Grid

Grid#

Class that houses the hybrid system performance and financials. Enforces interconnection and curtailment limits based on PySAM’s Grid module

Grid Model#

class hopp.simulation.technologies.grid.Grid(site, config, config_name='CustomGenerationProfileSingleOwner')#

Bases: PowerSource

Parameters:

site (SiteInfo)
config (GridConfig)
config_name (str | None)

site: SiteInfo#

config: GridConfig#

missed_load: ndarray[tuple[Any, ...], dtype[float64]]#

missed_load_percentage: float#

schedule_curtailed: ndarray[tuple[Any, ...], dtype[float64]]#

schedule_curtailed_percentage: float#

total_gen_max_feasible_year1: ndarray[tuple[Any, ...], dtype[float64]]#

config_name: str | None#

simulate_grid_connection(hybrid_size_kw, total_gen, project_life, lifetime_sim, total_gen_max_feasible_year1, dispatch_options=None)#

Sets up and simulates hybrid system grid connection. Additionally, calculates missed load and curtailment (due to schedule) when a desired load is provided.

Parameters:

hybrid_size_kw (float) – Hybrid system capacity [kW]
total_gen (List[float] | ndarray[tuple[Any, ...], dtype[float64]]) – Hybrid system generation profile [kWh]
project_life (int) – Number of year in the analysis period (expected project lifetime) [years]
lifetime_sim (bool) – For simulation modules which support simulating each year of the project_life, whether or not to do so; otherwise the first year data is repeated
total_gen_max_feasible_year1 (List[float] | ndarray[tuple[Any, ...], dtype[float64]]) – Maximum generation profile of the hybrid system (for capacity payments) [kWh]
dispatch_options (HybridDispatchOptions | None) – Hybrid dispatch options class, deliminates if the higher power analysis for frequency regulation is run

calc_gen_max_feasible_kwh(interconnect_kw)#

Calculates the maximum feasible generation profile that could have occurred (year 1)

Args: :param interconnect_kw: Interconnection limit [kW]

Returns:: maximum feasible generation [kWh]
Parameters:: interconnect_kw (float)
Return type:: list

property system_capacity_kw: float#: System’s nameplate capacity [kW]

property interconnect_kw: float#: Interconnection limit [kW]

property curtailment_ts_kw: list#: Grid curtailment as energy delivery limit (first year) [MW]

property generation_profile: Sequence#: System power generated [kW]

property generation_profile_wo_battery: Sequence#: System power generated without battery [kW]

__init__(site, config, config_name='CustomGenerationProfileSingleOwner')#

Method generated by attrs for class Grid.

Parameters:

site (SiteInfo)
config (GridConfig)
config_name (str | None)

Return type:

None

_get_model_dict()#

Convenience method that wraps the attrs.asdict method. Returns the object’s parameters as a dictionary.

Returns:: dict – The provided or default, if no input provided, model settings as a dictionary.
Return type:: dict

property annual_energy_kwh: float#: Annual energy [kWh]

as_dict()#

Creates a JSON and YAML friendly dictionary that can be save for future reloading. This dictionary will contain only Python types that can later be converted to their proper Turbine formats.

Returns:: dict – All key, vaue pais required for class recreation.
Return type:: dict

assign(input_dict)#

Sets input variables in the PowerSource class or any of its subclasses (system or financial models)

Parameters:: input_dict (dict)

property benefit_cost_ratio: float#

Benefit cost ratio [-] = Benefits / Costs

Benefits include (using present values):

PPA, capacity payment, and curtailment revenues
Federal, state, utility, and other production-based incentive income
Salvage value

Costs: uses the present value of annual costs

calc_capacity_credit_percent(interconnect_kw)#

Calculates the capacity credit (value) using the last simulated year’s max feasible generation profile.

