Consortium White Papers, Articles and Notes
New Online Benchmarking Widget Motivates Utility Customer “Smart” Decisions
August 31, 2020
An Innovative Web Benchmarking Tool Applies a 7+ Million US Utility Customer Energy
Use Database to Motivate Customer Energy and Peak Savings
Current building/population benchmarking comparisons, which are applied mostly at larger investor-owned-utilities, report as much as a 2 percent electricity use reduction impact from residential benchmarking. There is some question though about how accurate these claims are since lighting and air conditioning efficiency standards continue to decrease electricity use over time as existing incandescent light bulbs and AC units are replaced by higher efficiency equipment.
However, even if building/ population estimates of a 2 percent reduction in electricity use are reasonably accurate, this reduction is hardly a significant achievement considering that NREL estimates cost-effective residential electric savings potentials of about 20 percent. In addition, Department of Energy data show average 2018 residential DR program peak kW savings of 1 percent (for utilities that reported DR savings) and efficiency kWh savings less than ½ percent.
This paper identifies shortcomings of currently available benchmarking tools and introduces the MAISY Energy Apps Utility Customer Benchmarking Widgets developed to overcome these shortcomings.
Widget buttons or links are installed on utility Web sites with a few lines of coding. Widgets pop up on the utility Web sites but are housed on our servers so no maintenance is required to provide continuous support to utility customers. All background analysis and data support is provided by the Smart Grid Research Consortium/Jackson Associates. At the completion of the Widget benchmarking analysis control is returned to the utility Web site at the end of the process.
Widget support includes optional Web pages with energy efficiency/DR suggestions customized to each user session. The widely-used and continuously updated Jackson Associates 7+ million MAISY Utility Customer and Energy Use Databases provides a statistically reliable and accurate source of ZIP area comparisons. Widgets:
- Install easily as a button or link on utility Web sites
- Provide utility customers with a comparison of their electricity costs and carbon emissions with similar customers in their ZIP code.
- Provide the most accurate benchmarking evaluations by matching utility customer dwelling unit, occupancy, appliance and other characteristics to more than 7 million customers in the widely-used MAISY Utility Customer Energy Database.
- Show utility customer cost savings and sustainability reductions based on new target energy use reductions
- Increase utility customer satisfaction with one of the lowest cost outreach programs available
- Do not require utilities to provide customer billing or other information
- Provide automated utility email reports on each user session
- Reduce system peak by emphasizing recommendations that reduce peak period electricity use
- Increase utility program visibility
- Provide both residential and commercial benchmarking
- Provide electric only or electric and natural gas for combination utilities
Widgets are designed to increase customer satisfaction by providing customized customer information regarding their electricity use and carbon emissions.
Click here to download the PDF White Paper: New Online Benchmarking Widget Motivates Utility Customer “Smart” Decisions
The Widget is implemented on utility Web sites will several lines of coding. Click here to access Widget demonstration sessions.
New Study Shows SCE Electric Vehicle Virtual Power Plant Eliminates Residential Peak Demand and Saves Each EV Customer $560/Year
October 16, 2019, Preface added January 2021
As little as a 10% EV market saturation eliminates residential peak period demands
Industry experts agree that recent technology cost reductions will significantly increase the percentage of electric vehicles (EVs) in the coming decade. Deloitte forecasts a tipping point in 2022 where the cost of ownership of an EV will reach parity with the cost of an internal combustion engine vehicle. LMC Automotive, a consulting firm that provides market forecasts estimates that EVs will make up 30 percent of the new car market sales by 2030.
Clearly a significant technological transformation is taking place. Utility pilot programs show that potential recharging kW spikes at the end of daily commutes can be distributed with managed charging to shift recharging demands across nighttime hours.
This study analyzed data on 5,000 individual SCE utility customers commuting data and battery reserves after afternoon commuting to simulate the ability of a VPP to clip the residential sector peak while constraining EV overnight recharging to avoid an overnight peak.
Study results show that, at a market share of just 10 %, combining EV battery electricity supply to the grid in peak period hours along with managed overnight charging can provide an EV virtual power plant (VPP) that completely shaves residential peak demands. Savings are estimated at $560 per EV customer, even after accounting for the cost of recharging the EV battery.
