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Smart meters and the data-directed grid.

Smart meters are physically located on the periphery of smart grids, but from a data perspective, they are at the heart of data-driven electric-energy distribution systems. At a time when computational resources are small, cheap, readily available, and easily deployed, smartness isn't the most salient smart grid feature, data-directedness is.

Grid-power distribution networks service large, growing, and highly dynamic loads with transmission--and distribution-infrastructure equipment that provide service lifetimes as long as 35 years. Such long operating periods paired with high equipment costs means that electric-energy providers must make equipment selection and deployment commitments in the context of timescales that far outstrip even the most far-reaching energy-demand forecasts. Consequently, with aging power infrastructure in much of the developed world, grids experience growing strain that can reduce customer power-supply reliability and provider-cost predictability.

Illustrative of this trend, the increase in US electricity demand has significantly outpaced population growth over the 60-year period from 1950 to 2010. This trend continues with some degree of mitigation in the last decade after significant efforts by the electronics industry to improve energy efficiency and campaigns to encourage energy-use awareness among end-users.

Annualized grid-utilization data, however doesn't tell the whole story. Electric energy demand peak-to-average ratios (PARs) have been climbing, according to the U.S. DOE's Energy Information Administration. For example, the New England region's peak electrical-energy demand hit 89 percent above its average in 2010 and Southern California peak struck 96 percent above its average in the same year (Table 1). Since then preliminary data suggest PARs beyond 2.00 in both areas.

Consequently, grid capacity has to exceed twice the average utilization. Basic economics have run in opposition to the old central-generation model of electric-power production. The alternative is distributed generation, requires active management that, at the network level, is driven by real-time geographically specific data. Intelligence on that topic derives from more spatially fine-grained sources, with smart meters being the obvious choice.

In the beginning ... or sometime thereafter

More than a century after the first true watt-hour meter entered the electric-utility equipment market in 1889, meter makers introduced the earliest electronic (non-electromechanical) meters. The first electronic models brought the promise of expanded functionality beyond simply totalizing energy use for monthly billing.

Electronic metering resulted from advances in integrated ADC designs and semiconductor processes that made single-chip multiple, synchronized ADCs possible. The ADC advancements also increased conversion rates and resolution sufficiently to satisfy utility metering requirements. Since then, those requirements have broadened as metering standards expanded to accommodate comparatively wideband artifacts. These terms have become increasingly important as the load profile moved away from tungsten lighting devices, which do not degrade the line power factor, and non-switching motor controls, for which simple capacitive power-factor correction suffices.

The proverbial last mile

Since metering technology has addressed the broader scope of measurements, the measurement function is not necessarily the primary challenge to smart meter designs. For example, deployments do not allow for a one-technology-serves-all approach to the communication link, so manufacturers must often take a modular approach, particularly in selecting physical media and communication protocols for last-mile service.

Utilities exploit a variety of communication technologies for data transfer and control signaling between the distribution system's head end and individual customer locations. As in other large scale point-to-multipoint networks, such as those for telephone, internet, or video services, utilities consider a variety of factors when choosing communication technologies for smart-meter deployments. These include terrain, population density, and access to various physical media. Available physical media and communication technologies include wire-line, fiber, radio mesh, and cellular networks. Notable is that these include both utility-proprietary and public-network infrastructure--an issue that affects implementation and operating costs as well as data security requirements. Some deployments take advantage of IEEE 802.15.4g Smart Utility Networks or EN 13757 M-Bus.

In locales where smart meter rollouts have enjoyed large market-penetration rates, smart meters provide energy use data logs between once and four times per hour. Aggregated data helps coordinate distributed power-generation capacity with demand in ways that reduce stress on transmission and distribution networks, where generation resource and load distributions allow. In this regard, intelligence gleaned from smart-meter data flows can dynamically drive smart-grid-resource allocations in real or near-real time. The fine granularity of temporal and geographic energy-use data coupled with sensor-based monitors on transformers and switches also allows utilities to maintain awareness of patterns of use; health of distribution-equipment; and status of solar, wind, and other renewable power-generation resources.

Failure to communicate

Given importance of smart metering in realizing the full value and capability of smart grids, the electric-power distribution sector needs to address technical marketing issues, not just technology issues. Customer perception has reached sufficiently low points as to engender ratepayer resistance to smart-meter deployments.

One issue, data security, is an oft-mentioned concern. Utilities frequently claim they use encryption methods similar to those employed for banking transactions. This, however, may offer little consolation to consumers who read with worrisome regularity of credit card data security breaches, such as those at Target and Home Depot or the massive data security failure at Sony. To those well steeped in data security practices, these examples may not be interchangeable with risks to energy use data, but to the average ratepayer, the distinctions are likely less than apparent.

Another issue, raised by a small, but loud cadre of RF-phobes is that of exposure to radio frequency energy emanating from smart meters. These groups often quotes WHO statements--that they've evidently not read--about RF exposure and its relationship to human health.

WHO states that current research literature reports no correlation between long-term exposure to low-level RF fields and human health problems.

Meanwhile, studies of smart meters that use RF links indicate that, when active, they emit no more RF energy than a cell phone--often less. Due to their low duty cycles, their transmitters are typically active for a total of no more than three minutes per day. Additionally, rarely does one see a homeowner or business operator with their head pressed against their electric-energy meter. Please send pictures if you do.

The behavior of technical markets, however, is not that different from nontechnical ones, where perception all too often is reality. The industry has done a poor job in answering either concern effectively, as frivolous as they may seem to insiders. Most of the utilities' statements I've found on these topics have appeared either on company or industry-association websites or in fliers like those that accompany monthly energy bills. Admittedly, mine has not been an exhaustive search on the subject, but what I've found thus far reminds me that if McDonalds only placed their advertising messages on hamburger wrappers, they'd still be looking forward to serving their first million.

Table 1: U.S. grid power: average, peak, and PAR data, 1993 through
2012 (Data source: Energy Information Administration, US Department
of Energy)

Region                PAR               Year            Trends

               Min    Max    2012   Min    Max           Avg

New England    1.52   1.89   1.78   1996   2011      [down arrow]
New York       1.56   1.83   1.75   2004   2006   [left right arrow]
Mid-Atlantic   1.52   1.82   1.74   2000   2006   [left right arrow]
Midwest        1.56   1.77   1.76   1994   2006   [left right arrow]
Southeast      1.63   1.78   1.75   2003   2005       [up arrow]
Texas          1.70   1.84   1.80   1994   2003       [up arrow]
Southern CA    1.76   1.96   1.84   2003   2010      [down arrow]
Northern CA    1.61   1.87   1.73   1994   2010      [down arrow]
Northwest      1.45   1.71   1.47   2003   2011   [left right arrow]
Min            1.45   1.71   1.47   Data source: US Energy
Max            1.76   1.96   1.84   Information Administration

Region                              Trends

                      Peak                 PAR

New England    [left right arrow]       [up arrow]
New York       [left right arrow]   [left right arrow]
Mid-Atlantic       [up arrow]           [up arrow]
Midwest            [up arrow]           [up arrow]
Southeast          [up arrow]       [left right arrow]
Texas              [up arrow]           [up arrow]
Southern CA       [down arrow]         [down arrow]
Northern CA       [down arrow]         [down arrow]
Northwest      [left right arrow]   [left right arrow]
Min            Data source: US Energy Information Administration
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Title Annotation:ON DESIGN
Author:Israelsohn, Joshua
Publication:ECN-Electronic Component News
Date:Feb 1, 2015
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