When it comes to analytics, it’s long been understood that how you develop your algorithms, what criteria and attributes you use and when you perform functions are all important to the accuracy and potential value of the results. With the advent of grid edge computing, we also consider a fourth key dimension: where.
How. What. When…Where.

The Evolution of Edge Computing

Edge computing has gained traction across various industries, bringing computation and data storage closer to the source of data. This proximity can reduce latency and increases efficiency to enable faster and more intelligent decision-making.

In the utility space, edge computing and grid edge intelligence reshapes how we monitor, manage and control the grid—particularly in distribution. While our transmission systems are typically already highly instrumented and optimized, edge computing can be highly impactful at the distribution level where the same levels of instrumentation and control have historically proven to be prohibitive.

With the growth of distributed energy resources (DERs) and increasing interest in demand response (DR) programs, the ability to coordinate and optimize these assets in real-time can be crucial for grid stability and cost efficiency. As DERs such as EVs, solar PV, and battery storage become more prevalent on the distribution grid, we can expect the need for localized, real‑time intelligence is likely to become increasingly important.In this context, grid edge intelligence can be understood as the use of centralized and distributed analytics and decision-making capabilities deployed closer to where data is generated, enabling more localized and timely operational insights (Preuss & Spencer, 2021).

A highly effective and strategic locations for grid edge intelligence capabilities is at the grid edge itself. As the demarcation point between utility infrastructure and the customer premise, grid‑edge intelligence points—such as advanced meters and edge‑enabled devices—can be positioned to:

Access detailed, real-time energy usage data

Communicate with local devices (including other endpoints) directly, relying less on cloud-based systems or ISPs
Host localized solutions through embedded processing, enabling fast analysis and response on high-fidelity data
Leverage communication networks to deliver valuable information in real time.

Grid edge platforms with dedicated processing capabilities can support real-time, locally hosted solutions for advanced grid reliability (such as low-voltage feeder visibility and VVO, fault prevention and outage location analytics), DER management (such as PV/EV detection, 100A panel protection and management, and demand flexibility controls) and customer experience (such as proactive outage and restoration communications, personalized usage and cost insights and targeted program eligibility and enrollment). These represent some essential grid edge intelligence use cases and capabilities. These grid-edge endpoints can support sampling rates and localized data processing beyond what is practical to transmit to and analyze in centralized systems. By performing analytics at the edge, insights can be derived in near real time—while reducing latency, bandwidth constraints, and cost of sending all raw data to the head end.

Additionally, when grid‑edge sensors are aware of their electrical location—such as being connected on the same phase of a transformer—they can communicate locally and correlate data across neighboring endpoints. This location and topology awareness further enables deeper insights into grid health, asset performance, and emerging issues that were previously considered unreachable with centralized approaches alone. This enhanced visibility and coordination can also contribute to improved outage detection and restoration efficiency, particularly in more complex distribution scenarios.

Key Use Cases for Grid Edge Intelligence

Advanced Grid Reliability

Advanced grid reliability can transform utility operations through increased distribution system reliability, boosted capacity, and accelerated outage response. These use cases leverage real-time grid edge data and distributed intelligence to address critical utility business objectives which can deliver measurable improvements in operational efficiency (reducing OpEx), customer satisfaction (improving SAIDI and SAIFI), and asset utilization (optimizing CapEx investments). By integrating advanced analytics and automation, utilities can proactively manage outages, optimize grid performance, and support the integration of distributed energy resources.

Value Summary:

Outage Awareness
- High‑confidence outage scope determination enables faster, more accurate response and restoration
- Precise outage location and extent reduce SAIDI through targeted crew dispatch
- Rapid nested outage detection helps prevent redundant field visits and speeds restoration
- Automated restoration reporting and verification improves customer communications
Grid Awareness
- Real‑time load flow and state awareness improve control room efficiency and response speed
- Optimized VVO and power factor management can increase system capacityand reduce losses and defer infrastructure investment
- Improved DER hosting capacity through location‑aware grid insight
Fault Prevention
- Early detection of overhead and underground faults helps prevent outages before they occur
- Transformer and secondary asset protection enables planned, data‑driven maintenance
- Location‑aware anomaly detection identifies emerging safety risks
- Reduced SAIFI through proactive fault identification

These solutions collectively contribute to reliability, resiliency, and safety objectives by leveraging grid edge intelligence—delivering value in terms of reduced outage duration, increased grid capacity, lower operational costs, and enhanced customer satisfaction.

Running these solutions on the meter itself—sampling at high frequency—enables real-time detection and action, contributing to improved reliability and grid protection while reducing the need to backhaul large volumes of data to the back office.

DER Management

Today’s distribution grid faces increasing pressures: accelerating electrification, rapid adoption of DERs, decarbonization mandates, aging infrastructure and increasingly volatile weather events. Traditional, centralized grid management methods may no longer be sufficient to address these complex and evolving challenges, particularly as utilities must integrate a diverse array of DERs—including electric vehicles (EVs), rooftop solar photovoltaics (PV), battery storage and other flexible loads. To navigate this dynamic landscape, utilities increasingly require scalable, agile solutions that can integrate customer-owned assets and convert them into manageable, grid-supporting resources.

