Granted Patent B2

US 10,282,796 · App. 15/405,236 · Granted May 7, 2019

Abstract

Claims (64)

1. An energy storage system for a building, the system comprising:

a battery configured to store electrical energy purchased from a utility and to discharge the stored electrical energy for use in satisfying a building energy load; and

an energy storage controller configured to:

generate a cost function comprising multiple demand charges and a nonlinear maximum value function for each of the multiple demand charges, each of the demand charges corresponding to a demand charge period and defining a cost based on a maximum amount of the electrical energy purchased from the utility during any time step within the corresponding demand charge period, wherein the cost is a function of one or more decision variables representing an amount of the electrical energy to store in the battery or discharge from the battery during each time step of an optimization period;

linearize the cost function by replacing each nonlinear maximum value function with an auxiliary demand charge variable;

modify the cost function by applying a demand charge mask to each of the multiple demand charges, wherein the demand charge masks cause the energy storage controller to disregard the electrical energy purchased from the utility during any time steps that occur outside the corresponding demand charge period when calculating a value for the demand charge; and

allocate, to each of a plurality of time steps within the optimization period, an optimal amount of electrical energy to store in the battery or discharge from the battery during the time step by optimizing the modified cost function, wherein optimizing the modified cost function comprises performing an optimization process to determine optimal values of the decision variables that optimize the cost defined by the modified cost function.

2. The energy storage system of claim 1 , wherein the energy storage controller is configured to create the demand charge masks for each of the multiple demand charges, each demand charge mask defining one or more time steps that occur within the corresponding demand charge period.

3. The energy storage system of claim 2 , wherein each demand charge mask comprises a vector of binary values, each of the binary values corresponding to a time step that occurs within the optimization period and indicating whether the demand charge is active or inactive during the corresponding time step.

4. The energy storage system of claim 1 , wherein the energy storage controller is configured to impose an optimization constraint on each auxiliary demand charge variable, each optimization constraint requiring the auxiliary demand charge variable to be greater than or equal to the amount of electrical energy purchased from the utility during each time step that occurs within the corresponding demand charge period.

5. The energy storage system of claim 1 , wherein the energy storage controller is configured to apply a weighting factor to each of the multiple demand charges in the cost function, each weighting factor scaling the corresponding demand charge to the optimization period.

6. The energy storage system of claim 5 , wherein the energy storage controller is configured to calculate each weighting factor based on a number of time steps the corresponding demand charge is active within the optimization period and a number of time steps the corresponding demand charge is active outside the optimization period.

7. The energy storage system of claim 5 , wherein the energy storage controller is configured to calculate each weighting factor by:

determining a first number of time steps that occur within both the optimization period and the corresponding demand charge period;

determining a second number of time steps that occur within the corresponding demand charge period but not within the optimization period; and

calculating a ratio of the first number of time steps to the second number of time steps.

8. A method for allocating a battery asset in an energy storage system, the method comprising:

generating a cost function comprising multiple demand charges and a nonlinear maximum value function for each of the multiple demand charges, each of the demand charges corresponding to a demand charge period and defining a cost based on a maximum amount of electrical energy purchased from a utility during any time step within the corresponding demand charge period, wherein the cost is a function of one or more decision variables representing an amount of the electrical energy to store in a battery or discharge from the battery during each time step of an optimization period;

linearizing the cost function by replacing each nonlinear maximum value function with an auxiliary demand charge variable;

modifying the cost function by applying a demand charge mask to each of the multiple demand charges;

calculating a value for each of the multiple demand charges in the modified cost function, wherein each demand charge mask causes the electrical energy purchased from the utility during any time steps that occur outside the corresponding demand charge period to be disregarded when calculating the value for the corresponding demand charge;

allocating, to each of a plurality of time steps within the optimization period, an optimal amount of electrical energy to store in the battery or discharge from the battery during the time step by optimizing the modified cost function, wherein optimizing the modified cost function comprises performing an optimization process to determine optimal values of the decision variables that optimize the cost defined by the modified cost function; and

operating the battery to store electrical energy purchased from the utility and discharge the stored electrical energy based on the optimal amounts of electrical energy allocated to each time step.

9. The method of claim 8 , further comprising creating the demand charge masks for each of the multiple demand charges, each demand charge mask defining one or more time steps that occur within the corresponding demand charge period.

