We have heard a lot about the Internet of Things (IoT) in our daily lives in recent years, from smart thermostats, smartphones, wearables and other connected devices to the myriad of apps that run them. Behind the IoT hype – and, indeed, the phrase itself – what we’re really talking about here is the proliferation of ‘things’ or devices with intelligent sensors, networked connectivity of those things, and the ability to leverage the data being gathered by them.

The utility industry is no stranger to the Internet of Things. The smart grid, after all, is essentially the application of IoT technology (smart sensors, two-way communications, and analytics) to our electric grid infrastructure to enable better efficiency, improved reliability, the integration of more renewables and distributed energy resources, reduced emissions, and more engaged and empowered customers.

In coming years, this industry is expected to drive exponential growth of new IoT applications to communicate machine-to-machine to new field devices and consumer energy technology devices at the edge of the grid. But even more important than this ubiquitous communication is the sensor data being gathered by these machines, and how that data can be operationalized for more efficient and proactive efforts on the part of the utility.

Utilities are already leveraging IoT
Leveraging technology is nothing new to electric utilities. As Deloitte Consulting’s Rob Young, John McCue and Christian Grant wrote in The power is on: How IoT technology is driving energy innovation: “The electric utility industry’s use of IoT applications has closely followed the arc of technology availability. While seldom on the bleeding edge, utilities have always leveraged available technology to optimize and control assets, increase safety, control the grid, and keep the lights on.”¹

Young and his colleagues go on to point out that supervisory control and data acquisition (SCADA) sensors and actuators already communicate with and are controlled by a central master unit, providing a user interface ‘through a human-machine interface.’ As well, they say, advanced metering infrastructure (AMI) provides for a two-way communication system of smart devices ‘on both the utility and customer sides of the meter,’ consisting of home area networks, in-home displays, energy management systems, smart meters, communication networks, and data management systems.²

By applying analytics to all of this near-real-time sensor and device data, utilities are now able to leverage existing technologies with even more prowess, unlocking actionable insights improving asset performance management and grid reliability as well as customer satisfaction.

There are also a number of other ways in which utilities are already leveraging, or planning to leverage, IoT technologies to enable the vision of the future distribution utility. These include asset performance management and grid and energy optimization (including distributed energy resources and consumer energy technologies). And ultimately, it will be IoT technologies that will help to facilitate a consumer-driven (or crowd-sourced) energy market.

Let’s look at each of these areas in more detail.

Leveraging sensors for asset performance management
With operating costs skyrocketing, utilities are being pressed to reduce expenses while finding new ways to decrease their environmental impact and deliver more customer-centric service. Asset performance management is a key part of that equation. Low-cost, smart field sensors are providing real-time eyes in the field, enabling utilities to proactively determine whether it is necessary to replace or repair an asset before it breaks down. This determination requires an understanding of that asset’s condition and importance to the organization, and is best gained by aggregating all asset data, including work history and condition rating, into a single system, balancing the importance of one factor versus another, and updating any condition changes as they occur. Armed with this data in real time, the utility has a more reliable view of asset health and can make more meaningful investment and work decisions on how to best balance compliance, reliability, safety and risk.

In the past, business insight was based upon intuition and subjective or observed assessment. In an increasingly digitized utility environment, this type of assessment is being replaced by objective data analysis that is by its very nature more accurate, providing utilities with far more actionable insight than before. This analysis, when automated as a core business process, has demonstrated significant capacity to affect margin by lowering operating costs while increasing revenue.

How, specifically, does this occur? Proactive work has been shown to reduce asset failure rates and drive down the cost to operate each asset. For example, by scheduling proactive work during normal business hours instead of having to react to a failure with an after-hours call-out, costs are reduced and reliability is greatly improved. If you add automation to this equation, made possible by smart sensor and control devices – many of which are IP-addressable and wireless connected – along the utility’s infrastructure, you can also add real-time asset analysis to the asset management toolset. Advanced asset risk analytics than then correlate the appropriate data from across the enterprise (for example, correlating specific sensor data with advanced metering data for a certain area) to provide immediate prescriptive maintenance work requests, thereby alleviating a problem while it is still minor.

Leveraging new resources for grid optimization
The modern grid is changing as many consumers are choosing to become prosumers. Empowered by technology innovation and sustained by changing regulatory policies, consumers are adopting distributed energy resources (DER) such as rooftop solar and on-site energy storage in record numbers, far more quickly than anticipated. By managing their energy consumption behavior with little or no input from their utilities, these consumers are driving change in how the modern distribution grid will work in the near future. These DER, too, are pushing massive amounts of real-time data back onto the distribution grid, providing utilities with the means to change the way they manage this evolving distribution grid.

Made possible by IoT technologies, modern distribution management requires utilities to take a data-centric approach to monitoring, control and optimization of both traditional distribution and new edge-of-grid needs. Optimizing the grid and leveraging these distributed energy resources and consumer energy technologies begins with the ability to model the generation output profile of each of these DER, accounting for location, condition of use, and other attributes unique to each asset. (If the DER is a rooftop solar resource, for example, it is important to account for clear or cloudy skies, latitude, time of day, day of year, and the direction and pitch of panels.)

Each DER asset type is different, as are their impacts on the distribution grid. But by being able to more accurately model their generation output profiles, utilities can better forecast where and how DER growth will impact their distribution grid and use that information to improve their long-term resource planning to support this growth. Further benefits include reducing the capacity for intermittency to cause disruption and safety issues; eliminating the need to bring additional, costly generation resources online; and minimizing customer interruption minutes by way of improved generation output profiling.

Facilitating a consumer-driven energy market
As defined by the GridWise Architectural Council in its initial framework document, the term ‘transactive energy’ (or TE) ‘refers to techniques for managing the generation, consumption or flow of electric power within an electric power system through the use of economic or market-based constructs while considering grid reliability constraints. The term ‘transactive’ comes from considering that decisions are made based on a value. These decisions may be analogous to or literally economic transactions.’³

With consumer-owned DER continuing to proliferate, a consumer-driven, transactive energy market is expected to take shape in the future. Utilities have a choice: they can view this challenge as a threat to their business, or they can turn it into a business opportunity to manage the evolving energy distribution platform, improving their investment performance and lowering operating costs along the way. The TE construct provides for this future by including utility customers’ and other third-party distributed energy resources in the new market exchange equation, a dynamic balance of energy supply and demand. IoT technologies will provide the means for utilities to model and manage the granularity and scale of DER and other consumer energy technologies involved in this dynamic market.

Going forward
End-to-end visibility, and the ability to model, manage, analyze and control these new grid-edge resources, will be key to the advanced distribution management required of utilities, both now and in the future. IoT is the vital link connecting this end-to-end utility IT/OT technology platform both to the consumer and to the edge of the grid. Are you ready to make this shift?
 

About the Author

Bradley Williams is vice president of industry strategy, Oracle Utilities. Williams is responsible for Oracle’s smart grid strategy as well as utility solutions for outage management, advanced distribution management, mobile workforce management, work and asset management, and OT analytics. Williams has spent the last 30 years driving innovation in the utility industry in roles, including T&D power system engineering, technology development, asset management, and industry analyst.
 
 

¹ Deloitte, The power is on: How IoT technology is driving energy innovation by Rob Young,
   John McCue and Christian Grant, Jan 21, 2016
² Ibid
³ The GridWise Architectural Council, “GridWise Transactive Energy Framework Version 1.0,” January 2015.
   http://www.gridwiseac.org/pdfs/te_framework_report_pnnl-22946.pdf