For a solar PV lender, the core challenge in ensuring the profitability of a PV investment lies in making correct assumptions at the planning phase and in using the right values for financial modeling. But financial modeling is not entirely a finance opinion, since a relatively young industry like the solar PV industry inherently has diverse forms of economic and technical risks, implicated in the typical lifecycle of a PV plant.
The wide range of technical risks associated with different PV components and the different lifecycle phases of PV projects constitute a real threat to proper project modeling for a lender or an investor. Therefore, a water-tight PV investment risk management strategy/framework – deployed for the entire credit duration will be inevitable for a lender. Incorrect assumptions in PV financial modeling by a lender or investor broadly leads to one of two outcomes; a) Acceptance of an otherwise unworthy investment or (b) rejection of an otherwise attractive investment.
Saying yes to an unprofitable PV project?
Since lenders utilize the Levelized Cost of Electricity (LCOE) to estimate a PV project’s energy generation cost over its lifespan, it is considered an important and sensitive tool for financial modeling. However, one core concern is the underestimation of LCOE for a project/plant which will give a false projection of the Return on Investment (ROI) and also the wrong value for Debt Service Coverage Ratio (DSCR). The challenge for a lender may therefore lie in proper estimation of LCOE.
While a lender is concerned about an obligor’s ability (PV project developer) to maintain a positive Debt Service Recovery Ratio (DSCR above 1.0), the obligor’s ability to do so is squarely hinged on the quality of PV risk management framework installed throughout the project lifecycle. From design to decommissioning phase, the PV lender/investor’s assumptions will be extensively challenged by technical considerations; some of which will only manifest at the operation phase and may also be difficult to accurately model to a point of reasonable statistical confidence.
Since values that go into the calculation of important financial models (such as project’s LCOE) are subject to changes and diverse degrees of changes in different operational environments; managing LCOE input uncertainties - through sensitivity analysis - becomes very important for a lender/investor. Furthermore, because LCOE and most of its input variables do not have universal uniformity in values (i.e. they are dependent on the environment/project setting), obtaining statistically significant and accurate data may be tough for some projects especially in primary solar markets, where installations are predominantly new.
How then should a lender increase his confidence in a project’s bankability assessment and how can PV project activities at all phase be properly and financially de-risked?
Technical risks have been identified in several PV operation reports/studies as capable of causing economic losses associated with energy yield. They also cause PV failure/degradation and are therefore likely to introduce distortions in project cash flows. It suggests that lenders should optimally adjust for technical risks and encourage obligors or project developers to systematically identify and mitigate all possible PV project technical risks.
Some key considerations are important. For example, LCOE input values such as CAPEX, OPEX, yield estimation; project lifespan and PV degradation rate have to be accurately and correctly calculated. In addition the LCOE’s sensitivity to changes in each of the inputs should be correctly modeled and understood. For a lender who is exposed to multiple (and different PV) markets LCOE sensitivity analysis provides a tool not just for modeling the Return On Investment (ROI) but also for selecting PV markets to play in, as well as, the PV business model/segment to adopt (rooftop, mini-grid, commercial & industrial and utility scale).For example, The Operating Expenditure (OPEX) for equal-sized PV plants in Oman and Ivory Coast may not only vary but its variation could have distinct levels of impact on the ultimate value of LCOE (degree of change). Similarly while a small change in energy yield may cause a disproportionately large change in LCOE value in one market or class of markets, the impact of yield variation on LCOE may be less dramatic in other markets. Understanding these behaviors empower lenders to make better business decisions in a given PV market. Take Africa for example - which is a primary market and also a region with somewhat distinct irradiance; both of which could pose some threat to accurate and objective energy yield estimation. (Reference is made here to the likelihood of significant disparities between Laboratory PV quality behavior and Field quality/performance)
A clear strategy for characterizing, mitigating, managing, transferring or bearing technical risks in PV projects is therefore fundamental to profitability. Since values such as estimated PV yield, PV degradation rate, project CAPEX, project OPEX and estimated lifespan that are used as LCOE inputs are directly influenced by the nature of the technical risks confronting the plant/project.
The core task for a lender is to ensure that proper values and assumptions are used in all models, that includes; the uncertainties around energy yield; probabilities of rising OPEX; proper estimation of lifecycle degradation rate; proper estimation of economic losses due to downtime (downtime from full or partial module/plant failure); mid phase and late-phase component behavior modeling (cost of repairs and substitution of components), EPC settings, warranty settings and recoverability.
The whole essence is that correct financial models for PV investment require strong understanding and monitoring of several technical risks and that’s the only way that a project could be made to live up to its fiscal expectations. Apparently, understanding PV technical risks is a conspicuous aspect of bankability assessment. comment↓