IP Library Granted Patent US 11,705,726
Granted Patent B2
US 11,705,726 · App. 17/080,583 · Granted Jul 18, 2023

Building energy system with predictive control of battery and green energy resources

Inventors: Robert D. Turney (Watertown, WI); Nishith R. Patel (Madison, WI)
Assignee: Johnson Controls Tyco IP Holdings LLP
H02J3/003F24F11/47F24F11/56F24F11/64F24F11/65F25B27/00G05B13/021G05B13/048G05B15/02G06Q10/06G06Q50/06H02J3/32F24F2130/10F24F2140/60G05B2219/2642H02J2310/14
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Quick Facts
Patent No.
US 11,705,726
App. No.
17/080,583
Granted
Jul 18, 2023
Kind
B2
Abstract

A building energy system includes HVAC equipment, green energy generation, a battery, and a predictive controller. The HVAC equipment provide heating or cooling for a building. The green energy generation collect green energy from a green energy source. The battery stores electric energy including at least a portion of the green energy provided by the green energy generation and grid energy purchased from an energy grid and discharges the stored electric energy for use in powering the HVAC equipment. The predictive controller generates a constraint that defines a total energy consumption of the HVAC equipment at each time step of an optimization period as a summation of multiple source-specific energy components and optimizes the predictive cost function subject to the constraint to determine values for each of the source-specific energy components at each time step of the optimization period.

Claims (57)

1. A predictive controller for a building energy system, the predictive controller comprising one or more processing circuits configured to:

generate a constraint that defines a total electric load to be served by the building energy system at each time step of an optimization period as a summation of multiple source-specific energy components comprising:

a grid energy component indicating an amount of grid energy to purchase from an energy grid during the time step; and

a green energy component indicating an amount of green energy provided by green energy generation during the time step;

perform an optimization of a predictive objective function subject to the constraint to determine values of the source-specific energy components at each time step of the optimization period; and

operate equipment of the building energy system using the values of the source-specific energy components.

2. The predictive controller of claim 1 , wherein the source-specific energy components further comprise a battery energy component indicating an amount of electric energy to store in a battery or discharge from the battery during the time step;

wherein operating the equipment of the building energy system using the values of the source-specific energy components comprises operating the battery to store or discharge the amount of electric energy indicated by the battery energy component.

3. The predictive controller of claim 1 , wherein the predictive objective function accounts for:

an amount of the grid energy or cost of the grid energy purchased from the energy grid; and

an amount of energy savings or cost savings resulting from discharging stored electric energy during the optimization period.

4. The predictive controller of claim 1 , wherein the one or more processing circuits are configured to:

obtain energy pricing data defining a cost per unit of the grid energy purchased from the energy grid at each time step of the optimization period; and

use the energy pricing data as inputs to the predictive objective function.

5. The predictive controller of claim 1 , wherein the building energy system comprises HVAC equipment comprising airside equipment and waterside equipment; and

wherein the predictive objective function accounts for an amount of energy consumed or cost of the energy consumed by both the waterside equipment and the airside equipment during the optimization period.

6. The predictive controller of claim 5 , wherein the one or more processing circuits are configured to:

generate a second constraint that defines the total energy consumption of the HVAC equipment at each time step as a summation of multiple equipment-specific energy components comprising:

a waterside energy component indicating an amount of energy consumed by the waterside equipment during the time step; and

one or more airside energy components indicating one or more amounts of energy consumed by the airside equipment during the time step; and

optimize the predictive objective function subject to the second constraint to determine values for each of the equipment-specific energy components at each time step of the optimization period.

7. The predictive controller of claim 6 , wherein the one or more airside energy components comprise at least one of:

an air handler unit (AHU) energy component indicating an amount of energy consumed by one or more AHUs of the building at each time step; or

a rooftop unit (RTU) energy component indicating an amount of energy consumed by one or more TRUs of the building at each time step.

8. The predictive controller of claim 1 , wherein the predictive objective function accounts for a demand charge based on a maximum power consumption of the building energy system during a demand charge period that overlaps at least partially with the optimization period.

9. The predictive controller of claim 1 , wherein the one or more processing circuits are further configured to determine optimal temperature setpoints for one or more building zones based on the values of the source-specific energy components.

10. A method of operating a building energy system, comprising:

generating a constraint that defines a total electric load to be served by the building energy system at each time step of an optimization period as a summation of multiple source-specific energy components comprising:

a grid energy component indicating an amount of grid energy to purchase from an energy grid; and

a green energy component indicating an amount of green energy provided by green energy generation;

performing an optimization of an objective function subject to the constraint to determine values of the source-specific energy components for the optimization period; and

operating equipment of the building energy system using the values of the source-specific energy components.

11. The method of claim 10 , wherein the multiple source-specific energy components further comprise a battery energy component indicating an amount of electrical energy to store in a battery or discharge from the battery.

12. The method of claim 11 , wherein the battery energy component:

adds to the grid energy component and the green energy component when the amount of the electric energy is discharged from the battery during the time step; and

subtracts from the grid energy component and the green energy component when the amount of the electric energy is stored in the battery during the time step.

13. The method of claim 10 , wherein the objective function accounts for:

an amount of the grid energy or cost of the grid energy purchased from the energy grid; and

an amount of energy savings or cost savings resulting from using the green energy.

14. The method of claim 10 , wherein the objective function accounts for a cost savings resulting from discharging stored energy from a battery during the optimization period.

15. The method of claim 10 , wherein operating the equipment of the building energy system using the values of the source-specific energy components comprises:

determining temperature setpoints for one or more building zones based on the values of the source-specific energy components; and

controlling the equipment using the temperature setpoints.

16. A method of operating a building energy system, comprising:

generating a visualization of a total electric load to be served by the building energy system, the visualization comprising multiple source-specific energy components comprising:

a grid energy component indicating an amount of grid energy to purchase from an energy grid; and

a green energy component indicating an amount of green energy provided by green energy generation;

an energy storage component indicating an amount of energy stored by or discharged from energy storage equipment;

performing a predictive control process subject to a constraint that defines a total a total electric load to be served by the building energy system at each time step of a future time period as a summation of the source-specific energy components to determine target values of the source-specific energy components for the future time period;

causing the visualization to show the target values of the source-specific energy components for the future time period; and

operating equipment of the building energy system using the values of the source-specific energy components.

17. The method of claim 16 , wherein the visualization comprises a plot of the values of the multiple source-specific energy components at a plurality of time steps in the future time period.

18. The method of claim 16 , wherein the visualization further comprises a charge level of energy storage equipment.

19. The method of claim 16 , wherein the visualization further comprises an indication of a heating or cooling load served by the building energy system over the future time period.

20. The method of claim 16 , wherein operating the equipment of the building energy system using the values of the source-specific energy components comprises:

determining temperature setpoints for one or more building zones based on the values of the source-specific energy components; and

controlling the equipment using the temperature setpoints.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 9, 2024
From: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
To: TYCO FIRE & SECURITY GMBH
Reel/Frame 067056/0552 →
NUNC PRO TUNC ASSIGNMENT Recorded Feb 4, 2022
From: JOHNSON CONTROLS TECHNOLOGY COMPANY
To: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
Reel/Frame 058959/0764 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 26, 2020
From: TURNEY, ROBERT D.; PATEL, NISHITH R.
To: JOHNSON CONTROLS TECHNOLOGY COMPANY
Reel/Frame 054169/0927 →