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The Case for AMI in the Public Power Sector – A Review Summary

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This item is part of the Special Issue - 2020-12 - Data Analytics & Intelligence, click here for more

Background:

Over the past two decades, the electric power industry has undergone substantial change due to market restructuring, ecological and climate pressures, and technology advancements. The evolution of the US electric power industry is tied to the evolution of electricity metering technology in a co-evolutionary process.  Changes in the industry have led to changes in metering technology and vis-a-verse – with the latter continuing to drive change to the industry. (For a detailed history on the evolution of electricity metering see (Strong, 2017))

The invention of automatic meter reading (AMR) in the 1970s shifted the focus of labor from manual meter readings to combine an electronic meter with a communication module.  AMR evolved into smart meters in the 1980s and 1990s as micro-processor technology improved, and data could be recorded in separate time intervals, as well as higher resolution.  These meters can be programmed to register schedules for use with time-varying and season-varying programs.  The most important change to these electronic meters in recent years has been the upgrade to function with two-way communication. [ (Capehart & Storin, 1983), (Sioshansi, 1991)]

Smart meters are the technology at the core of Advanced Metering Infrastructure (AMI).  Smart meters have the capability to not only measure electric usage, according to the previous industry standard, but can also collect, store, and communicate usage, as well as other data (such as voltage, reactive power, and current), and at more frequent intervals, which include alerts that notify the utility of an outage or potential issue. (US DoE OEDER, 2016)

As the smart meter technology has evolved, so has the AMI technology landscape.  From enhancements on network architectures [ (Bikmetov, et al., 2015), (Mendi, Akinc,, Başalan, Atli, & Civelek, 2019), (Huh & Seo)], communication strategies [ (Chen, Lukkien, & Zhang, 2010), (Ghasempour, 2016), (Vaidya, Makrakis, & Mouftah, 2012)], data architectures for optimized data mining  [ (Foreman & Pacheco, 2017), (Marimuthu, Devaraj, & Srinivasan, 2017)], and data analytics strategies [ (Kojury-Naftchali, Fereidunian, & Lesani, 2017 ), (Chelmis, Kolte , & Prasanna, 2015), (Alahakoon & Yu, 2013)], a robust set of literature (of which this is merely an extract) exists on advancements to the field of AMI and shows that these technologies are expanding into and adding benefit to a global community.

Clear Challenges for AMI Installations:

While smart meters and AMI systems have been present in the US since approximately 2006—and indeed have enjoyed support from such federal grants as the Smart Grid Investment Grant program [ (Department of Energy (DoE), 2019)] under the American Recovery and Investment Act of 2009 [ (US DOE EPA , 2009)] and the National Action Plan for Energy Efficiency [ (US EPA, 2008)]—there are still significant challenges to the utility sector, even with the advancements to the science and engineering of smart meters and AMI.  To date, there are 94.8 million smart meter installations in the US, of which 88 percent are residential.  This is just over 50 percent of US households.

Key challenges that may affect this low deployment rate include:

Costs - According to [ (Strong, 2017)], smart meters have four major elements of cost: expense, privacy, security and health. 

  • Expense: Smart meters must be part of an AMI system to realize their full potential.  To fully deploy AMI, it is estimated that more than half of spending will go to smart field devices and communications networks, while an increasing percentage will be spent on control equipment, control center, and substation-based software and platforms.  For utilities with small-scale metering (e.g., under 750,000 meters), the cost of back-office software systems can sometimes outweigh the benefits. (US DoE, 2018)  However, maximizing the value of AMI investments through multiple functions and non-metering capabilities may be crucial to justifying the costs.  Smart meters can serve as a technology platform on which to expand grid modernization and can do so at a relatively small marginal cost, such as through upgrading of communication networks for non-metering purposes. [ (Levy, Herter, & Wilson, 2004), (US NETL, 2008), (MITEI, 2011)] 

Results from the Department of Energy (DoE) Smart Grid Investment Grant Program show costs per meter ranged from $130 to $1895.  Of the 77 projects analyzed, only six reported a total installation cost above $600/meter. (US DoE OEDER, 2016)

