High level central plant optimization

• US 10,915,094 B2
• Filed: 12/10/2018
• Issued: 02/09/2021
• Est. Priority Date: 05/01/2014
• Status: Active Grant

• Current Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Wenzel, Michael J., Turney, Robert D., Drees, Kirk H., Asmus, Matthew J.
• Primary Examiner(s): Masinick, Michael D
• Application Number:
  US16/214,984
• Publication Number:
  US 20190107825A1
• Time in Patent Office: 792 Days
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/418 Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
• 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 2219/31414 Calculate amount of production energy, waste and toxic release
• CPC Class Codes: G05B 2219/32021 Energy management, balance and limit power to tools
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• 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
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02P 70/10 Greenhouse gas [GHG] capture, material saving, heat recovery or other energy efficient measures, e.g. motor control, characterised by manufacturing processes, e.g. for rolling metal or metal working
• CPC Class Codes: Y02P 80/10 Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
• CPC Class Codes: Y02P 90/80 Management or planning
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving

Abstract

A controller for equipment obtains utility rate data indicating a price of one or more resources consumed by the equipment to serve energy loads. The controller generates an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the equipment at each of a plurality of time steps. The controller optimizes the objective function to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps. Load equality constraints on the objective function ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps. The controller operates the equipment to achieve the distribution of the predicted energy loads at each of the plurality of time steps.

Claims

1. A controller for equipment configured to serve energy loads, the controller comprising:

a processing circuit configured to;

obtain utility rate data indicating a price of one or more resources consumed by the equipment to serve the energy loads at each of a plurality of time steps of an optimization period;

generate an objective function that expresses a total monetary cost of operating the equipment over the optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the equipment to serve the energy loads at each of the plurality of time steps;

generate a resource consumption curve for each of a plurality of groups of the equipment, each resource consumption curve indicating a relationship between resource consumption and load production for one of the groups of the equipment;

use the resource consumption curves to impose constraints on the objective function; and

optimize the objective function over the optimization period subject to the constraints imposed using the resource consumption curves and subject to load equality constraints on the equipment to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps, wherein the load equality constraints ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps; and

operate the equipment to achieve the distribution at each of the plurality of time steps.

2. The controller of claim 1, wherein the processing circuit is configured to predict the predicted energy loads using at least one of input from sensors within a building served by the equipment or weather data for a location of the building served by the equipment.

3. The controller of claim 1, wherein:

the equipment comprise a plurality of subplants within a central plant; and

the distribution of the predicted energy loads across the equipment comprises a distribution of the predicted energy loads across the plurality of subplants at each of the plurality of time steps.

4. The controller of claim 1, wherein:

the equipment comprise one or more energy storage devices configured to store energy generated or obtained in one of the plurality of time steps for use in another of the plurality of time steps; and

the distribution of the predicted energy loads across the equipment comprises an amount of the predicted energy loads to store or discharge from the one or more energy storage devices at each of the plurality of time steps.

5. The controller of claim 1, wherein the processing circuit is configured to:

organize the equipment into a plurality of type groups, each type group of the equipment comprising one or more devices of the equipment that operate to consume a same type of the one or more resources and produce a same type of the energy loads; and

distribute the predicted energy loads across the plurality of type groups of the equipment such that each of the type groups of the equipment is assigned to produce a portion of the predicted energy loads at each of the plurality of time steps.

6. The controller of claim 1, wherein generating the resource consumption curve for a group of the equipment comprises at least one of:

converting a nonlinear resource consumption curve into a linear resource consumption curve comprising one or more piecewise linear segments;

or

converting a non-convex resource consumption curve into a convex resource consumption curve.

7. The controller of claim 1, wherein generating the resource consumption curve for a group of the equipment comprises:

receiving an initial resource consumption curve based on manufacturer data for the group of the equipment; and

updating the initial resource consumption curve using experimental data obtained by operating the group of the equipment.

8. The controller of claim 1, wherein generating the resource consumption curve for a group of the equipment comprises:

receiving a plurality of device-specific equipment curves, each of the device-specific equipment curves indicating the relationship between resource consumption and load production for a particular device of the equipment within the group of the equipment; and

combining the plurality of device-specific equipment curves to generate the resource consumption curve for the group of the equipment.

9. A method for controlling equipment to serve energy loads, the method comprising:

obtaining utility rate data indicating a price of one or more resources consumed by the equipment to serve the energy loads at each of a plurality of time steps of a time period;

generating an objective function that expresses a total monetary cost of operating the equipment over the time period as a function of the utility rate data and an amount of the one or more resources consumed by the equipment to serve the energy loads at each of the plurality of time steps;

generating a resource consumption curve for each of a plurality of groups of the equipment, each resource consumption curve indicating a relationship between resource consumption and load production for one of the groups of the equipment;

using the resource consumption curves to impose constraints on the objective function;

performing an optimization of the objective function over the time period subject to the constraints imposed using the resource consumption curves and subject to load equality constraints on the equipment to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps, wherein the load equality constraints ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps; and

operating the equipment to achieve the distribution of the predicted energy loads at each of the plurality of time steps.

