Allocation of energy production changes to meet demand changes

• US 9,898,787 B2
• Filed: 10/16/2014
• Issued: 02/20/2018
• Est. Priority Date: 10/16/2014
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Schindler, Zdenek
• Primary Examiner(s): Barnes-Bullock, Crystal J
• Application Number:
  US14/515,706
• Publication Number:
  US 20160109895A1
• Time in Patent Office: 1,223 Days
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

A computer implemented method optimizes a utility plant having multiple devices to convert input energy into output energy for a building. The method includes dividing a utility plant scheduling interval into several control intervals and for each control interval, obtaining a difference between a desired and a measured in-building condition controlled by output power from the utility plant, obtaining current values of multiple factors that influence operation of the utility plant, determining a new power demand of the building expected to decrease the difference, and finding set points for the multiple devices that satisfy the new power demand, take into account response times of the devices and their capacities, and optimize utility plant operation costs.

Claims

1. A method of controlling a utility plant having multiple devices which consume power and provide energy, the method comprising:

receiving a forecast of building energy demand;

receiving a forecast of factors influencing the operation of the utility plant;

operating an optimizer in a scheduling mode to determine which devices should be on and to provide control actions to specify a set point for each running device so that the operation is economically optimal and no constraint is violated;

operating the optimizer such that the optimizer takes into account a cost or penalty for starting up and shutting down the multiple devices between neighboring time intervals; and

operating the optimizer in a control mode to distribute energy demand between the devices based on efficiency and response times of the devices to minimize purchased energy cost;

wherein the method is performed for each of multiple scheduling intervals constituting an operation schedule for a scheduling time horizon; and

wherein device set points are determined to minimize cost (C) in accordance with

2. The method of claim 1 wherein the multiple factors that influence operation of the utility plant include weather conditions, building temperature set point and building occupancy.

3. A computer implemented method of optimizing a utility plant having multiple devices to convert input energy into output energy for a building, the method comprising:

dividing a utility plant scheduling interval into several control intervals and for each control interval;

obtain a difference between a desired and a measured in-building condition controlled by a device from the utility plant;

obtain current values of multiple factors that influence operation of the utility plant;

determine a power demand of the building expected to decrease the difference;

find set points for the multiple devices that satisfy the power demand, take into account response times of the devices and their capacities, and optimize utility plant operation costs; and

for each control interval where no such set points are found;

remove an exact match requirement for meeting the power demand;

modify an optimization objective by adding a member representing weighted energy imbalance which is a difference between a desired demand and the energy available; and

accept the result as the best possible step in the direction of eliminating the difference between the desired and measured in-building condition;

determining if the desired indoor air conditions cannot be achieved in a specified number of intervals; and

changing a configuration of devices operating in the utility plant by;

receiving a forecast of building energy demand;

receiving a forecast of factors influencing the operation of the utility plant; and

operating the optimizer in a scheduling mode to determine which devices should be on and to provide control actions to specify a set point for each running device so that the operation is economically optimal and no constraint is violated as a function of energy cost.

4. The computer implemented method of claim 3 wherein the set points for the multiple devices are selected as a function of cost.

5. The computer implemented method of claim 3 wherein the set points for the multiple devices are set to minimize the difference between a desired and a measured in-building condition and modified to optimize cost in later intervals.

6. The computer implemented method of claim 5 wherein the difference is minimized as a function of weighted difference between the desired and measured in-building condition.

7. The computer implemented method of claim 5 wherein the set points are changed on devices having faster response times to minimize the difference.

8. The computer implemented method of claim 5 wherein set points for more efficient devices are changed to optimize the cost in the later intervals.

9. The method of claim 3 wherein device set points are determined to minimize cost of energy from public resources (C) in accordance with

10. The computer implemented method of claim 3 wherein the multiple factors that influence operation of the utility plant include ambient temperature.

11. The computer implemented method of claim 3 wherein the multiple factors that influence operation of the utility plant include purchased energy cost.

12. The computer implemented method of claim 3 wherein the control intervals are treated by the method as a series of independent self-contained optimizations.

13. A device comprising:

a processor;

input connections to receive energy demand, real time energy price data, and influencing condition information; and

a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform utility plant optimization by dividing a utility plant scheduling interval into several control intervals and for each control interval;

obtaining a difference between a desired and a measured in-building condition controlled by a device from the utility plant;

obtaining current values of multiple factors that influence operation of the utility plant;

determining a power demand of the building expected to decrease the difference;

finding set points for the multiple devices that satisfy the new power demand and take into account response times of the devices and their capacities; and

for each control interval where no such set points are found, optimization further comprises;

removing an exact match requirement for meeting the new output power demand;

modifying an optimization objective by adding a member representing weighted energy imbalance which is a difference between a desired demand and the energy available; and

accepting the result as the best possible step in the direction of eliminating the difference between the desired and measured in-building condition;

determining if the desired indoor air conditions cannot be achieved in a specified number of intervals with a current configuration of multiple devices; and

changing the configuration of multiple devices operating in the utility plant by;

receiving a forecast of building energy demand;

receiving a forecast of factors influencing the operation of the utility plant; and

operating the optimizer in a scheduling mode to determine which devices should be in the changed configuration of device and to provide control actions to specify a set point for each such device so that the operation is economically optimal and no constraint is violated.

14. The device of claim 13 wherein the set points for the multiple devices are set to minimize the difference between a desired and a measured in-building condition and modified to optimize cost in later intervals.