Distribution Grid Real-Time Visibility: An Essential for Right-Sizing Distribution Grids & DER Deployment

A new paradigm is emerging, one that combines real-time visibility, dynamic management, and data-driven decision making to transform how we plan to invest in and operate the grid edge. These decisions will impact US competitiveness in manufacturing applications as much as they impact choices and affordability of DER-based resilience devices for residential customers, and routine electrification choices for buildings. Check out the issue brief and full article bellow.

ISSUE BRIEF

Current Transformer Infrastructure Challenge on the Low Voltage Grid

• End-of-the-line transformers are far smaller in the US for residential and small customers (secondary customers); but these transformers are also where routine electrification rates are increasing, and loads added are becoming more variable and significant.
• The impact of electrification will be most severe on distribution systems where grid edge equipment - particularly transformers and secondary feeders - are already operating near their capacity limits.

• Grid stress patterns are becoming more complex and localized, with some transformers potentially experiencing dramatic load increases while others remain underutilized. This new reality demands more sophisticated, data-driven planning tools that can identify and respond to emerging stress points before they become reliability issues.

Traditional grid planning approaches have to evolve to suit these new conditions

• Conventional grid planning centers on managing summer peaks from air conditioning loads, but the fundamental nature of electricity demand is shifting. As heat pumps and electric vehicles proliferate, winter peaks are emerging as a critical constraint. This seasonal shift, combined with the clustering effect of new technology adoption in certain neighborhoods, means utilities must fundamentally reimagine their planning approaches.
• Current utility interconnection assessments are fundamentally constrained by their reliance on "Connected kVA" calculations - a conservative approach that simply sums the rated capacity of all transformers on a circuit to determine available capacity. This method, while straightforward and commonly reflected in public hosting capacity maps, leads to unnecessarily restrictive interconnection decisions that fail to capture real system capabilities. This approach tends to assume worst-case loading scenarios that rarely materialize in actual operations, effectively stranding significant feeder and transformer capacity that could safely support new distributed energy resources or electric vehicle charging. The result is a systematic underutilization of grid assets that could otherwise enable greater DER integration and electrification efforts.

The Core Dilemma

• Utilities face a challenging dilemma in this situation. Their primary responsibility is ensuring system and public safety: the lack of real-time visibility into actual system conditions compels electric service utilities to maintain conservative positions in the face of uncertainty about what they don't know.
• Meanwhile, developers and customers experience the other side of this "unknown" actual hosting capacity equation. Whether working with residential customers or commercial clients seeking to site larger DER aggregations, they frequently encounter unexpected transformer and power train costs or face surprising export limits in interconnection studies. These uncertainties create barriers to project development and increase costs throughout the system, ultimately slowing the pace of grid modernization.

The Static Threshold Problem

• The persistence of static interconnection thresholds based on model information – not real time dynamics of individual transformer service equipment - represents a significant barrier to renewable energy integration, exemplified by current industry practices that typically limit DER penetration to a certain low percentage of a feeder's annual peak demand. This situation highlights a broader industry problem where conservative static thresholds trigger costly supplemental studies for voltage regulation, thermal violations, and reverse power flow, even when unnecessary if real data was available to show that a far higher tolerance than say, "15% of peak demand" is actually possible to absorb at a specific feeder.
• Further, DERs can help the problem of averting transformer or feeder loading challenges: but the price signals and program designs allowing this useful marriage of technology behind the meter to system needs in front of it, cannot materialize at scale without data sets showing real-time trends that should motivate these programs into existence.

The Path Forward

• The solution lies in shifting from static thresholds to dynamic, data-driven approaches that can accurately assess real grid conditions and support higher hosting capacities while maintaining system reliability. This transition requires not just technical solutions like real-time monitoring, but also updated regulatory frameworks that align utility incentives with grid modernization goals.

