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How to Reassess Pricing Pilot Data with COVID-19 Impacts

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Pricing pilots are a key step for Utilities on the path to large scale alternative rate programs.  The objectives of pricing pilots vary, but some of the main goals include measuring the behavioral impact of price incentives and testing rate education and marketing strategies.  The results and lessons learned during the pilot are then used to design larger scale alternative rate programs.

The most common pilot changes the price from a time independent rate (same cost for every hour of the day) to a time-varying price.  With a time-varying price, or Time-of-Use rate, the cost of electricity is higher during system peak demand and lower during periods of reduced system demand.  The purpose of this design is to align the retail price of electricity with the wholesale price and enable customers to choose whether using electricity during peak periods is worth the higher cost. 

Many, if not most, large utilities are performing pricing pilots during 2020.  Almost all of these pilots are being impacted by COVID-19 Shelter in Place orders.  Some pilots are performed as ‘opt-in’, which requires customers to volunteer, while others are performed as ‘opt-out’ in which customers unilaterally switched to the new rate and must actively opt-out to return their previous rate plan.

As mentioned, pricing pilots measure how changing the price for electricity changes customer demand for electricity.  Economists refer to this relationship as Price Elasticity.  In order to isolate and accurately measure this change in consumption due to price, all other circumstances must be as close to equal as possible.  The imposition of a Shelter in Place order is clearly a fly in the ointment.  For example, Renewable Energy World reports that residential demand is up as much as 20%; refrigerators are working overtime, EVs are getting a rest and HVACs are working all day.  Overall load is down by as much as 15% in parts of the country, implying that C&I load decrease is more than offsetting the residential increase. 

Executing and learning from pricing pilot in 2020 will face at least three distinct challenges.  First, in addition to weather normalizing usage data, the data will also need to be normalized for COVID-19 impacts.  Second, load changes for residential customers working from home vs. residential customers working in essential jobs will have distinctly different COVID-19 normalization.  The situation is similar for C&I customers that are essential vs. non-essential business.  The third challenge involves taking into account the COVID-19 normalization when providing customers with guidance on their bill impact between a non-time varying rate and a time varying rate.

While it may seem no small task for utilities to solve the three challenges outlined, that is not the case.  Big Data technology enables utilities to examine every interval for every customer.  This is a vast departure from the more common practice of “sampling”.  For sampling to be effective, a small number of customers need to be reflective of the entire population and historically observed patterns need to continue.  Clearly, COVID-19 is making those assumptions weak at best.

The level of granularity resulting from a full population, complete interval analysis is tremendously beneficial in the current situation:

  • To accurately assess customers’ Price Elasticity, each customer’s load will need to be COVID-19 normalized;
  • A more granular analysis is required to differentiate between new COVID affected load shapes;
  • After the pricing pilot, when the new pricing program is made available to the full utility population, every customer load profile will need to be COVID-19 normalized, increasing the data set by 10 – 100X, depending on the number of utility customers.

Clearly COVID-19 has introduced data challenges needing new processes and technology.  Today’s utility systems may not be able to meet these new challenges.

Applying an Enterprise Rating Engine to Pricing Pilot Data

An Enterprise Rating Engine has key capabilities to meet the unique pricing pilot challenges facing utilities during COVID-19.  An Enterprise Rating Engine can house every customer’s interval data for 13+ months.  For COVID-19 normalization, individual customer interval data can be compared over the COVID-19 shelter-at-home time period with the same period from 2019, to establish the COVID-19 normalization, at the interval level.  Algorithms can be used to categorize residential customers as affected or not affected by shelter at home (essential workers).  A similar exercise can be performed to establish C&I COVID-19 normalization and segment customers as essential or non-essential businesses.

The results can then be used to establish behavior changes due to COVID-19 from behavior changes from the pricing pilot.  In addition, the results establish the baseline for bill impact comparison moving from pilot to a full program.

The Enterprise Rating Engine has an additional key capability – whole population rate analysis.  Whole population rate analysis provides a revenue grade bill impact for new rates, for each customer, calculated using the customer’s unique historical (or COVID-19 normalized) interval data.  Providing bill impact to each customer is a critical step during rate marketing and education.

With the new customer behavior introduced by COVID-19, an Enterprise Rating Engine can provide the solution to creating meaningful results from pricing pilots and moving forward to full rate transition.

Rob Girvan's picture

Thank Rob for the Post!

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Discussions

Eric Van Orden's picture
Eric Van Orden on May 20, 2020 3:42 pm GMT

Given the expected increase in residential load and decrease in business load, thoughtful allocation for cost of service seems extra important over the next couple of years or so. Cost of service allocation is nothing new, John Hendrickson Illinois Commerce Commission shared the following bullet points on a NARUC presentation summarizing Cost of Service and Rate Design in 2009.

  • Customer class Peak Demands
  • Customer class Usage
  • Number of Customers
  • Number of Meters
  • Number of Services
  • Many Others

What has changed since 2009 is increased access to data and enabling technologies for consumers. Still, the collection, organization, segmentation, and analyzation this information needs to be thoroughly considered when it comes to comparing year-over-year trends.

Additionally, to manage risk, consider how to balance attention on residential vs. commercial programs when implementing dynamic rates. Many utility rates for businesses have demand charges and/or time of use components. Residential energy users often have flat volumetric rates. When accounting for changing weather/climate and black swan events, does that mean there is more risk in aligning cost of service with the residential customer class?

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