  • Privacy: Smart meters record consumption data.  Some consumers may be uncomfortable with utilities, or third parties, having access to their detailed usage data.  Such information can signify patterns of presence or absence in the home.  Utilities need to be aware of the sensitivity of Personally Identifiable Information (PII), defined as “information that can be used to distinguish or trace an individual’s identity, either alone or when combined with other personal or identifying information that is linked or linkable to a specific individual.” [ (US GSA, 2019)]
  • Security: The National Institute of Standards and Technology (NIST), as the national coordinator for smart grid technology standards and cybersecurity guidelines, is collaborating with relevant industry stakeholders (e.g. meter manufactures, technology providers, utilities) to produce secure meters and networks.  The North American Electric Reliability Corporation (NERC), whose mission is to ensure grid reliability, has also issued cybersecurity standards, and the American Recovery and Reinvestment Act of 2009 also increased cybersecurity measures through its smart grid programs. [ (US NETL, 2008), (Edison Electric Institute (EEI), 2011), (MITEI, 2011)]
  • Health: Smart meters that use wireless transmission for communication, leading to RF exposure, can potentially impose health costs.  Scientific studies have shown, however, that RF exposure from smart meters is negligible.  Smart meters fall under a low power, unlicensed category, similar to wireless Internet routers.  They also emit substantially less RF exposure than cell phones. RF exposure from smart meters, if it occurs at all, has been shown to be well within legal limits. [ (US NETL, 2008), (Edison Electric Institute (EEI), 2011)]

Policy: According to DoE AMI in Review Report [26], smart meters and AMI lack a standard set of processes and procedures for procurement and implementation.

  • AMI is different than other utility investments and is an early indicator of how the review process is changing. AMI is part of a suite of technologies and has the potential to provide a foundation for a future that can enable new products and services.  To enable that future grid, AMI is often proposed to replace functioning equipment. Realizing the potential value streams depends on how AMI is implemented, the capabilities of other utility systems, and how the utility proposes to use or allow access to the data.  These factors affect how an AMI investment is reviewed or assessed and provide a preview of the changing nature of utility investments, which have identifiable costs but uncertain future benefits.  Commissions and advocates will have to increasingly grapple with greater uncertainty in benefits and outcomes.
  • The view of AMI’s role as a foundational technology can be significant. The degree to which the commission and advocacy groups agree on the timing of a transition to a more distributed energy and consumer-centric electricity system has an impact on the review of an investment such as AMI.  Considered as both a meter with multiple uses cases and an enabler of new products and services, the business case is more multi-faceted and has differing elements from other utility investments.
  • There is no standard regulatory template for AMI applications.  Currently, proposals presented to regulatory commissions for investment in AMI systems do not follow a standard template or format.  The use cases identified in [ (US DoE OoE, 2020)] showed that proposals ranged from rate cases to grid modernization proposals to stand-alone applications.  The diversity of regulatory approaches reflects both the diversity of state regulatory environments and the differing strategies of the utilities involved.

Key Benefits to Utilities:

  • The development of the smart grid. A smart grid combines information and communication technology with sensing and control technology applied to the power grid in order to increase the economic efficiency and physical reliability of electricity supply.  The essence of the smart grid, synonymous with grid modernization, is the use of digital technologies allowing situational awareness through micro-level visibility of grid operations.
  • Together with smart meters, AMI lets utilities offer value-added services to consumers, such as direct usage feedback, flexible tariffs, and smart-home applications.  AMI also adds value by detecting tampering, identifying and isolating outages, monitoring voltage, eliminating the need for manual truck rolls and labor to read meters, connect/disconnect service, and diagnosing several meter issues.  This results in lower outage costs and fewer inconveniences for customers, few customer complaints and the ability to resolve billing disputes faster. (IBM, 2020)
  • Real-time monitoring and optimal decision making through automated control of the power grid.  Sensors placed along the distribution grid, for example, can detect power outages and associated automation controls can re-route power in order to minimize the number of consumers affected.  Another important aspect of the smart grid is the integration of intermittent and distributed generation and storage resources, often customer-owned, onto a transactive grid. [ (US NETL, 2009), (Joskow, 2012)]
  • Demand response programs for load control or peak shaving.  Defined as changes in electricity consumption in response to changes in electricity prices over time.  Mechanisms for changing consumption include incentive-based programs, such as direct load control or interruptible rates, as well as price-based programs, such as time-of-use or real-time pricing.  Times of high demand and stress on the electric power grid, the peak load problem, motivates demand response programs.  The costs include the necessary metering infrastructure, other enabling technologies, and management of demand response programs.  The benefits include bill savings, avoided infrastructure costs, improved reliability, and reductions in market power. [ (US FERC, 2016), (Albadi, 2008)]
  • According to the DoE SGIG Program results, utilities that implemented customer behavior studies (CBS) showed a reduction in customer peak demand by up to 23.5 percent.  Customers who participated in CBS reduced average demand by 30 percent with Critical Peak Pricing (CPP) and 29 percent with Critical Peak Rebates (CPR).