ARTICLE

Low Voltage Distribution Grid Real-Time Visibility: an Essential Practice for Right-Sizing Distribution Grids for US Manufacturing and Residential Loads

Introduction and Background

The distribution grid stands at a critical inflection point. As electrification accelerates and distributed energy resources proliferate, electric distribution system owners and operators are discovering that traditional planning and operational approaches no longer suffice.? On the other side of the meter, power customers are rethinking conventional approaches to monitoring and measuring power quality at the point of service received from the distribution system.? As the U.S. pursues a dramatic reindustrialization and manufacturing renaissance, the power quality and reliability requirements of modern industrial processes present both challenges and opportunities – whether it is the unusual oscillation characteristics of data center loads, or modern manufacturing and processing facilities which face power quality challenges from frequency converters, LED lighting, and controlled systems and the like.? (A load oscillation in a data center context is particularly challenging because of the rapid switching of many digital loads (servers, storage devices, networking equipment) that can create repetitive power fluctuations or "oscillations.")? In all of these applications, issues like power and voltage fluctuations can damage expensive equipment, and also violate manufacturer warranties and compromise insurance coverage. ?IEEE Standard 1100-2005 provides the “business standard” list of electrical power quality problems and categories them as follows – Transient; Interruptions; Sag / Undervoltage; Swell / Overvoltage; Waveform Distortion; Voltage Fluctuations; Frequency Variations.?

This issue presents economic sensitivities that need to be solved for either at the distribution system service, and/or at the interconnected points that step down service from the distribution transformer to the onsite components of the power train. The components of a power train include: the utility feeders, the transformers, switchgear at the site, any uninterruptible power supply sources installed by the facility taking that power, as well as power distribution units, circuit breakers and wires feeding the final distribution of power to the equipment in service.? (A power train, or chain, is simply the series of electrical equipment components which deliver power from the grid source (distribution transformer) to the end use application inside the facility connected into the electric service provider’s point of delivery, measured through that provider’s meters.? The provider may add additional high-cost meters for certain customers to specifically measure power quality parameters (e.g., devices used for Industrial Metering).?

The challenge can be solved on either side of the electric service meter and is a significant investment for all parties.? ?The private sector – customers of electric distribution systems – can relatively quickly justify the investment in power quality monitoring on its distribution service power train.? Utilities and other conventionally regulated service providers however, have more work to do to get to this point: integrating new technology for low-voltage grid monitoring into distribution planning and operations planning.? Both sides of the system need to make winner investments to support the competitiveness of the U.S. in the basic thing that drives our economic engine: electricity. ??

Below I explore some of the nuances of this issue, which result in a thesis I am actively exploring with stakeholders in multiple states.?

How and why should we move away from static modeling and assumptions about the LV grid equipment service life, to an industry standard that embraces real-time visibility into distribution service equipment, so we can do better to right-size the distribution grid? ?

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The Distribution Challenge

Traditional models are breaking down under new pressures, and recent regulatory acknowledgments underscore this reality:

EV adoption is straining smaller transformers. California electric system operator SMUD submitted a study, used in the 2024 DOE Final Rule on Energy Conservation Standards for Distribution Transformers, supporting a conclusion in that rule that EVs can cause a 100% overload risk for 15 kVA transformer units in high-adoption scenarios. (This size is a fairly typical distribution transformer size for residential neighborhoods throughout the U.S.).? Several stakeholders, including APPA, NEPPA, and others, emphasized in the record in this rulemaking that operating these smaller transformers efficiently at higher loading is becoming "a distinct value for consumer service." (This recognition is particularly critical for Equipment Class 1B - single-phase liquid-immersed distribution transformers ranging from 5-100 kVA, which are most directly placed near consumer loads).? In a high-EV penetration scenario (50% by 2035), overloading likelihood ranges from 100% for 15 kVA transformers to 2.5% for 100 kVA transformers, according to the study – presenting the obvious case for electric system planners to understand the life span of smaller transformers today relative to potential electrification loads on neighborhood feeders and do more faster to prepare the system for these demands. (See Dalah, S., Aswani, D., Geraghty, M., Dunckley, J., Impact of Increasing Replacement Transformer Size on the Probability of Transformer Overloads with Increasing EV Adoption, 36th International Electric Vehicle Symposium and Exhibition, June, 2023.)

