uesgraphs.systemmodels_pp.systemmodelheating

This module includes the uesgraph to write a pandapipes model and simulation

Functions

`set_up_logger`(name[, log_dir, level])	Sets up a configured logger with file handler.
`set_up_terminal_logger`(name[, level])	Set up a simple console-only logger for small functions.

Classes

SystemModelHeating(stop_time, start_time, ...)

Writes pandapipes model for system models from uesgraphs information

uesgraphs.systemmodels_pp.systemmodelheating.set_up_logger(name, log_dir=None, level=40)[source]

Sets up a configured logger with file handler.

Creates a logger with specified name and logging level. Log files are stored in a directory with timestamp in filename. If no directory is specified, the system’s temporary directory is used.

Parameters:

name (str) – Name of the logger, also used for filename
log_dir (str, optional) – Directory for log files. Defaults to None (uses temp directory)
level (int, optional) – Logging level (e.g. logging.ERROR, logging.INFO). Defaults to logging.ERROR

Returns:

Configured logger object

Return type:

logging.Logger

Example

>>> logger = set_up_logger("my_app", "/var/log", logging.INFO)
>>> logger.info("Application started")

Notes

Log filename format: {name}_{YYYYMMDD_HHMMSS}.log
Log entry format: time - logger_name - [file:line] - level - message

uesgraphs.systemmodels_pp.systemmodelheating.set_up_terminal_logger(name: str, level: int = 20) → Logger[source]

Set up a simple console-only logger for small functions.

Parameters:

name – Logger name
level – Logging level (default: INFO)

Returns:

Configured console logger

class uesgraphs.systemmodels_pp.systemmodelheating.SystemModelHeating(stop_time, start_time, timestep, network_type='heating', logger=None)[source]

Writes pandapipes model for system models from uesgraphs information

While a uesgraph object uses edges and nodes to describe the network topology, the pandapipes model uses junctions and pipes. Therefore, each node in the uesgraph is represented by two junctions in the pandapipes model (supply and return). At the current stage, only heating networks are supported.

stop_time

Stop time for simulation in seconds

Type:: float

timestep

Timestep for simulation in seconds

Type:: float

timesteps

Number of timesteps for simulation calculated from ‘timestep’ and ‘stop_time’

Type:: int

graph["T_ground"]

Ground temperature in Kelvin

Type:: list

pp_network

Pandapipes network object

Type:: pp.net

__init__(stop_time, start_time, timestep, network_type='heating', logger=None)[source]: Constructor for UESGraph class.

import_nodes_from_uesgraph(uesgraph_input, logger=None)[source]

Imports nodes from UESGraph into pandapipes junctions.

Parameters:

uesgraph_input (uesgraphs.uesgraph.UESGraph object) – At current stage, this uesgraph should contain only 1 network of 1 type that is indexed in the corrsponding nodelist as ‘default’
logger (logging.Logger, optional) – Logger instance for debugging

Returns:

Tuple containing: - junction_ids: Mapping of uesgraph node IDs to pandapipes junction IDs - heat_source_id: ID of the heat source junction - heat_source_r_id: ID of the heat source return junction

Return type:

Tuple[dict, int, int]

import_edges_from_uesgraph(uesgraph_input, junction_ids, logger=None)[source]

Imports edges from UESGraph into pandapipes pipes.

Parameters:

uesgraph_input (uesgraphs.uesgraph.UESGraph object) – At current stage, this uesgraph should contain only 1 network of 1 type that is indexed in the corrsponding nodelist as ‘default’
junction_ids (dict) – Mapping of uesgraph node IDs to pandapipes junction IDs
logger (logging.Logger, optional) – Logger instance for debugging

Returns:

Tuple containing: - pipe_list: List of pipes with their details

Return type:

Tuple[list, dict]

import_from_uesgraph(uesgraph_input, logger=None)[source]

Imports full UESGraph into pandapipes model.