Conclusions:

Smart meters and advanced metering infrastructure have both an interesting history and a bright future.  Their role is deeply integral to a fully connected and modernized, low-carbon electricity grid.  A few key takeaways from the information presented are:

  • Inconsistent implementation results have increased review scrutiny. The value that can be achieved from AMI varies.  There are utilities that have deployed AMI and are realizing benefits for customers and across the utility enterprise.  There are also examples of utilities that have not achieved the benefits included in the business case or are using AMI solely to measure consumption and generate billing. 
  • AMI deployment supports utilities core values. Safety, reliability, and affordability can achieve substantial grid impacts and benefits for customers and utilities.
  • AMI installations are not without cost. However, utilities that deployed partial-scale implementations generally had a lower total cost per meter.
  • AMI is a big project that needs a multidisciplinary team with executive support. AMI is one of the first milestones towards the modernized grid and the potential to both revolutionize electricity grid operations and transform the relationship between utilities and their customers.   This also means that an AMI investment will create a paradigm shift across the organization and involve more departments than just metering.  Ensuring maximum benefits requires cross-departmental conversations to consider future scenarios and opportunities.  AMI needs to become an integral part of the utilities vision

References

Alahakoon, D., & Yu, X. (2013). Advanced analytics for harnessing the power of smart meter big data. IEEE International Workshop on Intelligent Energy Systems (IWIES), (pp. 40-45). Vienna. doi:10.1109/IWIES.2013.6698559

Albadi, M. H.-S. (2008). A Summary of Demand Response in Electricity Markets. Electric Power Systems Research, 78(11), 1989–1996. doi:10.1016/j.epsr.2008.04.002.

Bikmetov, R., Raja, M. Y., Kazi, K., Ibragimov, R., C. B., & Enslin, J. (2015). A perspective of RoF implementation for an advanced Smart Metering Infrastructure. North American Power Symposium (NAPS) (pp. 1-4). Charlotte: NAPS. doi:doi: 10.1109/NAP

Capehart, B. L., & Storin, M. O. (1983). Technological Advances in Metering. In Innovative Electric Rates: Issues in Cost-Benefit Analysis. (S. V. Berg, Ed.) Lexington, USA: Lexington Books.

Chelmis, C., Kolte , J., & Prasanna, V. (2015). Big data analytics for demand response: Clustering over space and time. International Conference on Big Data (Big Data) (pp. 2223-2232). Santa Clara: IEEE. doi:10.1109/BigData.2015.7364011.

Chen, S., Lukkien, J., & Zhang, L. (2010). Service-oriented Advanced Metering Infrastructure for Smart Grids. Asia-Pacific Power and Energy Engineering Conference (pp. 1-4). Chengdu: Asia-Pacific P&EE Conf. doi:doi: 10.1109/APPEEC.2010.5448792.

Department of Energy (DoE). (2019). Recovery Act Deployment Status. Retrieved November 20, 2020, from Smartgrid.gov: https://www.smartgrid.gov/recovery_act/deployment_status/

Edison Electric Institute (EEI). (2011). Smart Meters and Smart Meter Systems: A Metering Industry Perspective. Washington D.C.: EEI. Retrieved from https://www.cpsenergy.com/content/dam/corporate/en/Documents/SmartGrid/A...

Foreman, J., & Pacheco, F. (2017). Aggregation architecture for data reduction and privacy in advanced metering infrastructure. Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) (pp. 1-5). Quito: IEEE PES. doi:10.1109/ISGT-LA.2017.8126704.

Ghasempour, A. (2016). Optimized advanced metering infrastructure architecture of smart grid based on total cost, energy, and delay. IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1-6). Minneapolis: ISGT. doi:10.1109/ISGT.2016.7781250

Huh, J. O., & Seo, K. (n.d.). Advanced metering infrastructure design and test bed experiment using intelligent agents: focusing on the PLC network base technology for Smart Grid systems. Journal of Supercomputing, 72, 1862–1877. doi:https://doi.org/10.1007

IBM. (2020). Digitizing Electric Utilities - Whitepaper. International Business Machines. Retrieved from https://www.ibm.com/thought-leadership/institute-business-value/report/d...