Building electrification (routine basic electrification) is also straining smaller transformers.? A recent webinar held by the ESIG (Energy Systems Integration Group) helps support the case for understanding what is happening in real-time on a distribution transformer, as opposed to operating under modeled assumptions about how much of? “beating” a LV transformer can take as more customers choose to electrify their buildings (e.g., conversion to electric heat).? “Building electrification is expected to affect all facets of the power system, and the effects will be pronounced for distribution systems where many pieces of grid edge equipment are already heavily loaded. In many ways, our thinking about grid planning will need to change as new technologies are adopted that change the fundamentals of load, and shift stress towards winter. Because of the timelines associated with building new distribution and transmission infrastructure, decisions today, or lack thereof, will impact the preparedness of the grid for the expected demand. Despite the high uncertainty of the timing and makeup of this demand and that different jurisdictions may experience the impacts in different ways, there are opportunities to lay a grid planning foundation today that will support a grid that enables widespread building electrification.” [Source, Webinar Abstract, Sean Morash Principal, Telos Energy https://www.esig.energy/event/webinar-grid-planning-for-building-electrification/ ].

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Right-Sizing Distribution Systems? Relies on Understanding Where to Invest and Where to Defer? - Dynamic Decisions that Rely on Dynamic, Granular, Real-Time Data Sets, Not Models

·?????? Distribution planning must shift from reactive to proactive approaches.? Earlier this year in Dallas, EPRI and DOE partnered to host 17 electric distribution companies to discuss "right-sizing" the grid for decarbonization.

·?????? ?The key insight was that utilities can no longer wait for load growth to appear before planning upgrades. EPRI’s CEO Arshad Mansoor recently noted regarding this effort, "We not only have to right size the grid but build the grid in advance of the anticipated electricity demand growth which we haven't seen in decades."

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·?????? Real-time data sets provider by smart sensors located on distribution grid equipment (LV transformers, LV substations, relays, primary and secondary) are being recognized by some electric service providers as critical tools to supplement or ?fill in for more complex, expensive challenges of extrapolating this information from comprehensive smart meter deployments.??

·?????? These technologies support right-sizing exercises because they provide real-time data sets that provide intra-hour and inter-hour live feeds on power quality metrics tied to how a single piece of utility equipment is faring as loads utilize the system – that data, coming from digitized sensor equipment, provides meaningful analytics that show whether or not there is a problem, where there is an opportunity, and how much load and what kind of load can come onto that feeder and equipment without causing a major issue (real-time issue or longer-term asset degradation issue).?

• Vermont Electric Cooperative prevented outages within the first month of deployment and identified three faulty transformers using ~ $2,000-per unit real-time grid monitoring sensors from Edge Zero on its LV transformers.? Edge Zero came into existence some years ago in Australia, focused at that time on the surprises of grid right-sizing that accompanied the boom in rooftop solar.? Edge Zero began building low-cost monitoring equipment for Australian DSOs and ended up providing this tech and the software platform to enable visibility and data manipulation toolsets - real time power quality mapping - to over half of the Australian distribution network operators. VEC’s innovation lead Cyril Brunner stated: “VEC is seeking to integrate a variety of distributed energy resources to maintain affordability and reliability for our members… Edge Zero’s solution will help VEC identify grid constraints to generate a complete picture of our distribution grid so that we can manage distributed energy resources to reduce infrastructure impacts resulting from the energy transition.” (VEC operates 159 miles of transmission lines and approximately 2,500 miles of distribution overhead and underground lines. VEC owns and operates 32 substations and 4 primary metering points. VEC is interconnected with, and served by 5 VELCO connections, 13 GMP connections, 2 Eversource (formerly PSNH) connections, 3 Hydro-Quebec connections, and 1 connection to Swanton Village Electric.) In addition to real-time power quality monitoring across the low voltage network, this projects supports VEC’s fault detection and equipment risk identification priorities and helps VEC create a realistic view of what dynamic operating thresholds could look like on their system as more DERs and EVs interconnect in their service territory.? Source:? Report Link.

