Tutorial

If you correctly installed pytrnsys and were able to use the two commands pytrnsys-run and pytrnsys-process you most likely already simulated and processed your first system with pytrnsys. But for sure you would like to adapt the example system to your weather data and to your demand profile, excecute other parametric studies, use different system sizing, analyse other simulation results etc.. In this tutorial, you learn how to do all this and much more. We will work with the solar domestic hot water example system that looks like this:

Monthly heat balance

Create your own local system

You can set up a local copy of the pytrnsys example systems and the pytrnsys ddck-repository by executing pytrnsys-load in the target folder of your choice. This process is explained in the getting started section. Once you have done this, you should see a sub-folder pytrnsys_examples that contains the following files:

pytrnsys_examples
+-- solar_dhw
    +--latexNames.json
    +--process_solar_dhw.config
    +--run_solar_dhw.config
    +--solar_dhw_control.ddck
    +--solar_dhw_control_plotter.ddck
    +--solar_dhw_hydraulic.ddck
    +--solar_dhw_storage1.ddck

Besides the two configuration files, the folder contains some ddck files that are custom to the project. The file latexNames.json contains a dictionary that is used to translate TRNSYS variable names into the latex format used in the results pdf file.

If this system is executed with pytrnsys-run, a new simulation folder with the name solar_dhw is created that has the following structure:

solar_dhw (simulation base folder)
+-- debugParralelRun.dat
+-- runLogFile.config
+-- solar_dhw.dck
+-- UnitsType.info
+-- (location of comparison plots)
+-- solar_dhw-Ac2-VTes75 (simulation sub folder)
    +--solar_dhw-Ac2-VTes75.dck
    +--solar_dhw-Ac2-VTes75.log
    +--solar_dhw-Ac2-VTes75.lst
    +-- (location of single simulation results and plots)
    +--temp
       +--*.Prt

As you can see, pytrnsys creates a base dck file solar_dhw.dck that is then copied to the subfolders where the actual dck file containing possible changes that were defined in the configuration file are included. So in a parametric study, each single simulation will have its own subfolder. In the temp folder of each subfolder, the simulation results defined in the TRNSYS printers will be stored.

The finished simulation results can be processed by running pytrnsys-process inside the base simulation folder solar_dhw. The processing will add various files to the folder structure. Single simulation plots and results will be placed in the subfolders while comparison plots and comparison data will be added to the simulation base folder.

As in all pytrnsys projects, a heat balance will be produced for the solar domestic hot water example system. The heat balance plot of the unchanged example system looks like

Monthly heat balance

To have an overview of the by default created plots and result files please go through the different simulation folders of the example system.

Work with the run-configuration file

The easiest way to work with pytrnsys is to use a pre-defined system and to modify it with the configuration files. The pytrnsys configuration files offer a large amount of functionalities that are described in detail in in the config file page. In the following sections, some of the most important functions are explained in a step-by-step guide.

Change TRNSYS variables

Constants and Equation of the TRNSYS dck-file can be changed by the following line in the config file:

deck trnsysVariableName value

In the runconfiguration file run_solar_dhw.config, we can see that there are already three such lines that change the dck-file:

deck START 0    # 0 is midnight new year
deck STOP  8760 #
deck sizeAux 3

It is recommended to always have the START and STOP variable exposed in the configuration file since they define the simulated timespan and are of high importance. In addition, the variable sizeAux is changed to a value of 3. This variable defines the power in kW of the auxiliary heater inside the thermal storage. We can choose now any other variable in one of the used ddck files that we would like to change. Let us say we would like to change the slope of the thermal collector. In order to identify the relevant parameter we have to open the ddck of the used solar collector model /solar_collector/type1/type1.ddck. In there we see that the collector surface tilt definition C_tilt is a dependency of another ddck, in particular the variable slopeSurfUser_1. Looking at the solar_dhw.dck file we see that the definition of slopeSurfUser_1 is done in the file /weather/weather_data_base.ddck. Therefore, in the configfile we can add the following line to simulate a facade collector with slope 90:

deck slopeSurfUser_1 90

If we would like to add the collector slope to the parametric study, we can use the variation keyword:

variation slopeSurfUser_1 30 45 60 75 90

When this line is added without removing any of the other variation lines the total amount of simulations will increase to 6x2x5=60 which will take a while. Feel free to reduce the number of values per variation to save time.