Expected rapid growth of the EV market along with the significant benefits shown here of even limited EV market saturation highlight the urgency of developing appropriate utility programs, EV technologies and regulations to take advantage of this new VPP resource.
Chart and graphs in the white paper
It's Time for Utilities to Plan for Disruptive Solar PV Impacts
July 28, 2015
Paper Describes Dramatic PV Growth, Utility Operational and Business Model Impacts
Abstract: This white paper details the dramatic growth in residential solar PV systems and discusses utility operational and business model challenges associated with these technologies. The paper describes factors that explain past and future growth in PV installations including forecasts from the Consortiums Utility Solar PV Forecasting Model that shows a 60 percent nationwide increase in residential solar PV over the next year-and-a-half.
The paper illustrates how the clustering of residential solar PV installations on individual circuits and rapid market growth can create power quality issues for utilities whose system-wide PV saturation is still quite small. These power quality and control issues have already occurred at utilities in California, Hawaii, Arizona and other states, and, with the rapidly increasing saturation of PV installations over the next several years, will soon become a common problem for many utilities.
Statistics on US installations are presented by year from 2008 through 2016 along with 15-year historical trends in average residential PV installation costs. Detail on PV electric bill savings for 40 US cities and current best-practice installation costs by state reveal geographic detail. Finally, system economic analysis is provided including paybacks for cost/savings combinations and total value of PV systems over 10 years including the capitalized value that owners realize when they sell their homes.
Taken together, this information suggests a more rapidly developing residential solar PV market than generally anticipated and underscores the need to begin addressing the impacts of residential solar PV installation growth on both utility operations and utility business model issues including revisions of rate structures to more accurately reflect costs and benefit associated with increasing distributed solar PV installations.
A unique and important contribution of this paper is that it presents market information that is both intuitive and detailed enough that readers can draw their own conclusions regarding the growth and timing of solar PV-related issues in their own utility service areas.
PV market trends shown in the paper indicate that most utilities should begin an assessment of the impacts of likely future distributed solar on distribution systems power quality and other operating issues along with an evaluation of PV impacts on revenue, cross subsidization, rate restructuring and other utility business model issues. The Consortiums Solar PV Forecasting Models and Forecasting Services are designed to provide this utility planning support with analysis that applies utility rates, electric PV kWh and bill savings, dwelling unit, household, and neighborhood characteristics, utility policies and other factors providing forecasts over ten years. Installation and load impact forecasts are provided for distribution feeders, substations, ZIP areas, and the entire utility service areas. Low, medium and high forecasts are provided to reflect the range of likely PV installation and load impacts. Additional information on the Models and Service is available at
Click here to download the PDF White Paper: It's Time for Utilities to Plan for Disruptive Solar PV Impacts
Developing a Timely, Cost-Effective Customer Engagement Demand Response Strategy
January 27, 2015
A Roadmap for Utilities with AMI and Older AMR/Electromechanical Metering Systems
Abstract: A recently completed Smart Grid Research Consortium (SGRC) study of utility customer engagement demand response (DR) programs identifies new technologies and opportunities for utilities with both AMI and older AMR and electromechanical metering systems. This information is applied to develop a customer engagement DR roadmap applicable to all utilities.
Newer programs provide avenues for utilities with older metering systems to capture DR benefits and provide interested customers with the most important benefits of an AMI-based system. For example, programmable communicating thermostats that communicate with the utility via WiFi and the internet provide nearly all the functionality of AMI-based systems. Interestingly, these programs can be extremely cost-effective with their ability to target high-value customers.
Many customer engagement programs can significantly boost returns by revisiting objectives and revising technology and program choices to more effectively match top down requirements and bottom up capabilities.
The Consortiums customer engagement DR roadmap includes four steps:
1. Develop and continually revise specific customer engagement DR objectives. Use these objectives to guide activities in step 2.
