Are “Smart Meters” a Costly Diversion From the “Smart Grid” We Really Need?

I recently read a paper entitled “Getting Smarter About the Smart Grid” by Timothy Schoechle. Though the paper’s primary focus is the electricity industry, it seems a fitting topic for the Quello Center blog because it raises important questions about how best to use information and communication technology to support a sustainable energy system that addresses the increasingly urgent issue of climate change and other environmental challenges we now face. And it does so in a way that challenges conventional wisdom regarding the massive and costly deployment of so-called “smart meters” now underway across the country.

The paper’s core argument, as I understand it, is that the nationwide smart meter deployment catalyzed by the 2008 federal stimulus bill is actually a step in the wrong direction. This is because it supports a continuation of utilities’ existing reliance on:

carbon-intensive and environmentally destructive baseload generation;
centralized, inefficient & vulnerable generation and transmission facilities;
outdated business and regulatory models that are intimately linked to the first two items, and are slowing the integration of renewables into the energy mix at a time when such integration needs to be accelerated.

Instead of deploying utility-controlled “smart meters” (which, as he explains, are 20+ year-old technology originally designed to reduce meter reading costs and aren’t really very smart at all), Shoechle advocates a different approach to addressing climate change and transitioning as quickly as possible to a sustainable energy infrastructure. This approach would focus on and combine:

increased integration of and reliance on distributed renewable generation of electricity (e.g., via rooftop solar panels rather than massive, centralized solar installations);
reduced dependence on and strategies to phase out carbon-intensive and highly centralized baseload generation, which is the current foundation of utility business models, but stands in the way of the needed transition;
more efficient, resilient and secure micro-grids that support the migration to distributed generation;
distributed storage and control technologies that also support distributed generation and allow consumers rather than utilities to control their energy use, and keep detailed usage and other personal data private and secure;
an electric power sector that is not driven by the increasingly obsolete business models of investor owned utilities (IOUs) and the inefficient and substantially “captured” regulatory models and agencies that have developed alongside the IOUs.

A key question, according to Schoechle, is who controls the gateway device linking end user premises to the electrical grid. In his view, the electric utility industry should look to the model created years ago in the telecom sector:

The demarcation between monopoly utility space and customer market space was clarified over two decades ago in the case of wire-line telephone monopolies with the decisions and policy changes culminating in the divestiture of AT&T. One result was enormous…growth in new markets for premises equipment and services. The electricity grid today is facing the same demarcation inflection point as the telephone network experienced. The gateway belongs to the consumer, not to the electric utility. A demarcation and opening of the consumer premises space to market competition could unleash the creative energy of the consumer electronics industry, the home appliance industry, and others. Full two-way smart grid communication among premises-based systems, products, and services—facilitated by a consumer-controlled gateway device and already available data services (i.e., Internet and Web access via DSL, cable, fiber, etc.) —would free the smart grid from the stifling control of utilities and their proprietary meter-reading networks.

Part of the problem, explains Schoechle, is that one of the stimulus bill’s central goals was to spend money fast. At that time, the networked digital meters being deployed were the only off-the-shelf technology available to quickly absorb the bulk of the budgeted investment in a “smart grid.” And dubbing them “smart” meters made this budget allocation seem all the more reasonable (if we need a “smart grid,” then “smart meters” sure seems like a good place to start).

Since it allowed them to retain control of the next generation of customer-premise equipment while expanding the cost-basis on which their profits are calculated, utilities (and the vendors from whom they were buying the devices), embraced the large scale deployment of these meters. And, to most politicians and citizens, it sounded like a sensible (though not well understood) step toward a future “smart grid,” especially when federal stimulus funds were available—but only for a short time—to finance the purchase of whatever form of “smart grid” technology was available at that time.