Parameters:

uesgraph_input (uesgraphs.uesgraph.UESGraph object) – At current stage, this uesgraph should contain only 1 network of 1 type that is indexed in the corrsponding nodelist as ‘default’
logger (logging.Logger, optional) – Logger instance for debugging

Returns:

Tuple containing: - junction_ids: Mapping of uesgraph node IDs to pandapipes junction IDs - pipe_list: List of pipes with their details - heat_source_id: ID of the heat source junction - heat_source_r_id: ID of the heat source return junction

Return type:

Tuple[dict, list, dict, int, int]

find_pipe_parameter(inner_diameter: float)[source]

Finds the insulation thickness for a given inner diameter of the pipe.

Uses a lookup table based on standard district heating pipe dimensions for insulation class 3 according to district heating planning handbook.

Parameters:: (float) (inner_diameter) –
Returns:: Tuple[float, float]
Return type:: Inner diameter in meters, insulation thickness in meters

Note

If the exact pipe diameter is not in the reference table, the closest size will be selected.

estimate_xi(l_pipe: float)[source]

Estimates the total loss coefficient for a pipe based on its length.

Calculates pressure losses from: - 90° elbows (approx. every 25m a U-form with 4x90° elbows) - Tee junctions (flow separation and association at ends) - Installed slide valves

Parameters:: l_pipe (float) – Length of the pipe in meters
Returns:: float
Return type:: Total loss coefficient (xi) for the pipe

References

Horlacher2016, Rohrleitungen 2 (S.519-521)
Boehmer Fernwärme documentation (S.46)

run_test_simulation(logger=None)[source]

Does a test simulation of the pandapipes network to check if it runs without errors.

Parameters:: logger (logging.Logger, optional) – Logger instance for debugging
Raises:: Exception – If the test simulation fails

pipe_order(pipe_list, heat_source_ids, heat_source_r_ids, logger=None)[source]

calculate_temperature_pipe_profile_implicit(T_in, T_prev, mdot, cp, alpha, d_in, T_ground, dx, dt, rho, sections)[source]

This function calculates with an implicit euler scheme the temperature distribution within one pipe.

Parameters:

(float) (rho) –
(list) (T_prev) –
(float) –
(float) –
(float) –
(float) –
(float) –
(float) –
(float) –
(float) –
(int) (sections) –

Returns:

list

Return type:

Temperature distrubtion list in the pipe for the next time step

mix_junction_temperatures(pp_network, pipe_temp_history, pipe_subset, logger=None)[source]

This function calculates a mixing temperature if two pipes flow into one pipe.

Parameters:

pp_network (pandapipes-network) – Pandapipes network
pipe_temp_history (dict) – Temperatures of pipes to different time steps
pipe_subset (list) – Pipes that are used to find the inflowing pipes

Return type:

—

init_output_writer(log_variables)[source]

log_current_state(pp_network, output_data, log_variables, current_time)[source]

write_results_to_csv(output_data, log_variables, base_folder)[source]

run_timeseries_dpp(pipe_list, heat_source_ids, heat_source_r_ids, save_at, logger=None)[source]

This function runs the dynamic simulation of the pandapipes network and logs the results.

Parameters:

pipe_list (list) – List of pipes with their details
heat_source_ids (list) – List of heat source junction IDs
heat_source_r_ids (list) – List of heat source return junction IDs
save_at (str) – Folder path to save the results
logger (logging.Logger, optional) – Logger instance for debugging

Return type:

—

resample_profile_constant(df, factor)[source]

duplicate each row in the dataframe factor times to resample the profile with a constant value.

df: DataFrame with timesteps as index factor: int, number of times each row should be duplicated

run_timeseries_spp(save_at, mode, logger=None)[source]

Does timeseries calculation of the pandapipes network and saves the results in save_at.

Parameters:

save_at (str) – Path where the results will be saved
mode (str) – Mode of the simulation, either “static” for static simulation or “transient” for transient pandapipes simulation.
logger (logging.Logger, optional) – Logger instance for debugging

Raises:

Exception – If the timeseries simulation fails