Incorporating a demand charge in central plant optimization

• US 10,386,820 B2
• Filed: 02/27/2015
• Issued: 08/20/2019
• 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., Drees, Kirk H.
• Primary Examiner(s): Saavedra, Emilio J
• Application Number:
  US14/634,599
• Publication Number:
  US 20150316901A1
• Time in Patent Office: 1,635 Days
• Field of Search: 700297
• 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 optimization system includes a demand charge module configured to modify the objective function to account for a demand charge indicating a cost associated with maximum power consumption during a demand charge period. The high level optimization module is configured to optimize the objective function over the demand charge period.

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 load prediction data indicating building energy loads for a plurality of time steps in an optimization period 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 at each of the plurality of time steps, the central plant controller comprising;

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; and

a demand charge module configured to modify the objective function to account for a demand charge indicating a cost associated with a maximum power consumption during a demand charge period different from the optimization period, wherein modifying the objective function comprises applying a weighting factor to one of a demand charge term or a consumption term of the objective function, wherein the weighting factor equalizes the optimization period and the different demand charge period, the objective function comprising a summation of the consumption term and the demand charge term in which one of the consumption term or the demand charge term is multiplied by the weighting factor, wherein the consumption term comprises the amount of the one or more resources consumed by the central plant equipment at each time step of the optimization period multiplied by a cost per unit of the one or more resources, and the demand charge term comprises the maximum power consumption during the demand charge period multiplied by a cost per unit of power consumption;

wherein the high level optimization module is configured to optimize the objective function over the demand charge period subject to a set of constraints to determine an optimal distribution of the building energy loads over multiple groups of the central plant equipment at each of the plurality of time steps, and configured to use the weighting factor to evaluate trade-offs between increasing the consumption term and increasing the demand charge term when optimizing the objective function;

wherein the central plant controller is configured to control the central plant equipment according to a result of optimizing the objective function 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 modifying the objective function to account for the demand charge comprises:

adding the demand charge term to the objective function, wherein the demand charge term comprises a nonlinear function that selects the maximum power consumption during the demand charge period; and

linearizing the demand charge term by adding one or more demand charge constraints to the set of constraints.

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

the objective function comprises a cost vector including cost variables that represent 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

modifying the objective function to account for the demand charge comprises adding a demand charge rate to the cost vector.

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

the objective function comprises a decision matrix including load variables that represent an energy load for each of the multiple groups of the central plant equipment at each of the plurality of time steps;

modifying the objective function to account for the demand charge comprises adding a demand charge variable to the decision matrix; and

optimizing the objective function comprises determining optimal values for the variables in the decision matrix.

6. The optimization and control system of claim 5, wherein modifying the objective function to account for the demand charge comprises adding one or more constraints on the demand charge variable to the set of constraints for each of the plurality of time steps in the demand charge period;

wherein the constraints on the demand charge variable for each time step require the demand charge variable to be greater than or equal to a total energy consumption at the time step.

7. The optimization and control system of claim 6, wherein the total energy consumption at a time step is a total electric power consumed by the central plant equipment and one or more buildings served by the central plant equipment at the time step.

8. The optimization and control system of claim 6, wherein the demand charge constraint for each time step requires the demand charge variable to be greater than or equal to a value of the demand charge variable at a previous time step in the demand charge period.

9. The optimization and control system of claim 1, wherein the weighting factor comprises at least one of:

a ratio of a duration of the demand charge period to a duration of the optimization period applied to the consumption term of the objective function; and

a ratio of the duration of the optimization period to the duration of the demand charge period applied to the demand charge term of the objective function.

10. A method for incorporating a demand charge in an optimization and control process for a central plant, the method comprising:

receiving, at a central plant controller, load prediction data indicating building energy loads for a plurality of time steps in an optimization period 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 at each of the plurality of time steps;

generating, by a high level optimization module, 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;

modifying, by a demand charge module, the objective function to account for a demand charge indicating a cost associated with a maximum power consumption during a demand charge period different from the optimization period, wherein modifying the objective function comprises applying a weighting factor to one of a demand charge term or a consumption term of the objective function, wherein the weighting factor equalizes the optimization period and the different demand charge period, the objective function comprising a summation of the consumption term and the demand charge term in which one of the consumption term or the demand charge term is multiplied by the weighting factor, wherein the consumption term comprises the amount of the one or more resources consumed by the central plant equipment at each time step of the optimization period multiplied by a cost per unit of the one or more resources, and the demand charge term comprises the maximum power consumption during the demand charge period multiplied by a cost per unit of power consumption;

optimizing, by the high level optimization module, the objective function over the demand charge period subject to a set of constraints to determine an optimal distribution of the building energy loads over multiple groups of the central plant equipment at each of the plurality of time steps, wherein optimizing the objective function comprises using the weighting factor to evaluate trade-offs between increasing the consumption term and increasing the demand charge term; and

controlling, by the central plant controller, the central plant equipment according to a result of optimizing the objective function 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.

11. The method of claim 10, wherein the high level optimization module uses linear programming to generate and optimize the objective function.

12. The method of claim 10, wherein modifying the objective function to account for the demand charge comprises:

adding the demand charge term to the objective function, wherein the demand charge term comprises a nonlinear function that selects the maximum power consumption during the demand charge period; and

linearizing the demand charge term by adding one or more demand charge constraints to the set of constraints.

13. The method of claim 10, wherein:

the objective function comprises a cost vector including cost variables that represent 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

modifying the objective function to account for the demand charge comprises adding a demand charge rate to the cost vector.

14. The method of claim 10, wherein:

the objective function comprises a decision matrix including load variables that represent an energy load for each of the multiple groups of the central plant equipment at each of the plurality of time steps;

modifying the objective function to account for the demand charge comprises adding a demand charge variable to the decision matrix; and

optimizing the objective function comprises determining optimal values for the variables in the decision matrix.

15. The method of claim 14, wherein modifying the objective function to account for the demand charge comprises adding one or more constraints on the demand charge variable to the set of constraints for each of the plurality of time steps in the demand charge period;

wherein the demand charge constraint for each time step requires the demand charge variable to be greater than or equal to a total energy consumption at the time step.

16. The method of claim 15, wherein the total energy consumption at a time step is a total electric power consumed by the central plant equipment and one or more buildings served by the central plant equipment at the time step.

17. The method of claim 15, wherein the demand charge constraint for each time step requires the demand charge variable to be greater than or equal to a value of the demand charge variable at a previous time step in the demand charge period.

18. The method of claim 10, wherein the weighting factor comprises at least one of:

a ratio of a duration of the demand charge period to a duration of the optimization period applied to the consumption term of the objective function; and

a ratio of the duration of the optimization period to the duration of the demand charge period applied to the demand charge term of the objective function.