VeraGridEngine.Simulations.InvestmentsEvaluation package

Subpackages

• VeraGridEngine.Simulations.InvestmentsEvaluation.Methods package
  • Submodules
  • VeraGridEngine.Simulations.InvestmentsEvaluation.Methods.NSGA_3 module
    • BitflipMutation
    • GridNsga
    • IntegerRandomSamplingVeraGrid
    • NSGA_3()
    • QuadBinarySampling
    • SkewedBinarySampling
    • SkewedIntegerSamplingRange
    • UniformBinarySampling
    • finalize_seed_population()
    • get_nsga3_initial_population()
  • VeraGridEngine.Simulations.InvestmentsEvaluation.Methods.mixed_variable_NSGA_2 module
    • MixedVariableProblem
    • NSGA_2()
  • VeraGridEngine.Simulations.InvestmentsEvaluation.Methods.random_eval module
    • random_trial()
  • VeraGridEngine.Simulations.InvestmentsEvaluation.Methods.stop_crits module
    • MaxItersStopCriterion
      • MaxItersStopCriterion.add()
    • StochStopCriterion
      • StochStopCriterion.add()
    • StopCriterion
      • StopCriterion.add()
  • Module contents
• VeraGridEngine.Simulations.InvestmentsEvaluation.Problems package
  • Submodules
  • VeraGridEngine.Simulations.InvestmentsEvaluation.Problems.adequacy_problem module
    • AdequacyInvestmentProblem
      • AdequacyInvestmentProblem.get_objectives_names()
      • AdequacyInvestmentProblem.get_vars_names()
      • AdequacyInvestmentProblem.n_objectives()
      • AdequacyInvestmentProblem.n_vars()
      • AdequacyInvestmentProblem.objective_function()
    • apply_actives_mask()
    • correct_x()
    • determine_starting_index_of_every_year()
  • VeraGridEngine.Simulations.InvestmentsEvaluation.Problems.black_box_problem_template module
    • BlackBoxProblemTemplate
      • BlackBoxProblemTemplate.get_investments_for_combination()
      • BlackBoxProblemTemplate.get_objectives_names()
      • BlackBoxProblemTemplate.get_vars_names()
      • BlackBoxProblemTemplate.n_objectives()
      • BlackBoxProblemTemplate.n_vars()
      • BlackBoxProblemTemplate.objective_function()
  • VeraGridEngine.Simulations.InvestmentsEvaluation.Problems.power_flow_problem module
    • PowerFlowInvestmentProblem
      • PowerFlowInvestmentProblem.get_objectives_names()
      • PowerFlowInvestmentProblem.get_vars_names()
      • PowerFlowInvestmentProblem.n_objectives()
      • PowerFlowInvestmentProblem.n_vars()
      • PowerFlowInvestmentProblem.objective_function()
    • power_flow_function()
  • VeraGridEngine.Simulations.InvestmentsEvaluation.Problems.power_flow_ts_problem module
    • TimeSeriesPowerFlowInvestmentProblem
      • TimeSeriesPowerFlowInvestmentProblem.get_objectives_names()
      • TimeSeriesPowerFlowInvestmentProblem.get_vars_names()
      • TimeSeriesPowerFlowInvestmentProblem.n_objectives()
      • TimeSeriesPowerFlowInvestmentProblem.n_vars()
      • TimeSeriesPowerFlowInvestmentProblem.objective_function()
    • power_flow_ts_function()
  • Module contents

Submodules

VeraGridEngine.Simulations.InvestmentsEvaluation.investments_evaluation_driver module

class VeraGridEngine.Simulations.InvestmentsEvaluation.investments_evaluation_driver.InvestmentsEvaluationDriver(grid: MultiCircuit, options: InvestmentsEvaluationOptions, problem: BlackBoxProblemTemplate, engine: EngineType = VeraGrid)

Bases: DriverTemplate

cancel()

Cancel the simulation

evaluate_individual_investments()

Run a one-by-one investment evaluation without considering multiple evaluation groups at a time

get_steps()

Returns:

independent_evaluation() → None

Evaluate projects with a CBA-like reference/PINT/TOOT ranking and then build a cumulative portfolio.

initialize(max_iter: int)

Initialize the results :param max_iter: Maximum iterations

name = 'Investments evaluation'

objective_function(x: ndarray[tuple[Any, ...], dtype[int64]], record_results: bool = True) → ndarray[tuple[Any, ...], dtype[float64]]

Function to evaluate a combination of investments :param x: vector of investments (yes/no). Length = number of investment groups :param record_results: record the results or not :return: multi-objective function criteria values

objective_function_so(x: ndarray[tuple[Any, ...], dtype[int64]]) → float

Single objective version of the objective function :param x: vector of investments (yes/no). Length = number of investment groups :return: summation of the objectives

optimized_evaluation_mixed_nsga2() → None

Run an optimized investment evaluation on mixed variables with NSGA2

optimized_evaluation_mvrsm_pareto() → None

Run an optimized investment evaluation without considering multiple evaluation groups at a time

optimized_evaluation_nsga3() → None

Run an optimized investment evaluation with NSGA3

optimized_evaluation_pint_toot_nsga3() → None

Run an NSGA3 search warm-started with the direct PINT and TOOT evaluations.

