by Matt Dinsmore and Laurence Wong, Altman & Vilandrie Co.
Electric utilities face a series of dovetailing technology and competitive trends that point to a significant change for the residential energy market. These trends pose risks to utility investors and might require changes to many established business practices, including new ways to think about and plan for changing load curves.
The first wave of an industrywide transition to advanced metering infrastructure (AMI) is nearing completion. Many utilities are investing to educate consumers and launch variable pricing and other consumer programs that were critical components to the business case for many of these large smart grid investments.
This transition will not be easy. Utilities need to become more savvy and agile at consumer engagement and marketing while facing faster development cycles more in line with competitive industries. Utilities that committed early to AMI, such as Pacific Gas & Electric Co. (PG&E), San Diego Gas & Electric Co. (SDG&E) and OG&E Electric Services, already grapple with this transition and are augmenting their customer engagement processes and internal expertise.
During the five years needed to develop and deploy AMI at many utilities, players from other industries saw financial and environmental opportunities. As a result, those industries are providing new efficiency and environmental products and services. They are giving consumers an unprecedented number of energy-related options that introduce new volatility to long-term electricity consumption patterns that challenge utility planning and business models.
The largest service providers, such as AT&T Inc., Verizon Communications Inc., Comcast Corp. and ADT LLC, each with tens of millions of established customers, have entered with their first but not last generation of home energy offers (see Figure 1). Similarly, consumer product companies big and small are beginning to offer innovative consumer energy products such as LED lighting, smart high-efficiency home appliances, home automation and energy management systems, fully automated learning thermostats such as Nest, electric vehicles and financed rooftop solar. The numerous start-ups focused on energy-related applications, products and services are just the beginning. New offers will continue to multiply. Competitive retail electric providers also are expanding their geographic focus and intensity and are rolling out innovative offers such as Reliant’s Learn & Conserve Plan that bundles a free Nest Learning Thermostat with a higher energy rate.
In some cases these offers are introduced with the utility in mind, but because regulated utility markets are not structured to foster innovation and evolution as quickly as competitive product markets, significant focus is placed on alternative channels such as big box and online retail, direct marketing, or, in the cases of service providers, up-sell strategies to their established customer bases.
These new energy products and services will affect load curves dramatically, subjecting them to evolving, often fickle and diverse consumer behaviors. When consumers are given choices, they don’t choose the same thing. Traditional product adoption curves, driven by perceived value propositions, personal preferences, marketing and channel effectiveness, product and technology associations, etc., will introduce variability to consumer usage and continuously evolve demand profiles. More important, as more of this happens outside the traditional utility viewpoint and ecosystem, it will be increasingly difficult for utility planners to develop forecasts with the level of reliability they’ve come to expect. As each product appeals to a consumer segment and follows distinct adoption curves, the potential aggregate impact can vary over a wide range. Figure 2 shows the individual impact of each technology for an average household.
Electric utility load forecasting based solely on traditional factors such as weather patterns, macroeconomic trends and limited microeconomic indicators will be increasingly exposed to error from these new and evolving consumer adoption trends.
For example, a significant cost of increasing forecast error is less efficient capital allocation. Assume a feeder has shown higher than average load growth and is scheduled for a capital reinforcement program. If the consumers on this feeder, however, are those most likely to opt for emerging energy-reducing products—the higher-income, earlier adopters with larger houses—this might be an inefficient use of capital because energy reductions from these products could defer reinforcement needs, freeing up capital for other priorities. Alternatively, if a new demand trend exists, such as unexpected electric vehicle penetration in neighborhoods not previously thought to be addressable, a utility might underdeploy capital, risking customer service and reliability impacts. Even if the impacts of these inefficiencies are less than 5 percent of addressable capital budgets, this is a significant number, given that an annual utility capital budget can be hundreds of millions to $1 billion or more.
Missing these trends also can mean the difference between meeting and not meeting key performance measures such as reliability.
