John Echols

We know energy prices can swing wildly, but we don’t know when these swings will occur. In April 2020, oil at Cushing, OK, traded at negative $38 a barrel, and in February 2021, power prices in ERCOT traded $9,000 per kilowatt-hour (kwh). During these stressed times, which both occurred inside a 12-month window, certain organizations prospered while others stumbled. How can an enterprise best prepare itself to be one that prospers?

In the summer of 2019, Opportune began a series explaining its views about the essential capabilities organizations need to have to effectively manage commodity price risk. In prior articles (here, here and here), it outlined how an enterprise might approach risk management and its view of essential capabilities for effective commodity risk management. One such capability is evaluating economic performance during unknown conditions, namely unknown price conditions.

In this article, I’ll present a case for an enterprise to develop a robust economic forecasting model that simulates future cash flows, allowing for performance evaluation of a specific energy asset or portfolio of assets.

Energy assets come in many forms. Let’s consider those that offer an owner choices in how they operate it in physical markets. Storage tanks provide flexibility as to when product is purchased or sold. Pipeline capacity provides flexibility as to where product is purchased and sold. Rail cars and marine vessels create flexibility as to where and when product is purchased and sold. A gas-fired merchant power plant creates flexibility around when natural gas is purchased to make electricity.

Energy prices are volatile, but they’re discoverable in both spot and forward markets. Forward markets provide the basis for defensive commercial strategies that protect financial outcomes. But such markets also provide opportunities for optimization strategies that can add significant value beyond the pure logistical purpose for which an asset may have been originally intended. Furthermore, investment decisions that expand operational flexibility, such as adding battery storage to a wind farm or insulating a gas turbine enclosure, should also be contemplated within an economic model that uses statistically-driven forward prices to produce probable forward cash flows supporting asset valuation.

Typically, a physical market participant will realize the modeled value of their asset position in spot or near-spot markets, capturing market moves as part of the logistical exercise embedded in the company operations. Monetizing an asset’s potential full value requires awareness of forward markets and the capability to prudently engage in transactions in the forward markets. Such activity can add a lot of value, but it also introduces new requirements for risk management, analytics and liquidity management.

There are numerous benefits in having an objective, statistically-driven economic model, including:

  • Evaluating investment opportunities for facility expansion
  • Understanding both the intrinsic and extrinsic (optionality) value of a facility
  • Evaluating choices between different commercial marketing and hedging strategies
  • Evaluating when it may make sense to fully hedge a facility
  • Understanding potential credit exposures with significant trading partners
  • Understanding potential liquidity requirements in different price/operating conditions

Normally, these models reside in an Excel workbook with individual sheets representing different elements and user interfaces of the tool. Standard user interfaces include historical market settlement data, forward market data (fixed price and volatility), parameter inputs and the simulated model results.

It’s important to distinguish between a normal financial model that may be run occasionally to predict future results, perhaps for investor guidance, and a risk management valuation model outlined in the next few sentences. The valuation model should include multiple variables to facilitate future decisions made in response to continuously changing market conditions. Such a problem often fits nicely into a Monte Carlo framework that utilizes historical price data, forward market prices and volatilities in forecasting future prices and, subsequently, how the asset might operate and its expected future cash flows.

Ideally, enterprises will assign risk management accountability to an independent risk manager and provide tools like the model described above to understand likely outcomes and assist with determining what might go wrong. A statistically-driven economic forecasting and valuation model should be incorporated within the value-at-risk and stressed exposure process employed by the risk manager to understand risks to the enterprise.

In prior writings, we at Opportune have suggested the benefits of employing rigor and learning with respect to risk management. Powerful synergies occur throughout an organization when senior management, commercial personnel and risk managers share modeling expertise for investment decisions, portfolio valuation and risk management.

Negative oil prices and polar vortexes remind us that energy prices are volatile and can go places no one is contemplating. Companies that prosper in these unknown and extreme conditions rely on tools such as an asset valuation model and related processes to understand risk, as well as embedded optionality in their operations.

John Echols is a partner in charge of Opportune’s commodity trading & risk management group. Echols has more than 30 years of business experience, including six years as a senior executive in the energy industry.