h2integrate.control.control_rules.plant_dispatch_model

Classes

PyomoDispatchPlantModel(pyomo_model, ...[, ...])

Class defining Pyomo model and rule for the optimized dispatch for load following for the overall optimization problem describing the system.

class h2integrate.control.control_rules.plant_dispatch_model.PyomoDispatchPlantModel(pyomo_model, index_set, source_techs, tech_dispatch_models, time_weighting_factor, round_digits, block_set_name='plant')

Class defining Pyomo model and rule for the optimized dispatch for load following for the overall optimization problem describing the system.

Parameters:

• pyomo_model (pyo.ConcreteModel) – Externally defined Pyomo model that works as the base model that this class builds off of.

• index_set (pyo.Set) – Externally defined Pyomo index set for time steps. This should be consistent with the forecast horizon of the optimization problem.

• source_techs (list) – List of technology names that are being dispatched in the system.

• tech_dispatch_models (pyo.ConcreteModel) – Externally defined Pyomo model that contains the technology-specific dispatch rules and components for each technology in the system.

• time_weighting_factor (float) – Exponential time weighting factor for the optimization problem that defines if/how future time steps are discounted relative to the current time step in the optimization problem.

• round_digits (int) – Number of digits to round to in the Pyomo model.

• block_set_name (str, optional) – Name of the block set (model variables). Defaults to “plant”.

dispatch_block_rule(hybrid, t)

Creates and initializes pyomo dispatch model components for a the system-level dispatch

This method sets up all model elements (parameters, variables, constraints, and ports) associated with a pyomo block within the dispatch model.

Parameters:

• hybrid (pyo.ConcreteModel) – The Pyomo model to which the technology components will be added.

• t (int) – integer location of variables in the control time window

initialize_parameters(inputs, dispatch_params)

Initialize parameters for optimization model

inputs (dict):

Dictionary of numpy arrays (length = self.n_timesteps) containing at least:

f”{commodity}_in” : Available generated commodity profile. f”{commodity}_demand” : Demanded commodity output profile.

dispatch_inputs (dict): Dictionary of the dispatch input parameters from config

Parameters:

• inputs (dict)

• dispatch_params (dict)

_create_variables_and_ports(hybrid, t)

Connect variables and ports from individual technology model to system-level pyomo model instance.

Parameters:

• hybrid (pyo.ConcreteModel) – The Pyomo model to which the technology components will be added.

• t (int) – integer location of variables in the control time window

static _create_parameters(hybrid)

Create system-level pyomo model parameters

Parameters:

hybrid (pyo.ConcreteModel) – The Pyomo model to which the technology components will be added.

_create_hybrid_constraints(hybrid, t)

Define system-level constraints for pyomo model.

Parameters:

• hybrid (pyo.ConcreteModel) – The Pyomo model to which the technology components will be added.

• t (int) – integer location of variables in the control time window

create_arcs()

Defines the mapping between individual technology variables to system level

update_time_series_parameters(commodity_in=<class 'list'>, commodity_demand=<class 'list'>, updated_initial_soc=<class 'float'>)

Updates the pyomo optimization problem with parameters that change with time

Parameters:

• commodity_in (list) – List of generated commodity in for this time slice.

• commodity_demand (list) – The demanded commodity for this time slice.

• updated_initial_soc (float) – The updated initial state of charge for storage technologies for the current time slice.

create_min_operating_cost_expression()

Creates system-level instance of minimum operating cost objective for pyomo solver.

_delete_objective()

property time_weighting_factor: float

property storage_commodity_out: list

Storage commodity out.