Schoechle paints a very different and more problematic picture of the stimulus-financed rush to deploy so-called smart meters (bolding for emphasis is mine):

The [smart] meter networks squander vast sums of money, create enormous risks to privacy and security, introduce known and still unknown possible risks to public health, and sour the public on the true promise of the smart grid. Data to be collected by the smart meters, including intimate personal details of citizens’ lives, is not necessary to the basic purpose of the smart grid—supply/demand balancing, demand response (DR), dynamic pricing, renewable integration, or local generation and storage—as promoters of the meters, and uninformed parties, routinely claim. Instead, the meter data is serving to create an extraneous market for consumer data mining and advertising (i.e., “big data” analytics)…

[S]mart meters have failed to deliver smart grid benefits for fundamentally technical reasons. Examples include that 1) the networks do not generally provide full two-way communication, 2) customer usage display was, in most cases, of stale data (24 hour delayed) on a third-party website—on-site real-time display is not feasible using most meter backhaul networks—and 3) smart meters and their networks cannot or are ill-equipped to implement demand response load control strategies…

What is almost always assumed or alluded to by meter advocates, but never explained, is how reading meters, however frequently, can serve the goals of functions of the smart grid—i.e., balancing supply and demand. Never explained is how granular personal meter data helps manage the grid. It is believed by some that consumer electricity usage behavior data may be useful to utilities or to consumers. But it is not clear how such data would actually be applied, nor is it clear that there are not cheaper and more benign ways to acquire it. SCADA [supervisory control and data acquisition] networks already provide utilities with the aggregate transformer or substation load data needed to assess distribution loads and conditions. A premises meter is not needed, or would be impractically cumbersome to use, to aggregate data to derive distribution grid load information.

Schoechle sees a different path leading to a truly smart and sustainable electric power grid. As he explains:

[M]anagement of premises demand response, supply/demand balancing or control/monitoring of solar systems, electric vehicles (EVs), or batteries would be better accomplished by distributed control through intelligent energy management devices and transactional control strategies. What is needed is not meter data flowing out of the premises, but rather grid load, time-of-use signals, or electricity transactional data flowing into the premises so that the premises can manage its own energy. This would require full two-way communication via a gateway with premises-based equipment such as home automation systems (HA), smart inverters, smart appliances, energy management systems, etc. that do the job of managing energy on-premises.

Present day meters do not provide such a gateway. The meters generally do not provide data directly to the customer, but rather upload it to the utility, which may or may not provide it later to customers via a third-party web portal (usually delayed by at least 24 hours). Customer usage displays would need to be real-time or near real-time to be useful to consumers and even then the best displays are no substitute for premises-based automated energy management equipment that would act on behalf of consumer priorities and do so entirely within their own homes…

In another section of the paper, Schoechle provides more detail about what he sees as a true “smart grid” and how it can shift our electricity usage away from fuels and systems that contribute to climate change and pollution and are not sustainable. Unlike the widespread but superficially-driven enthusiasm for today’s “smart” meters, his perspective strikes me as grounded in an understanding of problems associated with current utility practices, what keeps these practices and problems in place, and what’s needed to change the practices and fix the problems.

The commodity sale of electricity and double-digit ROR on assets has resulted in a system historically dependent on “baseload” generation within a big-grid and big-transmission centralized structure. This means that to be economical, large centralized generating plants (primarily coal, nuclear or some types of natural gas fired plants) must run at a fixed optimum output level known as the baseload. Because the supply and demand for energy on the grid must be instantaneously matched, second by second, hourly variation in demand above the baseload supply curve is met by “peaking plants” (usually natural gas) that are more expensive to operate but can be quickly turned on or off.

Another method of dealing with variation in supply is known as demand-side management or “demand response” (DR). Demand response includes various techniques to manage demand to better match supply. DR offers ways to quickly shift peak demand by sending control signals that turn off or limit specific industrial or residential load devices (e.g., air conditioners, water heaters, etc.). However DR systems require communication pathways and special premises equipment in order to be implemented—products and services that are not yet standardized, fully developed, or readily available. Unfortunately, DR employed in a baseload system, while shaving peaks and improving system efficiency, may perversely serve to increase dependency on relatively dirtier baseload sources (e.g., coal, nuclear, etc.) and thus can actually result in higher pollution and CO2 emissions. However, properly implemented, new forms of DR (e.g., “transactive energy”) can play a crucial role in renewable integration if the resulting system is cheap, ubiquitous, and easy to use…

