pownet.optim_model package

Subpackages

• pownet.optim_model.constraints package
  • Submodules
  • pownet.optim_model.constraints.energy_storage_constr module
    • add_c_link_ess_charge()
    • add_c_link_ess_discharge()
    • add_c_link_ess_state()
    • add_c_unit_ess_balance()
    • add_c_unit_ess_balance_init()
  • pownet.optim_model.constraints.nondispatch_constr module
    • add_c_hourly_unit_ub()
    • add_c_hydro_limit_daily()
    • add_c_hydro_limit_daily_dict()
    • add_c_hydro_limit_weekly()
    • add_c_link_unit_pu()
  • pownet.optim_model.constraints.system_constr module
    • add_c_angle_diff()
    • add_c_flow_balance()
    • add_c_kirchhoff()
    • add_c_link_shortfall()
    • add_c_ref_node()
    • add_c_reserve_req_1()
    • add_c_reserve_req_2()
    • add_c_thermal_curtail_ess()
    • add_c_unit_curtail_ess()
    • add_c_unit_curtail_ess_daily()
  • pownet.optim_model.constraints.thermal_unit_constr module
    • add_c_link_ppbar()
    • add_c_link_pthermal()
    • add_c_link_pu_lower()
    • add_c_link_pu_upper()
    • add_c_link_spin()
    • add_c_link_uvw()
    • add_c_link_uvw_init()
    • add_c_min_down()
    • add_c_min_down_init()
    • add_c_min_up()
    • add_c_min_up_init()
    • add_c_peak_down_bound()
    • add_c_peak_up_bound()
    • add_c_ramp_down()
    • add_c_ramp_down_init()
    • add_c_ramp_up()
    • add_c_ramp_up_init()
  • Module contents

Submodules

pownet.optim_model.model module

model.py: PowerSystemModel is wrapper of gurobipy.Model and highspy.Highs to provide a more user-friendly interface while compatible with HiGHs solver.

class pownet.optim_model.model.PowerSystemModel(model)

Bases: object

check_feasible()

Return type:

bool

get_model()

get_objval()

Return type:

float

get_runtime()

Return type:

float

get_runtime_gurobi()

Return type:

float

get_runtime_highs()

Return type:

float

get_solution()

Return type:

DataFrame

get_solution_gurobi()

Return type:

dict

get_solution_highs()

Return type:

dict

get_status()

optimize(solver='gurobi', log_to_console=True, mipgap=0.001, timelimit=600, num_threads=0)

optimize_with_rounding(rounding_strategy, max_rounding_iter, threshold=0, mipgap=0.001, timelimit=600, num_threads=0, log_to_console=False)

Return type:

None

print_added_constraints()

solve_for_export_capacity(shared_nodes, sim_horizon, step_k)

Return the export capacity and hourly prices at the shared nodes

Return type:

tuple

solve_for_export_prices(shared_nodes, sim_horizon, step_k)

The export prices are locational marginal prices at the shared nodes.

Return type:

DataFrame

solve_for_lmp()

Return type:

dict

solve_for_lmp_gurobi()

Return the locational marginal price (LMP).

Parameters:

model – The Gurobi model.

Return type:

dict

Returns:

The LMP at each node.

solve_for_lmp_highs()

Return type:

dict

write_ilp_mps(output_folder, instance_name)

write_mps(output_folder, filename)

pownet.optim_model.objfunc module

objfunc.py: Functions for constructing the objective function.

pownet.optim_model.objfunc.get_marginal_cost_coeff(step_k, timesteps, units, nondispatch_contracts, contract_costs)

Generic helper function to calculate coefficients based on marginal cost or a similar attribute for a list of units.

Parameters:

• step_k (int) – Current step in the simulation

• timesteps (range) – Range of timesteps for the simulation

• units (list) – List of units to calculate coefficients for

• nondispatch_contracts (dict) – Dictionary mapping units to their respective contracts

• contract_costs (dict) – Dictionary mapping contracts to their respective costs

Return type:

dict

Returns

A dictionary mapping (unit, t) tuples to the calculated coefficients

pownet.optim_model.objfunc.get_thermal_fixed_coeff(timesteps, thermal_units, thermal_fixed_cost, thermal_rated_capacity)

Fixed cost is a function of rated capacity and fixed cost per unit.