options

problem: BlackBoxProblemTemplate

randomized_evaluation() → None

Run purely random evaluations, without any optimization

run() → None

run the QThread

tpe = 'Investments evaluation'

VeraGridEngine.Simulations.InvestmentsEvaluation.investments_evaluation_options module

class VeraGridEngine.Simulations.InvestmentsEvaluation.investments_evaluation_options.InvestmentsEvaluationOptions(max_eval: int, pf_options: PowerFlowOptions | None = None, opf_options: OptimalPowerFlowOptions | None = None, solver: InvestmentEvaluationMethod = NSGA3, obj_tpe: InvestmentsEvaluationObjectives = PowerFlow, plugin_fcn_ptr: Callable = None)

Bases: OptionsTemplate

Investments Evaluation Options

CLASS_NON_EDITABLE_PROPERTIES: Tuple[str, ...] = ('idtag', 'diff_changes')

CLASS_PROPERTIES_WITH_PROFILE: Dict[str, str] = {}

CLASS_PROPERTY_DECLARATIONS: Tuple[GCProp, ...] = (prop:idtag, prop:name, prop:code, prop:rdfid, prop:action, prop:comment, prop:diff_changes, prop:max_eval, prop:pf_options, prop:opf_options, prop:solver, prop:objf_tpe)

CLASS_PROPERTY_LIST: Tuple[GCProp, ...] = (prop:idtag, prop:name, prop:code, prop:rdfid, prop:action, prop:comment, prop:diff_changes, prop:max_eval, prop:pf_options, prop:opf_options, prop:solver, prop:objf_tpe)

CLASS_REGISTERED_PROPERTIES: Dict[str, GCProp] = {'action': prop:action, 'code': prop:code, 'comment': prop:comment, 'diff_changes': prop:diff_changes, 'idtag': prop:idtag, 'max_eval': prop:max_eval, 'name': prop:name, 'objf_tpe': prop:objf_tpe, 'opf_options': prop:opf_options, 'pf_options': prop:pf_options, 'rdfid': prop:rdfid, 'solver': prop:solver}

LOCAL_PROPERTY_DECLARATIONS: Tuple[GCProp, ...] = (prop:max_eval, prop:pf_options, prop:opf_options, prop:solver, prop:objf_tpe)

action: ActionType

comment: str

device_type: DeviceType

diff_changes

selected_to_merge

VeraGridEngine.Simulations.InvestmentsEvaluation.investments_evaluation_results module

class VeraGridEngine.Simulations.InvestmentsEvaluation.investments_evaluation_results.InvestmentsEvaluationResults(f_names: ndarray[tuple[Any, ...], dtype[str_]], x_names: ndarray[tuple[Any, ...], dtype[str_]], max_eval: int, plot_x_idx: int, plot_y_idx: int)

Bases: ResultsTemplate

CLASS_DATA_VARIABLES = {'f': <VeraGridEngine.Simulations.results_template.ResultsProperty object>, 'f_best': <VeraGridEngine.Simulations.results_template.ResultsProperty object>, 'f_names': <VeraGridEngine.Simulations.results_template.ResultsProperty object>, 'max_eval': <VeraGridEngine.Simulations.results_template.ResultsProperty object>, 'plot_x_idx': <VeraGridEngine.Simulations.results_template.ResultsProperty object>, 'plot_y_idx': <VeraGridEngine.Simulations.results_template.ResultsProperty object>, 'sorting_indices': <VeraGridEngine.Simulations.results_template.ResultsProperty object>, 'x': <VeraGridEngine.Simulations.results_template.ResultsProperty object>, 'x_names': <VeraGridEngine.Simulations.results_template.ResultsProperty object>}

CLASS_RESULTS_DECLARATIONS = (<VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>)

LOCAL_RESULTS_DECLARATIONS = (<VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>, <VeraGridEngine.Simulations.results_template.ResultsProperty object>)

add(x_vec: ndarray[tuple[Any, ...], dtype[float64]], f_vec: ndarray[tuple[Any, ...], dtype[float64]]) → None

Parameters:

• x_vec

• f_vec

Returns:

property current_evaluation: int

property f: ndarray[tuple[Any, ...], dtype[float64]] | ndarray[tuple[int, int], dtype[float64]]

property f_best: ndarray[tuple[Any, ...], dtype[int64]]

f_names: StrVec

finalize()

Finalize the results after simulation

get_index() → ndarray[tuple[Any, ...], dtype[str_]]

property max_eval: int

mdl(result_type) → ResultsTable

Plot the results :param result_type: type of results (string) :return: DataFrame of the results (or None if the result was not understood)

plot_x_idx

plot_y_idx

set_at(i: int, x_vec: ndarray[tuple[Any, ...], dtype[float64]], f_vec: ndarray[tuple[Any, ...], dtype[float64]])

Parameters:

• i

• x_vec

• f_vec

Returns:

set_best_combination(combination: ndarray[tuple[Any, ...], dtype[int64]]) → None

Set the best combination of investment groups :param combination: Vector of integers (0/1)

property sorting_indices: ndarray[tuple[Any, ...], dtype[int64]]

tpe = 'Investments Evaluation Results'

property x: ndarray[tuple[Any, ...], dtype[float64]] | ndarray[tuple[int, int], dtype[float64]]

x_names: StrVec

Module contents