Chris Lewis
Cognera Corporation
Calgary, AB Canada

What is Dynamic Pricing?
As wireless telecommunications companies came up to speed with consumer demand, prices slowly dropped and eventually tapered off at a “peak” and “off-peak” model. These pricing models incented customers to make calls during off-peak periods in exchange for significantly lowered rates. These plans soon impacted consumer behavior, and dynamic pricing came into effect. Today, dynamic pricing has been replaced by pre-paid and other variations of cost management programs, indicating that demand response initiatives are not only successful, but in many cases, inevitable.

Dynamic Pricing Meets the Utility Industry
No one would argue that the utilities industry is currently going through its own pricing revolution. Currently, an array of pricing structures and models are out in the industry being tested—all based on the Demand Response equation. After all, Dynamic Pricing is in many ways a more formal version of Demand Response programs, and according to the Department of Energy, there is a Federally mandated commitment this year to support Demand Response programs throughout the country – and stimulus funding to motivate utilities to adopt these programs.

But these demand response and dynamic pricing programs currently being adopted in the United States can be very confusing, and seem to have their own set of acronyms and technology terms. So, let’s take a minute to have a closer look at some of the current initiatives and associated terms before we delve into the “how’s-and-why’s” of dynamic pricing.

“For 2010, OE will support the development of demand response by providing technical assistance to independent system operators, utilities, state and regional policymakers to enhance the development of demand response programs, technologies, services infrastructure, dynamic pricing tariffs, and other related activities.” – Office of Electricity Delivery & Reliability Website

Principal Pricing Structures
The two main pricing structures that have been tested and communicated to date are Real Time Pricing (RTP) and Critical Peak Pricing (CPP). Both of these pricing structures allow for very short notice – usually between an hour and a day – for pricing changes during periods when the cost of electricity is high. The thought is that the stimulus of increased economic burden in times of high cost power will encourage reduction in usage or a change in process to shift a given load to another time period or, eliminate it altogether.

In June of 2008, Baltimore Gas & Electric conducted a four-month smart energy pricing pilot. Peak period hours were defined from 2-7pm on weekdays. All remaining hours were considered off-peak.

As part of their pilot, the utility provided a rebate for customers willing to reduce their energy consumption during peak hours. The results showed an overall reduction of energy usage during these peak hours from 18% to 33%. (The higher numbers reflected consumers who were provided with some enabling technologies to help indicate peak and off-peak hours¹).

In addition, initial programs for customer pricing have focused on Time of Use (TOU) pricing structures. These pricing structures set the prices for certain hours of the day or times of the year based on historical expectations of increased prices. These “block pricing” structures have, by themselves, shown little impact on actual usage (e.g., PSE&G myPower pricing pilot 2008), and some customers have expressed concerns that the pricing structures are too complicated. Although the incentive exists to shift usage to alternate times in TOU structures, the economic stimulus does not seem to be enough to drive substantive changes in behavior.

In the summer of 2008, Pacific Gas & Electric deployed the first large-scale critical peak-pricing program in the United States. The pilot was conducted over a six-month period. Once again, the utility determined the peak period to be from 2pm to 7pm on weekdays with significant cost savings applied to non-peak hours. Residents received direct mail and other forms of marketing promotions encouraging them to enroll in the program.

As a result, standard customers reduced their peak loads by 16.6 percent on average and 11 percent of customers who qualified for low-income program reduced their peak load by 11 percent. Ultimately, the results proved that with the right amount of notification and education as well as the right cost incentives, customers did respond to behavioral change. However, those numbers could potentially be greater and the question is then: What will ultimately drive large-scale change?

Incenting the Customers
Initial feedback from pilots involving CPP has been quite favorable, as evidenced by a recent study by The Brattle Group. In instances where critical peak prices were introduced and enabling technology utilized, as high as 80% of customers changed consumption behavior, and reductions of 25-44% of peak load have occurred. Therefore, it would seem that the concept of dynamic pricing structures has the potential to meet the goals of peak load reduction. One must ask, however, “Is it really that simple?”

There has been much debate over the actual consumer benefit of pricing structures that link electricity usage to the actual costs at any given time. There is little debate, however, regarding the benefit to the utility. Cost savings in automated meter reading, remote connect/disconnect, and billing and collection accuracy are well documented. There are also savings associated with reduction in peak demand by way of a reduction in the use of high priced peak generation and a reduction in capacity maintenance that is well beyond typical base load needs.

For the end-use customer, the stimulus must be relevant enough – and notably, adoption easy enough – to generate a change in behavior. For commercial and industrial customers where power is a significant cost, dynamic pricing structures can generate significant bottom line savings and create win-win scenarios for both the utility and the customer. For residential customers it is more difficult to justify significant behavioral changes for weekly savings amounting to less than the cost of a cup of coffee. This is where the use of the data will prove to be significant in creating products and rates that are meaningful to target customer subsets.

Dynamic Pricing Options
Various packaging options exist for dynamic pricing, tailored to specific customer groups and sub-groups. Pricing plans vary according to the needs and requirements of each category, as briefly discussed below.

Residential Customers
Of course, the real impact for customers when real-time or variable pricing begins to be reality in the market will be a result of the products and services that the utility puts in place to allow end use customers to adequately manage their risk based on their own personal risk profiles. Like the telecommunications industry, consumers must have adequate choice in the products and services to weigh against the benefits of changing their behavioral patterns. For example, consumers are well aware that making calls after 5pm reduces their bill. This continues to be a strong motivator, as are packaged family plans and various other off-peak incentives.

C&I Customers
To date, many utilities have adopted business intelligence tools to offer C&I (Commercial and Industrial) customers multiple levels of products designed to mitigate risk and allow for behavior change. As with similar tools used in the communications industry, they allow the utility to offer stability and incentives to businesses for bulk purchases (in the case of telecom – wholesale pricing).

BI is setting a trend in the utilities industry to enable utilities to offer custom designed programs to meet the needs of these large consuming customers. Some utilities have begun offering the ability to transfer energy usage to less expensive times and take advantage of lower pricing. Moreover, a number of utilities are currently adopting technologies to integrate energy usage and cost management. These more forward-thinking programs are incenting business owners and providing a model for dynamic energy management and real-time cost savings.

Institutional Customers
In the world of government and institutional users, however, there is a key requirement for stability and budget predictability. This does not remove the requirement for real-time pricing, but it provides an opportunity for the utility to build products that allow for the institutional client to plan their costs and monitor them closely to ensure that budgets are not breached.

Conclusion
Over the longer term – as with any open commodity market – the concept of variability in the electricity price allows for the utility to transfer some of the pricing risk to the end user, which should produce an environment that allows users to understand the market better and make decisions based on the real risks of the commodity they consume.

About the Author
Chris Lewis is Head of Marketing for Cognera, where he brings extensive knowledge of the deregulated utilities market and a strong understanding of customer data analytics from direct utility experience. A seasoned utility professional, Chris has held leadership positions with both Enmax Energy and Direct Energy where he was responsible for driving more than $2 billion in revenue growth over five years. He is a graduate of the University of Calgary and a former professional football player for the Calgary Stampede in the Canadian Football League.

¹ “Moving Toward Utility-Scale Deployment if Dynamic Pricing in Mass Markets,” IEEE Whitepaper; June 2009, by Ahmed Faruqui and Sanem Sergici of The Brattle Group, and Lisa Wood of the Institute for Electric Efficiency.