Meistaravörn við Iceland School of Energy - Chagaka Kalimbia
Financial modelling and analysis of power project finance. A case study of Ngozi geothermal power project, southwest Tanzania
Fimmtudaginn 11. apríl nk. kl. 13:00 í stofu M325 heldur Chagaka Kalimbia fyrirlestur um 60 eininga verkefni sitt til meistaraprófs í orkuverkfræði við Háskólann í Reykjavík.
Verkefnið heitir "Financial modelling and analysis of power project finance. A case study of Ngozi geothermal power project, southwest Tanzania." Fyrirlesturinn verður fluttur á ensku er öllum heimill aðgangur.
Leiðbeinandi: Dr Páll Jensson, prófessor í rekstrarverkfræði við Háskólann í Reykjavík.
Prófdómari: Dr Bjarni Pálsson deildarstjóri hjá Landsvirkjun.
Abstract:
This study undertook a 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 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. The results of the analysis are useful to lenders, sponsors and off-taker in evaluating the attractiveness of the investment and subsequently facilitate making financial decisions. 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 model analysis demonstrated the viability of power project investment to all project stakeholders. The risk analysis indicated the price of electricity and energy production to be the most sensitive parameters to the KPIs results.
