Title: Financial modelling and analysis of power project finance. A case study of Ngozi geothermal power project, southwest Tanzania

Abstract

This study undertook Excel-based financial modelling which entailed construction of an analytical instrument that performs detailed financial and economic analysis for a 30 MW Ngozi geothermal power project investment over its economic useful life. The tool facilitates making the most important financial decisions and solves complex questions about the future performance of an investment. The new bankable project is transacted using project finance structure with a 70% debt share and 6% interest arrangement in long-term debt financing. The model was built to quickly process a comprehensive list of project input assumptions to establish investment key performance indicators (KPIs) from the detailed analysis of projected cash flows. Results of the analysis are useful to the key power project stakeholders namely lenders, sponsors and off-taker in evaluating the attractiveness of the investment and subsequently facilitate making strategic decisions required for the project implementation. The deterministic KPIs results were subjected to the risk analysis to mirror a range of data variations and thus account for future uncertainties of the cash flows. The probabilistic estimation of project CAPEX made with a 90% confidence level amounts to 129 million USD. The project required a total of 21 million USD to meet the lender's fees, interest during construction and initial funding of reserve accounts. The project yielded an equity and project NPV of 24 million USD and 62 million USD respectively. The IRR of the project was 10% > calculated WACC of 6% while the IRR of equity was 17% > 10% of expected return on equity. The model yielded lender’s ADSCR of > 1.4, LLCR of > 2.1 and PLCR of 1.9, all the cover ratios above their minimum requirement, indicating the robustness and ability of project cashflow to service and repay debt. The project produced the LCOE of 60 $/ MWh with the exclusion of the effects of taxes on costs. The risk analysis indicated the price of electricity and energy production to be the most sensitive parameters to the KPIs results. Overall, the model analysis demonstrated the viability of geothermal power project investment to the key project stakeholders.