Value Summary:

Flexible Interconnection of DERs
- Dynamic management of behind-the-meter DERs in response to real-time grid conditions
- Reduce customer costs of electrification and DER adoption
- Optimize utility CAPEX costs by avoiding service upgrades
Transformer & Panel Protection
- Proactively manage service transformer and home electrical service panel loading to help prevent localized overloads
- Allow safely deferred infrastructure upgrades by balancing load growth at both the transformer and customer panel, optimizing utility CAPEX
- Increase low‑voltage DER hosting capacity by coordinating panel‑level demand with grid constraints—while maintaining or improving reliability
Emergency Grid Balancing & Resilient Outage Response (DER‑Enabled)

Automated islanding and DER coordination to help maintain critical customer and community energy services during outages
Smart cold load pickup management using DERs and localized controls to enable faster, safer, and more predictable restorations

Customer Experience

Customer experience solutions are intended to empower utilities to deliver more reliable service, proactive communications and personalized energy insights. By leveraging real-time data, automation and advanced analytics, these solutions help utilities enhance customer satisfaction, streamline operations, and support the evolving needs of energy consumers. This can contribute to a more resilient grid and a more engaged, informed customer base. Proactive outage communications and improved restoration visibility can also help manage customer expectations during major events and reduce inbound inquiry volumes.

Value Summary:

Real-Time Outage and Restoration Communications
- Deliver timely, accurate outage alerts and restoration notifications to customers to reduce call center volume and improving satisfaction.
- Automated restoration reporting enables fast, reliable customer communications.
Customer Energy Awareness
- Provide high-accuracy DER awareness and load disaggregation, helping customers understand their energy use and bills.
- Data-driven recommendations support energy savings and program participation.
Multi-Channel Engagement
- Integrate with smart home apps and OEM platforms to reach customers through their preferred digital channels.
- Enhance satisfaction by meeting customers where they are.
Program and Rate Design
- Use load disaggregation and grid constraint data to design better programs and rates that promote higher incentives and more enrollments.
Program Targeting and Integration
- Improve program segmentation and targeting for cost effectiveness.
- Automate eligibility screening and customer data provision to reduce integration costs and customer churn.

These use cases collectively contribute to improvements in customer satisfaction, engagement and operational efficiency by leveraging grid edge intelligence —positioning utilities to meet modern expectations and regulatory requirements while building trust and loyalty.

A Practical Use Case: Real-Time EV Charging Management

The Challenge

The growing adoption of EVs and other DERs in residential neighborhoods is stressing distribution transformers and customer infrastructure.

The Goal

Avoid or defer costly service upgrades—such as transformer replacements or customer panel upgrades—while supporting clean technology adoption.

The Solution

Enable real-time monitoring and control of EV charging to:

Autonomously adjust charging rates to protect the customer’s service panel, allowing homes with limited electrical service to safely add Level 2 charging
Protect distribution transformers and secondary bus conductors from these added loads by aggregating data and actions across shared infrastructure and maintaining adequate local grid capacity
Use customer-specific demand triggers (kW thresholds) to initiate local, autonomous demand response events

Solution in Action

Utilities are piloting real‑time EV charging control to safely enable Level 2 charging without panel or service upgrades. Charging rates are automatically adjusted to protect both customer infrastructure and the local grid.

The Future of the Grid Edge

In an industry where change is constant, but slow, and innovation is non-negotiable, grid edge computing stands out not just as another solution option, but as a strategic approach that leverages existing infrastructure and expands its value. By enabling utilities to operationalize data in real time, distributed intelligence empowers teams to make more informed decisions, respond faster, and plan more effectively. It serves as a bridge between data and decision, creating a unified operational view that enhances situational awareness, supports cross-functional collaboration – breaking down operational silos – and drives better outcomes. The path to a more resilient, responsive, and efficient grid increasingly runs through the grid edge—because where really matters.

Resources

Preuss, C., & Spencer, L. (2021, August). Utilities on the edge: Powering transformation with grid edge intelligence. IEEE Smart Grid. https://smartgrid.ieee.org/bulletins/august-2021/utilities-on-the-edge-powering-transformation-with-grid-edge-intelligence

Additional Reading

The following sources provide additional context and supporting background for the industry perspectives presented in this article.

Federal Energy Regulatory Commission. (2024, November). 2024 assessment of demand response and advanced metering. https://www.ferc.gov/sites/default/files/2024-11/Annual%20Assessment%20of%20Demand%20Response_1119_1400.pdf

Liu, C.-C., & Stewart, E. M. (2021). Distribution systems in the evolving grid: An opportunity and challenge for grid modernization. U.S. Department of Energy, Grid Modernization Laboratory Consortium. https://www.energy.gov/sites/default/files/2021-05/Distribution%20Liu%20Stewart_0.pdf

Sodiya, E. O., Umoga, U. J., Obaigbena, A., Jacks, B. S., Ugwuanyi, E. D., Daraojimba, A. I., & Lottu, O. A. (2024). Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem. International Journal of Science and Research Archive, 11(1), 1863–1873. https://doi.org/10.30574/ijsra.2024.11.1.0287

U.S. Department of Energy. (2022, June 20). A system in transition. https://www.smartgrid.gov/files/20-06-2022_doe-voe-a-system-in-transition-report.pdf

Xu, K., Zhang, Y. M., Hardison, R., & Weber, E. (2021). Business models to accelerate the utilization of distributed energy resources (NREL/TP-6A20-79549). National Renewable Energy Laboratory. https://www.govinfo.gov/content/pkg/GOVPUB-E9-PURL-gpo184434/pdf/GOVPUB-E9-PURL-gpo184434.pdf

About the Author

Stefan Zschiegner joined Itron in March 2020 as VP Product Management for the Outcomes business. Prior to joining Itron, he held product business leadership roles driving digital transformation in telecom (leading Mitel’s Cloud business) and in manufacturing (Velo3D). Previously, Zschiegner held product leadership positions in energy solutions at Enphase Energy (global leader in residential solar solutions) and driving global growth with grid-connected solutions for First Solar (global leader in utility scale turnkey solar power plants). His education includes the Executive Marketing Management Program at the Stanford Graduate School of Business, and a masters’ equivalent degree in electrical engineering from Technical-University Hamburg in Hamburg Germany.

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