Part I, The Mechanics of Grid Edge Intelligence | Black & Veatch

Part I, The Mechanics of Grid Edge Intelligence

The first half of Black & Veatch two-part series addressing grid edge intelligence.

Part I, The Mechanics of Grid Edge Intelligence

Perhaps the one constant in today’s changing energy landscape is disruption. Distributed energy resources (DER) are accelerating us past the days of a single-direction, centralized electric grid and towards a bi-directional, distributed grid faster than any of us ever imagined.

The increased penetration of DER (whether it's rooftop solar or large commercial applications) is happening as the future also comes to us through advanced battery applications, electric vehicles (EVs), and thousands of smart devices. In addition, COVID-19 has emphasized the need to manage all aspects of our grid from anywhere at any time. This combination is amplifying the need to move digital intelligence to the “grid edge” to connect with the smart devices located there, potentially even talking to devices on the customer side of the meter.

This article is the first half of Black & Veatch’s two-part series addressing grid edge intelligence. As the industry moves towards a smarter, more flexible advanced grid, Part II, Roadmap to an Interconnected Grid Infrastructure, offers a deeper dive into this topic by outlining the roadmap process that is critical in leading utilities to an interconnected grid infrastructure.


Approaches to a Smarter Grid

So what is the “grid edge”? Grid edge refers to the location of the grid relative to utility customers – whether they are five miles from a substation, or five blocks. Truly defining the grid edge depends on customer demographics, system topography, load profile and technology adoption, all of which will change over time. Even today, COVID-19 has shifted load peaks by driving more and more residential customers to work from home.

Creating a grid architecture is a complex concept, but utilities have options when it comes to adding intelligence to the grid edge. There are three main approaches: a centralized intelligence approach, a distributed intelligence approach, or a hybrid approach.

Centralized intelligence involves simpler devices talking to the headend using traditional poll response protocols like DNP3 (IEEE 1815). The headend analytics are going to require reliable communications to the edge, although issues can arise around late, missing or low-quality data. More polled data will be transmitted to the headend for analytics and response, so polling is less on-demand. The benefit is that the headend analytics typically have better insight into the system and adaptable logic, and thus is considered “smarter” to grid conditions because it provides a holistic system-wide view.

Distributed intelligence involves more complex devices at the grid edge with analytics and high-speed peer-to-peer communications. The grid edge analytics may be disrupted by the quality of that peer-to-peer communication. But even with low latency and less polled data traveling back to the headend, the data is still available on-demand. Edge analytics typically have reduced system perspective and more hard-coded logic. The benefit is a faster rate of change and lightning-quick reaction of the grid edge devices even when communications to the headend may be cut or compromised.

There is a third option, the hybrid approach, which has both advanced distribution management solutions (ADMS) and grid edge intelligence. The hybrid approach combines the best of both worlds because even a distributed intelligence system with edge analytics may need to report back significant information to the headend, resulting in heavy bandwidth. This approach balances the need for more oversight (distributed) with the need for high initial investment (centralized), providing quality data to drive decision making.


Other Considerations of Grid Edge Intelligence

Utilities must understand the other impacts of grid edge intelligence. The first one worth mentioning is cybersecurity, which is a critical component of any grid edge strategy. Looking at the distribution automation industry, many original equipment manufacturers (OEMs) have not yet adopted the cybersecurity measures common to IT systems today – e.g., user controls, passwords, password controls, logging controls, encryption. While this is improving, we are still a little way away from seeing OEMs adopt a comprehensive OT approach that matches the IT side.

This leads to the next consideration – IT/ OT convergence. As we move to a converged field area network (FAN) that is managed by one team, versus multiple disparate groups and technologies, education will be key to enabling the team to perform maintenance and troubleshooting that system on the distribution side. This may mean adapting a utility’s processes and procedures – for example, a utility that selects a centralized architecture may need to perform significant work to implement an ADMS and the procedures that work with the ADMS and grid edge devices.

This effort may demand new skillsets, and could impact the utility’s line crews, operations team, control centers and engineering teams. That said, gone are the days when utilities had very rigid, defined engineering teams, whether it was the distribution engineers, the planning engineers, etc. The utility may need to work with its change management group to understand the necessary skillsets going forward, and how those needs will impact any union operating agreements or engineering skillsets. This new operating environment will require the collaboration of skillsets that typically didn’t work together in the past, at least not officially. Working teams will draw on resources from across the organization requiring utilities to create official pathways across silos.

Lastly, advanced communications networks will be key to enabling grid edge intelligence. The communications network must be robust enough to handle today’s applications while also considering tomorrow’s needs – for example, will the utility want to add future applications such as gas devices, transmission devices or energy supply devices onto the network? By controlling their own private network, utilities can prioritize their needs accordingly, e.g., making distributed automation traffic a high priority, or making meter traffic a low priority. The more thought and planning a utility puts in up front, the better the tools it can develop and the more prepared it is for whatever opportunity presents itself in the future.


Grid Edge in the Field

Utilities are very familiar with large capital investment-type projects and these applications can all be built on top of those existing projects. For example, let’s look at how FLISR can benefit from additional grid intelligence. With a system like AMI or some other telemetry from customer meters, a utility can leverage that information to pinpoint exactly which customers are out of service during an outage.

While the FLISR system may reduce that number down to the 500 or 1,500 customers who are out of power – instead of an entire feeder – the utility can take that AMI telemetry and zero in on exactly what caused the outage, while identifying other information that can help the utility greatly improve the speed and accuracy of FLISR.

The data from those meters may then be leveraged to fit a state estimation profile within an ADMS, allowing the utility to look at a customer’s load profile to better understand what that customer is doing – or what that class of customer is doing – and forecast what they will do in the future.

The utility can also use those AMI meters as sensor points to provide telemetry to VVO, allowing the utility to take action that would benefit the customer. This enables installing an active voltage regulation device in the customer’s proximity. Then, as the utility starts to look at additional DER across its system, it could leverage batteries both in front of and behind the meter, and leverage smart inverters behind the meter, to further improve VVO performance, saving its customers money while delivering better benefits.



Grid edge intelligence is indeed complex, but not impossible. Utilities have options when it comes to implementing an interconnected grid infrastructure, whether it’s by pursuing a centralized, decentralized or hybrid approach. To help develop this process, utilities should consider building a roadmap and strategic plan. This topic is discussed in Part II, Roadmap to an Interconnected Grid Infrastructure.

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