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There is a power project in an Asian country. An equity investor holds 40% of the shares. The objective of this case study is to quantify the impact of political risks on the tariffand dispatch and subsequently on the returns and debt-service capacity of the project. It is assumed that technical, operational, maintenance, or supply risks are successfully hedged and cause no losses to the project.
METHOD
The risk exposure quantification consists of an analysis of the power purchase agreement (PPA), two surveys, and cashflow modeling and simulation. The PPA and financial model were provided on an anonymous and confidential basis. First, the PPA was analyzed to identify which risk events have an impact on demand and pricing. Then, by questionnaire, factors that may influence these events were identified. In a second questionnaire, these identified factors were assessed by expert opinion in terms of likelihood of occurrence and possible consequence. The risk perceptions were quantified with the method for quantifying qualitative information on risks (QQIR) that uses trapezoidal fuzzy numbers (TrFN) to map human intuition (Sachs, Tiong, and Wagner [2008]) and converted into a probability density function. The results of this assessment were then simulated on the cash flow model with the commercial software @RISK from Palisade. The difference between the base case and the case of political risks is the investors' risk exposure.
POLITICAL RISK PERCEPTIONS AND MODELING ASSUMPTIONS
The foreign investor holds 40% of the total equity. The power off-taker and the fuel supplier of liquid natural gas (LNG) are state-owned enterprises (SOE). These SOEs have direct impact with their actions on the investment returns. Exhibit 1 shows the project and risk structure.
Forced Outage
Based on the PPA, there are four risk cases that may cause forced outage. In the base case cash-flow model, forced outage is 0%.
Risk identification. The risk events that can cause forced outage were identified from the power purchase agreement. The risk factors that may trigger such an event were identified by questionnaire. The possible maximum range of forced outage was estimated at 7.5%; see Exhibit 2.
Risk assessment and modeling assumptions. The foreign investor comments on the risk assessment on forced outage:
In general, war, civil disturbance, terrorism, and unions covering an area beyond the...