High level central plant optimization

• US 10,175,681 B2
• Filed: 02/27/2015
• Issued: 01/08/2019
• 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:
  US14/634,609
• Publication Number:
  US 20150316902A1
• Time in Patent Office: 1,411 Days
• Field of Search: 700291
• 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

An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment. The high level optimization module is configured to optimize the objective function over the optimization period subject to load equality constraints and capacity constraints on the central plant equipment to determine an optimal distribution of the building energy loads over multiple groups of the central plant equipment.

Claims

1. An optimization and control system for a central plant configured to serve building energy loads, the optimization and control system comprising:

a central plant controller configured to receive utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve building energy loads at each of the plurality of time steps, the central plant controller comprising;

a load/rate prediction module configured to use feedback from a building automation system to predict the building energy loads for a plurality of time steps in an optimization period, the feedback from the building automation system comprising input from one or more sensors configured to monitor conditions within a controlled building; and

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

wherein the high level optimization module is configured to optimize the objective function over the optimization period subject to load equality constraints and capacity constraints on the central plant equipment to determine an optimal distribution of the predicted building energy loads over multiple groups of the central plant equipment at each of the plurality of time steps, wherein the load equality constraints ensure that the optimal distribution satisfies the predicted building energy loads at each of the plurality of time steps;

wherein the central plant controller is configured to control the central plant equipment such that the central plant equipment operate to achieve the optimal distribution of the building energy loads at each of the plurality of time steps.

2. The optimization and control system of claim 1, wherein the high level optimization module uses linear programming to generate and optimize the objective function.

3. The optimization and control system of claim 1, 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 central plant equipment to serve the building 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 multiple groups of the central plant equipment at each of the plurality of time steps, wherein the high level optimization module is configured to determine optimal values for the load variables in the decision matrix.

4. The optimization and control system of claim 1, wherein:

the central plant comprises a plurality of subplants; and

each of the multiple groups of the central plant equipment corresponds to one of the plurality of subplants.

5. The optimization and control system of claim 4, wherein:

the plurality of subplants comprise at least one of a hot thermal energy storage subplant and a cold thermal energy storage subplant; and

the thermal energy storage subplants are configured to store thermal energy generated in one of the plurality of time steps for use in another of the plurality of time steps.

6. The optimization and control system of claim 4, wherein the high level optimization module is configured to:

generate a subplant curve for each of the plurality of subplants, wherein each subplant curve indicates a relationship between resource consumption and load production for one of the plurality of subplants;

use the subplant curves to formulate subplant curve constraints; and

optimize the objective function subject to the subplant curve constraints.

7. The optimization and control system of claim 6, wherein generating the subplant curve comprises at least one of:

converting a nonlinear subplant curve into a linear subplant curve comprising one or more piecewise linear segments; and

converting a non-convex subplant curve into a convex subplant curve.

8. The optimization and control system of claim 6, wherein generating the subplant curve comprises:

receiving an initial subplant curve based on manufacturer data for the group of equipment corresponding to the subplant; and

updating the initial subplant curve using experimental data from the central plant.

9. A cascaded optimization and control system for a central plant configured to serve building energy loads, the cascaded optimization system comprising:

a central plant controller configured to use dynamic programming to split an optimization problem for the central plant into a high level optimization and a low level optimization, the central plant controller comprising;

a load/rate prediction module configured to use feedback from a building automation system to predict building energy loads for a plurality of time steps in an optimization period, the feedback from the building automation system comprising input from one or more sensors configured to monitor conditions within a controlled building;

a high level optimization module configured to perform the high level optimization, wherein the high level optimization comprises determining an optimal distribution of the predicted building energy loads over multiple groups of central plant equipment subject to load equality constraints that ensure the optimal distribution satisfies the predicted building energy loads at each of the plurality of time steps; and

a low level optimization module configured to perform the low level optimization, wherein the low level optimization comprises determining optimal operating statuses for individual devices within each of the multiple groups of the central plant equipment;

wherein the central plant controller is configured to control the central plant equipment such that the central plant equipment operate to achieve the optimal distribution of the building energy loads at each of the plurality of time steps.

