reV.bespoke.place_turbines.PlaceTurbines

class PlaceTurbines(wind_plant, objective_function, capital_cost_function, fixed_operating_cost_function, variable_operating_cost_function, balance_of_system_cost_function, include_mask, pixel_side_length, min_spacing, wake_loss_multiplier=1)[source]

Bases: object

Framework for optimizing turbine locations for site specific exclusions, wind resources, and objective

Parameters:

wind_plant (WindPowerPD) – wind plant object to analyze wind plant performance. This object should have everything in the plant defined, such that only the turbine coordinates and plant capacity need to be defined during the optimization.
objective_function (str) – The objective function of the optimization as a string, should return the objective to be minimized during layout optimization. Variables available are:
- n_turbines: the number of turbines
- system_capacity: wind plant capacity
- aep: annual energy production
- avg_sl_dist_to_center_m: Average straight-line distance to the supply curve point center from all turbine locations (in m). Useful for computing plant BOS costs.
- avg_sl_dist_to_medoid_m: Average straight-line distance to the medoid of all turbine locations (in m). Useful for computing plant BOS costs.
- nn_conn_dist_m: Total BOS connection distance using nearest-neighbor connections. This variable is only available for the balance_of_system_cost_function equation.
- fixed_charge_rate: user input fixed_charge_rate if included as part of the sam system config.
- capital_cost: plant capital cost as evaluated by capital_cost_function
- fixed_operating_cost: plant fixed annual operating cost as evaluated by fixed_operating_cost_function
- variable_operating_cost: plant variable annual operating cost as evaluated by variable_operating_cost_function
- balance_of_system_cost: plant balance of system cost as evaluated by balance_of_system_cost_function
- self.wind_plant: the SAM wind plant object, through which all SAM variables can be accessed
capital_cost_function (str) – The plant capital cost function as a string, must return the total capital cost in $. Has access to the same variables as the objective_function.
fixed_operating_cost_function (str) – The plant annual fixed operating cost function as a string, must return the fixed operating cost in $/year. Has access to the same variables as the objective_function.
variable_operating_cost_function (str) – The plant annual variable operating cost function as a string, must return the variable operating cost in $/kWh. Has access to the same variables as the objective_function. You can set this to “0” to effectively ignore variable operating costs.
balance_of_system_cost_function (str) – The plant balance-of-system cost function as a string, must return the variable operating cost in $. Has access to the same variables as the objective_function. You can set this to “0” to effectively ignore balance-of-system costs.
include_mask (np.ndarray) – Supply curve point 2D inclusion mask where included pixels are set to 1 and excluded pixels are set to 0.
pixel_side_length (int) – Side length (m) of a single pixel of the include_mask.
min_spacing (float) – The minimum spacing between turbines (in meters).
wake_loss_multiplier (float, optional) – A multiplier used to scale the annual energy lost due to wake losses. IMPORTANT: This multiplier will ONLY be applied during the optimization process and will NOT be come through in output values such as aep, any of the cost functions, or even the output objective.

Methods

`capital_cost_per_kw`(capacity_mw)	Capital cost function ($ per kW) evaluated for a given capacity.
`define_exclusions`()	From the exclusions data, create a shapely MultiPolygon as self.safe_polygons that defines where turbines can be placed.
`initialize_packing`()	Run the turbine packing algorithm (maximizing plant capacity) to define potential turbine locations that will be used as design variables in the gentic algorithm.
`optimization_objective`(x)	The optimization objective used in the bespoke optimization
`optimize`(**kwargs)	Optimize wind farm layout.
`place_turbines`(**kwargs)	Define bespoke wind plant turbine layouts.

Attributes

`aep`	Wrapper to return None if PlaceTurbines is not optimized
`area`	Wrapper to return None if PlaceTurbines is not optimized
`avg_sl_dist_to_center_m`	Wrapper to return None if PlaceTurbines is not optimized
`avg_sl_dist_to_medoid_m`	Wrapper to return None if PlaceTurbines is not optimized
`balance_of_system_cost`	Wrapper to return None if PlaceTurbines is not optimized
`capacity`	Wrapper to return None if PlaceTurbines is not optimized
`capacity_density`	Wrapper to return None if PlaceTurbines is not optimized
`capital_cost`	Wrapper to return None if PlaceTurbines is not optimized
`convex_hull`	Wrapper to return None if PlaceTurbines is not optimized
`convex_hull_area`	Wrapper to return None if PlaceTurbines is not optimized
`convex_hull_capacity_density`	Wrapper to return None if PlaceTurbines is not optimized
`fixed_charge_rate`	Fixed charge rate if input to the SAM WindPowerPD object, None if not found in inputs.
`fixed_operating_cost`	Wrapper to return None if PlaceTurbines is not optimized
`full_cell_area`	Wrapper to return None if PlaceTurbines is not optimized
`full_cell_capacity_density`	Wrapper to return None if PlaceTurbines is not optimized
`nn_conn_dist_m`	Wrapper to return None if PlaceTurbines is not optimized
`nturbs`	Wrapper to return None if PlaceTurbines is not optimized
`objective`	Wrapper to return None if PlaceTurbines is not optimized
`turbine_x`	Wrapper to return None if PlaceTurbines is not optimized
`turbine_y`	Wrapper to return None if PlaceTurbines is not optimized
`variable_operating_cost`	Wrapper to return None if PlaceTurbines is not optimized

define_exclusions()[source]: From the exclusions data, create a shapely MultiPolygon as self.safe_polygons that defines where turbines can be placed.

initialize_packing()[source]: Run the turbine packing algorithm (maximizing plant capacity) to define potential turbine locations that will be used as design variables in the gentic algorithm.

optimization_objective(x)[source]: The optimization objective used in the bespoke optimization

optimize(**kwargs)[source]

Optimize wind farm layout.

Use a genetic algorithm to optimize wind plant layout for the user-defined objective function.

Parameters:: **kwargs – Keyword arguments to pass to GA initialization.