Dynamic load curtailment system and method
First Claim
1. A method comprising:
- examining, with a processor, historical information about one or more loads during a historical time interval to identify a historical demand peak for the historical time interval, wherein the examining step includes;
determining, with the processor, a highest demand peak for the one or more loads during the historical time interval by comparing the respective demand peaks of the one or more loads during the historical time interval;
determining, with the processor, an average total demand for the one or more loads during the historical time interval; and
calculating, with the processor, a target total demand for the one or more loads as the greater of;
a first proportion times the highest total demand for one or more loads, anda second proportion times the average total demand for the one or more loads;
predicting, with the processor, utilizing real-time information about the one or more loads, a future demand peak for a future time interval subsequent to the historical time interval; and
curtailing, with the processor, using a tuning operation, the one or more loads to prevent the future demand peak from exceeding the historical demand peak.
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Abstract
A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.
15 Citations
19 Claims
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1. A method comprising:
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examining, with a processor, historical information about one or more loads during a historical time interval to identify a historical demand peak for the historical time interval, wherein the examining step includes; determining, with the processor, a highest demand peak for the one or more loads during the historical time interval by comparing the respective demand peaks of the one or more loads during the historical time interval; determining, with the processor, an average total demand for the one or more loads during the historical time interval; and calculating, with the processor, a target total demand for the one or more loads as the greater of; a first proportion times the highest total demand for one or more loads, and a second proportion times the average total demand for the one or more loads; predicting, with the processor, utilizing real-time information about the one or more loads, a future demand peak for a future time interval subsequent to the historical time interval; and curtailing, with the processor, using a tuning operation, the one or more loads to prevent the future demand peak from exceeding the historical demand peak. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer program process code, tangibly stored on at least one non-transitory computer readable medium, the computer program process code comprising instructions implementing a method for using a computing device to perform dynamic load curtailment, the method comprising instructions for:
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examining, with the computing device, historical information about one or more loads during a historical time interval to identify a historical demand peak for the historical time interval, wherein the examining step further comprises; determining, with the computing device, a highest demand peak for the one or more loads during the historical time intervals by comparing the respective demand peaks of the one or more loads during each of the plurality of historical time intervals; determining, with the computing device, an average total demand for the one or more loads during each of the plurality of historical time intervals; and calculating, with the computing device, a target total demand for the one or more loads as the greater of; a first proportion times the highest demand peak, and a second proportion times the average total demand; predicting, with the computing device, based on real-time information about the one or more loads, a future demand peak for a future time interval subsequent to the historical time interval; and curtailing, with the computing device, using a tuning operation, the one or more loads in order to prevent the future demand peak from exceeding the historical demand peak. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A system comprising:
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a site controller including a processor and a memory; one or more sensors coupled to the processor and a plurality of loads; and a plurality of circuit controllers coupled to the processor and the plurality of loads, wherein a control algorithm performs the steps of; receiving, at the processor, from the one or more sensors, historical information about the plurality of loads; identifying, with the processor, based on the historical information, a highest demand peak for the one or more loads during a historical time interval by comparing respective demand peaks of the one or more loads during the historical time interval; determining, with the processor, an average total demand for the one or more loads during the historical time interval; and calculating, with the processor, a target total demand for the one or more loads as the greater of; a first proportion times the highest total demand for one or more loads, and a second proportion times the average total demand for the one or more loads; receiving, at the processor, from the one or more sensors, real-time information about the plurality of loads during a current time interval subsequent to the historic time interval; predicting, with the processor, based on the real-time information, a future demand peak for a future time interval subsequent to the current time interval; and curtailing, with the plurality of circuit controllers, using a tuning operation, the plurality of loads in order to prevent the future demand peak from exceeding the historical demand peak. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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Specification