One of the most important risks to the Indian renewable energy sector is the counterparty credit risk, associated with the risk of state-owned utilities delaying or defaulting on their contractual payments to power producers, adding as much as 1.07% of additional risk premium to the cost of debt for renewable energy projects (CPI, 2018), and also limiting the availability of capital.

This risk mainly arises from systematic inefficiencies in the public sector electricity utility sector in India. State-owned electricity distribution companies, or DISCOMs, form the largest set of power offtakers for the Indian renewable energy sector, under long-term power purchase agreements (PPAs) at pre-decided tariffs with independent renewable energy power producers. As a result of these inefficiencies, these companies are plagued by poor financial health.

Mitigating this risk requires long-term structural fixes aimed at solving the systematic failures of the utilities sector through coordinated efforts by the central and state governments and DISCOMs. The Ujwal DISCOM Assurance Yojana (UDAY) program; which envisages financial turnaround, operational improvements, and reduction in power generation costs, is a step in this direction (PIB, 2015).

This paper, besides providing a methodology for answering these questions also provides illustrative calculations for a range of sample DISCOMs and prescribes methods using data available in the public domain, and under certain assumptions.
Methodology overview

The payment security mechanism as designed in this study builds on existing work by CPI (CPI, 2016). The approach builds on the existing frameworks for credit guarantees for the purpose of enhancing the credit quality by means of providing protection against defaults/ delays in payment obligations due towards a project.

The study uses a probabilistic methodology, calibrated on empirically derived proxies for past payment history, for calculating the optimum PSM size and the credit enhancement achieved using it for a project selling electricity to a given DISCOM. We consider two piecemeal component risk scenarios: Default by the project owing to all risks outside of the counterparty (DISCOM) risk ; and default by the project owing to the default/ delay on payments by the counterparty (DISCOM).

Next, we study the probability of default by the project in the two cases: without the presence of payment support (the base case), and in the presence of a given payment support mechanism (post-intervention). The base case corresponds to the probability of default associated with the base credit rating of the project, whereas the second probability of default corresponds to the probability of default associated with the target credit rating intended to be achieved by the intervention. The underlying assumption is that, for project finance, credit ratings are completely specified by the probability of default.

The study outlines a five-step process to arrive at the optimal size of the PSM that can achieve the intended goal of the intervention, and also illustrates the results by applying this framework to a representative sample set of 8 diverse DISCOMs.

Key findings

The key insights derived from these results are:

We find that the maximum possible credit enhancement that a PSM can achieve is up to BB rating. Against a target of BBB, this is the theoretical maximum credit enhancement that can be achieved through the use of a payment security mechanism under the assumptions made in the study. Further credit enhancement would require the mitigation of other risk factors beyond the counterparty credit risk.

Projects associated with certain DISCOMs do not require payment security support. For instance, the Gujarat DISCOM requires minimal payment support (1 month or less) to enhance the credit quality of its projects. Similarly, the West Bengal DISCOM, in fact, does not require a PSM to achieve the BB rating limit.

We also find the projects associated with some DISCOMs require impractically high payment support. The unusually high payment support (> 45 months) requirement for Uttarakhand DISCOM, as seen in the results, seems anomalous. On one hand, it may point towards an unusually bad DISCOM. On the other hand, this may be due to the lack of granularity of liability data in the published financial reports, which results in unusually high payments month outstanding (PMO) proxies computed for the Uttarakhand DISCOM.

In general, we find that most DISCOMs require moderately high payment support of 12 months’ payment. Barring the above outliers, the rest of the DISCOMs require 8-17 months payment support for associated projects to achieve BB rating, and on average, 12 months of payment support. This translates to a fund size equivalent to approximately 10%-20% of the total capital expenditure of the project being supported. The size requirement for this fund may be further reduced to 6%-18% of the capital expenditure by requiring the DISCOM to furnish a revolving letter of credit of 3 months’ payment, thus off-loading part of the cost of payment support to the DISCOMs.

We recommend the adoption of the five-step methodology outlined in this paper by central and state level government agencies looking to provide financial risk mitigation interventions targeting the counterparty risk due to DISCOM default, with an implicit goal of raising the credit profile of affected projects. We recognize the possibility of refining these methods and the resultant outputs in the presence of data currently not available in public domain, but likely available to policy-makers. This requires a coordinated effort between various stakeholders such as central and state governments, DISCOMs, regulators and power aggregators. To make PSMs more efficient over multi-year horizons as well as to assess the cost of implementing such a funded PSM, further research is required.