It’s easy to argue that utilities are facing several “impossible” challenges in the wake of the energy transition.
The problem stems from trying to solve for now and what’s coming next at the same time.
Utilities are trying to manage aging infrastructure, while integrating cutting edge technology. They’re trying to build a progressive, sustainable grid while maintaining reliance and reliability. They’re facing soaring operating costs and dropping commodity returns. All while trying to stay competitive and relevant today while they reinvent themselves for the future.
But it is possible – and profitable - to solve these pressing, dichotomous problems.
The foundations of Layered Intelligence
Dealing with new difficulties requires a fresh approach that starts at the edge of the grid - the point where homes and businesses connect to the power network.
It’s (relatively) new territory for many utilities who have traditionally focused on the infrastructure that delivers power to those connection points, but it’s loaded with physical and digital points that can reveal key insights.
The smart meters, software, and analytics tools at the edge of the grid all offer invaluable insights into the power demands of the buildings and customers beyond. And the good news is, these devices and platforms are getting smarter.
That means it’s possible to get near real-time insight into the demands of customers, recognize usage habits and patterns, and truly understand the health of the entire energy system.
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With more renewables and connections being added every day, this level of insight is now essential for utilities trying to take charge of the energy transition.
But it’s not just a case of plugging into an existing platform. Layered Intelligence – real-time, data-driven information of grid performance right to the edge – is not only about equipping devices at the edge with computer-like capabilities.
Making the most of this powerful potential – and dealing with the dual challenges above - means getting every level of data right.
4 different data types
One of the challenges in leveraging Layered Intelligence is understanding the different data sources that need to be considered, and the different timelines on which they deliver insight. Some data sources offer instant insight that makes real-time adjustment possible, while other are best analyzed over time as they reveal the impact of ongoing, historically-influenced trends.
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Data gathered from the edge provides insight into key events, safety and security, and overall consumption. It also enables device-to-device communication.
Strategically placed devices can provide data about the performance of both the Transmission and Distribution systems.
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Information about customers, their usage habits, their assets, and the audience they fall into can impact marketing, communication, and billing.
When combined with other types of data, information about local weather and potential safety risks can provide greater insights and context.
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While these layers of intelligence are valuable in their own right, their true value is realized when there is communication between the layers. Historically, that has only happened within the enterprise and substation layers. However, devices at the edge can share that data with enterprise systems or third-party providers in near real time.
By combining data sources, utilities can leverage insights to manage short-term operations and help them to rethink and proactively manage long-term operations.
So how can utilities get to this point?
Life on the edge
Taking a layered approach empowers utilities in various ways, but to get to the point where they can unlock that value, they need to:
- Understand what’s possible and prioritize use cases based on what it is they hope to achieve.
- Model the possible outcomes and build a business case (starting with the use cases that are accretive to their business) to understand how they might deliver customer and/or operational benefits.
- Understand the data required and how to process it: for example, what can be processed at the edge, and what needs to be processed by a third-party provider, or even the utility themselves.
- Test the use cases in a pilot, and then expand them over time based on the benefits that the program can deliver.
Much like the way in which this approach can help to solve the challenges of today and tomorrow, it can also generate benefits for customers and the grid itself (depending on the use case).
For customers, these benefits can include:
- Increased energy insights / awareness
- Enhanced EV targeted marketing and programs
- Advanced customer programs
- Reduction in duration of power outages.
Layered data can also improve grid performance in a multitude of ways, such as:
- Reduced truck rolls from theft false positives
- Enhanced grid planning capabilities
- Proactive asset management
- Increased grid resiliency
- More accurate connectivity models, and many more.
Timing is everything
The ability to embrace the massive potential of layered data depends heavily on having the latest generation of metering capabilities: meters that are grid-edge enabled are essential to developing the immediate and long-term insights needed to navigate a changing grid.
While there are devices like Sense that can provide behind-the-meter intelligence to the customer, and there may soon be distribution devices that are independent of the meter and capable of sharing data to improve grid operations, the defining factor for utilities hoping to implement grid edge intelligence solutions today is where they are in their meter replacement cycle.