Renewable energy sources are inherently incompatible with a conventional baseload generation-based electricity system. When variable and unpredictable power from wind and/or solar is fed into a baseload-supplied grid, occasionally too much electricity may be produced relative to demand. The electricity system requires that supply and demand be perfectly balanced second-by-second. If supply and demand become mismatched, even momentarily, the grid may become unstable and could quickly and completely fail. For both technical and economic reasons, baseload plant operators prefer to operate at a fixed optimal output level. Rather than turn down the baseload plants, operators prefer instead to “curtail” the renewable energy (Regelson, 2011; Farrell, 2011, p. 26). In such situations, ratepayers end up paying for both the baseload and the curtailed (i.e., wasted) renewable power. The higher the proportion of renewable energy available to the system, the bigger this problem becomes. Conventional baseload-oriented utilities are cautious about adding too much renewable energy because beyond a certain level, doing so raises total costs, which wastes energy and/or threatens to de-stabilize their grid…

But baseload is not essential for meeting demand…[T]he same total supply/demand profile [can be] met by a combination of renewable (variable) supply and peaking supply…[A] renewable non-baseload supply system…does not waste power but does, however, present significant technical challenges requiring careful and rapid rebalancing by quick response to changes in supply and demand—either by quickly adding fast peaking sources (e.g., hydro, storage sources, natural gas turbines) when needed or by quickly reducing or shifting demand (e.g., demand response). This rapid rebalancing represents the essential promise, and challenge, of smart grid technology.

While the development of systems to achieve this rapid rebalancing poses some very real technical challenges, the more daunting challenges facing Schoechle’s vision of a truly smart grid may relate to issues of political and economic power. One arena where this is playing out is the issue of “net metering,” which Schoechle considers in another paper, entitled “Green Electricity or Green Money?”

The issue [in Arizona and a growing number of other states] was “net metering” rules (also known as “distributed generation” or DG) that allow home operators of photovoltaic systems to feed excess solar power back into the electricity grid and essentially “run their meters backwards.” In late 2013, Arizona Public Service (APS), the local IOU, had proposed charging customers who install rooftop solar panels an additional $50-100 fee on their monthly bills. After tumultuous hearings, with public demonstrations, and millions of dollars spent by the electricity industry to lobby the Arizona regulators and influence public opinion, APS essentially lost. Although the ACC did approve a “connection fee,” it was only a tenth of what APS wanted—a nominal charge of about $5 per month on a typical household installation (Sweet, 2013)…

According to Shoechle, the fight over net metering reflects “a clash between the century-old method of monopoly “cost-of-service” (CoS) rate regulation and the recently emerging public desire for clean energy and the new concept of “value-of-solar” (VoS) utility rates.”

The traditional CoS model guarantees IOUs full recovery of costs of delivering electricity plus a significant guaranteed profit, regardless of how it is produced. But when the customers start producing power on their own roofs, this model doesn’t work anymore. If too many customers do it, the IOU no longer sells enough electricity to cover its fixed costs and costs of distribution to others, or to meet its investor’s expectations…). In summary, adopting VoS electricity rates encourages solar energy but is seen as threatening by IOUs to their traditional business practices and monopoly profits.

Schoechle cites “a nationwide effort initiated last year by the American Legislative Exchange Council (ALEC) to eliminate renewable portfolio standards in 16 states and to roll back or weaken net metering policies to delay solar energy.” Opposing this ALEC push at the state level is a grassroots effort that, in Schoechle’s view, is most powerful and effective at the local level, where more and more citizens and businesses are deploying rooftop solar and other renewable energy sources.

Taken together with local citizen concern over climate-change, oil and gas fracking, and the desire for clean energy sources, the small rebellion is beginning to morph into a bottom-up, community-based revolution in electricity and energy that could re-shape society—from a centralized fossil fuel-based economy to one that is decentralized, democratized, and sustainable… It will likely be left to the people to reinvent the electricity system largely through bottom up community initiatives and disruptive technologies—motivated by the desire for a clean energy future, control of energy costs, economic growth, and local control of environmental health.