Parameters:: interconnect_kw (float) – Interconnection limit [kW]
Returns:: capacity value [%]
Return type:: float

calc_nominal_capacity(interconnect_kw)#

Calculates the nominal AC net system capacity based on specific technology.

Parameters:: interconnect_kw (float) – Interconnection limit [kW]
Returns:: system’s nominal AC net capacity [kW]

calculate_total_installed_cost(cost)#

Parameters:: cost (float)
Return type:: float

property capacity_credit_percent: float#: Capacity credit (eligible portion of nameplate) [%]

property capacity_factor: float#: System capacity factor [%]

property capacity_payment: list#: Capacity payment revenue [$]

property capacity_price: list#: Capacity payment price [$/MW]

property construction_financing_cost: float#

copy()#

Returns:: new instance

property cost_installed: float#: Net capital cost [$]

property debt_payment: tuple#: Debt total payment [$]

property degradation: tuple#: Annual energy degradation [%/year]

property dispatch#: Dispatch object

property dispatch_factors: tuple#: Time-series dispatch factors normalized by PPA price [-]

property energy_purchases: tuple#: Energy purchases from grid [$]

property energy_sales: tuple#: PPA revenue gross [$]

property energy_value: tuple#: PPA revenue net [$]

export()#

Returns:: dictionary of variables for system and financial

property federal_depreciation_total: tuple#: Total federal tax depreciation [$]

property federal_taxes: tuple#: Federal tax benefit (liability) [$]

classmethod from_dict(data)#

Maps a data dictionary to an attr-defined class.

TODO: Add an error to ensure that either none or all the parameters are passed in

Parameters:

data (dict) – dict The data dictionary to be mapped.

Returns:

cls: The attr-defined class.

property gen_max_feasible: list#: Maximum feasible generation profile that could have occurred (year 1)

property generation_profile_pre_curtailment: Sequence#: System power before grid interconnect [kW]

classmethod get_model_defaults()#

Produces a dictionary of the keyword arguments and their defaults.

Returns:: Dict[str, Any] – Dictionary of keyword argument: default.
Return type:: Dict[str, Any]

static import_financial_model(financial_model, system_model, config_name)#

initialize_financial_values()#

These values are provided as default values from PySAM but should be customized by user

Debt, Reserve Account and Construction Financing Costs are initialized to 0 Federal Bonus Depreciation also initialized to 0

property insurance_expense: tuple#: Insurance expense [$]

property internal_rate_of_return: float#: Internal rate of return (after-tax) [%]

property levelized_cost_of_energy_nominal: float#: Levelized cost (nominal) [cents/kWh]

property levelized_cost_of_energy_real: float#: Levelized cost (real) [cents/kWh]

property logger#

property net_present_value: float#: After-tax cumulative NPV [$]

property om_capacity#: Capacity-based O&M amount [$/kWcap]

property om_capacity_expense#: O&M capacity-based expense [$]

property om_fixed#: Fixed O&M annual amount [$/year]

property om_fixed_expense#: O&M fixed expense [$]

property om_production#: Production-based O&M amount [$/Mwh]

property om_total_expense#: Total operating expenses [$]

property om_variable#

Production-based O&M amount [$/kWh] For battery: production-based System Costs amount [$/kWh-discharged]

Type:: For non-battery technologies

property om_variable_expense#: O&M production-based expense [$]

plot(figure=None, axes=None, color='b', site_border_color='k', site_alpha=0.95, linewidth=4.0)#

property ppa_price: tuple#: PPA price [$/kWh]

set_overnight_capital_cost(overnight_capital_cost)#: Set overnight capital costs [$/kW].

setup_performance_model()#: Sets up performance model to before simulating power production. Required by specific technologies

simulate(interconnect_kw, project_life=25, lifetime_sim=False)#

Run the system and financial model

Parameters:

project_life (int) – int, Number of year in the analysis period (execepted project lifetime) [years]
lifetime_sim – bool, For simulation modules which support simulating each year of the project_life, whether or not to do so; otherwise the first year data is repeated
interconnect_kw (float)

simulate_financials(interconnect_kw, project_life)#

Runs the finanical model for individual sub-systems

Parameters:

interconnect_kw (float) – float, Hybrid interconnect limit [kW]
project_life (int) – int, Number of year in the analysis period (execepted project lifetime) [years]