10. The cascaded optimization and control system of claim 9, wherein:

the optimal distribution of the building energy loads determined by the high level optimization module optimizes a monetary cost of operating the central plant over an optimization period; and

the optimal operating statuses determined by the low level optimization module optimize an amount of energy consumed by each of the multiple groups of the central plant equipment to achieve the optimal distribution of the building energy loads determined by the high level optimization module.

11. The cascaded optimization and control system of claim 9, wherein:

the low level optimization module is configured to generate a subplant curve for each of the groups of central plant equipment, wherein each subplant curve indicates a relationship between resource consumption and load production for one of the groups of central plant equipment; and

the high level optimization module is configured to use the subplant curves to formulate subplant curve constraints and to determine the optimal distribution of the building energy loads subject to the subplant curve constraints.

12. A method for optimizing cost in a central plant configured to serve building energy loads, the method comprising:

using feedback from a building automation system to predict building energy loads for a plurality of time steps in an optimization period, the feedback from the building automation system comprising input from one or more sensors configured to monitor conditions within a controlled building;

receiving, at a central plant controller, utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the predicted building energy loads at each of the plurality of time steps;

generating, by a high level optimization module of the central plant controller, an objective function that expresses a total monetary cost of operating the central plant over the optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment at each of the plurality of time steps;

optimizing, by the high level optimization module, the objective function over the optimization period subject to load equality constraints and capacity constraints on the central plant equipment to determine an optimal distribution of the predicted building energy loads over multiple groups of the central plant equipment at each of the plurality of time steps, wherein the load equality constraints ensure that the optimal distribution satisfies the predicted building energy loads at each of the plurality of time steps; and

controlling, by the central plant controller, the central plant equipment such that the central plant equipment operate to achieve the optimal distribution of the building energy loads at each of the plurality of time steps.

13. The method of claim 12, wherein the high level optimization module uses linear programming to generate and optimize the objective function.

14. The method of claim 12, 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 central plant equipment to serve the building 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 multiple groups of the central plant equipment at each of the plurality of time steps, wherein optimizing the objective function comprises determining optimal values for the load variables in the decision matrix.

15. The method of claim 12, further comprising:

using the building energy loads and capacity limits for the central plant equipment to generate the load equality constraints and the capacity constraints;

wherein the capacity constraints ensure that the multiple groups of central plant equipment are operated within the capacity limits at each of the plurality of time steps.

16. The method of claim 12, wherein:

the central plant comprises a plurality of subplants; and

each of the multiple groups of the central plant equipment corresponds to one of the plurality of subplants.

17. The method of claim 16, further comprising:

generating a subplant curve for each of the plurality of subplants, wherein each subplant curve indicates a relationship between resource consumption and load production for one of the plurality of subplants;

using the subplant curves to formulate subplant curve constraints; and

optimizing the objective function subject to the subplant curve constraints.

18. The method of claim 17, wherein generating the subplant curve comprises at least one of:

converting a nonlinear subplant curve into a linear subplant curve comprising one or more piecewise linear segments; and

converting a non-convex subplant curve into a convex subplant curve.

19. The method of claim 12, wherein the central plant optimization system uses dynamic programming to split the method for optimizing cost into a high level optimization and a low level optimization;

wherein the high level optimization comprises determining the optimal distribution of the building energy loads over the multiple groups of the central plant equipment; and

wherein the low level optimization comprises determining optimal operating statuses for individual devices within each of the multiple groups of the central plant equipment.

20. The method of claim 19, wherein the optimal distribution of the building energy loads optimizes the monetary cost of operating the central plant over the optimization period;

and

wherein the optimal operating statuses optimize an amount of energy consumed by each of the multiple groups of the central plant equipment to achieve the optimal distribution of the building energy loads.