Energy End-Use Forecasting
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Peak Demand Issues and Comparisons of Supply and Demand Technologies

Project Description

Electricity demand varies constantly. At times of low demand, only the utility’s lowest marginal cost plants operate, while at peak times, almost all of the utility’s available power plants must run to meet the demand and prevent system outages. The lowest marginal cost plants are often the most fuel efficient. The electric utility industry has traditionally focused on peak demand because the likelihood of system outages (often measured by the so-called “loss of load probability” or LOLP) is by far the greatest at peak times. LOLP is typically concentrated in a relatively small number of hours per year, and those hours are often near the time of system or seasonal peaks.

The reasons why peak times are so likely to be associated with system outages are several-fold:

• Real time delivery: Electricity cannot be cost-effectively stored, and thus must be supplied at the same time that it is being used.

• Long lead times: Generation capacity is fixed in the short term, and adding new capacity can take anywhere from two to ten years, and sometimes longer.<

• Lack of responsiveness to real-time costs: Demand is typically not responsive to the cost of supplying power in real time (costs per kWh at time of system peak can be several times the retail rates charged to customers). These retail rates might vary seasonally, but only rarely are responsive to daily changes in prices, in part due to the widespread lack of inexpensive metering technology capable of charging customers for their electricity use in real time, and an associated lack of end-use device technologies capable of tracking and responding to such time-varying price signals. Even when metering technologies are capable of monitoring such price signals, sometimes the bills are delivered on a monthly basis, thus sidestepping the most powerful potential effect of real-time prices, the immediate behavioral feedback.

For these reasons, the time of system peak demand has been a preoccupation of utility planners for many years. Society is rightly concerned about peak demand for other reasons as well, including economic efficiency, environmental quality, fuel security, and facility siting.

The quantitative analysis in this project area has focused on developing a simplified method for comparing supply and demand-side technologies that accurately accounts for peak demand savings from efficiency options. The two reports where this method is developed and used are Koomey et al. 1990a and 1990b, below.

The qualitative analysis in this area is summarized in Koomey and Brown 2002, where the reader will find a compilation of the key issues surrounding peak demand related to technologies, policies, behavior, and R&D priorities.

Project Staff

Jonathan Koomey

Rich Brown

Key Data

Publications

Koomey, Jonathan G., and Richard E. Brown. 2002. The role of building technologies in reducing and controlling peak electricity demand. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-49947. September. Abstract | 270K PDF

Koomey, Jonathan, Arthur Rosenfeld, and Ashok Gadgil. 1990. Conservation Screening Curves to Compare Efficiency Investments to Power Plants: Applications to Commercial Sector Conservation Programs. Proceedings of the 1990 ACEEE Summer Study on Energy Efficiency in Buildings. Asilomar, CA: American Council for an Energy Efficient Economy. Abstract | 44K PDF

Koomey, Jonathan, Arthur H. Rosenfeld, and Ashok K. Gadgil. 1990. "Conservation Screening Curves to Compare Efficiency Investments to Power Plants." Energy Policy. vol. 18, no. 8. October. pp. 774-782. Abstract | 44K PDF

Other Resources

Sponsors

Office of Building Technology, State and Community Programs of the U.S. Department of Energy

Energy Star energy conservation energy savings energy efficiency environment climate change weatherization solar energy appliances energy audit energy tools efficient buildings energy education heating cooling heat pumps furnaces boilers air conditioners insulation ducts skylights windows thermostats fuel switching mechanical ventilation water heater water heating refrigerator freezer dishwasher clotheswasher clothes dryer miscellaneous energy home energy comfort remodeling residential energy.

TEMA Group Site The Enduse Forecasting Group, Lawrence Berkeley National Laboratory
 Last Updated On: 5/26/04