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The TEMOA energy system formulation is written in Pyomo, which is in turn written in Python. For most users, the biggest implication of this is that it will run anywhere. TEMOA will run on Windows, Linux, Mac, or any operating system that supports Python. At the moment, since we haven’t yet created a single install package, it also means there is an initial hurdle for many to overcome. We explain the hurdle below, so hopefully you can get up and running. As always, we encourage you to contact us with any questions or problems.

Parts of TEMOA

To run TEMOA, you will need:

Python – a very robust open source scripting language that has become a staple of the scientific community. At this time (Jun, 2010), TEMOA requires at least v2.6. You may (not) have success with an earlier version. Unless you run Windows, it is likely already installed on your computer.
COOPR – an optimization suite of tools developed by Sandia National Labs. TEMOA heavily uses the Pyomo subproject. If you are aware of AMPL, Pyomo tackles roughly the same problem area, but with a different syntax. Coopr is open source, and is a free download from Sandia.
Solver – Any optimization solver. Pyomo is a description language for optimization models, but it is not a solver. It can, however, “speak the language” of a plethora of solvers. This means that a model that uses Pyomo can run on a university machine with a CPlex or Gurobi license, while a researcher works on the same model at home with the Free GLPK solver. No more is the model limited by a single solver.As GLPK is free, it offers the lowest barrier to entry of the solvers available, and consequently is what we explain how to install here. However, be warned that it is also the weakest in that it can only solve linear programming models (LPs) and is a good deal less efficient (slower) than almost every other solver on the market.
TEMOA model – we are in early development stages of the project, but we have a working a prototype of the U.S. Electric Energy Sector. (Interested developers: look to the section “Development Version” in this page for the svn checkout command and URL.)

In general, once you’ve downloaded and installed all of the above, working with the model is currently a command line venture. The command to run it is pyomo elc.py elc.dat

Installation for Windows Users

As outlined above, there are four parts to getting up and running with the TEMOA model.

Installing Python Download the “Python 2.6 Windows installer” package from the Python Download page. (Note that the release series is 2.6 and the bug-fix release is denoted by an additional decimal point. So 2.6.3 and 2.6.5 are both the same version of Python, but 2.6.5 contains more recent bug fixes.) There are no special caveats, just double-click the install package, and click next through the various wizard pages.
Install SetupTools Though not strictly necessary, installing PyPi’s SetupTools makes the following step much easier.Download and install the setuptools installer that matches the version of Python that you installed in the previous step.
Installing COOPR Now that you have Python and PyPi, the easiest method to install Coopr is to use PyPi’s Coopr easy_install method. Open a terminal by going to Start→Run, and typing in cmd in the window. You should be presented with a black window with a prompt that looks likeC:\Documents and Settings\your_user_name>. Type cd Desktop and hit enter. The prompt should change to C:\Documents and Settings\your_user_name\Desktop>. Now type \Python2.6\Scripts\easy_install.exe Coopr and hit enter. This will ask the PyPi website how to install Coopr, and then automatically do just that. When the script finishes, and you’re returned to a prompt, Coopr will be installed.
Installing GPLK GLPK is a free optimization solver from the fine GNU community. Download the GLPK for Windows setup program and run through its wizard installation.Note: Though Coopr can interface with a plethora of solvers, we discuss only GLPK because it is both philosophically free (open source), and also gratis. You are welcome to install other solvers but they are outside the scope of this introduction and download page.
Installing TEMOA Finally, download the TEMOA model [zip]. Unzip it to the folder of your choice.
Testing the installation Navigate to Start→Programs→Python→IDLE (Python GUI). At the prompt (>>>) type from coopr.pyomo import * If you get back an empty prompt on the very next line, then you have successfully installed Coopr.

Installing in Linux

Ubuntu

Install Python, GLPK Open up a terminal via Applications→Accessories→Terminal. At the prompt, type sudo apt-get install glpk-utils python subversion and hit enter. Type your password, and in a moment, you’ll have Pythonm, GLPK, and Subversion installed.For the curious, subversion is a very powerful “Source Code Management” (SCM) tool, but we only use it here in the next step to install Coopr.
Install COOPR The easiest way to install Coopr is with Sandia National Laboratories’ install script. Download it, and then, back at the prompt, type python coopr_install. The script is noisy, but in about 5 minutes, it should finish.When it finishes, add the Coopr binaries to your path so you can run it from the commandline:
```
$ echo "export PATH=\$PATH:$(pwd)/coopr/bin" >> ~/.bash_profile
```
Logout of the terminal by typing exit at the prompt. This is important to update the PATH variable you just set.
Installing TEMOA Finally, download TEMOA with subversion:
svn co http://svn.temoaproject.org/temoa/trunk/ temoa
Test the installation To test the installation, reopen a terminal, and navigate to the elc folder you just unzipped. From there, type pyomo elc.py elc.dat and hit enter. If you see output like below, then you have successfully installed the TEMOA project.