2. Evaluate, identify and initiate programs/technology applications that can most cost-effectively meet the objectives in step 1. A sample of these items includes:
a. Identify potential DR end-use targets (e.g., AC, water heating) based on contributions to system load reductions, required incentives and avoided costs characteristics
b. Design programs to recognize customer segment wants and needs and likely responses
c. Select appropriate, cost effective technology enablers (hardware and software)
d. Consider both in-house and turnkey solutions
e. Use social media, target marketing, messaging, PR and promotional activities
f. Consider newer, innovative technology applications, program designs and experiences at other utilities
g. Carefully identify and evaluate supporting data and analytics requirements. Gathering and processing more customer data than is required can make some of these programs uneconomical
3. Reconcile objectives and applications (steps 1 and 2); calculate costs and benefits including preliminary vendor costs. Consider results with various program participation and impact assumptions, prioritize program/technology applications
4. Proceed with program development including vendor evaluations, RPF development, proposal evaluation, vendor interviews. Revisit steps 1-3 with this information and adjust the strategy as appropriate. Develop a timeline to ensure a timely program development and implementation schedule. Timeliness is important as delays in developing and implementing programs bypass savings that can never be captured.
Click here to download the 4-page PDF White Paper: Developing a Timely, Cost-Effective Customer Engagement Demand Response Strategy
White Paper: 7 Reasons Why Smart Grid Investments Fail
July 29, 2014
New Study Identifies Pitfalls and Recommendations
This Smart Grid Research Consortium (SGRC) research report cautions electric cooperatives and municipal
utilities about pitfalls in achieving expected returns on smart grid investments.
For years, industry publications have touted smart grid cost-benefit study results that found smart grid investments more than paying for themselves with reduced utility costs. Smart grid investments seem like the perfect new technology application, transforming utility business practices, provide grid control capabilities that improve efficiency, provide enough cash flow to cover interest and principal payments and even give some rate relief. Those results can often be achieved if utility and customer characteristics are right, if smart grid investment strategies are designed appropriately and if implementation proceeds as planned.
However results at a growing number of utilities show that these conditions are often not met requiring unanticipated rate increases to make up for shortfalls in realized savings. The report identifies seven important reasons for disappointing smart grid investment returns including:
1. Vendor/integrator business case analysis
2. Absence of risk analysis
3. Failure to quantify unique utility and customer characteristics
4. Subjective system integrator/prime contractor selection
5. Software performance failures
6. Inadequate post-AMI implementation strategies
7. Insufficient utility due diligence
Each of the seven pitfalls is described in detail in the report along with recommendations to avoid each problem. The paper concludes with recommendations to fast-track certain smart grid benefits.
One of the interesting findings in our study was that many utilities who fail to achieve ROI targets are also failing to take advantage of opportunities to significantly improve smart grid investment returns. Traditional cautious utility approaches are unnecessary and detrimental to financial outcomes for certain smart grid initiatives. For example, the EPRI Guidebook for Cost/Benefit Analysis of Smart Grid Demonstration Projects (December 2103) suggests that after the VVO/CVR system is installed and tested, the efficacy of CVR will be examined through two years of day-on/day-off operation that will provide data to feed a regression analysis.
However, information from smart meters can be used in day-ahead experiments and real-time applications to fine-tune CVR applications as soon as smart meters begin transmitting information, two years in advance of the EPRI recommendation. Two years of CVR savings can be enough in some cases to pay one-third to one-half the cost of the AMI system that is providing this information. Similarly, delayed implementation of customer engagement programs dilute savings as these benefits remain unrealized long after they could be effective.
This last observations suggests that utilities who have embarked on smart grid projects should reassess post AMI project development and implementation plans as the project proceeds.
Download the 4-page PDF White Paper: 7 Reasons Why Smart Grid Investments Fail
Low-Cost CVR May Pay for Your AMI System
February 6, 2014
New Study Turns Traditional Smart Grid Business Case Analysis on its Head
A recently-completed Smart Grid Research Consortium (SGRC) study identifies a new smart grid investment
strategy that can transform a poor AMI business case into an attractive investment.
Many electric cooperatives and public utilities have rejected AMI systems because expected meter-related
benefits are not compelling enough to outweigh costs. Adding demand response savings boosts benefit-cost
ratios; however, the uncertainty and long lead times surrounding these customer engagement programs add
more risk. Adding distribution automation (DA) benefits and costs including customer valuations of
improved reliability provide added costs and benefits but leaves utility decision-makers skeptical.
