Open AccessThe Application of Numerical Simulation Result for Geothermal Financial Model with Probabilistic Approach: A Comprehensive Study
Author(s) -
Nevi Cahya Winofa,
Ade Lesmana,
Heru Berian Pratama,
Nenny Miryani Saptadji,
Ali Ashat
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/417/1/012020
Subject(s) - geothermal gradient , tariff , internal rate of return , rate of return , investment (military) , net present value , electricity generation , environmental economics , electricity , geothermal energy , grid , probabilistic logic , economics , finance , production (economics) , engineering , computer science , power (physics) , mathematics , microeconomics , geology , electrical engineering , artificial intelligence , law , geometry , quantum mechanics , political science , physics , politics , geophysics , international trade
Feasibility of developing a geothermal project depends on the financial return generated from the investment. One of the strategies to achieve optimum return is formulating a financial model with a high level of confidence. Technical input parameters in the financial model are determined by the amount of available geothermal reserve in the form of a field development scenario. The best method for predicting geothermal reserve is a numerical simulation. The objective of this study is to determine the electricity tariff to generate 30 MW, 60 MW, and 110 MW which meet the 50% of the Rate of Return value will be equal or not exceed 16% (P50) for a specific geothermal field with a probabilistic approach. This study started with determining the technical input parameters: the number of production wells; makeup wells; and injection wells from each development scenarios based on numerical simulation result that has been studied by another researcher. The electricity tariff that meets the P50 of Rate of Return at 16% was calculated for those scenarios. Then, the tariffs were evaluated based on the Average Cost of Electricity Generation (BPP) on the relevant local grid. The result shows that the tariff or/and generation cost need to be negotiated. Moreover, total investment and economic indicators forecasting indicated that the investment was attractive. Lastly, sensitivity analysis shows that Rate of Return strongly affected by well drilling cost and power plant cost (EPCC).