Change the used ddck-files

In the solar domestic hot water system, the following ddck files are used by default:

string PYTRNSYS$ "..\..\pytrnsys_ddck\"
string LOCAL$ ".\"

PYTRNSYS$ generic\head
PYTRNSYS$ demands\dhw\dhw_sfh_task44
PYTRNSYS$ weather\weather_data_base
PYTRNSYS$ weather\SIA\normal\CitySMA_dryN
PYTRNSYS$ solar_collector\type1\database\type1_constants_CobraAK2_8V
PYTRNSYS$ solar_collector\type1\type1
LOCAL$ solar_dhw_control
LOCAL$ solar_dhw_storage1
LOCAL$ solar_dhw_hydraulic
LOCAL$ solar_dhw_control_plotter
PYTRNSYS$ generic\end

In pytrnsys, it is possible to replace some of the ddck files depending on the structure of the project. In this case, it is possible to replace the domestic hot water with another demand as well as to chose another weather data location. The current city which is Zurich (SMA) can be replaced with Locarno in the south of Switzerland. In Locarno, there are more hours of sunlight in the year which will help us to have a better performance for the solar domestic hot water system. In the default database pytrnsys-ddck there are many different Swiss cities. Locarno can be chosen by:

PYTRNSYS$ weather\SIA\normal\CityOTL_dryN

You can go through the weatherSIA folder in the ddck repository to see all by default available weather data ddck files.

Run the modified configuration file

Now you are almost ready to run your new simulation. In order to not overwrite the default system run you should specify a new folder name. This can be done by changing the addResultsFolder parameter:

string addResultsFolder "my_new_solar_dhw"

Save you configuration file and use it with the pytrnsys-run command to start the simulation.

Work with the processing-configuration file

In the default example system processing file, there are already some custom calculations and custom plots given as examples. In this section be will go through the process of adding some more calculations and plot the results of the custom calculations.

Add custom calculations to the processing

In the default processing configuration file of the solar domestic hot water system, the monthly and overall solar fraction of the system is calculated:

calcMonthly fSolarMonthly = qSysIn_Collector/qSysOut_DhwDemand
calc fSolar = qSysIn_Collector_Tot/qSysOut_DhwDemand_Tot

Another interesting quantity to analyze the performance of a solar system is the Total Solar Efficiency

\[\eta^{coll} = \frac{Q^{collector}}{E_{irradiance}}\]

This can be implemented using the simulation results. In the monthly printer section of the collector ddck file /solar_collector/ype1/type1.ddck we can see that the power gain and the irradiated power per area are integrated, printed and accessible in the processing as PColl_kWm2 and IT_Coll_kWm2. So we have everything to calculate the Solar Efficiency. Again we calculate the monthly values as well as the overal yearly value:

calcMonthly solarEffMonthly = PColl_kWm2/IT_Coll_kWm2
calc solarEff = PColl_kWm2_Tot/IT_Coll_kWm2_Tot

Add custom plots to the processing

We can plot the new results in different ways. First of all, we can use the monthly values to create a monthly bar plot by including:

stringArray monthlyBars "solarEffMonthly"

This will result in a plot that looks like this:

SP

By default, pytrnsys will use the variable name in all the legends. We can change this to a nicer looking LaTeX-formatted string by adding an entry to the dictionary in the projects latexNames.json. Adding a line “effSolarMonthly”: “$\eta^{coll}$” in the json file and rerunning the processing will give a plot with a nicer legend:

SP

We can also create a comparison plot of the solar efficiency including the parametric runs on the collector area, the storage size and the collector slope by using the following line:

stringArray comparePlot "AcollAp" "solarEff" "volPerM2Col" "slopeSurfUser_1"
SP

Do parametric runs with different ddcks

For some tasks, it is not enough to replace a single number in the dck file. Such tasks are for example the replacement of the weather data, the replacement of the demand data or the change of the parametrization of a component i.e. the solar collector. In pytrnsys this is solved by the possibility to define a seperate ddck for each case and to loop though the ddck files during the parametric runs.

Let us say that instead of changing the collector slope in our example system, we would like to use different domestic hot water profiles. In the default ddck repository, pytrnsys offers both a domestic hot water profile for a single family house as well as one for a multi-family building. If we would like to include both in the same parametric run, we can include the following line:

changeDDckFile dhw_sfh_task44 dhw_sfh_task44 dhw_mfh

The changeDDckFile command interprets the first argument as the substring to be replaced in the ddck including line of the configuration file which in this case would be the following:

PYTRNSYS$ demands\dhw\dhw_sfh_task44

This line will internally be changed to the following arguments of changeDDckFile. Since the first argument is repeated, an unchanged variation will be used. The third argument will result in a variation that builds the dck file based on the line changed to:

PYTRNSYS$ demands\dhw\dhw_mfh

There is no restriction to the substrings used. It is also possible to write:

changeDDckFile dhw\dhw_sfh_task44 dhw\dhw_sfh_task44 dhw\dhw_mfh

That way ddck files that are located in other folders could be used. The file name of the changed ddck file will be used in the name of the variation’s subfolder and will also be saved to the results json-file.

