Systems and methods for energy cost optimization in a building system

• US 10,007,259 B2
• Filed: 03/11/2016
• Issued: 06/26/2018
• Est. Priority Date: 03/13/2013
• Status: Active Grant

• Current Assignee: Tyco Fire & Security GmbH (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Turney, Robert D., Wenzel, Michael J.
• Primary Examiner(s): Hartman, Jr., Ronald D
• Application Number:
  US15/068,470
• Publication Number:
  US 20160195866A1
• Time in Patent Office: 837 Days
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/042 using digital processors G05B19/05 takes precedence
• CPC Class Codes: G05B 2219/2639 Energy management, use maximum of cheap power, keep peak load low
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05B 23/02 Electric testing or monitoring
• CPC Class Codes: Y02B 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/20 End-user application control systems
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/30 Smart metering, e.g. specially adapted for remote reading

Abstract

A controller is configured to use an energy cost function to predict a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller. The energy cost function includes a demand charge term defining a cost per unit of power corresponding to a maximum power usage of the building system. The controller is configured to linearize the demand charge term by imposing demand charge constraints and to mask each of the demand charge constraints that applies to an inactive pricing period. The controller is configured to determine optimal values of the one or more setpoints by performing an optimization procedure that minimizes the total cost of energy subject to the demand charge constraints and to provide the optimal values of the one or more setpoints to equipment of the building system that operate to affect the maximum power usage.

Claims

1. A computer-implemented method for minimizing energy cost in a building system, the method comprising:

using an energy cost function to predict, by a controller for the building system, a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller, the energy cost function comprising a demand charge term defining a cost per unit of power corresponding to a maximum power usage of the building system;

linearizing, by the controller, the demand charge term by imposing demand charge constraints, wherein each of the demand charge constraints applies to a time-step and a pricing period;

masking, by the controller, each of the demand charge constraints that applies to an inactive pricing period, wherein a demand charge constraint applies to an inactive pricing period if the time-step to which the demand charge constraint applies does not occur during the pricing period to which the demand charge constraint applies;

determining, by the controller, optimal values of the one or more setpoints by performing an optimization procedure that minimizes the total cost of energy subject to the demand charge constraints;

providing the optimal values of the one or more setpoints to equipment of the building system; and

operating the equipment of the building system using the optimal values of the one or more setpoints so as to affect the maximum power usage.

2. The method of claim 1, wherein using the energy cost function to predict the total cost of energy comprises using time-varying pricing information to determine a cost of energy purchased from the energy provider during each of a plurality of time steps in a prediction period.

3. The method of claim 2, wherein the time-varying pricing information comprises a plurality of pricing structures including at least two of off-peak, partial-peak, peak, critical-peak, and real-time brokered.

4. The method of claim 1, wherein the energy cost function comprises a demand charge for each of a plurality of pricing periods.

5. The method of claim 1, further comprising receiving temperature constraints defining an acceptable temperature range for the building system;

wherein performing the optimization procedure comprises minimizing the total cost of energy subject to the temperature constraints.

6. The method of claim 1, further comprising using a dynamic model describing heat transfer characteristics of the building system to predict a temperature of the building system as a function of the one or more setpoints provided by the controller.

7. The method of claim 6, further comprising:

receiving a feedback signal indicating a measured temperature of the building system and a measured power usage of the building system; and

using the dynamic model and the feedback signal to formulate equality constraints;

wherein performing the optimization procedure comprises minimizing the total cost of energy subject to the equality constraints.

8. The method of claim 1, wherein linearizing the demand charge term by imposing demand charge constraints comprises:

replacing a nonlinear expression of the demand charge term with a linear expression that defines demand charge as a linear function of a demand charge variable; and

imposing a demand charge constraint for each time-step that constrains the demand charge variable to a value greater than or equal to power consumption of the building system during the time-step.

9. The method of claim 1, wherein masking each of the demand charge constraints that applies to an inactive pricing period comprises, for each of the demand charge constraints:

identifying the pricing period to which the demand charge constraint applies;

identifying the time-step to which the demand charge constraint applies;

determining whether the identified time-step occurs during the identified pricing period; and

selectively masking the demand charge constraint in response to a determination that the identified time-step does not occur during the identified pricing period.

10. The method of claim 1, wherein performing the optimization procedure comprises minimizing the total cost of energy subject to any demand charge constraints that have not been masked as a result of the masking.

11. A system for minimizing energy cost in a building system, the system comprising a controller configured to:

use an energy cost function to predict a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller, the energy cost function comprising a demand charge term defining a cost per unit of power corresponding to a maximum power usage of the building system;

linearize the demand charge term by imposing demand charge constraints, wherein each of the demand charge constraints applies to a time-step and a pricing period;

mask each of the demand charge constraints that applies to an inactive pricing period, wherein a demand charge constraint applies to an inactive pricing period if the time-step to which the demand charge constraint applies does not occur during the pricing period to which the demand charge constraint applies;

determine optimal values of the one or more setpoints by performing an optimization procedure that minimizes the total cost of energy subject to the demand charge constraints; and

provide the optimal values of the one or more setpoints to equipment of the building system; and

operate the equipment of the building system using the optimal values of the one or more setpoints so as to affect the maximum power usage.

12. The system of claim 11, wherein the controller is configured to predict the total cost of energy by using time-varying pricing information to determine a cost of energy purchased from the energy provider during each of a plurality of time steps in a prediction period.

13. The system of claim 12, wherein the time-varying pricing information comprises a plurality of pricing structures including at least two of off-peak, partial-peak, peak, critical-peak, and real-time brokered.

14. The system of claim 11, wherein the energy cost function comprises a demand charge for each of a plurality of pricing periods.

15. The system of claim 11, wherein the controller is configured to:

receive temperature constraints defining an acceptable temperature range for the building system; and

perform the optimization procedure by minimizing the total cost of energy subject to the temperature constraints.

16. The system of claim 11, wherein the controller is configured to use a dynamic model describing heat transfer characteristics of the building system to predict a temperature of the building system as a function of the one or more setpoints provided by the controller.

17. The system of claim 16, wherein the controller is configured to:

receive a feedback signal indicating a measured temperature of the building system and a measured power usage of the building system;

use the dynamic model and the feedback signal to formulate equality constraints; and

perform the optimization procedure by minimizing the total cost of energy subject to the equality constraints.

18. The system of claim 11, wherein the controller is configured to:

linearize the demand charge term by replacing a nonlinear expression of the demand charge term with a linear expression that defines demand charge as a linear function of a demand charge variable; and

impose a demand charge constraint for each time-step that constrains the demand charge variable to a value greater than or equal to power consumption of the building system during the time-step.

19. The system of claim 11, wherein the controller is configured to mask each of the demand charge constraints that applies to an inactive pricing period by:

identifying the pricing period to which the demand charge constraint applies;

identifying the time-step to which the demand charge constraint applies;

determining whether the identified time-step occurs during the identified pricing period; and

selectively masking the demand charge constraint in response to a determination that the identified time-step does not occur during the identified pricing period.

20. The system of claim 11, wherein the controller is configured to perform the optimization procedure by minimizing the total cost of energy subject to any demand charge constraints that have not been masked.