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_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.transmission_expansion_TS(grid)

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