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Simple energy model for educational purposes.
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josiahjohnston/simple_energy
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This git repository stores a brief introduction to Pyomo. This assumes you are familar with git, a python programming environment, and have installed pyomo and glpk on your machine. I should write instructions for that in a INSTALL file, but that can wait for another day. Assuming you have a python programming environment set up, you can test your installation by executing simple_energy.py. This should write the text file results.yml into your working directory. If that doesn't work, you'll have to troubleshoot & fix your installation before you can get your hands dirty with experiments. Learning Pyomo: * You can start by skimming through simple_energy.py to see what you are in for. * If you are an experienced programmer and energy modeller, this file as-is should get you started. * If you want a better walk-through, open the git repo and go back to the earliest commit. This model starts from a single-variable optimization problem and gradually introduces pyomo and python concepts, in steps recorded via git commits. Read through all of this. * If you want to get to the next level, I recommend practicing writing. Write your own version of this in a new file. Follow the pattern shown in this git history: * Start from something dead simple and test that it works. * Commit that working copy to your local copy of the git repo. * Move on in small increments, checking that it still works after every edit. * You can reuse the equations, but don't copy and paste or just retype everything verbatim. * If you are like most people, these tests will often fail .. from typos, having different expectactions of program/library behaviors, subtle mistakes with python syntax, etc. The process of debugging those errors is a huge part of this lesson. If you don't rewrite the model yourself, you'll miss out on that process of exploration and discovery. On the flip side, the model is simple enough that rewriting it won't take too long. * Saving working versions and testing at each edit is important to minimize debugging frustrations and maximizing productivity. ..If you know an error is the result of a few lines of edits, it's way easier to track down and figure out.
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Simple energy model for educational purposes.
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