This new SGRC study shows that the combination of AMI and low-cost conservation voltage reduction enabled with smart meters can provide a compelling business case for many of these utilities with little risk.
This study turns the traditional smart grid business case analysis approach on its head: instead of viewing AMI as the foundation, then adding demand response and then distribution automation benefits and costs, the analysis started with a joint AMI/low-cost conservation voltage reduction (CVR) strategy as the foundation for the business case. The low-cost CVR provides significant benefits and, because it is enabled with smart meter data, more than makes up shortcomings in the stand-alone AMI business case for many utilities without going on to more speculative smart grid benefits. A significant advantage of this new strategic approach is that costs and benefits of individual AMI and CVR elements can be determined with considerable certainty prior to initiating the project. In addition, the low-cost CVR component can be developed simultaneously with the AMI implementation avoiding the long delays that many utilities are experiencing with customer engagement infrastructure development. The CVR strategy requires utility distribution information including some voltage-demand experiments; however, this information is inexpensive to collect and analyze with our Smart Grid Investment Model.
The CVR strategy considered here is low cost, averaging about $15,000 per feeder for controls, communications and installation with no new investments voltage regulators or capacitor banks. This CVR strategy uses smart meters for voltage metering, retrofitted controls and communications to existing feeder equipment, where appropriate, and lowers and tightens grid voltage control at during peak periods.
This study and its implications for utilities are noteworthy for six reasons:
Study analysis is based on results from a recently completed SGRC CVR study conducted for an
electric cooperative utility and data on electric coops and municipal utilities drawn from existing
CVR, and other smart grid pilot studies and implementations.
This paper includes summary results of the new study for a generic electric cooperative utilizing the Consortiums Smart Grid Investment Model along with a description of the Consortiums AMI/low-cost CVR applications assessment and implementation services.
Who Will Control Your Customers' Thermostats and what are the Implications for Your Rates?
December 2, 2013
Selected Results from a Smart Grid Research Consortium Study of Programmable Communicating Thermostat Programs
Coops and public utilities can potentially reap large savings with new programmable communicating thermostat (PCT)
programs -- while
ignoring PCT opportunities exposes customer relationships to third-party providers whose initiatives may increases
in customers' rates.
Its time to reevaluate residential programmable communicating thermostat (PCT) programs, regardless of whether a program is already in place. Big changes have taken place recently in PCT technologies and programs. Compared to several years ago:
- PCT costs have dropped dramatically with PCTs that provide basic control functionality available for less than $100
- PCT functionality has increased including capabilities such as provision of HVAC maintenance diagnostics, and voice recognition,
- Control strategies have become more sophisticated and individualized to individual dwelling units and address bounce-back and other program complications, and
- Many PCTs do not require an AMI infrastructure.
Nearly all utilities can develop PCT programs that provide utility and customer value and many utilities and their
customers can potentially reap large savings with these technologies.
These PCT advances also define a huge mass market potential for third-party PCT program providers as
evidenced by the growing number of companies in this space.
While utility/third-party PCT provider relationships can provide net benefits to a utility and its
customers as described later in this paper, utilities face a risk that third-party relationships with
customers will increase customer rates. Impacts differ by utility depending on power cost characteristics,
customer rate structures and customer characteristics; however, customer rates will increase if participating
customer bill savings are greater than utility avoided power costs. Participating customers will see bill
reductions while other customers will face increased rates and monthly bills.
On the other hand, third-party providers of PCT services when can potentially provide more value for
participating customers and the utility than can be provided by the utilitys own program and still make a profit.
The advantage of in-house versus utility/third-party provided PCT programs depends on a variety of factors discussed
in a later section.
The remainder of this article incudes a brief overview of newer PCT technology and control strategy characteristics along with an evaluation of several of the factors that impact utility PCT program potential using the Consortiums Smart Grid Investment Model. Utility-provided versus third-party PCT program considerations are discussed in the final section of this paper.
