Articles The Texas Power Grid Fallout: View from a Risk Manager

The Texas Power Grid Fallout: View from a Risk Manager

March 1, 2021

The Texas fallout of February ’21 from severe winter weather is currently at the forefront of conversation. But this isn’t the first time in history that we’ve seen weather anomalies cripple the power grid and spell financial doom for organizations that were poorly positioned. Such “black swan” events are extremely low in probability but extremely high in impact. In fact, they are often so extreme that risk managers wouldn’t typically want to incorporate them explicitly into their analytical risk management framework. A risk manager may model prices occasionally hitting the system-wide offer cap of $9,000, but simulating these for an entire week, as we saw in the recent Texas power grid catastrophe, could very likely have the effect of over-valuing any options that exist within the portfolio. For example, real options like thermal generators and batteries or financial options would show significantly inflated valuations since their value is directly related to energy market volatility.

These “tail events” are more typically incorporated into risk management analytics through either scenario analysis or historical simulations, as part of risk reduction strategies. That is, a scenario or “stress test” is designed that reflects these extreme market conditions and/or historical simulations assess the impact of a previously-observed black swan event on the current state of a portfolio. Historical simulation approaches leverage realized historical events to understand how a particular portfolio would be impacted by a similar situation, even if this portfolio did not exist at the time of the historical event. They provide a powerful and objective methodology for portfolio stress testing, but naturally cannot be used to model events that have not previously occurred; this is where a more subjective scenario design and analysis process can augment the analytical framework.

Even with these approaches, the challenge is producing a scenario that is extreme enough to push the limits of what is possible, yet not so extreme that it provides little to no actionable business intelligence. Almost by definition, these black swan events have never occurred in the past, so there’s no basis for determining just how extreme they can be. The polar vortex of 2014 is another good example; at the time, it was one of the most extreme events the northeastern U.S. power grid had ever seen. If a portfolio manager had modeled the polar vortex prior to its occurrence and urged the company to manage to it, it’s likely this individual would have been laughed out of the room. Worse yet, if the organization did manage to this sort of event, they might have paid enormous premiums for financial protection against it, which could have negatively impacted margin and forced them into bankruptcy before the event ever even happened. However, after the polar vortex event occurred, management teams at many companies (particularly energy retailers with a short position in the market) were likely pointing fingers at the risk manager, criticizing the lack of management to the extreme event.

It is important that, as energy professionals, we look at black swan events through the eyes of our risk managers and shift our focus and mindset to how we can better prepare for these events in the future. For energy companies exposed to volatile natural gas and electricity spot markets, savvy hedging can help protect against the extreme. A happy medium can be achieved in terms of reducing some exposure to extreme events while acknowledging that completely removing the risk is either impossible or cost prohibitive. Where this middle-ground lies depends on the nature of the company, its risk tolerance, and the means it has to hedge its native position. Understanding a company’s future market exposure to extreme events can be complex and requires rigorous analysis accounting for a company’s unique portfolio of contractual commitments, physical assets, and in-place financial positions.

At, we’ve built an energy analytics platform with easy-to-use models that can help companies protect themselves from adverse market events. Our web-based interface provides access to powerful analytic models that let you understand your portfolio’s market exposure and take steps to mitigate your risk. Don’t let the next “Black Swan” weather event destroy your company’s future.