Plotting Module¶

This page has been pre-filled with the functions that are available in the Plotting module by AI, please check the code for more details.

This module provides functions for visualizing grid components and results.

functions are found in pyflow_acdc.Graph_and_plot

Time Series¶

Time series results¶

This function is used to plot the time series results of the grid.

plot_TS_res(grid, start, end, plotting_choices=[], show=True, path=None, save_format=None)¶

Creates plots for time series results. The possible plotting choices are:

‘Power Generation by price zone’
‘Power Generation by generator’
‘Curtailment’
‘Market Prices’
‘AC line loading’
‘DC line loading’
‘AC/DC Converters’
‘Power Generation by generator area chart’
‘Power Generation by price zone area chart’

Parameter	Type	Description	Default
`grid`	Grid	Grid with results	Required
`start`	int	Start timeframe	Required
`end`	int	End timeframe	Required
`plotting_choices`	list	Results types to plot	All
`show`	bool	Whether to show the plot in browser	True
`path`	str	Path to save the plot	Current working directory
`save_format`	str	Format to save the plot, if None, the plot are not saved	None

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/BernardoCV/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/BernardoCV/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)

pyf.plot_TS_res(grid,start,end,save_format='svg')

Plot shown in browser:

Time Series Plot: Power Generation by price zone

Plot saved in current working directory:

Time series probability¶

This function is used to plot the probability of the time series parameters or results of the grid. Results currently available are:

‘Power Generation by generator’
‘Prices by price zone’
‘AC line loading’
‘DC line loading’
‘AC/DC Converters loading’

Time_series_prob(grid, element_name, save_format=None, path=None)¶

Parameter	Type	Description	Default
`grid`	Grid Class	Grid to analyze	Required
`element_name`	str	Name of element to analyze	Required
`save_format`	str	Format to save the plot, if None, the plot are not saved	None
`path`	str	Path to save the plot	Current working directory

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/BernardoCV/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/BernardoCV/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)

pyf.Time_series_prob(grid,'OWPP_BE',save_format='svg')
pyf.Time_series_prob(grid,'BE_price',save_format='svg')
pyf.Time_series_prob(grid,'L_BE',save_format='svg')

Network Graph Visualization¶

Full grid visualization as a network graph¶

plot_Graph(Grid,text='inPu',base_node_size=10,G=None):

Creates an interactive network graph visualization using Plotly.

Parameter	Type	Description	Default
`Grid`	Grid	Grid to visualize	Required
`text`	str	Hover text format (‘data’ or ‘inPu’ or ‘abs’)	‘inPu’
`base_node_size`	int	Base size for nodes	10
`G`	Graph	Graph to visualize	Full grid

Example

import pyflow_acdc as pyf

grid,res = pyf.case24_3zones_acdc()

pyf.plot_Graph(grid)

Neighbor Graph¶

This function is used to plot the neighbor graph of a node. You can either provide a node or a node name, one or the other must be provided.

plot_neighbour_graph(grid, node=None, node_name=None, base_node_size=10, proximity=1)¶

Creates a graph visualization of a node’s neighbors.

Parameter	Type	Description	Default
`grid`	Grid	Grid to analyze	Required
`node`	Node	Node object	None
`node_name`	str	Node name	None
`base_node_size`	int	Base size for nodes	10
`proximity`	int	Proximity	1

Example

import pyflow_acdc as pyf

grid,res = pyf.case24_3zones_acdc()

pyf.plot_neighbour_graph(grid,node_name='111.0')

case24_3zones_acdc neighbour graph of node 111.0

Saving the Network Graph¶

For this function, you need to have the svgwrite library installed. You can install it using pip install svgwrite. geometry of objects is required.

save_network_svg(grid, name='grid_network', width=1000, height=800)¶

Saves the network graph as an SVG file.

Parameter	Type	Description	Default
`grid`	Grid	Grid to save	Required
`name`	str	Name of the file	‘grid_network’
`width`	int	Width of the file	1000
`height`	int	Height of the file	800

Example

import pyflow_acdc as pyf

grid,res = pyf.NS_MTDC()

grid.save_network_svg(grid)