Wind Farm Array Sizing Module¶

This module provides functions for wind farm array sizing based on [1].

Sequential Cable Sizing (CSS)¶

sequential_CSS(grid, NPV=True, n_years=25, Hy=8760, discount_rate=0.02, ObjRule=None, max_turbines_per_string=None, limit_crossings=True, MIP_solver='glpk', CSS_L_solver='gurobi', CSS_NL_solver='bonmin', svg=None, max_iter=None, time_limit=300, NL=False, tee=False, fs=False)¶

Iteratively alternates between a path selection MIP and a linear/nonlinear OPF-based cable type selection to converge to an efficient array layout. Returns models, a summary of iterations, timing info, solver stats, and the best iteration index.

Parameter	Type	Description	Default
`grid`	Grid	Grid with candidate array lines and cable options	Required
`max_turbines_per_string`	int	Optional cap on per-string turbines (sets MIP flow bound)	None
`limit_crossings`	bool	Enforce one-active-per-crossing-group	True
`MIP_solver`	str	Solver for path MIP (e.g., `glpk`/`gurobi`)	‘glpk’
`CSS_L_solver`	str	Solver for linear OPF step	‘gurobi’
`CSS_NL_solver`	str	Solver for nonlinear OPF step (if `NL=True`)	‘bonmin’
`time_limit`	int	Solver time limit in seconds	300
`NL`	bool	Use nonlinear OPF instead of linear in CSS	False

Example

import pyflow_acdc as pyf

grid,res = pyf.barrow()
print('grid loaded')
pyf.sequential_CSS(grid,NPV=True,max_turbines_per_string=None,MIP_solver='gurobi',CSS_L_solver='gurobi',max_iter=None,time_limit=300,tee=True)


res.All()

MIP Path Selection (Array)¶

MIP_path_graph(grid, max_flow=None, solver_name='glpk', crossings=False, tee=False, callback=False)¶

Solves a master MIP to select array connection paths minimizing total cable length, with optional crossing constraints and Gurobi callback to record feasible solutions over time. Activates cable types on candidate lines upon success.

Parameter	Type	Description	Default
`grid`	Grid	Grid with candidate array lines	Required
`max_flow`	int	Per-line absolute flow bound (≈ turbines per string)	`\|nodes\|-1`
`solver_name`	str	‘glpk’ or ‘gurobi’ (callback supported with Gurobi)	‘glpk’
`crossings`	bool	Enforce one-active-per-crossing-group	False
`callback`	bool	Enable Gurobi MIPSOL callback to track (time, objective)	False

Returns

flag (bool): feasible solution found
high_flow (int|None): maximum absolute line flow
model: Pyomo model
feasible_solutions: list of (time, objective) pairs (if callback)

Example

import pyflow_acdc as pyf

grid,res = pyf.anholt()

flag, high_flow,model_MIP,feasible_solutions_MIP = pyf.MIP_path_graph(grid, max_flow=10,
                                                                      solver_name='gurobi',
                                                                      crossings=True, tee=True,callback=True)
pyf.ACDC_TEP.plot_feasible_solutions(
                feasible_solutions_MIP,
                'MIP',
                show=True)

Simplified CSS Workflow¶

simple_CSS(grid, NPV=True, n_years=25, Hy=8760, discount_rate=0.02, ObjRule=None, CSS_L_solver='gurobi', CSS_NL_solver='bonmin', time_limit=1200, NL=False, tee=False, export=True, fs=False)¶: Runs a simplified sequential cable sizing workflow with reduced setup.

simple_assign_cable_types(grid, model, t_MW=None)¶: Assigns cable types from an optimized model back into the grid.

Linear CSS Solvers¶

Optimal_L_CSS_gurobi(grid, OPEX=True, NPV=True, n_years=25, Hy=8760, discount_rate=0.02, tee=False, time_limit=300)¶: Solves the linear CSS formulation with Gurobi.

Optimal_L_CSS_ortools(grid, OPEX=True, NPV=True, n_years=25, Hy=8760, discount_rate=0.02, tee=False, time_limit=300)¶: Solves the linear CSS formulation with OR-Tools.