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13.2: Demand Integrators
Power utilities take into account both energy consumption and peak demand when billing customers.
Peak demand, expressed in kilowatts (kW), is the highest level of demand recorded during a set period
of time, called the interval. The Nexus® 1262/1272 meter supports the following most popular conventions
for averaging demand and peak demand: Thermal Demand, Block Window Demand, Rolling Window
Demand and Predictive Window Demand. You may program and access all conventions concurrently
with the Communicator EXT software (see the Communicator EXT User Manual).
Thermal Demand: Traditional analog watt-hour (WH) meters use heat-sensitive elements to
measure temperature rises produced by an increase in current flowing through the meter. A pointer
moves in proportion to the temperature change, providing a record of demand. The pointer remains
at peak level until a subsequent increase in demand moves it again, or until it is manually reset. The
Nexus® meter mimics traditional meters to provide Thermal Demand readings.
Each second, as a new power level is computed, a recurrence relation formula is applied. This
formula recomputes the thermal demand by averaging a small portion of the new power value with a
large portion of the previous thermal demand value. The proportioning of new to previous is
programmable, set by an averaging interval. The averaging interval represents a 90% change in
thermal demand to a step change in power.
Block (Fixed) Window Demand: Fixed Window Demand produces an average (arithmetic mean) of
power readings over a programmed interval.
Example: A typical setting of 15 minutes produces an average value every 15 minutes (at 12:00,
12:15, 12:30, etc.) for power readings over the previous fifteen minute interval (11:45-12:00,
12:00-12:15, 12:15-12:30, etc.)
Rolling (Sliding) Window Demand: Rolling Window Demand functions like multiple, overlapping
Block Window Demands. The programmable settings provided are the number and length of
demand subintervals. Every subinterval, an average (arithmetic mean) of power readings over the
subinterval is internally calculated. This new subinterval average is then averaged (arithmetic mean)
with as many previous subinterval averages as programmed, to produce the Rolling Window
Demand.
Example: With settings of 3 five minute subintervals, subinterval averages are computed every 5
minutes (12:00, 12:05, 12:10, 12:15. etc.) for power readings over the previous five minute interval
(11:55-12:00, 12:00-12:05, 12:05-12:10, 12:10-12:15, etc.). Further, every 5 minutes, the subinterval
averages are averaged in groups of 3 (12:00, 12:05, 12:10; 12:05, 12:10, 12:15. etc.) to produce a
fifteen (5*3) minute average every 5 minutes (rolling (sliding) every 5 minutes) (11:55-12:10,
12:-12:15, etc.).
Predictive Window Demand: Predictive Window Demand expands on the above Rolling Window
Demand. Rolling Window Demand waits until the end of a subinterval to update the demand.
Predictive Window Demand uses the average of the previously completed subinterval, weighted by a
programmable factor, to guess at the average of the upcoming subinteval During the next
subinterval, as actual power readings occur, the value for the subinterval consists of the portion
actually mesured plus a predicted guess, prorated to the subinterval time remaining.
Electro Industries/GaugeTech
Doc # E151701
13-6
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