Development Version

If you would like the developer version, you can check it out from the source repository
svn checkout http://svn.temoaproject.org/temoa/trunk temoa

Successful Installation

Here is sample output from an initial run of the TEMOA model:

$ cd elc
$ pyomo elc.py elc.dat

# ==========================================================
# = Solver Results                                         =
# ==========================================================

# ----------------------------------------------------------
#   Problem Information
# ----------------------------------------------------------
Problem:
- Lower bound: 2740078.006
  Upper bound: inf
  Number of objectives: 1
  Number of constraints: 133
  Number of variables: 210
  Number of nonzeros: 606
  Sense: minimize

# ----------------------------------------------------------
#   Solver Information
# ----------------------------------------------------------
Solver:
- Status: ok
  Termination condition: unknown
  Error rc: 0

# ----------------------------------------------------------
#   Solution Information
# ----------------------------------------------------------
Solution:
- number of solutions: 1
  number of solutions displayed: 1
- Gap: 0.0
  Status: optimal
  Objective:
    f:
      Id: 0
      Value: 2740078.006
  Variable:
    xc[gt_p,2000]:
      Id: 0
      Value: 307.787
    xc[gt_p,2001]:
      Id: 1
      Value: 328.306
    xc[gt_p,2002]:
      Id: 2
      Value: 348.825
    xc[gt_p,2003]:
      Id: 3
      Value: 369.344
    xc[gt_p,2004]:
      Id: 4
      Value: 389.864
    xc[gt_p,2005]:
      Id: 5
      Value: 410.383
    xc[gt_p,2006]:
      Id: 6
      Value: 430.902
    xc[gt_p,2007]:
      Id: 7
      Value: 451.421
    xc[gt_p,2008]:
      Id: 8
      Value: 471.94
    xc[gt_p,2009]:
      Id: 9
      Value: 492.459
    xc[gt_p,2010]:
      Id: 10
      Value: 512.978
    xc[hydro_s,2000]:
      Id: 66
      Value: 2
    xc[hydro_s,2001]:
      Id: 67
      Value: 2
    xc[hydro_s,2002]:
      Id: 68
      Value: 2
    xc[hydro_s,2003]:
      Id: 69
      Value: 2
    xc[hydro_s,2004]:
      Id: 70
      Value: 2
    xc[hydro_s,2005]:
      Id: 71
      Value: 2
    xc[hydro_s,2006]:
      Id: 72
      Value: 2
    xc[hydro_s,2007]:
      Id: 73
      Value: 2
    xc[hydro_s,2008]:
      Id: 74
      Value: 2
    xc[hydro_s,2009]:
      Id: 75
      Value: 2
    xc[hydro_s,2010]:
      Id: 76
      Value: 2
    xc[nuclear,2008]:
      Id: 85
      Value: 239.996
    xc[nuclear,2009]:
      Id: 86
      Value: 298.211
    xc[nuclear,2010]:
      Id: 87
      Value: 299.263
    xc[coal,2000]:
      Id: 110
      Value: 310.526
    xc[coal,2001]:
      Id: 111
      Value: 311.579
    xc[coal,2002]:
      Id: 112
      Value: 312.632
    xc[coal,2003]:
      Id: 113
      Value: 313.684
    xc[coal,2004]:
      Id: 114
      Value: 314.737
    xc[coal,2005]:
      Id: 115
      Value: 315.789
    xc[coal,2006]:
      Id: 116
      Value: 316.842
    xc[coal,2007]:
      Id: 117
      Value: 317.895
    xc[coal,2008]:
      Id: 118
      Value: 57.162
    xc[wind_ons,2009]:
      Id: 152
      Value: 170.246
    xc[wind_ons,2010]:
      Id: 153
      Value: 749.001
    xc[gtcc_s,2000]:
      Id: 165
      Value: 260.826
    xc[gtcc_s,2001]:
      Id: 166
      Value: 262.368
    xc[gtcc_s,2002]:
      Id: 167
      Value: 263.91
    xc[gtcc_s,2003]:
      Id: 168
      Value: 265.452
    xc[gtcc_s,2004]:
      Id: 169
      Value: 266.995
    xc[gtcc_s,2005]:
      Id: 170
      Value: 268.537
    xc[gtcc_s,2006]:
      Id: 171
      Value: 270.079
    xc[gtcc_s,2007]:
      Id: 172
      Value: 271.622
    xc[gtcc_s,2008]:
      Id: 173
      Value: 273.164
    xc[gtcc_s,2009]:
      Id: 174
      Value: 211.984
    xc[gtcc_s,2010]:
      Id: 175
      Value: 0.300787
    xc[geo,2008]:
      Id: 206
      Value: 23
    xc[geo,2009]:
      Id: 207
      Value: 23
    xc[geo,2010]:
      Id: 208
      Value: 23

$

Tools for Energy Model Optimization and Analysis