Moreover, maximizing capital efficiency, especially in service of residential load, is becoming increasingly important. The Energy Information Administration’s Annual Energy Outlook expects industrial declines in usage to continue as costs of capital replacements for aging infrastructure are expected to drive industrywide spending to an estimated $1.5 trillion to $2 trillion by 2030. Mitigating the impacts of consumer adoption trends in load forecasting will require taking advantage of more probabilistic, less certain scenario-based methods. It also requires being more agile and responsive to deviations and new developments, much as companies in competitive, noncommodity markets are forced to do with their infrastructure investments.
The potential impact on load forecasting for utilities extends beyond capital inefficiency. If these energy-related offers from large established service providers and product companies penetrate the market, potential exists for these new energy services to disrupt or even preclude consumer adoption of the utility-led programs—a critical part of the business case justification of AMI and smart grid investments (see Figure 3).
This significant risk can hamstring the industry’s ability to achieve the peak-reduction benefits promised by AMI and that are needed to keep rates down.
For example, if AT&T applies the same marketing, sales and pricing tactics that is customary for its new products to its Digital Life platform, the company likely will achieve double-digit penetration rates among its customer base within two to four years. If, as is expected, home energy management is one of the primary functions of this platform, it has the potential to reduce greatly or preclude adoption of utility offers that require competing alternative technologies.
So how should utilities respond? These third-party offers are gaining momentum and will increase in number and improve in functionality, cost and customer appeal. Although this issue can be viewed as a risk to the financial and operational performance of the industry, utilities have an opportunity to mitigate these impacts, as well as drive customer adoption of new, high-priority programs by proactively responding to this trend.
à¢— Load forecasting and capital allocation. Utilities should rethink asset management and demand forecasting to better account for the inevitable error associated with new consumer behavioral trends. This might require incorporating significantly more consumer adoption trend analysis, aligning residential customer subsegments of demographic, psychographic and behavioral profiles with definable use cases and ranges of product adoption rates, integrating more detailed scenario planning into load forecasting and asset management, and providing a more robust understanding of the impacts of planning decisions and subsequent capital allocation. For example, conducting detailed segmentation to identify the feeders or substations with the highest density of customers likely to invest heavily in efficiency who also overlap with a service provider that offers a home automation or energy management product can drive alternative reinforcement plans based on adoption scenarios. This can result in a lower or more targeted reinforcement capital allocation and provide a better understanding of how adoption might progress across the broader footprint.
à¢— Enhanced customer marketing capabilities. Utilities will need to incorporate customer marketing and engagement tools and practices used in competitive markets. Utilities beginning to roll out AMI-related pricing programs are headed down this path, but it will be increasingly important to establish best-in-class capabilities as programs come under increasing competition from other offers and providers.
à¢— Proactive engagement of third-party offers. Utilities should not encumber third parties that look to enter the market at the risk of being placed on the outside with limited or no visibility and access to these offers. Engaging the service providers and product companies to identify and espouse winning approaches to the market should be a priority. This is especially true when considering third-party access to customer meter data.
Customer energy behavior is beginning to change and will grow in magnitude and variability as technologies move along traditional adoption and pricing curves. Customer preferences will become increasingly important for utilities to incorporate into their business processes, such as load forecasting, as new entrants create value propositions that appeal to specific customers and usage patterns change.
Don’t expect a wholesale change in customer behavior overnight, but expect the pace of change and innovation to converge with other traditional customer-focused businesses, and place an increased level of importance on changing and evolving utility business and operating models.
Matt Dinsmore is a director at Altman Vilandrie & Co. He has more than 17 years of experience in the electric utilities and telecommunications industries.
Laurence Wong is a manager at Altman Vilandrie & Co. He is an expert in energy strategy, go-to-market planning, market forecasting, business case development and energy technology grid benefits and cost modeling. He has worked closely with clients to develop growth, new offer and entry strategies and go-to-market plans within the energy market.