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How does more accuracy in end-use load shapes improve impact estimation of energy efficiency and DR programs?

Background of Demand-Side Management and Evaluation in the U.S.

The 1970’s saw two events that forever changed the landscape of electricity production in the United States. The Arab Oil Embargo of 1973 ended the era of cheap oil at a time when oil supported a peak of 17 percent of total U.S. power generation,[1] and the 1979 accident at the Three Mile Island[2] brought a halt to new nuclear plant orders for over 25 years, dashing hopes of a power source that was “too cheap to meter.” Prior to these events, the siting and construction of power plants by utilities was routine and lacked the controversy these events triggered, which remain to this day. These events prompted government and private sector concerns over national energy security and nuclear plant safety, along with increasing concerns over the environmental impacts of coal based power production. To address these concerns while meeting expected energy demands prompted interest in Energy Conservation, giving rise to demand-side management (DSM) as an alternative strategy, and began the industry practice for DSM services.

Government, university and utility industry research studies conducted throughout the 1980’s identified significant potential for DSM to have a measurable impact on energy consumption,[3] and utilities, spurred by government and regulators began to roll out programs to provide incentives for DSM “negawatts” as an alternative to building more power plants. Unlike power plants, however, reductions in energy consumption from demand-side measures could not be measured directly, so the demand for evaluation methods and services to estimate the energy impacts of programs emerged. During the early days of DSM impact evaluation, the emphasis was on estimating annual energy savings attributed to DSM programs. Often, impacts were calculated and reported using engineering estimates with demand impacts estimated using load factors and coincidence factors, without much detail or robust research foundation. As investments in DSM programs increased, evaluation became more rigorous and robust, requiring a better strategy for estimating more accurate time-differentiated impacts.

How Have Traditional Methods for Impact Estimation Incorporated End-Use Load Shapes?

DSM strategies targeted reductions in kWh consumption of specific appliances and end-uses (e.g. HVAC systems, lighting, domestic hot water, refrigeration), as well as building shell measures, which affect heating and cooling. The need for better data and more rigorous methods to estimate savings was apparent, given that these measures were designed to defer/replace power plants. This led to end use studies using data loggers, meters, and sensors placed on building systems and household appliances to capture time-differentiated kWh consumption and kW demand. The resulting end use interval data supported impact evaluation at the hourly and daily level, an increase in granularity over monthly and annual consumption, enabling utilities to estimate a more nuanced set of impacts with time-variant loads and support DSM investment cost-effectiveness calculations. End use metering of treatment and comparison groups became the preferred method for measuring impacts from DSM programs. However, with deregulation in the mid 1990’s much of the end use load research slowed, but has still been selectively adopted where it could be cost-justified.

What is the Current State of End Use Load Shape Development for Impact Estimation?

End use load studies have continued to be rare, as their cost and complexity remain high. New technology has helped somewhat, particularly with the advent of wireless communications, which enable remote data collection, replacing phone and on-site data collection. With the deployment of over approximately 50 million smart meters, translating to a national penetration rate of 37 percent,[4] remote access to metered utility consumption data is increasing, though higher saturations (over 70%) are limited to 10 states.  However, end use measurement and communication with end uses on the customer side of the meter for residential and commercial customers is still very limited. Non-intrusive methods derive end use consumption from whole-house loads using end use load “signatures” derived from the utility meter, along with appliance inventories. These emerging tools hold great promise for their non-intrusive characteristics and significant cost benefits over more traditional data collection technologies and are attracting R&D funding by both governments and utilities,[5] including several studies underway with DNV GL participation.

DNV GL assists their clients in deployment of a variety of advanced technologies for cost effective data collection, including smart thermostats that record HVAC system runtimes, indoor and outdoor temperatures, and customer response to direct load control. This remotely collected data, combined with estimates of maximum load draw (confirmed with spot-metering sample studies), enables accurate estimates of cooling and heating operating patterns. These types of techniques are typically used by DNV GL for evaluating energy efficiency and demand respond programs (e.g. dynamic pricing and load control). For other end uses, studies are limited, with most linked to specific DSM program impact analyses, leaving most utilities and regulators to choose between expensive data collection or borrowing from the few studies that have been done – and hoping old or other utility studies can be made applicable.

