Corporate buyers signing renewable power purchase agreements (PPAs) agree to take on market price risk in exchange for the energy and environmental attributes of their counterparty’s projects. This is a key principle underlying unit-contingent (also called as-generated), fixed-for-floating contract structures that are most commonly used in North America today. To the best of their abilities, corporate buyers must understand the risk they are assuming by signing a PPA by projecting the market value and testing sensitivities depending on how the grid may change over the PPA term. Once the risk is understood, they can then work to de-risk the contract with appropriate contract structuring. This post explores power market modeling methodologies used to make projections and explains why a fundamentals-based approach is most suitable for corporate renewable PPA analysis.
Two primary categorical approaches are available when making long-term projections in energy markets. Quantitative modeling – which includes Monte Carlo and other randomized approaches – uses historical statistical relationships to evaluate sensitivity of market price to underlying variables like natural gas price and heat rate. This lightweight, low-effort approach is most useful in financial markets where there is reason to believe that historical relationships will persist in the future.
This approach falls short in energy markets for two reasons: first, quantitative modeling does not predict price. Rather, it relies on an underlying long-term projection – such as market forwards – to make an assertion over how price changes with heat rate or natural gas. And, as we all know, predicting the future is challenging (See Figure 1).
Figure 1: ERCOT North Historical Prices & Forward Price Expectations Over Time
Second, do we really believe that the future energy market will reflect the past? We argue no. The last twenty years have brought more upheaval to energy markets than ever thought possible. Natural gas has usurped coal as the dominant fuel source in US power generation, led by unprecedented, sustained low commodity prices. Wind and solar have become mainstream, and their intermittent generation profiles materially impact regional on-peak and off-peak prices (See Figure 2). And as is playing out today, demand impact of COVID-19 has been material, dropping over ten percent year-on-year in most utility jurisdictions around the country.
Figure 2: ERCOT Houston Hub Forward Peak Around-the-Clock Monthly Prices
How will a renewable PPA stand up to continued market transition? Monte Carlo modeling will not answer that question. Instead, it may wrap the corporate buyer in a false sense of comfort by producing a perfect bell curve of probabilistic outcomes based on arbitrary changes to market fundamentals.
The Edison model forecasts hourly market prices by modeling production from generation assets to meet the grid’s regional demand subject to local transmission system constraints – in other words, we run the wholesale market every hour to understand how prices are set under each scenario. We gather forecasts of risk factors from industry-leading data providers, taking care to ensure a variety of data suppliers. We also develop our own proprietary forecasts of market risks informed by our longstanding expertise in energy market modeling.
By creating many scenarios based on combinations of data inputs across data providers and varied weather patterns, we ensure an unbiased analysis which is not dependent on “group think.” We work with clients to understand the expected value (i.e. the cashflow) of a PPA as well as the likelihood and cause of upside potential and downside risk. We help clients understand “what you need to believe” about risk factors for a specific cash flow range to materialize. This fundamentals-based approach creates unparalleled understanding of risk and insight to de-risk the PPA with appropriate contract structuring.