Time Series Module

This module provides functions for time series analysis of power flows and optimal power flow.

functions are found in pyflow_acdc.Time_series

Sequential AC/DC Time Series Power Flow

Cross-sectional time series

TS_ACDC_PF(grid, start=1, end=99999, print_step=False)

Performs sequential AC/DC power flow for time series data.

Parameter	Type	Description	Default
grid	Grid	Grid to analyze	Required
start	int	Start time step	1
end	int	End time step	99999
print_step	bool	Print progress	False

Returns

Results are stored in grid.time_series_results dictionary with the following keys:

• PF_results: Node voltages and power flows

• line_loading: Line loading percentages

• ac_line_loading: AC line loading percentages

• dc_line_loading: DC line loading percentages

• converter_loading: Converter loading percentages

• grid_loading: Overall grid loading

Example

pyf.TS_ACDC_PF(grid, start=1, end=24)

Simple Time-Series Power Flow

Time_series_PF(grid)

Runs time-series power flow using the configured grid time-series inputs.

Grid Data Update Helper

update_grid_data(grid, ts, idx, price_zone_restrictions=False, use_clusters=False, n_clusters=None)

Internal helper that applies one time-step (or one clustered state) to grid data before solving PF/OPF routines.

Optimal Power Flow Time Series

Cross-sectional time series

TS_ACDC_OPF(grid, start=1, end=99999, ObjRule=None, price_zone_restrictions=False, expand=False, print_step=False)

Performs time series optimal power flow analysis.

Parameter	Type	Description	Default
grid	Grid	Grid to analyze	Required
start	int	Start time step	1
end	int	End time step	99999
ObjRule	dict	Objective rule, check Objective Functions for more details	None
price_zone_restrictions	bool	Price zone restrictions, adds price zone restrictions to the model [1]	False
expand	bool	Expand price zone import limits	False
print_step	bool	Print step in the terminal	False

Returns

Results are stored in grid.time_series_results dictionary with the following keys:

• converter_p_dc - Converter power in DC side

• converter_q_ac - Converter power in AC side

• converter_p_ac - Converter power in AC side

• converter_loading - Converter loading percentages

• real_load_opf - Real load per node

• real_power_opf - Real power per generator

• reactive_power_opf - Reactive power per generator

• curtailment - Curtailment values

• line_loading - Line loading percentages

• grid_loading - Loading by unsynchronized grids

• prices_by_zone - Prices by price zone

• prices_by_zone_total - Total prices by price zone

• ac_line_loading - AC line loading percentages

• dc_line_loading - DC line loading percentages

• real_load_by_zone - Real load per price zone

• real_power_by_zone - Real power per price zone

It also returns a dictionary with the timing information.

Example

import pyflow_acdc as pyf
import pandas as pd

[grid,results] = pyf.NS_MTDC()

start = 5750
end = 6000
obj = {'Energy_cost': 1}

market_prices_url = "https://raw.githubusercontent.com/CITCEA-UPC/pyflow_acdc/main/examples/NS_MTDC_TS/NS_TS_marketPrices_data_sd2024.csv"
TS_MK = pd.read_csv(market_prices_url)
pyf.add_TimeSeries(grid,TS_MK)

wind_load_url = "https://raw.githubusercontent.com/CITCEA-UPC/pyflow_acdc/main/examples/NS_MTDC_TS/NS_TS_WL_data2024.csv"
TS_wl = pd.read_csv(wind_load_url)
pyf.add_TimeSeries(grid,TS_wl)

times=pyf.TS_ACDC_OPF(grid,start,end,ObjRule=obj)

res_dict = grid.time_series_results

Parallel cross-sectional time series

TS_ACDC_OPF_parallel(grid, ObjRule=None, PV_set=False, OnlyGen=True, Price_Zones=False)

Performs parallel time series optimal power flow analysis. Creates parallel sub-models to speed up the calculation.

Returns Results are saved in grid.time_series_results and the average elapsed time is returned.

Example

average_elapsed_time=pyf.TS_ACDC_OPF_parallel(grid)

Data handling

Statistical Analysis

Time_series_statistics(grid, curtail=0.99, over_loading=0.9)

Calculates statistical metrics for time series results.

Parameter	Type	Description	Default
grid	Grid	Grid with results	Required
curtail	float	Curtailment percentile	0.99
over_loading	float	Overloading threshold	0.9

Calculates for each time series:

• Mean

• Median

• Maximum/Minimum

• Mode

• IQR

• Percentiles

Results Export

results_TS_OPF(grid, excel_file_path, grid_names=None, stats=None, times=None)

Exports time series results to Excel file.

Parameter	Type	Description	Default
grid	Grid	Grid with results	Required
excel_file_path	str	Output file path	Required
grid_names	dict	Grid name mappings	None
stats	DataFrame	Statistical results	None
times	dict	Computation times	None

Exports sheets for:

• Time - Timing information

• All line loadings (AC/DC) - All line loadings (AC/DC)

• AC line loadings - AC line loading percentages

• DC line loadings - DC line loading percentages

• Grid loadings - Grid loading percentages

• Converter DC power - Converter power in DC side

• Converter AC power - Converter power in AC side

• Converter AC reactive power - Converter reactive power in AC side

• Real load per node - Real load per node

• Real power per generator - Real power per generator

• Reactive power per generator - Reactive power per generator

• Curtailment - Curtailment

• Converter loading - Converter loading percentages

• Real load by zone - Real load by zone

• Real power by zone - Real power by zone

• Reactive power by zone - Reactive power by zone

• Prices by zone - Prices by zone

• Statistics - Statistics

Example

pyf.results_TS_OPF(grid, "results", stats=stats_df)

References

[1]

B. C. Valerio, V. A. Lacerda, M. Cheah-Mañe, P. Gebraad, and O. Gomis-Bellmunt, “Optimizing offshore wind integration through multi-terminal DC grids: a market-based OPF framework for the North Sea interconnectors,” IET Conference Proceedings, vol. 2025, no. 6, pp. 150–155, 2025. doi: 10.1049/icp.2025.1198