Gleanings from the web and the world, condensed for convenience, illustrated for enlightenment, arranged for impact...

The challenge now: To make every day Earth Day.


  • TODAY’S STUDY: The Risk Of Natural Gas Vs. The Risk Of Wind
  • QUICK NEWS, April 24: The Health Impacts Of Climate Change; New Energy Is Everywhere; Study Shows LA Does Not Need Aliso Canyon

  • Weekend Video: How To Win Friends For New Energy
  • Weekend Video: The Electric Vehicle Highway
  • Weekend Video: Wind And The Economy

  • FRIDAY WORLD HEADLINE-A Deeper Look At The Heat
  • FRIDAY WORLD HEADLINE-Wind Gets Market Tough
  • FRIDAY WORLD HEADLINE-UK Gets Utility-Led Solar Plus Storage
  • FRIDAY WORLD HEADLINE-Germany’s VW Talking Its EV To China


  • TTTA Thursday-U.S. Military Affirms Climate Change-War Link
  • TTTA Thursday-Solar Plus Hydro Drive Wholesale Power Cost Sub-Zero
  • TTTA Thursday-Wind Boom Goes On Growing Midwest Wealth
  • TTTA Thursday-More Kentucky Jobs In New Energy Than In Coal

  • ORIGINAL REPORTING: Rocky Mountain compromise: Inside Xcel's landmark Colorado solar settlement
  • ORIGINAL REPORTING: Fixed charge battle looms in Texas as regulators tackle rate design reform
  • ORIGINAL REPORTING: No time to think: How utilities are handling the deluge of grid data


  • TODAY’S STUDY: Resource Diversity And Grid Reliability
  • QUICK NEWS, April 18: Study Puts 10-Year Timer On Climate Change; The War Between Wall Street And Solar; New Energy To Power Healthcare
  • --------------------------


    Anne B. Butterfield of Daily Camera and Huffington Post, f is an occasional contributor to NewEnergyNews


    Some of Anne's contributions:

  • Another Tipping Point: US Coal Supply Decline So Real Even West Virginia Concurs (REPORT), November 26, 2013
  • SOLAR FOR ME BUT NOT FOR THEE ~ Xcel's Push to Undermine Rooftop Solar, September 20, 2013
  • NEW BILLS AND NEW BIRDS in Colorado's recent session, May 20, 2013
  • Lies, damned lies and politicians (October 8, 2012)
  • Colorado's Elegant Solution to Fracking (April 23, 2012)
  • Shale Gas: From Geologic Bubble to Economic Bubble (March 15, 2012)
  • Taken for granted no more (February 5, 2012)
  • The Republican clown car circus (January 6, 2012)
  • Twenty-Somethings of Colorado With Skin in the Game (November 22, 2011)
  • Occupy, Xcel, and the Mother of All Cliffs (October 31, 2011)
  • Boulder Can Own Its Power With Distributed Generation (June 7, 2011)
  • The Plunging Cost of Renewables and Boulder's Energy Future (April 19, 2011)
  • Paddling Down the River Denial (January 12, 2011)
  • The Fox (News) That Jumped the Shark (December 16, 2010)
  • Click here for an archive of Butterfield columns


    Some details about NewEnergyNews and the man behind the curtain: Herman K. Trabish, Agua Dulce, CA., Doctor with my hands, Writer with my head, Student of New Energy and Human Experience with my heart




      A tip of the NewEnergyNews cap to Phillip Garcia for crucial assistance in the design implementation of this site. Thanks, Phillip.


    Pay a visit to the HARRY BOYKOFF page at Basketball Reference, sponsored by NewEnergyNews and Oil In Their Blood.

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  • TODAY AT NewEnergyNews, April 25:

  • TODAY’S STUDY: The Way To Grow EVs
  • QUICK NEWS, April 25: Private Sector Takes Over The Climate Fight; How Sea Level Rise Would Change The Map; Wind Jobs Top 100,000 As Wind Energy Booms

    Tuesday, February 17, 2015


    Updated Value of Service Reliability Estimates for Electric Utility Customers in the United States

    Michael J. Sullivan, Josh Schellenberg, Marshall Blundell, et. al., January 2015 (Lawrence Berkeley National Laboratory)


    This report updates the 2009 meta-analysis that provides estimates of the value of service reliability for electricity customers in the United States (U.S.). The meta-dataset now includes 34 different datasets from surveys fielded by 10 different utility companies between 1989 and 2012. Because these studies used nearly identical interruption cost estimation or willingness-topay/accept methods, it was possible to integrate their results into a single meta-dataset describing the value of electric service reliability observed in all of them. Once the datasets from the various studies were combined, a two-part regression model was used to estimate customer damage functions that can be generally applied to calculate customer interruption costs per event by season, time of day, day of week, and geographical regions within the U.S. for industrial, commercial, and residential customers. This report focuses on the backwards stepwise selection process that was used to develop the final revised model for all customer classes. Across customer classes, the revised customer interruption cost model has improved significantly because it incorporates more data and does not include the many extraneous variables that were in the original specification from the 2009 meta-analysis. The backwards stepwise selection process led to a more parsimonious model that only included key variables, while still achieving comparable out-of-sample predictive performance. In turn, users of interruption cost estimation tools such as the Interruption Cost Estimate (ICE) Calculator will have less customer characteristics information to provide and the associated inputs page will be far less cumbersome. The upcoming new version of the ICE Calculator is anticipated to be released in 2015.

