Running PySAM

There are two ways to run simulations within this project: using PySAM (recommended) and using PySSC (ideally for debugging).

The main way to run a full simulation in SSC with dispatch optimization is to use the NE2 modules. These are found in this repository under neup-ies/simulations/modules.

A number of example scripts for running a simulation can be found under neup-ies/simulations/scripts. There should be a guide on module-specific scripts within this documentation.

Here is a general outline of how to run a simulation.

0. Build SSC in export mode. Instructions found here:.

1. PYTHONPATH: Make sure the neup-ies/simulations/ directory is in your Python path, either in your terminal or whatever IDE you are using. This is slightly automated in current scripts by calling:

   import sys
   sys.path.append('..')
   

2. Select an NE2 module you want to run. For example, if you want to run an LFR + TES model (Model 1a) you would select the NuclearTES module. Import the module at the top of your script:

   import modules.NuclearTES as NuclearTES
   

3. Initialize an object instance of the module class as follows:

   nuctes = NuclearTES.NuclearTES()
   

   Note that there are several input parameters you can specify, though each module has default values preset.

   • plant_name (str) – name of SSC module to run

   • json_name (str) – name of JSON script with input data for module (just the name, don’t specify directory)

     Note

     JSON scripts can be found neup-ies/simulations/json. For more information on the structure of the JSON scripts, see this guide:.

   • is_dispatch (bool) – boolean, if True runs Pyomo dispatch optimization

   • dispatch_time_step (int) – time step for dispatch (hours)

   • log_dispatch_targets (bool) – boolean, if True logs dispatch targets calculated by Pyomo at each segment

     Note

     The log_dispatch_targets input, if set to True, saves all dispatch target time series that are sent to SSC from the Pyomo optimization model. These time series are saved to unique files ending in .dispatchtargets whose filenames are hash strings created from all JSON script parameter inputs. These files are found in neup-ies/simulations/outputs.

   • exec_debug (bool) – boolean, allows execution in “debug” mode that times out exec call

   • exec_timeout (float) – amount of time in seconds to timeout an exec call

     Note

     exec_debug and exec_timeout were intended to help debug sessions were calls to the root SSC program would get stuck. These inputs have limited success in helping debug those sessions…

4. Optional Changes for Sweeps: After Step 3, the JSON script parameters should be loaded into the object. If you want to make any alterations (say you are running a loop or parameter sweep), there are some extra lines you should run before runnning the simulation to update all stored dictionaries.

   • If the change is to a PySAM_dict parameter (say, the ssc_horizon or pyomo_horizon time):

     # horizons
     nuctes.PySAM_dict['ssc_horizon']   = sscH
     nuctes.ssc_horizon   = sscH * nuctes.u.hr
     nuctes.PySAM_dict['pyomo_horizon'] = pyoH
     nuctes.pyomo_horizon = pyoH * nuctes.u.hr
     
     # saving/updating PYSAM dict to nuctes
     nuctes.dispatch_wrap = nuctes.create_dispatch_wrapper( nuctes.PySAM_dict )
     

     The last line will re-create a new instance of a the Dispatch Wrapper class that communicates with the Pyomo model.

   • If the change is to an SSC_dict parameter (say, the design point P_ref or tshours):

     nuctes.SSC_dict['P_ref'] = Pref
     nuctes.SSC_dict['tshours'] = tshours
     nuctes.dispatch_wrap.set_design()
     

     The last line updates the existing Dispatch Wrapper class (which is a member of the module class) with the new design point.

5. Run Simulation:

   nuctes.run_sim(  )
   

   Here are some of the input parameters to this method:

   • run_loop (bool) – if true, runs simulation in segments. else, runs simulation all at once

     Note

     If running with dispatch optimization set to True, make sure run_loop is set to True.

   • export (bool) – if true, exports results to an Excel sheet

   • filename (str) – name for Excel sheet saved to the /outputs directory

   • overwrite_dispatch_targets (bool) – if true, overwrites the current stored dispatch target file

     Note

     Normally, if log_dispatch_targets is set to True in the init of the object, the object checks to see if a dispatch targets file exists for the unique hash signature created out of the JSON script input parameters.

     If the file exists, it doesn’t save the current simulations’s results to that file.

     If overwrite_dispatch_targets is set to True here, it overrides the previous functionality and overwrites the existing file.