Book Chapter: Smart Grids: An Optimized Electric Power SystemFuture Energy: Improved, Sustainable and Clean Options for our Planet, Elsevier Science; 2nd edition (January 7, 2014)
Jerry Jackson, Leader and Research Director, Smart Grid Research Consortium
Abstract: The smart grid reflects the most exciting paradigm change to impact the electric power system since its beginnings more than a century ago. Smart grids apply new metering, communications and control technologies and strategies to provide an optimized power system that integrates distributed energy resources and electric customer participation in maximizing power system efficiency and reliability. Smart grids will also contribute to achieving energy efficiency, conservation, power plant emissions goals. While the smart grid concept can be described relatively easily, the transition to smart power grids presents financial evaluation challenges that are unique to these new technologies and applications.
White Paper: Score Your Smart Grid IQ (Investment Quotient), August 31, 2011
Abstract: Within a decade every utility will have incorporated at least some aspects of smart grid technologies in their distribution system. While several rating systems benchmark utility success at achieving smart grid functionality none evaluate the effectiveness of the investment planning process required to achieve the most cost-effective investment strategy. The Smart Grid Research Consortium was formed at Texas A&M University in 2010 and established as an independent Consortium in January 2011 to support electric cooperative, municipal and other public utility smart grid investment analysis. The Consortiums experience developing and applying the Smart Grid Investment Model at 15 member utilities provides the basis for the Smart Grid IQ test presented here.
The objective of the scorecard presented in this white paper is to assist utilities in evaluating their current smart grid investment analysis and planning process. The ideal investment/planning framework, reflected by a score of 100, is capable of identifying specific technologies and programs that best meet utility financial requirements while considering unique infrastructure and customer characteristics.
For utilities who have not yet started the smart grid investment process, the scorecard provides guidance on issues to consider when developing in-house investment analysis/planning capabilities or when engaging consultants.
We have drawn on our experience developing and implementing the Smart Grid Investment Model at fifteen utilities since 2010 to provide this evaluation process, said Dr. Jerry Jackson, Leader and Research Director of the Smart Grid Research Consortium. This is the first benchmarking system to assist utilities in evaluating their approaches to this complex investment problem.
Smart Grid IQ scores are compiled in six categories including: AMI/DA Investment/Planning Scope, Customer Engagement Investment/Planning Scope, Other Financial Items, Utility Customer Detail, Investment Analysis Quantitative Framework, and Ease of Use/User Interface/ Results Presentation.
White Paper: The Utility Smart Grid Business Case: Problems, Pitfalls And Ten Real-World Recommendations, August 3, 2011.
Studies published over the past several years report impressive returns on smart grid investments. However,
these studies reflect cost/benefit evaluations and models that, for many reasons, cannot be applied directly
to evaluate individual utility investments. The lack of a standard, commonly accepted utility-level cost/benefit
framework has led to a number of utility smart grid investment analysis approaches that poorly serve utility decision-makers.
This paper describes the challenge utilities face in developing comprehensive investment strategies and identifies difficulties associated with several common approaches to smart grid investment analysis. The final section presents ten investment analysis recommendations based on the Smart Grid Research Consortiums cost/benefit model that has been applied at 15 utilities. These recommendations are offered both to guide utility in-house analysis and to assist utilities in evaluating smart grid analysis undertaken by vendors and consultants.
Article: Evaluating Smart Grid Investments at US Cooperative and Municipal Utilities
Abstract: A twelve-member utility Smart Grid Research Consortium was formed in May 2010 to assess the state of smart grid investment analysis at US cooperative and municipal utilities and to develop a Smart Grid Investment Model for each individual member utility. The study was completed in January 2011 with delivery of the model to each utility. This article presents the results of the Consortiums nationwide survey of utility smart grid activity, describes smart grid investment modeling issues and modeling objectives pursued in the Consortium project and presents several observations based on Consortium smart grid investment evaluations.
Presentation: Smart Grid Research Consortium/Smart Grid Investment Model
Abstract: This PowerPoint presentation presents Smart Grid Research Consortium objectives, describes the Smart Grid Investment Model, illustrates Model evaluations of several investment analyses and issues an invitation to join the 2012 Smart Grid Research Consortium.