    Executive Summary

    In 2009, Freeman, Sullivan & Co. (now Nexant) conducted a meta-analysis that provided estimates of the value of service reliability for electricity customers in the United States (U.S.). These estimates were obtained by analyzing the results from 28 customer value of service reliability studies conducted by 10 major U.S. electric utilities over the 16-year period from 1989 to 2005. Because these studies used nearly identical interruption cost estimation or willingnessto-pay/accept methods, it was possible to integrate their results into a single meta-dataset describing the value of electric service reliability observed in all of them. The meta-analysis and its associated econometric models were summarized in a report entitled “Estimated Value of Service Reliability for Electric Utility Customers in the United States,”1 which was prepared for Lawrence Berkeley National Laboratory (LBNL) and the Office of Electricity Delivery and Energy Reliability of the U.S. Department of Energy (DOE). The econometric models were subsequently integrated into the Interruption Cost Estimate (ICE) Calculator (available at, which is an online tool designed for electric reliability planners at utilities, government organizations or other entities that are interested in estimating interruption costs and/or the benefits associated with reliability improvements (also funded by LBNL and DOE).

    Since the report was finalized in June 2009 and the ICE Calculator was released in July 2011, Nexant, LBNL, DOE, and ICE Calculator users have identified several ways to improve the interruption cost estimates and the ICE Calculator user experience. These improvements include:

    • Incorporating more recent utility interruption cost studies;

    • Enabling the ICE Calculator to provide estimates for power interruptions lasting longer than eight hours;

    • Reducing the amount of detailed customer characteristics information that ICE Calculator users must provide;

    • Subjecting the econometric model selection process to rigorous cross-validation techniques, using the most recent model validation methods;2 and

    • Providing a batch processing feature that allows the user to save results and modify inputs.

    These improvements will be addressed through this updated report and the upcoming new version of the ICE Calculator, which is anticipated to be released in 2015. This report provides updated value of service reliability estimates and details the revised econometric model, which is based on a meta-analysis that includes two new interruption cost studies.

    The upcoming new version of the ICE Calculator will incorporate the revised econometric model and include a batch processing feature that will allow the user to save results and modify inputs.

    Updated Interruption Cost Estimates

    For each customer class, Table ES-1 provides the three key metrics that are most useful for planning purposes. These metrics are:

    • Cost per event (cost for an individual interruption for a typical customer3 );

    • Cost per average kW (cost per event normalized by average demand); and

    • Cost per unserved kWh (cost per event normalized by the expected amount of unserved kWh for each interruption duration).

    Cost per unserved kWh is relatively high for a momentary interruption because the expected amount of unserved kWh over a 5-minute period is relatively low.

    In general, even though the econometric model has been considerably simplified, it produces similar estimates to those of the 2009 model. As in the 2009 study, medium and large C&I customers have the highest interruption costs, but when normalized by average kW, interruption costs are highest in the small C&I customer class. On both an absolute and normalized basis, residential customers experience the lowest costs as a result of a power interruption.

    Table ES-2 shows how customer interruption costs vary by season and time of day, based on the key drivers of interruption costs that were identified in the model selection process. For medium and large C&I customers, interruption costs only meaningfully vary by season (summer vs. nonsummer). For medium and large C&I customers, the cost of a summer power interruption is around 21% to 43% higher than a non-summer one, depending on duration (the percent difference lowers as duration increases). For small C&I customers, the seasonal pattern is the opposite, with the cost of summer power interruptions lower by around 9% to 30%, depending on duration, season, and time of day. Small C&I interruption costs also vary by time of day, with the highest costs in the afternoon and morning. In the evening and nighttime, small C&I interruption costs are substantially lower, which makes sense given that small businesses typically operate during daytime hours. For residential customers, interruption costs are generally higher during the summer and in the morning and night (10 PM to 12 noon). The table also includes a weighted-average interruption cost estimate (equal to the cost per event estimates in Table ES-1), which is weighted by the proportion of hours of the year that each interruption scenario represents, depending on season and time of day. This weighted-average interruption cost estimate is most appropriate to use for planning purposes, unless the distribution of interruptions by season and time of day is known and accounted for in the analysis.

    Study Limitations

    As in the 2009 study, there are limitations to how the data from this meta-analysis should be used. It is important to fully understand these limitations, so they are further described in this section and in more detail in Section 6. These limitations are:

    • Certain very important variables in the data are confounded among the studies we examined. In particular, region of the country and year of the study are correlated in such a way that it is impossible to separate the effects of these two variables on customer interruption costs;

    • There is further correlation between regions and scenario characteristics. The sponsors of the interruption cost studies were generally interested in measuring interruption costs for conditions that were important for planning their specific systems. As a result, interruption conditions described in the surveys for a given region tended to focus on periods of time when interruptions were more problematic for that region;

    • A further limitation of our research is that the surveys that formed the basis of the studies we examined were limited to certain parts of the country. No data were available from the northeast/mid-Atlantic region, and limited data were available for cities along the Great Lakes;

    • Another caveat is that around half of the data from the meta-database is from surveys that are 15 or more years old. Although the intertemporal analysis in the 2009 study showed that interruption costs have not changed significantly over time, the outdated vintage of the data presents concerns that, in addition to the limitations above, underscore the need for a coordinated, nationwide effort that collects interruption cost estimates for many regions and utilities simultaneously, using a consistent survey design and data collection method; and

    • Finally, although the revised model is able to estimate costs for interruptions lasting longer than eight hours, it is important to note that the estimates in this report are not appropriate for resiliency planning. This meta-study focuses on the direct costs that customers experience as a result of relatively short power interruptions of up to 24 hours at most. For resiliency considerations that involve planning for long duration power interruptions of 24 hours or more, the nature of costs change and the indirect, spillover effects to the greater economy must be considered.4 These factors are not captured in this meta-analysis.


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