During the clean energy procurement process, buyers are tasked with evaluating dozens to hundreds of PPA contracts with varying terms, geographies, energy shapes, storage options, etc. If their value and risk are not properly understood from the date of signing through the life of the contract, PPA/VPPAs can result in significant negative cash flows for the company.
When evaluating PPA contracts, there are several factors that need to be examined before a final decision is made. These factors include;
- Generation shape and intermittency
- Price modeling: market forwards, fundamental forecasts, and the roles of simulation and scenario analysis
- Hub settlement and other contracting structures
- Price-Generation covariance
Generation shape is determined by weather and locational dynamics that impact renewable assets. An obvious example is that solar generation shape is dependent on solar radiation in a specific geographic area and during a specific month. In that same vein, intermittency refers to obscure factors that affect normal generation shape. An example of intermittency could be a rainstorm during a summer day where the sun typically shines brightly.
While shape and intermittency are pretty well understood, capturing the future risk of black swan events on generation (or load) remains elusive. There is not a crystal ball for predicting a specific event, but most will acknowledge that these events are happening more frequently and with devastating consequences. cQuant clients have adopted a much more robust scenario or stress testing methodology to at least visualize the exposure to these events. Understanding the portfolio exposure of an improbable storm can allow for processes to be put in place in advance of an event and guide the organization until normal patterns return.
cQuant often helps organizations simulate risk factors for renewable contracts or assets across significantly different time horizons. Over the last few years, the contract duration for PPA’s has decreased from 20 years to now 12–15-year time-frames. Fundamental curves are useful for the longer-term simulations as they capture the pricing impact of regulatory, environmental, and capacity changes to energy over years. For shorter duration analysis, market forward curves are a better fit. The market forward curves capture the short-term volatility that often cause price spikes and determine the value of a contract from next week to a year ahead. Sometimes it makes sense to separately simulate with both types of pricing forecasts. By changing the pricing curves and possibly simulating with other assumptions of market conditions/scenario analysis, our clients can bracket the uncertainty and better understand thew real cost/value of a PPA.
Understanding where a contract settles can be one of less understood but directly relevant factors of a PPA contract. The valuation of a PPA contract needs to specifically simulate the impact of a hub or node settlement. By simulating the basis spread between the node and hub and using historical basis spread to inform the simulations of likely future basis spread provides significantly more accurate valuations for the contract.
Another significant concern in valuing PPAs is price-generation covariance. Simply defined as the inverse relationship between price and generation in areas where there is significant generation. In areas with abundant generation like parts of California, adding additional solar generation may depress the real-time prices to the extent that the asset is not economically viable. A great indicator of this phenomenon is real-time negative pricing seen more often now in afternoon nodal prices.
To make the best decision regarding a PPA contract, it is vital that all of these factors be taken into account before moving forward. This is best done using robust statistical analysis to fully understand what a buyer is getting into with a PPA. With the use of cQuant.io, users have noted improved negotiations with contracting counterparties as well as visibility into their portfolio’s value and risk within the wholesale electricity market.
To learn more about the cQuant’s PPA Valuation solution, read our specific use case: Assessing the Value and Risk of Multiple PPA Structures.
cQuant.io is an industry leader in analytic solutions for energy and commodity companies. Specializing in Total Portfolio Analysis, cQuant’s cloud-native platform enables physical asset, financial contract, market simulation and risk management analytics in one place. cQuant is the leader in analytics for renewable, storage and other clean energy technologies. cQuant’s customers have greater insight into their financial forecasts and the drivers of value and risk in their business. Visit cQuant.io for more information and request your free demo today.