Not surprisingly, Schoechle is involved with one such localized “rebellion.” It is taking place in Boulder, Colorado, where the city government is forming a municipal utility and moving to take control of the local electric grid from Xcel Energy, the IOU that currently serves the city (for more details on what’s happening in Boulder see here).

As Schoechle explains in an article to be published in the May/June 2015 issue of Solar Today:

Boulder is inventing a new model for a “utility of the future.” Although a few other small cities such as Gainesville and Winter Park, Florida, and others have formed municipal utilities in recent years, they did so for financial, service-related, or other reasons. Boulder, on the other hand, is motivated by the desire for clean energy…

Although it must start by buying the existing wires and poles, the concept behind the Boulder muni is not to run a conventional electric utility that generates or purchases electricity. Rather, the idea is to provide energy services—health, comfort, safety, and economic vitality—to its customers, at the best price and with the least environmental impact…

Schoechle describes the model being pursued by Boulder as:

“[A]n entirely new electricity paradigm…based on distributed renewable energy…in which the users generate most of the power [via] community solar microgrids based on solar-plus-storage at scales ranging from single homes to community solar gardens to commercial and industrial buildings…[and] small-scale hydro..power.

As part of this model, Boulder has adopted an “energy localization framework,” which Schoechle describes this way:

This framework seeks to democratize energy decision making so customers have more direct control over and involvement in energy decisions. This includes opportunities to invest in their long-term energy needs and to have a say in energy investments made on their behalf.

Under the framework, energy would be generated locally or regionally whenever feasible, reducing reliance on external fuel sources. Customers would manage and reduce their energy use directly and effectively and energy service companies would compete and innovate within a diverse and robust local energy economy.

While the Boulder initiative faces and will no doubt continue to face significant challenges, it strikes me as a very worthy effort to develop practical models for:

energy systems that can avoid major destructive impacts from climate change and;
information and communication systems that enhance individual freedom and support healthy prosperous communities rather than the more dystopian possibilities related to increasingly centralized control and surveillance and the abuses of power these tend to feed.

At the close of his “Getting Smarter About the Smart Grid” paper, Schoechle shares some final thoughts on this, citing the work of Jeremy Rifkin, who has written several books addressing issues and trends closely tied to the paper (see here and here)

Due to emerging public skepticism and pushback, manufacturers, service providers, grid operators, and policymakers at all levels should begin by abandoning the term “smart grid” in favor of a more appropriate term. Intergrid was suggested by Jeremy Rifkin in his recent visionary work on energy, The Third Industrial Revolution (Rifkin, 2011). Rifkin compares the grid with the Internet, where intelligence is distributed to the periphery. He envisions that in the future, people will be “…generating their own green energy in their homes, offices, and factories and sharing it with one another across intelligent distributed electricity networks—an Intergrid—just like people now create their own information and share it on the Internet” (p. 36). This transformation took place in telecommunications well over a decade ago, bringing competition and the creativity of the market to the telephone network and customer premises. Now it is the time for electricity to do the same. America was a leader in the genesis of telephony and electricity. America now has an opportunity to be a leader in taking electricity and society to a new, clean, economically viable, and sustainable energy future.

Primary takeaways

Digital inequality shows larger impacts on youth academic performance as compared to time spent on screens.
Digital skills play a significant role in mediating unstructured online engagement (social media use, playing video games, browsing the web) and youth academic, social, and psychosocial development.
Unstructured online engagement and face-to-face social interaction are complementary and continuously interact to create and enhance youth capital outcomes.

A familiar story: concerns of screen time

Today’s discussions of adolescent well-being have coalesced around a clear narrative: teenagers spend too much time online, and their academic performance, mental health, and social lives are deteriorating as a result. A steady stream of academic papers, books, and op-eds, alongside a growing number of policy proposals––school phone bans, age-gated social media use, restrictive screen-time limits––rest on the same underlying claim, aligning with a contemporary, digitized version of the displacement hypothesis:

Screen time, particularly the unstructured, free-time spent on social media, gaming, watching video content, or browsing the web, is said to displace the productive face-to-face activities that build adolescents into capable adults.

