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

Carlos Pocasangre, Yasuhiro Fujimitsu

研究成果: ジャーナルへの寄稿記事

1 引用 (Scopus)

抄録

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.

元の言語英語
ページ(範囲)164-176
ページ数13
ジャーナルGeothermics
76
DOI
出版物ステータス出版済み - 11 1 2018

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geothermal power
liquid
Liquids
Geothermal power plants
Hot springs
power plant
Computer programming languages
simulation
Power plants
thermal spring
library
method
software

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Geotechnical Engineering and Engineering Geology
  • Geology

これを引用

A Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir. / Pocasangre, Carlos; Fujimitsu, Yasuhiro.

:: Geothermics, 巻 76, 01.11.2018, p. 164-176.

研究成果: ジャーナルへの寄稿記事

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