Joskow, P. L. (2012). Creating a smarter U.S. Electricity Grid. Journal of Economic Perspectives, 26(1), 29-48. doi:10.1257/jep.26.1.29

Kojury-Naftchali, M., Fereidunian, A., & Lesani, H. (2017 ). AMI data analytics; an investigation of the self-organizing maps capabilities in customers characterization and big data management. Smart Grid Conference (SGC) (pp. 1-6). Tehran: IEEE. doi:10.1109/SGC.2017.8308844

Levy, R., Herter, K., & Wilson, J. (2004). Unlocking the Potential for Efficiency and Demand Response through Advanced Metering. Berkley: Lawrence Berkeley National Laboratory. Retrieved from http://www.osti.gov/scitech/servlets/purl/890624

Marimuthu, K. P., Devaraj, D., & Srinivasan, S. (2017). Development and implementation of advanced metering infrastructure for efficient energy utilization in smart grid environment. International Transactions on Electrical Energy Systems, 28(3). Retrieved from https://doi.org/10.1002/etep.2504

Mendi, Y. M., A. H., Başalan, İ., Atli, D., & Civelek, Y. (2019). Design and Implementation of Smart Meters with Hybrid Communication System Architecture. IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), (pp. 1-5). Bucharest: IEEE. doi:10.1109/ISGTEurope.2019.8905774

MITEI. (2011). The Future of the Electric Grid. Cambridge, MA: Massachusetts Institute of Technology Energy Initiative. Retrieved November 20, 2020, from http://energy.mit.edu/wp-content/uploads/2011/12/MITEI-The-Future-of-the...

MITEI. (2011). The Future of the Electric Grid. Cambridge, MA: Massachusetts Institute of Technology Energy Initiative. Retrieved from http://energy.mit.edu/wp-content/uploads/2011/12/MITEI-The-Future-of-the...

Sioshansi, F. P. (1991). Electronic Metering and Two-Way Communications: The Electric Power Industry . Utilities Policy, 1(4), 294–307. doi:doi:10.1016/0957-1787(91) 90070-l

Strong, D. R. (2017). The early diffusion of smart meters in the us electric power industry. Greensboro: University of North Carolina. Retrieved November 20, 2020, from https://osf.io/axfqe/download

US DoE. (2018). Smart Grid System Report. Washington D.C.: US Department of Energy. Retrieved November 20, 2020, from https://www.energy.gov/sites/prod/files/2019/02/f59/Smart%20Grid%20Syste...

US DOE EPA . (2009, February 17). Retrieved November 20, 2020, from Congress.gov: https://www.congress.gov/111/plaws/publ5/PLAW-111publ5.pdf

US DoE OEDER. (2016). Advanced Metering Infrastructure and Customer Systems, Results from the Smart Grid Investment Grant Program. Washington D.C.: US Department of Energy Office of Electricity Delivery and Energy Reliability. Retrieved November 20, 2020, from https://www.energy.gov/sites/prod/files/2016/12/f34/AMI%20Summary%20Repo...

US DoE OoE. (2020). Advanced Grid Research, AMI In Review – Informing the Conversation. Washington D.C.: US DoE Office of Electricity. Retrieved November 20, 2020, from https://www.smartgrid.gov/files/documents/AMI_Report_7_8_20_final_compre...

US EPA. (2008). National Action Plan for Energy Efficiency Vision for 2025: A Framework for Change. Washington D.C.: US EPA. Retrieved November 20, 2020, from https://www.epa.gov/sites/production/files/2015-08/documents/vision.pdf

US FERC. (2016). Assessment of Demand Response and Advanced Metering. Washington D.C.: Federal Energy Regulatory Commission . Retrieved from https://www.ferc.gov/sites/default/files/2020-05/DR-AM-Report2016.pdf

US GSA. (2019, August 10). CIO GSA Rules of Behavior for Handling Personally Identifiable Information (PII). Retrieved from GSA Directive CIO P 2180.2 - an appendix of OMB M-10-23 (Guidance for Agency Use of Third-Party Website and Applications): https://www.gsa.gov/reference/gsa-privacy-program/rules-and-policies-pro...

US NETL. (2008). 2008. Advanced Metering Infrastructure. Modern Grid Strategy. Pittsburgh: National Energy Technology Laboratory. Retrieved from https://netl.doe.gov/sites/default/files/Smartgrid/AMI-White-paper-final...

US NETL. (2009). A Vision for the Smart Grid. Modern Grid Strategy. Pittsburgh: US National Energy Technology Laboratory . Retrieved from http://www.netl.doe.gov/File%20Library/research/energy%20efficiency/smar...

Vaidya, B., Makrakis, D., & Mouftah, H. (2012). Secure multipath routing for AMI network in Smart Grid. IEEE 31st International Performance Computing and Communications Conference (IPCCC) (pp. 408-415). Austin: IEEE. doi:10.1109/PCCC.2012.6407784.

Elizabeth Massey's picture

Thank Elizabeth for the Post!

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Matt Chester's picture
Matt Chester on Dec 2, 2020

Elizabeth-- can you highlight what some of the differences in implementation might be for public power utilities vs. IOUs or others? 

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