Code Snippets and Tutorials¶

The general workflow when approaching simulation of controlled elements within pandapower could be outlined like this:

1. Get an overview of your project. Which network elements do you need, what and how should these elements accomplish their task?
2. Checkout current implementations. Maybe there is a controller that does something similar, so you can save work and also get a point to start with.
4. Setup your simulation and give it a go.

In case of questions or problems come up, feel free to contact friederike.meier@iee.fraunhofer.de or jan.wiemer@iee.fraunhofer.de (for students).

For introduction purposes an easy example will be described. The task at hand would be to simulate a Trafo Controller with local continous tap changer voltage control. First we load a network and define it as net (if you dont know how, have a look at Pandapower Pro Networks). Next we need one object: an instance of of a ContinuousTapControl, for example StatCurtPv. We want the transformer with ID 114 to be controlled by this controller, hence we pass tid=114.

Note

Have a look at all transformer IDs by typing net.trafo.index and chose the ones to be controlled.

import pandapower as pp
import control
from pandapower.networks import mv_oberrhein

net = mv_oberrhein()

# initialising controller
tol = 1e-6
trafo_controller = control.ContinuousTapControl(net=net, tid=114, u_set=0.98, tol=tol)

# running a control-loop
control.run_control(net)


We imported pandapower and the control module and created the object of a controller we need. You can look up which parameters are mandatory and which are optional in the constructor of the class you are creating an instance of. In our example we need to pass a reference to the net, the ID of the controlled transformer, the voltage setpoint and a calculation tolerance.

Note

I wrote import pandapower as pp which provides me a handy abbreviation pp for the whole import-reference. These abbreviations have to be unique throughout your code.

Now we look at our network that contains our controller.

net.controller


The output in the console shows, that the controller is active and has the default values for order and level (we’ll look at these in more detail shortly). Now we run a loadflow-simulation with our controlling unit using the control.run_control(net) method. Have a look at net.res_trafo to check the results of the transformers. You can compare them with results of a normal loadflow-simulation by running pp.runpp(net) and checking net.res_trafo again. Check the results at the buses and lines in the network aswell for further informations.

Simulating time-series with Controllers¶

If you want to simulate time-series, you may also do so using the controller framework. First you need a DataSource for the profiles loads or pv-plants should be using. Most commonly CSV-files are being used to provide data values over time, but you could also implement a DataSource of your own which e.g. generates data on-the-fly. In our example we use an instance of CsvData. It expects a column named time containing consecutive timestamps. You may simply use values from zero counting upwards for each time step or use UNIX-timestamps if you like. Each column contains a profile with a value for each time step at the corresponding row. You have to pass a datasource as well as the name of the column a controller should use as profile as depicted below.

import pandapower as pp
import control
import timeseries

net = pp.networks.mv_oberrhein(scenario='generation')

ds = timeseries.CsvData("PATH\\FILE.csv", sep=";")

# initialising ConstControl to update values at the regenerative generators
const = control.ConstControl(net, element='sgen', element_index=net.sgen.index,
variable='p_mw',  data_source=ds, profile_name='P_PV_1', level=0)

# initialising controller
tol = 1e-6
trafo_controller = control.ContinuousTapControl(net=net, tid=114, u_set=0.98, tol=tol, level=0)

# starting the timeseries simulation for one day -> 96 15 min values.
timeseries.run_timeseries(net, time_steps=(0,95))


We created a DataSource and passed it to the ConstControl, while also providing the name of the P-profile. For simplification purposes we used one profile for all generators. We may want to save certain values at each calculated timestep. In order to do that, we build an OutputWriter.

# initialising the outputwriter to save data
ow = timeseries.OutputWriter(net)
ow.log_variable('res_sgen', 'p_kw')
ow.log_variable('res_bus', 'vm_pu')

# starting the timeseries simulation for one day -> 96 15 min values.
timeseries.run_timeseries(net, time_steps=(0,95))

# results in ow.output


We created an OutputWriter and added a few functions to store values we are intersted in. Have a look at the implementation of the OutputWriter to find out more about saving values during time-series simulation. Note that the invokation of the simulation differs from above: we use timeseries.run_timeseries() and pass on the start- and stop step of the simulation. Results of the simulation are being stored in a pandas dataframe called output in the OutputWriter.

Jupyter Notebook Tutorials¶

There are a few interactive tutorials to internalize this section: