Executive Insight, Metering

Seven Things Utilities Should do with Smart Meter Intel (that most aren’t)

Over the past several years, it’s safe to say that we’ve hit a point of near universal acceptance that smart metering- in whatever incarnation it shows up- is here to stay.

Penetration levels globally have hit all-time highs, and jurisdictions that have long been considered holdouts for safety, privacy or other political reasons have now set formal timelines for achieving full penetration. From an infrastructure planning standpoint, smart metering has now become a generally accepted part of our collective future. That’s good news given its strategic importance in almost every emerging “grid of the future” scenario.

Surprisingly though, this level of acceptance (and corresponding customer investment) has been achieved with most utilities only tapping into a small fraction of the value they could be extracting today from better utilization of this valuable intelligence. Most organizations are still limiting the use of this intelligence to basic customer service and billing functions, while missing valuable pockets of the business where far more significant savings could be harvested.

What are some of these opportunities that are being overlooked? Here are 7 examples that are costing the utility industry billions:

1. Optimizing commodity costs. Over 50 percent of a customer’s energy bill is pure energy costs whether provided by the company’s generation assets or procured in energy commodity markets. Yet almost all of today’s energy procurement is done on the basis of top-down planning – an approach that is exposed to uncertainties which the company must hedge against to securely cover its load obligations. This burden has only gotten worse with unexpected swings in climate, changing customer demand patterns and emergence of renewable generation, driving the cost of energy even higher.

By shifting to bottoms-up customer level planning approaches, companies can begin integrating machine learning and AI into their forecasting approaches. This in turn improves forecasting accuracy by 30-50 percent, impacting customer bills (or improving utility gross margins) by as much a 5-10 percent.

2. (Customer Centric) Rightsizing the Grid. Most leading utilities and regulators have begun shifting toward a model that makes utility earnings more agnostic between investments in traditional capital (pipes and wires) and other demand side options and DER alternatives. This could be a watershed event for the industry – one that experts have said can save ratepayers billions in unnecessary capital outlays and future stranded investments, and one that can create huge new revenue and earnings streams for companies positioned to take advantage of it.

The single biggest success factor in that kind of environment is the degree of transparency companies have into customer consumption (historical trends and future predictability) and how that consumption interacts with the tapestry of DER and energy delivery assets that are now part of that holistic energy equation. Better customer intel made possible with AMI means more transparency and more opportunity for growth of the LDC.

3. Placement and Positioning of DERs. Related to, yet strategically separate from, the previous point is where to place new revenue producing assets, such as EV charging facilities, in a way that takes advantage of underutilized assets while factoring in the changes in customer demand and usage patterns. Being able to forecast and simulate consumption at a customer and feeder level can ensure these decisions are optimized for the changing needs of the grid and its customers.

4. Optimizing System Reliability. Most of today’s grid executives admit to being starved for the customer level intelligence needed to make effective planning and operating decisions. And DERs have only complicated this challenge since the way in which these assets are metered can blur visibility into what customers are actually consuming. The promise of DERs being able to provide legitimate lower cost alternatives to traditional generation and wires investment only materializes if these assets do not create additional risks to existing reliability levels. Grid assets must be able to accommodate actual levels of customer consumption should circumstances require it. For example, cold load pickup periods where DERs are coming back online after interruption. New predictive analytics made possible through AMI not only close gaps in consumption visibility, but also can produce real time intelligence for grid operators that augments (and could even replace) traditional forms of assumptive models and simulation tools in place today.

5. Rationalizing energy efficiency spending. Today, budgets for utility energy efficiency programs are still largely driven by societal and public policy frameworks that establish limits or targets based on avoided costs of the current supply-side alternative. While there are many moving parts in how these energy efficiency budgets are set, the bottom line is that these approaches are too top down and policy centric in that they tend to spread strategy across large swaths of customers with widely carrying consumption characteristics and differences in their suitability for specific energy efficiency measures. Advances in customer level profiling, predictive load disaggregation, clustering and forecasting not only help identify which customers are right for which programs, but also when they are right for a particular program. This can rationalize energy efficiency budgets to 20-30% of their current levels while still achieving the utility or energy community’s energy efficiency and sustainability goals.

6. Redefining Engagement. Ever since Accenture identified that customers only spend a handful of minutes each year engaging with their utility, utilities have tried feverishly to reverse this paradigm. The Innowatts’ eUtility platform is built on the exact opposite premise–that customers, particularly utility customers, really don’t want to engage with service providers unless absolutely necessary. Benchmark research shows that most utilities average between one and five calls per customer annually. Most of these calls are related to problems with billing or payment, most of which can be averted by better, more proactive communications with customers. Call volumes, one of the biggest costs of a utility, can often be halved if not eliminated entirely from AMI enabled predictive communications.

But it doesn’t stop there. Good service solves problems for customers. A great customer experience anticipates and prevents issues before they even happen. Often, it’s the unexpected new source of value that a customer receives that changes their perception forever and solidifies loyalty. For example, with AMI enabled predictive intel, the utility can not only help identity problems with an HVAC unit, but signal how much it will cost a customer if unattended, and even go as far as scheduling an appointment with their preferred heating vendor. So many companies offer apps with cool information like energy charts and graphs, usage alerts and even access to product marketplaces. But developers of these apps usually fail to realize that these are now all simply expected features. AMI-enabled intel allows utilities to get ahead of a customer and delight them with truly insightful information and new ways of automating and simplifying that engagement

7. Expanding new revenue sources. Whether its new regulatory frameworks that allow earnings on non-wires investments, product sale or partnering revenue from digital marketplaces that utility customers access, or more effective customer acquisition and retention, bottoms up energy intelligence will help enable new top line growth. Customer level energy intel is a core competency that, when honed and integrated into every part of the enterprise, provides a digital utility infrastructure that can be the primary driver of growth. CCA’s, servicing arrangements for other smaller utilities, even strategic acquisitions can all be greatly enhanced by mastering customer level analytics that derive from AMI investments.

Despite the wealth of AMI enabled intelligence and big data capabilities that exist today, the utility industry is only scratching the surface on its potential for optimizing business operations and, as a result, firms are missing out on billions in energy savings for customers and for their own bottom line. Some of this is simply lack of awareness, but there are many political, technological, cultural, and regulatory barriers that are impeding progress.

Whether it is competitive retail organizations, CCA’s or DSPP’s, organizations that are moving quickly to harvest this intelligence are not only reaping the savings but positioning themselves to be competitive indefinitely in a rapidly changing energy marketplace.

About the author: Bob Champagne is SVP at Innowatts, an energy technology company focused on transforming the energy value chain through its use of predictive intel, machine learning and AI enabled technologies. To date, Innowatts’ eUtility platform  has enabled over 15 million meters with lower across the board costs and a personalized energy experience.