How has DNV GL furthered the Cause of Increased Accuracy in End Use Load Shape Development for Impact Estimation?

For its utility and government clients, DNV GL applies methods to identify load shape patterns using advanced data analytics that translate annual energy consumption into 8,760 load shapes. Detailed load shapes support impact evaluation, DSM potential studies, informs DSM efficiency and demand response programs, and aides in accurate and detailed load forecasting.

The ability to use highly granular data facilitates calibration and improved accuracy.  Experience gained over many years and many studies have helped to provide DNV GL staff with the understanding of these patterns and an extensive case study library. Using a set of ratios, each of which can be calibrated and weather-adjusted, where needed, raw data or borrowed load shapes can be applied to known parameters for use in more accurate estimates of end use load shapes for baseline, “treatment” and impacts. These ratios are:

  • Monthly breakdown – percentage of annual usage, reflecting both weather and other seasonal factors,
  • Weekend to weekday ratio – monthly ratios that reflect the typical pattern – especially useful for commercial business customers,
  • Peak Day Adjustment factor – ratio of peak to average day usage by month – subject to adjustment and calibration to local weather and design conditions (e.g. one in ten years),
  • Hourly per-unit ratios – by day type (peak, weekday and weekend) and by month, each end use and segment has a unique pattern.

Graphic depictions of the DNV GL load shape ratio model outputs are shown below for a Northeastern heat pump example, with (clockwise from top left), monthly breakdown (assuming 5,000 kWh), July/January weekday hourly shapes, monthly TOU/load factors, and July peak day/weekday/weekend day hourly shapes.

Example Load Shape Statistical graphics

In developing and using load shapes, the key questions are:

  • Which patterns are consistent, and across which variables?
  • How best to calibrate?
  • How or when to adjust patterns when borrowing from old or other utility studies?

Working with these ratios from a top-down approach can enable annual energy for any scenario (e.g. baseline, DSM treatment) to be translated to any hour of the year and any year, customized for weather and calendars, resulting in more accurate estimates of impacts.

These techniques apply to development of baselines, population segments, and specific participant and non-participant groups using advanced analytics. The value of accuracy of estimates using these techniques is that they are closer than ever to “metered” savings and can be relied upon as true alternatives to metered supply options on a real-time basis.

How can DNV GL Help You Develop End-Use Load Shape Data?

Based on DNV GL’s extensive experience and library of end use load shapes, we can identify the best sources, optimal methods for calibration and adjustment, and plans/support for utility-specific end use data collection, where warranted, including turn-key end use load studies. In some cases, especially for weather-sensitive loads, whole premise AMI data can be used to derive the ratios described above.

Once your information needs are identified, our teams of statisticians, engineers, market researchers, and load data modeling specialists have the experience and expertise to help you develop a comprehensive end use load shape library, incorporating borrowed, calibrated, weather-adjusted or primary data collection.

DNV GL is regular contributor to industry conferences, including recent papers presented at ACEEE, AEIC, BECC and IEPEC. Please contact us to discuss this blog or how we can help you develop and utilize end-use load shapes in your planning and evaluation efforts. Visit www.dnvgl.com to learn more.

Acknowledgements

Michelle Marean, Principal Consultant in DNV GL’s Policy Advisory and Research group, also contributed to this blog.

[1] U.S. Department of State, Office of the Historian, MILESTONES: 1969-1976 https://history.state.gov/milestones/1969-1976/oil-embargo

[2] U.S. Nuclear Regulatory Commission. Backgrounder on the Three Mile Island Accident. http://www.nrc.gov/reading-rm/doc-collection/fact-sheets/3mile-isle.html

[3] Socolow, Robert H. 1978. The Twin Rivers program on energy conservation in housing: highlights and conclusions. Washington: U.S. Dept. of Energy, Assistant Secretary for Conservation and Solar Applications, Division of Buildings and Community Systems

[4] U.S. Energy Information Administration. 2015. Electric power sales, revenue and energy efficiency Form EIA-861 detailed data files, October 21, 2015, Final 2014 data. http://www.eia.gov/electricity/data/eia861/

[5] Holmes, Chris. EPRI. 2014. Non-intrusive Load Monitoring (NILMs) Research Activity. http://publications.aeic.org/Irc/2014_Workshop_NonIntrusiveApplianceLoadMonitoring.pdf

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