Examples

Parse OpenDSS data

Parse an OpenDSS model and save it in a local directory. An example script to parse the OpenDSS master file can be accessed from examples/dataparser/data_parser.py

from ldrestoration import DSSManager  

# 1. instantiate the DSSManager with the OpenDSS master file
dss_object = DSSManager(r"../test_cases/ieee123Bus/Run_IEEE123Bus.dss")

# 2. Parse the dss data to the ldrestoration data structure
dss_object.parsedss()

# 3. Save the parsed data in the desired output folder  
dss_object.saveparseddss(folder_name="parsed_data_ieee123")

Instantiate restoration model

Instantiate a restoration model by loading the data into the model.

from ldrestoration import RestorationBase

# 1. identify the path of the parsed data files
parsed_data_path = "parsed_data_iee123/"

# 2. instantiate the restoration model
rm = RestorationBase(parsed_data_path, base_kV_LL=4.16)

Solve the restoration model

solve the restoration model. To solve the model it needs to be instantiated first as shown above.

# 3. identify objective function
rm.objective_load_only()

# 4. solve the restoration model with the solver of your choice 
rm_solved, results = rm.solve_model(solver='glpk')

Extract results and plot

Once the model is solved, it can be plotted on the map to observe the restoration policy visually. To save the results in a csv file, you can pass save_results=True and results_filename as

rm.solve_model(solver='glpk', save_results=True, results_filename="ieee123solved.csv")

The solved restoration model can be plotted on a geographical map using the following snippet

from ldrestoration.utils.plotnetwork import plot_solution_map

# plot the restoration solution. It requires the solved pyomo model, networkx tree, and
# networkx graph. The latter two are embedded within the pyomo model for easier access.
plot_solution_map(rm_solved, rm.network_tree, rm.network_graph)