Returns:

simulate_power(project_life, lifetime_sim=False)#

Runs the system models for individual sub-systems

Parameters:

project_life – int, Number of year in the analysis period (execepted project lifetime) [years]
lifetime_sim – bool, For simulation modules which support simulating each year of the project_life, whether or not to do so; otherwise the first year data is repeated

Returns:

property system_nameplate_mw: float#: System nameplate [MW]

property tax_incentives: list#: The sum of Federal and State PTC and ITC tax incentives [$]

property total_installed_cost: float#: Installed cost [$]

property total_revenue: list#: Total revenue [$]

value(var_name, var_value=None)#

Gets or Sets a variable value within either the system or financial PySAM models. Method looks in system model first. If unsuccessful, then it looks in the financial model.

Note

If system and financial models contain a variable with the same name, only the system model variable will be set.

value(var_name) Gets variable value

value(var_name, var_value) Sets variable value

Parameters:

var_name (str) – PySAM variable name
var_value – (optional) PySAM variable value

Returns:

Variable value (when getter)

property generation_curtailed: Sequence#: Generation curtailed due to interconnect limit [kW]

property curtailment_percent: float#: Annual energy loss from curtailment and interconnection limit [%]

property capacity_factor_after_curtailment: float#: Capacity factor of the curtailment (year 1) [%]

property capacity_factor_at_interconnect: float#: Capacity factor of the curtailment (year 1) [%]

Grid Configuration#

class hopp.simulation.technologies.grid.GridConfig(interconnect_kw, fin_model=None, ppa_price=None, name='Grid')#

Bases: BaseClass

Configuration data class for Grid.

Parameters:

interconnect_kw (float) – grid interconnection limit (kW)
fin_model (str | dict | Singleowner | CustomFinancialModel | None) –
Financial model. Can be any of the following:
- a string representing an argument to Singleowner.default
- a dict representing a CustomFinancialModel
- an object representing a CustomFinancialModel or Singleowner.Singleowner instance
ppa_price (Iterable | float | None) – PPA price [$/kWh] used in the financial model
name (str)

interconnect_kw: float#

fin_model: str | dict | Singleowner | CustomFinancialModel | None#

ppa_price: Iterable | float | None#

name: str#

__init__(interconnect_kw, fin_model=None, ppa_price=None, name='Grid')#

Method generated by attrs for class GridConfig.

Parameters:

interconnect_kw (float)
fin_model (str | dict | Singleowner | CustomFinancialModel | None)
ppa_price (Iterable | float | None)
name (str)

Return type:

None

_get_model_dict()#

Convenience method that wraps the attrs.asdict method. Returns the object’s parameters as a dictionary.

Returns:: dict – The provided or default, if no input provided, model settings as a dictionary.
Return type:: dict

as_dict()#

Creates a JSON and YAML friendly dictionary that can be save for future reloading. This dictionary will contain only Python types that can later be converted to their proper Turbine formats.

Returns:: dict – All key, vaue pais required for class recreation.
Return type:: dict

classmethod from_dict(data)#

Maps a data dictionary to an attr-defined class.

TODO: Add an error to ensure that either none or all the parameters are passed in

Parameters:

data (dict) – dict The data dictionary to be mapped.

Returns:

cls: The attr-defined class.

classmethod get_model_defaults()#

Produces a dictionary of the keyword arguments and their defaults.

Returns:: Dict[str, Any] – Dictionary of keyword argument: default.
Return type:: Dict[str, Any]

property logger#

Grid

Contents

Grid#

Grid Model#

Grid Configuration#