Our PPA Valuation Model

Each benchmark shows the value over time of a specific combination of technology, start date, term length, and delivery point. We calculate each benchmark by multiplying the expected hourly price of electricity by the expected hourly generation of the relevant technology (solar or wind) over the lifetime of the contract. The result is an objective estimate of the PPA’s value. We then compare that value to the average price of similar offers from our marketplace. The delta between these two figures represents a “green premium” – the extra money a buyer would need to spend above the expected value of the electricity and RECs to enter into the PPA.

To get the expected hourly generation of the relevant facility, we start with the facility’s shape, which describes how much and when it is expected to generate. Because a facility’s actual hourly generation can vary materially from its expected hourly generation, we consider the risk of over or under delivering to expectations. To quantify that risk, we compare actual, historical generation data from ERCOT to the given P50 generation profile in order to understand shape risk and adjust the PPA value accordingly. Finally, if the PPA does not include a $0 price floor, we also adjust the shape for economic curtailment risk at the delivery point.

Forward block prices represent where traders agreed to transact today for energy that will be delivered at different times in the future. These block contracts typically trade over-the-counter and describe the price and delivery of energy during a pre-specified group of hours for an entire month (e.g., the August 2026, ‘on-peak' block). We calculate the forward block prices by averaging several forward block price curves, which we source daily from large brokers.

Since solar and wind are intermittent resources, their generation fluctuates dramatically, and the value of the generation is highly sensitive to hourly price movements. To analyze these intermittent resources, we therefore need to transform forward price curves into hourly, rather than monthly, granularity. To convert the monthly block prices to hourly prices, we use hourly scalars. We derive the scalars by looking at the variability in hourly prices from the past three years across Houston, North, West, and South hubs. Specifically, we divide each hourly price by its respective monthly block price (7x8, 5x16, or 2x16) to match the format of the forward block prices. We calculate scalars for each hour of each month. For hour 5 (5:00am) in October, for example, we calculate 31 distinct scalars representing each day in October. Since we look at three years of data, our set of scalars for this hour is three times as large: 93 in total. To estimate the future value of hour 5 in October, we start by selecting a scalar from these 93 possibilities. We follow this process for every hour of every day for the next 20 years. Once we multiply the randomly selected scalar by the relevant block price, we get a new price forecast with hourly granularity.

We adjust our scalars to reflect how market conditions are expected to change in the future. We do this by looking at net load. Net load is the amount of electricity demand that cannot be met with renewables. It’s calculated as load minus supply from wind and solar. As net load increases, more supply comes from fossil fuels, which are more expensive than renewables. So when net load is high, we expect price to be high as well. The Price Tracker currently uses a base-case scenario for net load based on ERCOT and EIA forecasts. In the future, users will be able to choose from additional net-load scenarios or customize their own.

PPAs can be offered in one of two main structures: “unit contingent” or “fixed shape.” Sometimes unit contingent is referred to as “as generated” or “as produced.” In a unit contingent PPA, the buyer agrees to purchase the seller’s energy whenever it is produced. In a fixed shape PPA, the seller promises to deliver a specific, pre-agreed shape regardless of whether or how much the underlying facility actually generates. Valuing unit contingent PPAs presents a challenge because actual generation may differ substantially from the expected generation profile. This is known as shape risk. To calculate shape risk, we compare how much the given solar or wind shape would have earned over the past three years against what solar and wind facilities actually earned over the same time period. This gives us a series of covariance factors for wind and solar. When we multiply these covariance factors by every hour in the 12x24, we convert what is essentially a fixed shape generation profile into a new generation profile with unit contingent granularity.

When electricity supply outstrips demand, the grid sends negative price signals to discourage further generation. This is called economic curtailment risk. A $0 price floor is a PPA term designed to help protect offtakers from paying for renewable energy when power prices are negative. A PPA can be offered with or without a $0 price floor. To quantify curtailment risk for PPAs without a $0 price floor, we compare the value of the given PPA if it were to settle during all hours against the value of the same PPA if it were to settle only during hours when prices are positive.