value()

get_dx(x: ndarray[tuple[Any, ...], dtype[float64]], xn: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float) → ndarray[tuple[Any, ...], dtype[float64]]

get_init_guess_info() → DataFrame

returns a df with uid, name, and initial value for the system variables :return: :rtype:

get_j11(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j12(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j21(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_j22(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]], h: float)

get_states_number() → int

get_var_idx(v: Var) → int

Parameters:

v

Returns:

get_variable_parameter_number() → int

get_x0() → ndarray[tuple[Any, ...], dtype[float64]]

Helper function to build the initial vector :return: array matching with the mapping, matching the solver ordering

rhs_algebraic(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

rhs_state(x: ndarray[tuple[Any, ...], dtype[float64]], dx: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

set_events_group(rms_events_group: RmsEventsGroup)

Set the events group to use for this simulation. This filters events by group and recompiles the event parameters function.

Parameters:

rms_events_group – The RmsEventsGroup to use

set_init_guess(mdl: Block, reference_powerflow: VarPowerFlowReferenceType, val: float)

add values from powerflow to initial guess

Parameters:

• mdl

• reference_powerflow

• val

Returns:

Return type:

property state_and_algebraic_vars: List[Var]

Returns:

property state_vars

Returns:

property uid2idx_vars

Returns:

update_variable_params(t: float, x_snapshot: ndarray[tuple[Any, ...], dtype[float64]] | None = None)

Update the variable parameters :param t: :return:

update_variable_params_ts(x: ndarray[tuple[Any, ...], dtype[float64]], t: float)

Update the variable parameters :param t: :return:

property vars_glob_name2uid

Returns:

VeraGridEngine.Simulations.Rms.problems.rms_problem_tensygrid.setP(P: ndarray[tuple[Any, ...], dtype[object_]], P_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Parameters:

• P

• P_used

• k

• val

Returns:

VeraGridEngine.Simulations.Rms.problems.rms_problem_tensygrid.setQ(Q: ndarray[tuple[Any, ...], dtype[object_]], Q_used: ndarray[tuple[Any, ...], dtype[bool]], k: int, val: object)

Parameters:

• Q

• Q_used

• k

• val

Returns:

VeraGridEngine.Simulations.Rms.problems.single_block_dae_problem module

class VeraGridEngine.Simulations.Rms.problems.single_block_dae_problem.SingleBlockDaeProblem(block: Block)

Bases: object

Generic single-block DAE problem adapter.

This class is shaped like the problem API consumed by implicit RMS solvers, but focuses on one symbolic block and optional externally-driven inputs.

CONSTANT_PARAMS_NAME = 'cprms'

DIFF_NAME = 'diff'

VARIABLE_PARAMS_NAME = 'vprms'

VARS_NAME = 'vrs'

advance_fmu_cs_devices(t: float, x_snapshot: ndarray, h: float) → None

advance_fmu_me_devices(t: float, x_snapshot: ndarray, h: float) → None

property algebraic_vars

bind_input_constant(input_name: str, value: float) → None

bind_input_profile(input_name: str, profile: Callable[[float], float]) → None

close_fmu_cs_devices() → None

close_fmu_me_devices() → None

get_algebraic_var_number() → int

get_all_vars_number() → int

get_diff_var_number() → int

get_dx(x: ndarray, xn: ndarray, dx: ndarray, h: float) → ndarray

get_j11(x: ndarray, dx: ndarray, h: float) → csc_matrix

get_j12(x: ndarray, dx: ndarray, h: float) → csc_matrix

get_j21(x: ndarray, dx: ndarray, h: float) → csc_matrix

get_j22(x: ndarray, dx: ndarray, h: float) → csc_matrix

get_next_forced_event_time(t_prev: float, t_target: float)

get_states_number() → int

get_var_idx(v: Var) → int

get_x0() → ndarray

initialize_fmu_cs_devices(x0: ndarray, t0: float) → None

initialize_fmu_me_devices(x0: ndarray, t0: float) → None

list_inputs() → list[str]

report_progress2(step_idx: int, steps: int) → None

rhs_algebraic(x: ndarray, dx: ndarray) → ndarray

rhs_state(x: ndarray, dx: ndarray) → ndarray

update(t: float, x_snapshot: ndarray, event_values: ndarray) → None

update_variable_params(t: float, x_snapshot: ndarray | None = None) → None

update_variable_params_ts(x_snapshot: ndarray, t: float) → None

Module contents