#BLIS - Balancing Load of Intermittent Solar:

A characteristic-based transient power plant model

Copyright (C) 2020. University of Virginia Licensing & Ventures Group (UVA LVG). All Rights Reserved.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

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Requirements:

Python 3.7 with the following libraries:
	pandas
	numpy
	joblib
	matplotlib
	seaborn
	xlrd

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To install:

With git and anaconda 3 installed:

1. Start anaconda 3 terminal
   Windows: Anaconda Prompt (anaconda3)
   Linux: start terminal

   module load anaconda

2. Change to directory for installation

   cd directory

3. Clone to desired directory (if git not installed, download directly from github)

   git clone https://www.github.com/EnergyModels/blis

4. Move to blis directory

   cd blis

5. Create and activate environment

   conda env create
   source activate blis-py3

6. Install blis

   pip install .

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File Overview:

blis:
    power_plant	- defines PowerPlant class
	fuel - defines class Fuel
	solar - defines class Solar
	storage - defines classes Storage, and Battery
	grid - defines class Grid
	hres - defines HRES (Hybrid Renewable Energy System) class, alternative control schemes are intended to be children of HRES
	     - also defines SBGS (solar-battery-grid system) class

examples:
	1) hres - hybrid renewable energy system
	    run_single_case 	- demonstration of running a single day using a CCGT with data file data063_Oct30th
	    run_parameter_sweep - demonstration of parameterizing the model using data file data063_Oct30th
	    data063_Oct30th 	- one day of demand and solar data (MW) for Oct 30, 2017    
	
	2) sbgs - solar-battery-grid system
	    same test files as hres, for a different energy system
	    
	3) preprocess_solar - supporting scripts which process solar data into a form useable by BLIS
	
projects
    3) sCO2_feasibility - scripts and code used for the study "Feasibility of using sCO2 turbines to Balance Load in power grids with a high deployment of solar generation" by Bennett et al., available at https://doi.org/10.1016/j.energy.2019.05.143
	
    4) sCO2_feasibility_results - same as #3, with the results of the simulations provided as .csv files

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Release history: \

• V1.0 - Uses Python 2.7, version used in "Feasibility of using sCO2 turbines to Balance Load in power grids with a high deployment of solar generation" by Bennett et al., available at https://doi.org/10.1016/j.energy.2019.05.143 \
• V2.0.0 - Updated to for Python 3.7 \

Notes:

• To run with SLURM files, replace ncpus = -1 with ncpus = int(os.getenv('NUM_PROCS'))