SWRV Methodology

SWRV stands for Shape Weighted RenewaFi Value. RenewaFi's SWRV methodology powers several products that aim to help renewable energy professionals better understand the value of ERCOT energy products. This document provides an explanation of that methodology.

PPA Terms

Each SWRV estimate is based on the following PPA terms:

• Delivery Point (Houston, North. South, West)
• Transaction Type (Wind, Solar)
• Start (COD)
• Term (Years)
• Structure (Fixed Shape, Unit Contingent)
• Product (Energy, Energy+REC)
• 12x24 shape

The SWRV methodology is powered by a proprietary algorithm with multiple data sources, including forward block prices and REC values, as well as historical day-ahead prices and power generation data. For unit contingent deals, the algorithm incorporates solar and wind covariance factors.

RenewaFi's Hourly Price Curve

RenewaFi applies forward block prices for three peak-types (5x16, 2x16, 7x8) for each ERCOT major trading hub (Houston, North, South, West). Since these block prices are based on current market expectations, they are meant to be objective and independent of any particular view of the market.

RenewaFi calculates hourly scalars based on three years of historical day-ahead price data for each hub. Then, RenewaFi simulates a statistically significant amount of hourly price forecasts using the Monte Carlo method and selects the average curve to achieve the most likely outcome. Finally, to generate the hourly price curve, RenewaFi multiplies the average simulated scalars by the respective forward block price.

To maintain the integrity of this methodology, RenewaFi updates the scalars quarterly, the block prices daily, and makes certain common-sense modifications for special circumstances, such as winter storm Uri in 2021.ⁱ

Renewable Energy Credit (REC) Price Assumptions

For deals that include RECs, RenewaFi adds a technology-specific REC value based on quotes that are provided by a third-party.

Covariance Factors for Unit Contingent Shapes

For unit contingent deals, RenewaFi calculates solar and wind covariance factors. These factors reflect the generally inverse relationship between price and generation for solar and wind. For example, LMPs may be higher than expected during certain hourly intervals when wind generation underperforms to forecast.

The covariance factor is a ratio between the energy value generated by the generation type and the implied historical firm energy value.

To calculate the actual energy value, day-ahead energy price by hour, block and month are multiplied by actual generation by hour, block and month. To calculate the historical firm energy value, the average day-ahead energy price by hour, block and month is multiplied by the average historical generation volume for the given hour, block and month.  

To get the unit contingent energy valuation, the hourly price is multiplied by the hourly generation shape then multiplied by the hourly covariance.

Disclaimer

Each Shape Weighted RenewaFi Value (“SWRV”) is nothing more than indicative, estimated calculation. These estimations do not represent actual opportunities available in RenewaFi’s marketplace. Do not use these estimates to make investment or trading decisions or to “mark” investments to market. Further, information provided via SWRV, or any RenewaFi product, is not to be considered advice. Buying and selling energy and renewable energy credits can be complex and involves substantial risk of loss. The use of any RenewaFi software or services does not guarantee performance. Renewable energy participants should hire and retain professional legal counsel prior to using RenewaFi software and evaluating or completing any commodity transactions.

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[i] In the case of winter storm Uri, RenewaFi excluded data from 2/17/2021 to 2/20/2021 for both the scalar and covariance calculations. We cap all input prices at $5,000/MWh due to ERCOT’s recent price cap adjustment to $5,000/MWh from $9,000/MWh.

[ii] Season is defined as Winter (Dec., Jan., Feb.), Summer (Jun., Jul., Aug., Sep.) and Other (Mar. Apr., May, Oct., Nov.)