Transmission Expansion Planning Module

This module provides functions for transmission expansion planning (TEP) analysis of AC/DC hybrid power systems. [1]

Functions are found in pyflow_acdc.ACDC_Static_TEP

Transmission Expansion Planning

This section creates an OPF model, chooses a state objective function. Afterwards it will include transmission expansion planning in the model and TEP objectives, finally solves the model.

Running one state transmission expansion planning

transmission_expansion(grid, NPV=True, n_years=25, Hy=8760, discount_rate=0.02, ObjRule=None, solver='bonmin')

Performs transmission expansion planning analysis.

Parameter	Type	Description	Default
grid	Grid	Grid to analyze	Required
NPV	bool	Calculate net present value	True
n_years	int	Number of years for NPV calculation	25
Hy	int	Hours per year	8760
discount_rate	float	Discount rate for NPV	0.02
ObjRule	dict	Objective function weights	None
solver	str	Solver to use	‘bonmin’

Returns

Returns a tuple containing:

• Model object

• Model results

• Timing information

• Solver statistics

Example

model, results, timing, stats = pyf.transmission_expansion(grid)

Running multiple scenario based transmission expansion planning

multi_scenario_TEP(grid, increase_Pmin=False, NPV=True, n_years=25, Hy=8760, discount_rate=0.02, clustering_options=None, ObjRule=None, solver='bonmin')

Performs a multiple scenario based transmission expansion planning analysis. It utilizes the clustering module to cluster the time series data into different states.

Parameter	Type	Description	Default
grid	Grid	Grid to analyze	Required
increase_Pmin	bool	Increase minimum power limit	False
NPV	bool	Calculate net present value	True
n_years	int	Number of years for NPV	25
Hy	int	Hours per year	8760
discount_rate	float	Discount rate for NPV	0.02
clustering_options	dict	Time series clustering options	None
ObjRule	dict	Objective function weights	None
solver	str	Solver to use	‘bonmin’

Returns

Returns a tuple containing:

• Model object

• Model results

• Timing information

• Solver statistics

• TEP time series results

Example

model, results, timing, stats, ts_results = pyf.multi_scenario_TEP(grid)

Linear and Sensitivity Utilities

linear_transmission_expansion(grid, NPV=True, n_years=25, Hy=8760, discount_rate=0.02, ObjRule=None, solver='gurobi', time_limit=300, tee=False, export=True, fs=False, obj_scaling=1.0)

Linearized TEP workflow suitable for faster studies and large sweeps.

alpha_paretto(grid, steps, ObjRule, NPV=True, n_years=25, Hy=8760, discount_rate=0.02, solver='bonmin', time_limit=None, tee=False, save_name=None, obj_scaling=1.0)

Computes Pareto-like trade-off points by sweeping alpha-style objective mixing.

rate_sensitivity(grid, steps, ObjRule, min_rate=0.0, max_rate=0.1, NPV=True, n_years=25, Hy=8760, solver='bonmin', time_limit=None, tee=False, obj_scaling=1.0)

Runs discount-rate sensitivity for TEP objective outcomes.

kappa_sensitivity(grid, steps, ObjRule, min_kappa=0.0, max_kappa=1.0, NPV=True, n_years=25, Hy=8760, discount_rate=0.02, solver='bonmin', time_limit=None, tee=False, obj_scaling=1.0)

Runs kappa-weight sensitivity for TEP objective outcomes.

comprehensive_sensitivity_analysis(grid, ObjRule, alpha_steps=None, rate_steps=None, kappa_steps=None, alpha_range=(0.0, 1.0), rate_range=(0.01, 0.1), kappa_range=(0.0, 1.0), n_years=25, Hy=8760, discount_rate=0.02, solver='bonmin', time_limit=None, tee=False, obj_scaling=1.0)

Convenience wrapper to execute multiple TEP sensitivity studies.

Element Expansion Helpers

expand_elements_from_pd(grid, exp_elements)

Applies expansion definitions from a pandas table.

repurpose_element_from_pd(grid, rec_elements)

Applies reconductoring/repurposing definitions from a pandas table.

update_attributes(element, n_b, n_i, n_max, life_time, base_cost, per_unit_cost, exp, n_inv_max=None)

Updates expansion-related attributes of a TEP-enabled element.

Expand_element(grid, name, n_b=None, n_i=None, n_max=None, life_time=None, base_cost=None, per_unit_cost=None, exp=None, update_grid=True, n_inv_max=None, **legacy_kwargs)

Enables or updates one element for TEP investment modeling.

Translate_pd_TEP(grid)

Builds pandas summaries from solved TEP model variables.

Transmission Expansion Planning objectives

TEP_obj(model, grid, NPV)

Returns the objective function for the transmission expansion planning based on [1]:

Description	Formula
AC expansion	\(\Psi_{exp}=\sum_{h \in \mathcal{E}_{ac}} \left[(n_h - n_{h,\text{b}}) \cdot \psi_h(L_h) \right]\)
AC reconducting	\(\Psi_{rec}=\sum_{u \in \mathcal{U}_{ac}} \left[\xi_u \cdot \psi_u(L_u) \right]\)
AC line selection	\(\Psi_{a}=\sum_{a \in \mathcal{E}_a} \sum_{n \in \mathcal{CT}} \left[ \xi_{a,n} \cdot \psi_n(L_a) \right]\)
DC expansion	\(\Psi_{dc}=\sum_{e \in \mathcal{E}_{dc}} \left[(n_e - n_{e,\text{b}}) \cdot \psi_e(L_e, p_e) \right]\)
Converter expansion	\(\Psi_{conv}=\sum_{cn \in \mathcal{E}_{cn}} \left[(n_{cn} - n_{cn,\text{b}}) \cdot \psi_{cn}(p_{cn}) \right]\)
General objective function	\(\Psi = \Psi_{exp}+\Psi_{rec}+\Psi_{a}+\Psi_{dc}+\Psi_{conv}\)
State objective function	\(\phi =\) OPF function
Net present value	\(\min \left[\frac{1 - \left(1 + r\right)^{-y}}{r} \cdot H_y \cdot \phi + \Psi \right]\)

Export Results

export_TEP_TS_results_to_excel(grid, export)

Exports time series TEP results to Excel file.

Parameter	Type	Description	Default
grid	Grid	Grid with results	Required
export	str	Export file path	Required

Example

pyf.export_TEP_TS_results_to_excel(grid, "results.xlsx")

References

[1] (1,2)

Castro Valerio, Bernardo and Cheah-Mane, Marc and Albernaz, Vinícius and Gebraad, Pieter and Gomis-Bellmunt, Oriol, Transmission Expansion Planning for Hybrid Ac/Dc Grids Using a Mixed-Integer Non-Linear Programming Approach. Available at SSRN: https://ssrn.com/abstract=5385596