The Chilling Tale of Texas Power Woes: A Credit Risk Management Perspective

This blog is written and published by S&P Global Market Intelligence, a division independent from S&P Global Ratings. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit scores from the credit ratings issued by S&P Global Ratings.

Many energy companies are increasingly addressing transition risks by investing in greener technologies to mitigate environmental impacts and avoid rising greenhouse gas emission taxes. However, they are also facing growing challenges from physical risks such as natural disasters, like winter freezes and hurricanes in North America or severe floods in Central, Southern and Eastern European regions, that disrupt operations and severely impact earnings. To ensure resilience, more companies are now reassessing their credit risk frameworks to enhance robustness against both environmental shifts and immediate operational threats.

Let us take RWE AktienGesellschaft, for instance. During the Texas freeze in February 2021, outages of its onshore wind turbines, compounded by unfavorable energy pricing conditions, caused losses of around $ 400m.^[1] Many other power companies faced similar fates, with only a select few, like Vistra Corporation, successfully mitigating the negative financial impact through a combination of self-help and securitization proceeds. In contrast, other companies, including Exelon Corporation, opted to challenge energy regulators, while entities like Griddy Energy LLC, Entrust Energy Inc., and Power of Texas Holdings resorted tofiling for bankruptcy after failing to meet their payment obligations to Ercot, the central counterparty agency for the local electrical energy markets.^[2]

Though the Texas deep freeze is deemed a once-in-a-lifetime event, global natural disaster losses has been on the rise between 2000 and 2023 (Figure 1).

Figure 1: Cost of natural disaster losses worldwide from 2000 to 2023.

Thus, it is crucial to perform scenario analysis and understand the financial and credit risk implications of extreme events before they materialize, both for a company and its counterparties.

Figure 2 illustrates a four-step approach to risk decision-making, emphasizing key dimensions and relevant Credit Analytics tools that can assist in this endeavor.

First, a robust risk management framework begins with a comprehensive credit assessment of a firm and its counterparties, considering fundamentals, business, market and qualitative aspects. Depending on portfolio size, available time, in-house resources, and expertise for the analysis, an institution may decide to employ S&P Global Market Intelligence’s Credit Assessment Scorecards that are broadly aligned with S&P Global Ratings’ criteria or leverage RiskGauge™ statistical scoring model, or a combination of both. The selected/largest exposures are assessed using the former approach, while the latter approach is employed for the bulk of smaller exposures.

Secondly, with increasing disclosure mandates on climate related financial risks and capital allocation towards energy transition, regular climate scenario analyses are essential. S&P Global Market Intelligence provides integrated tools for what-if and stress-testing analyses on climate scenarios, encompassing both energy-transition and physical risk scenarios:

• Climate Credit Analytics was developed in collaboration with Oliver Wyman and is particularly suited to analyze large-revenue companies, for which S&P Global has available a plethora of detailed, granular and sector-specific information.
• Climate RiskGauge, was optimized to scale the analysis of large portfolios of companies, in particular small-revenue firms that usually report limited information.

This exercise should occur at least annually, reflecting evolving country-specific energy transition policies, as well as potential physical events that may affect both a company and its counterparties:

• The company: depending on the nature of its business and the time-horizons of the exposures, the firm may in fact need to focus on short-term (e.g. 12 months) scenarios, where taxation policy shocks and extreme physical (i.e. acute) events play a more significant role than long-term (i.e. chronic) average trends. Based on the results of this exercise, a company may develop a business continuity plan should specific extreme hazards materialize or decide to buy insurance cover on those events that may disrupt its operations and potentially lead it to bankruptcy.
• The counterparties: looking across its whole customer base, a firm can identify businesses that are most exposed to specific shocks, perform further due diligence and adjust their credit limits, or rather buy trade credit insurance.

For example, different utilities companies (power generation companies) may perform in an unexpected manner under various climate scenarios depending on the sources of power generation and on its regulatory status as noted in Figure 3 below.

Figure 3: PD projections of four different power generation companies under NGFS IV - Current Policy versus Net Zero scenario.

Thirdly, it is vital to establish a proactive surveillance process that continuously evaluates counterparties’ creditworthiness, ideally on a daily basis. This involves distilling market information, news and alternative datasets into high-accuracy and predictive signals that herald imminent credit risk deterioration. Credit Analytics’ Early Warning System covers millions of companies, both public and private, globally, and helps risk managers efficiently prioritize and address areas of concern with its intuitive traffic-light scale and customizable framework that aligns with user risk appetite.

Finally, a firm should assess the credit limit towards its counterparties, both on a standalone basis, and at portfolio-level, taking into account both sides of the business relationship (i.e. both the firm’s and customers’ risk profiles), the internal risk appetite and the impact that the additional credit limit may have on both the customers (a higher credit limit puts a strain on their liquidity, when the invoice becomes due) and the company (in case the customer pays late, or not at all). In fact, a potential early warning from the third step may automatically trigger a review and reduction of the credit limit associated to a specific counterparty.

Employing these four components, we firmly believe in the feasibility to develop robust and automated end-to-end risk management frameworks. These frameworks will empower energy companies to enhance their preparedness in the face of unexpected events and make sound business decisions. 
 

^{[1] “February storm caused over $10B in losses for investor-owned power companies”, S&P Global Market Intelligence (May 2021), available here.}

^{[2] “The timeline and events of the February 2021 Texas electric grid blackouts”, The University of Texas at Austin – Energy Institute (July 2021), available here.}