• Southern Company/Georgia Power has deployed Energy 4.0 sensors across 1100+ high voltage transformers, extending maintenance cycles by 200%.? In October, T&D World featured the company’s usage of smart, contactless sensors to optimize their grid asset maintenance.?? The speakers from Georgia Power and Systems with Intelligence provided “a review of the security architecture and the benefits that Southern Company realized by using real-time sensors to move to a condition-based maintenance program. This includes revealing the technology used in Energy 4.0 sensors and the supporting architecture, and how the data and access to the network is safely managed” and “how 24/7 monitoring of critical equipment and assets increased safety and reliability and allowed Southern Company to implement a condition-based maintenance program and extend maintenance cycles by 200%.”?

Real-Time Visibility Supports Meaningful Hosting Capacity DER Interconnection Policy

• Traditional utility interconnection assessments rely heavily on "Connected kVA" calculations - a conservative approach that simply sums the rated capacity of all transformers on a circuit to determine available capacity. This method, while straightforward, often appears in public hosting capacity maps and leads to unnecessarily restrictive interconnection decisions.

o?? It assumes worst-case loading scenarios that may rarely materialize in real-world conditions, effectively stranding potential feeder/transformer capacity that could safely support new distributed energy resources or electric vehicle charging.

o?? The essence of the problem is twofold: on one hand, this conventional approach puts utilities in the position of being uncertain about what they don’t know.? That is a dynamic that is materially difficult to square with the primary role of a utility service provider, which is system safety and relatedly, public safety. ?On the other hand, developers working with residential customers, or commercial customers, who are looking for opportunities to site larger aggregations of DERs, install more electrification devices that support customer demand, building new large load centers that require investment in interconnection upgrades, experience the other side of the “unknown” actual hosting capacity – surprise transformer/power train costs or restrictions like export limits in interconnection studies.

• Non-traditional utility interconnection assessments rely on real-time data about the utility system, and support realistic, cost-saving outcomes for utility assets and interconnecting customers.?

o?? Sensor devices installed directly on transformers offer a compelling alternative by providing precise, real-time visibility into actual loading conditions. This granular data enables utilities to make more informed interconnection decisions. For example, if a transformer's connected homes lack high-draw amenities like swimming pools or large AC units that were initially planned for, utilities can confidently approve EV charging connections despite the transformer's nominal "full" rating.

o?? Crucially, these devices also provide early warning capabilities, alerting utilities when transformers approach overload conditions and enabling proactive upgrades.?

o?? The same data sets support DER integration, as a tool for designing distribution tariff price signals that motivate loads to achieve goals that extend utility equipment service life, i.e., DERS can help utilities avoid loading on distribution service equipment.? See this study for some excellent research on “how DERs can be appropriately valued in light of increasing electrification and EV adoption in an era when traditional grid enhancements are hobbled by cost and policy hurdles.” Source: Valuing distributed energy resources for non-wires alternatives - https://www.sciencedirect.com/science/article/pii/S0378779624004073.?

• The success of this approach is exemplified by Endeavour Energy's EV bus depot project, where real-time monitoring enabled precise load management and infrastructure optimization. By understanding actual usage patterns rather than relying on theoretical maximums, Endeavour reduced capital expenditures from $6.5 million to $2.5 million (AUD). The project demonstrated how granular, real-time monitoring can improve grid stability while enabling faster, more cost-effective electrification. This success provides a scalable model for utility service providers, showing how transformer-level monitoring can accelerate clean energy adoption while maintaining grid reliability and reducing unnecessary infrastructure investments. ?Edge Zero sensors were part of the real-time suite of monitoring, allowing the utility to create data sets on inter-hour loading, optimize their spending and maintenance strategy for transformer upgrades, and support a strategic EV charge scheduling program for the customer. Source: Aussie Innovation Supports Electric Bus Rollout; Commercial Electric Vehicle charging program management.