The implied and often practiced solution is restriction. In response, this dissertation tested this claim directly, and placed it within the broader context of adolescence.

Across three years, I followed 653 Michigan adolescents from early through late adolescence: in grades 8 or 9 (survey one, 2019) to grades 11 or 12 (survey two, 2022). Notably, these students, studied over time, were part of a broader pooled sample of 5,825 students across the same eighteen highschools. The study window captured the year before and the year after the peak of the COVID-19 pandemic and related lockdown orders, functioning as an unprecedented stress test for theories of adolescent social, academic, and digital life and, importantly, as a benchmark to compare the effects of pandemic-related change and inequality to those effects from screen time alone.

Across four studies of adolescents, consisting of six cross-sectional and longitudinal analyses, findings are not consistent with the displacement narrative, nor the broader concerns about the time youth spend on screens.

Findings are, however, consistent with something the current public and (most) academic discussions have largely overlooked or ignored: the gaps and inequalities that determine whether adolescents can access and use the internet meaningfully in the first place.

What the displacement hypothesis overlooks

Displacement and related research and policy concerning the time young people spend online assumes a “zero-sum” model of adolescent day-to-day time. An hour online is an hour not spent studying, reading, sleeping, or interacting face-to-face (i.e., time spent on more productive or developmentally “better” activity).

Indeed, this makes sense logically. However, as an empirical claim, this model requires time spent online to behave differently from all other ways adolescents allocate time; it must produce uniquely negative outcomes and be inherently harmful across digital contexts, rather than the typical mix of trade-offs corresponding to, and often overlooked among any other social or developmental context.

Yet, online time does not differ from other youth activity. Instead, I find it has a mix of pros, cons, and even some “uniquely digital” benefits which youth utilize for social and academic gains. When I compared unstructured digital media use against traditional face-to-face interaction and activities, both produced similar patterns: some negative associations with academic outcomes, some null, and some positive.

Trade-offs within traditional face-to-face activity (for example, social time with friends and family, or time spent in after-school extracurriculars) are treated as ordinary developmental experiences that must be experienced for the betterment of development. The identical trade-offs involving digital time tend to be overlooked or ignored, and online engagement is perceived as altogether harmful.

A growing body of evidence, including this dissertation, do not support that distinction. Indeed, the developmental context is routinely misread, leaving out the context of the experiences and time spent on digital, as well as face-to-face activities, interactions, existing inequalities, and changes inherent to development. As such, I proposed a novel framework to understand these contexts:

Digital capital exchange

Rather than treating screen time as a unified harm, this dissertation advances an “exchange”-based framework, grounded in James Coleman’s theories of youth capital and digital inequality scholarship, particularly following Eszter Hargittai, Jan van Dijk, and Alexander van Deursen (see this list of all dissertation references for full works).

The core proposition is that adolescents’ online engagement is not an alternative to developmental activity but another, albiet modern domain through which young people accumulate and mobilize online resources––particularly digital skills––that work alongside existing social networks and experiences to be exchanged for human capital (measured as: academic achievement, aspirations, STEM interest) and social capital (peer networks, community participation, extracurricular involvement).

Online time is not the mechanism; instead, it is digital skills that I find to be the most vital component in youth capital exchange and enhancement. Unstructured online engagement contributes to online skills; those skills, accumulated and mobilized alongside existing peer, family, and community networks, translate into the outcomes researchers and parents care about, i.e., academic achievement, aspirations, and face-to-face interaction and social networks.

This digital capital framework treats online and in-person contexts as complementary rather than antagonistic, and it situates adolescents’ digital lives within the structural conditions––connectivity quality, device reliability, autonomy of use––that determine whether exchange can occur at all.

Methods (in brief)

Paper-and-pencil surveys were administered to students in classrooms at two time-points: spring 2019 (N=2,876) and spring 2022 (N=2,949), across the same eighteen predominantly rural Michigan schools, grades 8–12. Official, nationally-ranked standardized reading, writing, and math test scores (PSAT 8/9, PSAT 10, SAT; College Board) were then anonymously linked to students’ survey responses with the help of participating districts.

