Systems and methods for energy cost optimization in a building system

• US 9,436,179 B1
• Filed: 03/13/2013
• Issued: 09/06/2016
• 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
• Application Number:
  US13/802,279
• Time in Patent Office: 1,273 Days
• Field of Search: 700/286, 700/295, 705/412
• US Class Current: 1/1
• 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

Methods and systems to minimize energy cost in response to time-varying energy prices are presented for a variety of different pricing scenarios. A cascaded model predictive control system is disclosed comprising an inner controller and an outer controller. The inner controller controls power use using a derivative of a temperature setpoint and the outer controller controls temperature via a power setpoint or power deferral. An optimization procedure is used to minimize a cost function within a time horizon subject to temperature constraints, equality constraints, and demand charge constraints. Equality constraints are formulated using system model information and system state information whereas demand charge constraints are formulated using system state information and pricing information. A masking procedure is used to invalidate demand charge constraints for inactive pricing periods including peak, partial-peak, off-peak, critical-peak, and real-time.

Claims

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

receiving, at a processing circuit, a dynamic model describing heat transfer characteristics of the building system, a feedback signal including information representative of a measured temperature and information representative of a measured power usage of the building system, and temperature constraints defining an acceptable range for the measured temperature;

using, by the processing circuit, time-varying pricing information for a plurality of pricing structures to define an energy cost function, wherein the energy cost function expresses a total cost of energy as a function of power used by the building system and includes a demand charge term defining a cost per unit of power corresponding to a maximum power usage within a pricing period, wherein the plurality of pricing structures include at least two of off-peak, partial-peak, peak, critical-peak, and real-time brokered;

using, by the processing circuit, the dynamic model and the feedback signal to formulate equality constraints;

linearizing, by the processing circuit, the demand charge term by imposing demand charge constraints, wherein the demand charge constraints are based on previous demand charges;

masking, by the processing circuit, any demand charge constraint which applies to an inactive pricing period, wherein a demand charge constraint applies to a time-step and a pricing period and wherein the 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;

using, by the processing circuit, an optimization procedure to minimize the total cost of energy while satisfying the equality constraints, the temperature constraints, and the demand charge constraints.

2. The method of claim 1, wherein the time-varying pricing information includes a negotiated pricing structure brokered in real-time.

3. The method of claim 1, wherein the time-varying pricing information includes demand charge information for two or more pricing periods.

4. The method of claim 1, wherein the time-varying pricing information includes energy charge information defining a cost per unit of energy for each of the plurality of pricing structures.

5. The method of claim 1, further comprising:

updating states of the dynamic model using an updated feedback signal, wherein the updated feedback signal includes updated values for the measured temperature and the measured power usage; and

repeating the ‘

using’ and

‘

updating’

steps recursively.

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

using, by processing circuit, time-varying pricing information to define an energy cost function, wherein the energy cost function expresses a total cost of energy as a function of power used by the building system, wherein the energy cost function includes a demand charge term defining a cost per unit of power corresponding to a maximum power usage within a pricing period;

linearizing, by the processing circuit, the demand charge term in the energy cost function by introducing demand charge constraints;

masking, by the processing circuit, any demand charge constraint which applies to an inactive pricing period, wherein a demand charge constraint applies to a time-step and a pricing period, wherein the 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; and

using, by the processing circuit, an optimization procedure to minimize the total cost of energy while satisfying the demand charge constraints.

7. The method of claim 6, further comprising:

receiving a dynamic model describing heat transfer characteristics of the building system, a feedback signal including a measured temperature and a measured power usage of the building system, and temperature constraints defining an acceptable range for the measured temperature;

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

wherein the optimization procedure minimizes the total cost of energy while satisfying the equality constraints and the temperature constraints.

8. The method of claim 7, further comprising:

updating the dynamic model using an updated feedback signal, wherein the updated feedback signal includes updated values for the measured temperature and the measured power usage; and

repeating the ‘

using’ and

‘

updating’

steps recursively.

9. The method of claim 6, wherein the time-varying pricing information includes pricing information for a plurality of pricing structures including at least two of off-peak, partial-peak, peak, critical-peak, and real-time brokered.

10. The method of claim 6, wherein the time-varying pricing information includes demand charge information for two or more pricing periods.

11. The method of claim 6, wherein the time-varying pricing information includes energy charge information defining a cost per unit of energy for each of the plurality of pricing structures.

12. The method of claim 6, wherein the time-varying pricing information includes a negotiated pricing profile brokered in real-time.

13. A system for minimizing a cost of energy used by a control process, the system comprising:

a processing circuit configured to;

receive a dynamic model describing heat transfer characteristics of the control process, a feedback signal including a measured temperature and a measured power usage of the control process, and temperature constraints defining an acceptable range for the measured temperature;

define an energy cost function, wherein the energy cost function uses the time-varying pricing information to express the cost of energy as a function of power used by the control process,

use the dynamic model and the feedback signal to estimate a temperature state for the control process a function of power usage;

mask any demand charge constraint which applies to an inactive pricing period, wherein a demand charge constraint applies to a time-step and a pricing period and wherein the 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; and

use an optimization procedure to minimize the cost of energy while maintaining the estimated temperature state within the acceptable range.

14. The system of claim 13, wherein the processing circuit is further configured to:

update the dynamic model using an updated feedback signal, wherein the updated feedback signal includes updated values for the measured temperature and the measured power usage; and

repeat the ‘

use’ and

‘

update’

steps recursively.

15. The system of claim 13, wherein the energy cost function includes a demand charge term defining a cost per unit of power corresponding to a maximum power usage within a pricing period, wherein the processing circuit is further configured to:

linearize the demand charge term by imposing demand charge constraints on the optimization procedure, wherein the demand charge constraints are based on previous demand charges and wherein the optimization procedure minimizes the cost of energy while satisfying the demand charge constraints.