Use the scaling functionality

Now that we changed the demand profiles of the simulation, we will end up with very different solar fractions for the two cases since a solar collector field that is designed for a single family home will be much too small for a multi-family building. In reality, it is a standard procedure to size the collector field relative two the expected demand.

In order to define relative system dimensioning, pytrnsys offers to possibility to read in the results file of an earlier simulation run and to use the values as a scaling parameter. In our case this requires that we pre-run the simulation in order to find the exact domestic hot water demand of the two different profiles. We can do this by running a configuration file that consists of no other variations but the changeDDckFiles line defined in the previous chapter:

changeDDckFile dhw_sfh_task44 dhw_sfh_task44 dhw_mfh

This should result in a simulation folder with the following subfolders:

solar_dhw (simulation base folder)
+-- SFH_DHW-dhw_mfh (simulation sub folder)
+-- SFH_DHW-dhw_sfh_task44 (simulation sub folder)

Before we are able to process we should make sure that we add the simulation result that we would like to use for scaling to the results file. In our case this is the yearly sum of the monthly integrated and printed values of P_dhw_kW that are by default available in the processing as P_dhw_kW_Tot. In the results file definition line we add:

stringArray results  "Pdhw_kW_Tot" "**" "**"

We now have our simulation results ready to be used in the scaling. The scaling can be activated by setting the scaling parameter in the run configuration file from “False” to “toDemand”. We then have to tell pytrnsys where it can find the scaling values. This is done by adding the following line:

string scalingReference "absolutePathToYourBaseResltsFile\SFH_DHW-dhw_sfh_task44-results.json"

The argument of the parameter scalingReference should be the results json-file of the simulation that corresponds to the first argument of the changeDDckFiles line. For each ddck-variation defined in changeDDckFiles Pytrnsys will take the file names and do the same substring replacement in the path in scalingReference. When the folder with the scaling values is onmodified, pytrnsys should be able to find the correct values for each variation.

Finally, we should also tell pytrnsys which value in the results file it should use. We can do this by adding the following line:

string scalingVariable "Pdhw_kW_Tot/1000"

As you can see we can also add arithmetic operations to the value. As an example, here the value is converted from kW to MW.

We are now ready to define our parametric study using relative sizing of parameters. As soon as the scaling is set to “toDemand”, pytrnsys will always multiply the values given in the variation statement with the scaling variable. So we can now size our collector area relative to the domestic hot water demand. A realistic sizing would be to have about 1.5 m2MWh so we add slight variations as:

variation Ac AcollAp 1 1.5 2

This will finally result in a more comparable results for the solar fraction:

SP

Run pytrnsys with an external deck file

Pytrnsys can also be used if you want to use its functionality on a full external dck file of your TRNSYS project that you have exported from the TRNSYS Studio or have created in your own way. To do this simply use this file as a single entry in the ddck section of the run configuration file:

string LOCAL$ "pathToYourDckFile"
LOCAL$ yourDckFile

Create your own ddck files

You already learned how to replace a ddck file with another one that is available in the ddck repository. Pytrnsys also allows you to create your own custom ddck files and include them into your project. In this chapter, we will go through the process of creating and including a new domestic hot water profile ddck that we can use in the simulation of the solar domestic hot water system.

If you executed the pytrnsys-load command you have your own local pytrnsys ddck repository that you are free to change. It is however recommended to save your own ddck files in a different folder that is under version control by GIT. That way, you can keep track of your work and savely overwrite the pytrnsys_examples when an update of the base repository is released. We also recommend to have your own repository in the same structure as the ddck repository.

In order to do so, in the folder or GIT repository of your choice, create a subfolder called demands that contains another subfolder called dhw. Inside this folder we create the new ddck that contains our custom domestic hot water reader. To have the right ddck structure you can for example copy the file dhw_mfh.ddck from the pytrnsys_ddck repository. Now you can perform any changes that you like, for example exchange the file that is used and adapt the TRNSYS type 9 accordingly.

After you created your new ddck file you can add your custom ddck repository to the ddck paths in the run config file and add replace the the domestic hot water line:

CUSTOMREPOSITORY$ demands\dhw\dhw_your_file

Get access to the pytrnsys GUI

Pytrnsys is still under development by the SPF Institute for Solar Technology. Therefore, up to this point the pytrnsys GUI is not available for the public. If you would like to use pytrnsys to create you own new system hydraulics please contact dani.carbonell@spf.ch.