Cross-sectional path analyses modeled pooled and wave-specific samples (pooled N=5,825); two-wave cross-lagged panel models tested reciprocal, longitudinal relationships on the 653 students who completed both surveys. Multi-group analyses of the cross-lagged panel models compared relationships between girls (N=345) and boys (N=308). All longitudinal models included time-invariant socioeconomic covariates as well as time-varying covariates to reduce omitted-variable bias.

Key findings: an overview

To summarize, to the best of my ability, eight chapters across 376 pages, I present two primary findings:

First: digital inequality predicted larger and more consistent declines in human capital than screen time did.

Unreliable home internet and technology maintenance problems––experiencing and/or dealing with broken or outdated devices and software, restrictive school-issued hardware, issues with connecting to or maintaining internet access––decreased youth GPA and standardized test achievement. And, these effect sizes were substantially larger than any negative direct effect from unstructured digital media use.

Across all four empirical studies, digital inequality emerged as the most substantial predictor of academic and developmental decline.

Second: digital skills mediated the relationship between online time and adolescent academic and social outcomes.

Unstructured digital media use, particularly online gaming and web browsing, predicted higher internet and social media skills for adolescents, which in turn predicted stronger academic achievement and self-efficacy (human capital), and social interaction and extracurricular participation (social capital). The positive indirect effect of screen time through skills offset or exceeded any small negative direct effects across several outcomes (supporting our existing peer-reviewed work: Hales & Hampton, 2025, and which you can read more about here).

These exchange processes were amplified when peer and family networks were modeled alongside digital skills, consistent with the premise that online and offline contexts operate together rather than in competition. The effect was not universal: social media skills amplified rather than offset a negative association with consistency of interest, one of the two subscales of grit. The exchange framework describes a contextual and conditional, domain-specific mechanism, not a blanket defense of time spent online.

Implications

If digital inequality, and not screen time, is the primary predictor of adolescent academic and developmental decline, and still warrants concern regarding access quality and experience even with the broader adoption of digital devices across the United States, the current policy emphasis on restriction is pointed at the wrong target. The evidence supports a different set of priorities.

Stable, reliable home (fast) broadband should be treated as an educational prerequisite rather than a consumer amenity. Unreliable connectivity exerted larger downward pressure on human capital than any measure of screen time, and that pressure intensified during the pandemic-era reliance on digital infrastructure. Technology maintenance, device repair, replacement, technical support, and the flexibility to install software and explore the web autonomously, matters as much as initial access, and school-issued devices that restrict autonomous use appear to hinder skill accumulation rather than support it.

Restrictive parental mediation of internet use was negatively associated with grit and self-efficacy at magnitudes comparable to the positive contributions of face-to-face activity. This challenges the assumption that digital restriction functions protectively. Instructive mediation, teaching adolescents to verify information, navigate platforms critically, and mobilize online resources toward meaningful ends, is the posture the data supports.

Finally, the technical skill-building that occurs through gaming, self-directed exploration, and deep web use is skill-building, not wasted time. Closing the persistent gender gap in these domains likely requires legitimizing technical play for girls, rather than restricting it for everyone.

None of the above is an argument that screen time is benign. It is an argument that screen time is the wrong focus, particularly when studied mostly in isolation. Context matters substantially, whether that is time spent on other activities during adolescence, the period of adolescence itself, digital inequality, resources gained from such online use, and how all such factors interact. The factor that predicts whether a given adolescent can convert online engagement into capital outcomes is structural: access, infrastructure, skills, and the autonomy to use them. These factors are distributed unevenly, and its uneven distribution, not hours logged, is what separates adolescents who thrive from those who fall behind.

The full dissertation is available through Michigan State University’s ProQuest archive, or see the embedded full-text PDF below. I’m happy to share papers, preprints, or the underlying framework with anyone interested and working in this area––don’t hesitate to reach out via my contact form. Thanks for reading.

Tags: distributed intelligence Internet of Things smart grid smart meters utilities

Are “Smart Meters” a Costly Diversion From the “Smart Grid” We Really Need?