Wind Plant

Wind Generation class based on PySAM’s Windpower module

class hopp.simulation.technologies.wind.wind_plant.WindPlant(site: SiteInfo, config: WindConfig)

Bases: PowerSource

site: SiteInfo

config: WindConfig

config_name: str

_rating_range_kw: Tuple[int, int]

property wake_model: str

property num_turbines

property rotor_diameter

property turb_rating

kw rating of turbine

Type:

return

modify_powercurve(rotor_diam, rating_kw)

Recalculate the turbine power curve

Parameters:

• rotor_diam – meters

• rating_kw – kw

Returns:

__init__(site: SiteInfo, config: WindConfig) → None

Method generated by attrs for class WindPlant.

_get_model_dict() → dict

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

Returns:

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() → 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:

All key, vaue pais required for class recreation.

Return type:

dict

assign(input_dict: dict)

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

property benefit_cost_ratio: float

Benefit cost ratio [-] = Benefits / Costs

Benefits include (using present values):

1. PPA, capacity payment, and curtailment revenues

2. Federal, state, utility, and other production-based incentive income

3. Salvage value

Costs: uses the present value of annual costs

calc_capacity_credit_percent(interconnect_kw: float) → float

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

Parameters:

interconnect_kw – Interconnection limit [kW]

Returns:

capacity value [%]

calc_gen_max_feasible_kwh(interconnect_kw: float) → list

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

Parameters:

interconnect_kw – Interconnection limit [kW]

Returns:

maximum feasible generation [kWh]

calc_nominal_capacity(interconnect_kw: float)

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

Parameters:

interconnect_kw – Interconnection limit [kW]

Returns:

system’s nominal AC net capacity [kW]

calculate_total_installed_cost(cost: float) → 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: dict)

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 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: list

System power generated [kW]

classmethod get_model_defaults() → Dict[str, Any]

Produces a dictionary of the keyword arguments and their defaults.

Returns:

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

modify_coordinates(xcoords: Sequence, ycoords: Sequence)

Change the location of the turbines

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: float, project_life: int = 25, lifetime_sim=False)

Run the system and financial model

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

simulate_financials(interconnect_kw: float, project_life: int)

Runs the finanical model for individual sub-systems

Parameters:

• interconnect_kw – float, Hybrid interconnect limit [kW]

• project_life – 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: str, 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 – PySAM variable name

• var_value – (optional) PySAM variable value

Returns:

Variable value (when getter)

system_capacity_by_rating(wind_size_kw: float)

Sets the system capacity by adjusting the rating of the turbines within the provided boundaries

Parameters:

wind_size_kw – desired system capacity in kW

system_capacity_by_num_turbines(wind_size_kw)

Sets the system capacity by adjusting the number of turbines

Parameters:

wind_size_kw – desired system capacity in kW

property system_capacity_kw

System’s nameplate capacity [kW]

class hopp.simulation.technologies.wind.wind_plant.WindConfig(num_turbines: int, turbine_rating_kw: float, rotor_diameter: float | None = None, layout_params: dict | WindBoundaryGridParameters | None = None, hub_height: float | None = None, layout_mode: str = 'grid', model_name: str = 'pysam', model_input_file: str | None = None, rating_range_kw: Tuple[int, int] = (1000, 3000), floris_config: dict | str | Path | None = None, operational_losses: float = 12.83, timestep: Tuple[int, int] | None = None, fin_model: dict | Singleowner | CustomFinancialModel | None = None)

Bases: BaseClass

Configuration class for WindPlant.

Parameters:

• num_turbines – number of turbines in the farm

• turbine_rating_kw – turbine rating

• rotor_diameter – turbine rotor diameter

• hub_height – turbine hub height

• layout_mode –

  • ‘boundarygrid’: regular grid with boundary turbines, requires WindBoundaryGridParameters as ‘params’

  • ’grid’: regular grid with dx, dy distance, 0 angle; does not require ‘params’

• model_name – which model to use. Options are ‘floris’ and ‘pysam’

• model_input_file – file specifying a full PySAM input

• layout_params – layout configuration

• rating_range_kw – allowable kw range of turbines, default is 1000 - 3000 kW

• floris_config – Floris configuration, only used if model_name == ‘floris’

• operational_losses – total percentage losses in addition to wake losses, defaults based on PySAM (only used for Floris model)

• timestep – Timestep (required for floris runs, otherwise optional)

• fin_model –

  Optional 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

num_turbines: int

turbine_rating_kw: float

rotor_diameter: float | None

layout_params: dict | WindBoundaryGridParameters | None

hub_height: float | None

layout_mode: str

model_name: str

model_input_file: str | None

rating_range_kw: Tuple[int, int]

floris_config: dict | str | Path | None

operational_losses: float

timestep: Tuple[int, int] | None

fin_model: dict | Singleowner | CustomFinancialModel | None

__init__(num_turbines: int, turbine_rating_kw: float, rotor_diameter: float | None = None, layout_params: dict | WindBoundaryGridParameters | None = None, hub_height: float | None = None, layout_mode: str = 'grid', model_name: str = 'pysam', model_input_file: str | None = None, rating_range_kw: Tuple[int, int] = (1000, 3000), floris_config: dict | str | Path | None = None, operational_losses: float = 12.83, timestep: Tuple[int, int] | None = None, fin_model: dict | Singleowner | CustomFinancialModel | None = None) → None

Method generated by attrs for class WindConfig.

_get_model_dict() → dict

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

Returns:

The provided or default, if no input provided, model settings as a dictionary.

Return type:

dict

as_dict() → 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:

All key, vaue pais required for class recreation.

Return type:

dict

classmethod from_dict(data: dict)

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 The data dictionary to be mapped.

Returns:

cls

The attr-defined class.

classmethod get_model_defaults() → Dict[str, Any]

Produces a dictionary of the keyword arguments and their defaults.

Returns:

Dictionary of keyword argument: default.

Return type:

Dict[str, Any]

property logger