Return type:

dict

pownet.optim_model.objfunc.get_thermal_opex_coeff(step_k, timesteps, thermal_units, thermal_opex, fuel_contracts, contract_costs, thermal_heat_rate)

Variable cost is a function of fuel cost, heat rate, and opex.

Return type:

dict

pownet.optim_model.objfunc.get_thermal_startup_coeff(timesteps, thermal_units, thermal_startup_cost, thermal_rated_capacity)

Startup cost is a function of rated capacity and startup cost per unit.

Return type:

dict

pownet.optim_model.rounding_algo module

rounding_algo.py: Functions to perform iterative rounding.

pownet.optim_model.rounding_algo.check_binary_values(var_dict)

Check if all variables in a dictionary have binary values (0 or 1).

Parameters:

var_dict (dict) – A dictionary where keys are variable names and values are gurobipy.Var objects.

Returns:

True if all variables have binary values, False otherwise.

Return type:

bool

pownet.optim_model.rounding_algo.fast_rounding(fraction_vars, threshold=0)

Return type:

None

pownet.optim_model.rounding_algo.find_fraction_vars(binary_vars, atol=1e-05)

Return a list of variable names when their values are fractional.

Return type:

dict

pownet.optim_model.rounding_algo.get_variables(model, target_varnames=None)

Extract non-binary variables from a Gurobi model.

Parameters:

• model (gp.Model) – The Gurobi model to extract variables from.

• target_varnames (list[str], optional) – A list of variable name prefixes to include. If None, defaults to [“status”].

Returns:

A dictionary mapping variable names to their corresponding

non-binary values (v.X).

Return type:

dict

pownet.optim_model.rounding_algo.optimize_with_rounding(model, rounding_strategy, threshold, max_rounding_iter, mipgap, timelimit, num_threads, log_to_console)

Optimize a Gurobi model using iterative rounding with a given threshold.

This function first relaxes the input model and then iteratively rounds fractional variables until an integer solution is found or the maximum number of iterations is reached.

Parameters:

• model (gp.Model) – The Gurobi model to optimize.

• threshold (float) – The threshold for rounding fractional variables.

• max_rounding_iter (int) – The maximum number of rounding iterations.

• log_to_console (bool) – Whether to log optimization output to the console.

• mipgap (float) – The relative MIP optimality gap.

• timelimit (int) – The time limit for the optimization in seconds.

• num_threads (int) – The number of threads to use for optimization.

Returns:

The optimized Gurobi model.

Return type:

gp.Model

pownet.optim_model.rounding_algo.round_down(variable)

Return type:

None

pownet.optim_model.rounding_algo.round_up(variable)

Return type:

None

pownet.optim_model.rounding_algo.slow_rounding(fraction_vars, threshold=0)

Iteratively rounding variables with the largest value at each iteration. Values above the threshold are rounded up. Values below the threshold are rounded down.

Return type:

None

pownet.optim_model.variable_func module

variable_func.py: Contains functions for adding variables to the optimization model.

pownet.optim_model.variable_func.add_var_with_variable_ub(model, varname, timesteps, step_k, units, capacity_df)

Add a variable with a variable upper bound in a day-ahead rolling horizon optimization.

Parameters:

• model (gp.Model) – The optimization model.

• varname (str) – The name of the variable.

• timesteps (range) – The range of timesteps.

• step_k (int) – The step index.

• units (list) – The list of units.

• capacity_df (pd.DataFrame) – The dataframe of capacities.

Returns:

The variable with a variable upper bound.

Return type:

gp.tupledict

pownet.optim_model.variable_func.update_flow_vars(flow_variables, step_k, capacity_df, line_capacity_factor)

Update the lower and upper bounds of the flow variables based on the capacity dataframes

Return type:

None

pownet.optim_model.variable_func.update_var_with_variable_ub(variables, step_k, capacity_df)

Update the time-dependent upper bound of the variable.

Parameters:

• variables (gp.tupledict) – The variable with a variable upper bound.

• step_k (int) – The step index.

• capacity_df (pd.DataFrame) – The dataframe of capacities.

Return type:

None

Returns:

None

Module contents

The optim_model module provides the core optimization model for power system operations.