12. The method of claim 9, predicting the predicted energy loads using at least one of input from sensors within a building served by the equipment or weather data for a location of the building served by the equipment.

13. The method of claim 9, wherein:

the equipment comprise a plurality of subplants within a central plant; and

the distribution of the predicted energy loads across the equipment comprises a distribution of the predicted energy loads across the plurality of subplants at each of the plurality of time steps.

14. The method of claim 9, wherein:

the equipment comprise one or more energy storage devices configured to store energy generated or obtained in one of the plurality of time steps for use in another of the plurality of time steps; and

the distribution of the predicted energy loads across the equipment comprises an amount of the predicted energy loads to store or discharge from the one or more energy storage devices at each of the plurality of time steps.

15. The method of claim 9, further comprising:

organizing the equipment into a plurality of type groups, each type group of the equipment comprising one or more devices of the equipment that operate to consume a same type of the one or more resources and produce a same type of the energy loads; and

distributing the predicted energy loads across the plurality of type groups of the equipment such that each of the type groups of the equipment is assigned to produce a portion of the predicted energy loads at each of the plurality of time steps.

16. The method of claim 9, wherein generating the resource consumption curve for a group of the equipment comprises at least one of:

converting a nonlinear resource consumption curve into a linear resource consumption curve comprising one or more piecewise linear segments;

or

converting a non-convex resource consumption curve into a convex resource consumption curve.

17. The method of claim 9, wherein generating the resource consumption curve for a group of the equipment comprises:

receiving an initial resource consumption curve based on manufacturer data for the group of the equipment; and

updating the initial resource consumption curve using experimental data obtained by operating the group of the equipment.

18. The method of claim 9, wherein generating the resource consumption curve for a group of the equipment comprises:

receiving a plurality of device-specific equipment curves, each of the device-specific equipment curves indicating the relationship between resource consumption and load production for a particular device of the equipment within the group of the equipment; and

combining the plurality of device-specific equipment curves to generate the resource consumption curve for the group of the equipment.

10. A controller for equipment configured to serve energy loads, the controller comprising:

a processing circuit configured to;

obtain utility rate data indicating a price of one or more resources consumed by the equipment to serve the energy loads at each of a plurality of time steps of an optimization period;

generate an objective function that expresses a total monetary cost of operating the equipment over the optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the equipment to serve the energy loads at each of the plurality of time steps, wherein the objective function comprises;

a cost vector comprising cost variables representing a monetary cost associated with each of the one or more resources consumed by the equipment to serve the energy loads at each of the plurality of time steps; and

a decision matrix comprising load variables representing an energy load for each of the equipment at each of the plurality of time steps;

optimize the objective function over the optimization period subject to load equality constraints on the equipment to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps, wherein the load equality constraints ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps and wherein determining the distribution of the predicted energy loads comprises determining values for the load variables in the decision matrix; and

operate the equipment to achieve the distribution at each of the plurality of time steps.

11. The controller of claim 10, wherein the processing circuit is configured to:

generate a resource consumption curve for each of a plurality of groups of the equipment, each resource consumption curve indicating a relationship between resource consumption and load production for one of the groups of the equipment;

use the resource consumption curves to impose constraints on the objective function; and

optimize the objective function subject to the constraints.

19. A method for controlling equipment to serve energy loads, the method comprising:

obtaining utility rate data indicating a price of one or more resources consumed by the equipment to serve the energy loads at each of a plurality of time steps of a time period;

generating an objective function that expresses a total monetary cost of operating the equipment over the time period as a function of the utility rate data and an amount of the one or more resources consumed by the equipment to serve the energy loads at each of the plurality of time steps, wherein the objective function comprises;

a cost vector comprising cost variables representing a monetary cost associated with each of the one or more resources consumed by the equipment to serve the energy loads at each of the plurality of time steps; and

a decision matrix comprising load variables representing an energy load for each of the equipment at each of the plurality of time steps;

performing an optimization of the objective function over the time period subject to load equality constraints on the equipment to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps, wherein the load equality constraints ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps and wherein determining the distribution of the predicted energy loads comprises determining values for the load variables in the decision matrix; and

operating the equipment to achieve the distribution of the predicted energy loads at each of the plurality of time steps.

20. The method of claim 19, further comprising:

generating a resource consumption curve for each of a plurality of groups of the equipment, each resource consumption curve indicating a relationship between resource consumption and load production for one of the groups of the equipment;

using the resource consumption curves to impose constraints on the objective function; and

performing the optimization of the objective function subject to the constraints.