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Geothermal Power Potential assessment

Project description

TOPIC

A Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir.

ABSTRACT

We present a Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir. The specific aims of this study are to use the volumetric method, heat in place, to estimate electrical energy production ability from a geothermal liquid-dominated reservoir, and to build a Python-based stochastic library with useful methods for running such simulations. Although licensed software is available, we selected the open-source programming language Python for this task. The Geothermal Power Potential Evaluation stochastic library (gppeval) is structured as three essential objects including a geothermal power plant module, a Monte Carlo simulation module, and a tools module. In this study, we use hot spring data from the municipality of Nombre de Jesus, El Salvador, to demonstrate how the gppeval can be used to assess geothermal power potential. Frequency distribution results from the stochastic simulation shows that this area could initially support a 9.16-MWe power plant for 25 years, with a possible expansion to 17.1 MWe. Further investigations into the geothermal power potential will be conducted to validate the new data.

For testing the application, a Jupyter Notebook example has been included in the example folder.

HINT: Now, this application is available for Python 3.5

Reference

Pocasangre, C., & Fujimitsu, Y. (2018). A Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir. Geothermics, 76, 164-176. https://doi.org/10.1016/J.GEOTHERMICS.2018.07.009

INSTALLATION

Required Packages

The following packages should be installed automatically (if using ‘pip’ or ‘easy_install’), otherwise they will need to be installed manually:

  • NumPy : Numeric Python
  • SciPy : Scientific Python
  • Matplotlib : Python plotting library
  • Mcerp : Monte Carlo Error Propagation
  • Beautifultable : Utility package to print visually appealing ASCII tables to terminal

How to install

You have several easy, convenient options to install the ‘gppeval’ package (administrative privileges may be required).

  1. Simply copy the unzipped ‘gppeval folder’ directory to any other location that python can find it and rename it ‘gppeval’.

  2. From the command-line, do one of the following:

    1. Manually download the package files below, unzip to any directory, and run:

      $ [sudo] python setup.py install

    2. If ‘pip’ is installed, run the follow command (stable version and internet connection is required)

      $ [sudo] pip install [–upgrade] gppeval

CHANGES OF NEW ISSUE

  1. gppeval (2019.4.17.0.3.dev1).

    Fixed bugs using “print” on Python 2.7

  2. gppeval (2019.4.17.0.2.dev1).

    Python 3.5 available

  3. gppeval (2018.10.11.0.1.dev1).

    The input file csv has been modified. It includes the possibility of using volume as a input reservoir parameter. Using the word none is possible to exchange between either to use Area and Thickness or to use only Volume as a reservoir geometric parameter.

    Example: Using Area and Thickness

    0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,T 1,,,,Thickness,h,m,450,500,600,0,0,T 2,,,,Volume,v,km3,4,6,8.2,0,0,none

    Example: Using only Volume

    0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,None 1,,,,Thickness,h,m,450,500,600,0,0,None 2,,,,Volume,v,km3,4,6,8.2,0,0,T

  4. gppeval (2018.4.6.0.1.dev1).

    Original issue after have been upload as a stable.

CONTACT

Please send feature requests, bug reports, or feedback to: Carlos O. POCASANGRE JIMENEZ

Project details


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