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Sensors versus AMI: Key Differentiators and AMI Problems Raised by Mission Data and ACEEE

·?????? Some US utilities have begun incorporating AMI data to refine their connected kVA/capacity calculations, but this approach comes with significant limitations.

o?? While #AMI can provide actual load data, it's often aggregated at the circuit level rather than associated with specific transformers.

o?? Furthermore, the sheer volume of data in Meter Data Management Systems (MDMS), combined with cybersecurity requirements due to integration with Customer Information Systems (CIS), makes granular, real-time analysis impractical and cumbersome.

o?? This is a thesis that is well explored in a #Mission:Data publication, highlighted last year by Utility Dive. ?The article states: “[R]esults from a range of studies suggest that many of the promised benefits of AMI have yet to be delivered even after a decade of implementation. For instance, a 2022 analysis from the Mission:data Coalition found that?97% of smart meters fail to provide promised customer benefits, and a?2020 report from the American Council for an Energy Efficient Economy?(ACEEE) found that utilities “are largely missing the opportunity to utilize AMI data to improve their energy efficiency and demand response offerings, in part due to regulatory, administrative, and technological barriers.” Unsurprisingly, a handful of states?have blocked multimillion-dollar smart meter deployments?over the past few years, and there is growing regulatory scrutiny of the benefits that AMI deployments actually provide.

• · My own experiences learning about AMI rollouts at utility events have further informed this perspective. At this year’s Edison Electric Institute TDMMA and the NARUC summer meeting (both in “Palms” on different sides of the country), I learned from utility presentations that AMI's limitations as a grid management tool stem from several factors.

o?? First, AMI's primary design purpose was billing and basic consumption monitoring, not real-time grid operations. While AMI is valuable for time-of-use pricing and billing accuracy, it cannot address critical grid edge challenges that rely on granular information about the loading and power quality and performance status of specific pieces of high-value utility equipment. (That thesis is laid out in the case for sensor-based approaches utilities are adopting regardless of AMI deployments).? Monitoring solutions must be positioned in different parts of the network of the utility operator itself: while AMI manages customer usage relationships with utility equipment, grid sensors manage infrastructure health and operations of the equipment itself

o?? Second, AMI deployments face significant equity challenges - consumers without technology eligible for AMI-enabled programs (they are not the people who have EVs and batteries already) can face regressive costs, as they often get AMI last but bear the deployment costs first because of the manner in which utilities must spread the global costs of new capex expenses for these deployments.

o?? Third, customers are not excited about proprietary AI tools providing hyper-sophisticated views to electric service provides about their consumption behaviors.

o?? Fourth, there are cost and timing tradeoffs with AMI 1.0 and 2.0 deployments, and solving for real-time grid visibility at the grid edge will come from serious integration work into AMI rollouts, OR, from supplementing those rollouts with low cost monitoring of transformer equipment itself.? There is a data type and data volume tradeoff here to explore that befuddles me as much as it seemed to overwhelm some of the audience members considering this question at EEI and NARUC events: if an AMI 2.0 meter takes continuous sampling of voltage and current waveforms at every residential home on a circuit 15,000 times per second, what sort of data processing cost is the utility looking at to use that information?? Indeed, as shared by the CEO of Sense, working on real-time integrations for AMI 2.0, “To make use of that high-resolution data…utilities need enough computational capabilities to run software in the meters, the ability to have real-time networking, and the software to protect their consumers’ privacy.” Source Reference, Latitude Media:? “The key issue with the first generation of smart meters, according to Phillips, was that the architecture didn’t allow for real time visibility on the grid. The smart meter collected low resolution data and sent it to the service provider to be made available at some point in the future… Sense is not an impartial observer. The company built an energy monitor that gives real-time information on every device in a home using machine learning techniques. In 2019, Sense partnered with smart grid technology producer Landis+Gyr to put its technology in smart meters.”

Utilities managing growing DER penetration and EV adoption should adopt low-cost monitoring approaches where subsecond-level visibility of power quality and transformer health at specific feeders on their system, could be as or more critical for managing assets and system planning at a lower cost and a faster timescale, than replacing every AMI device. ?Accomplishing more with less is the motto for efficiency everywhere - it should drive our approaches nationwide to right-size the distribution system.