10. The method of claim 9 , wherein each demand charge mask comprises a vector of binary values, each of the binary values corresponding to a time step that occurs within the optimization period and indicating whether the demand charge is active or inactive during the corresponding time step.

11. The method of claim 8 , wherein linearizing the cost function comprises imposing an optimization constraint on each auxiliary demand charge variable, each optimization constraint requiring the auxiliary demand charge variable to be greater than or equal to the amount of electrical energy purchased from the utility during each time step that occurs within the corresponding demand charge period.

12. The method of claim 8 , further comprising applying a weighting factor to each of the multiple demand charges in the cost function, each weighting factor scaling the corresponding demand charge to the optimization period.

13. The method of claim 12 , further comprising calculating each weighting factor based on a number of time steps the corresponding demand charge is active within the optimization period and a number of time steps the corresponding demand charge is active outside the optimization period.

14. The method of claim 12 , further comprising calculating each weighting factor by:

determining a first number of time steps that occur within both the optimization period and the corresponding demand charge period;

determining a second number of time steps that occur within the corresponding demand charge period but not within the optimization period; and

calculating a ratio of the first number of time steps to the second number of time steps.

15. An energy cost optimization system for a building, the system comprising:

HVAC equipment configured to consume energy purchased from a utility for use in satisfying a building energy load; and

a controller configured to:

generate a cost function comprising multiple demand charges and a nonlinear maximum value function for each of the multiple demand charges, each of the demand charges corresponding to a demand charge period and defining a cost based on a maximum amount of energy purchased from the utility during any time step within the corresponding demand charge period, wherein the cost is a function of one or more decision variables representing an amount of the energy to be consumed by the HVAC equipment during each time step of an optimization period;

linearize the cost function by replacing each nonlinear maximum value function with an auxiliary demand charge variable;

modify the cost function by applying a demand charge mask to each of the multiple demand charges, wherein the demand charge masks cause the controller to disregard the energy purchased from the utility during any time steps that occur outside the corresponding demand charge period when calculating a value for the demand charge; and

allocate, to each of a plurality of time steps within an optimization period, an optimal amount of energy to be consumed by the HVAC equipment during the time step by optimizing the modified cost function, wherein optimizing the modified cost function comprises performing an optimization process to determine optimal values of the decision variables that optimize the cost defined by the modified cost function.

16. The energy cost optimization system of claim 15 , wherein the controller is configured to create the demand charge masks for each of the multiple demand charges, each demand charge mask defining one or more time steps that occur within the corresponding demand charge period.

17. The energy cost optimization system of claim 15 , wherein the controller is configured to impose an optimization constraint on each auxiliary demand charge variable, each optimization constraint requiring the auxiliary demand charge variable to be greater than or equal to the amount of energy purchased from the utility during each time step that occurs within the corresponding demand charge period.

18. The energy cost optimization system of claim 15 , wherein the controller is configured to apply a weighting factor to each of the multiple demand charges in the cost function, each weighting factor scaling the corresponding demand charge to the optimization period.

19. An energy storage system for a building, the system comprising:

a battery configured to store electrical energy purchased from a utility and to discharge the stored electrical energy for use in satisfying a building energy load; and

an energy storage controller configured to:

generate a cost function comprising multiple demand charges, each of the demand charges corresponding to a demand charge period and defining a cost based on a maximum amount of the electrical energy purchased from the utility during any time step within the corresponding demand charge period, wherein the cost is a function of one or more decision variables representing an amount of the electrical energy to store in the battery or discharge from the battery during each time step of an optimization period;

apply a weighting factor to each of the multiple demand charges in the cost function, each weighting factor scaling the corresponding demand charge to the optimization period;

calculate each weighting factor by:

determining a first number of time steps that occur within both the optimization period and the corresponding demand charge period;

determining a second number of time steps that occur within the corresponding demand charge period but not within the optimization period; and

calculating a ratio of the first number of time steps to the second number of time steps;

modify the cost function by applying a demand charge mask to each of the multiple demand charges, wherein the demand charge masks cause the energy storage controller to disregard the electrical energy purchased from the utility during any time steps that occur outside the corresponding demand charge period when calculating a value for the demand charge; and

allocate, to each of a plurality of time steps within the optimization period, an optimal amount of electrical energy to store in the battery or discharge from the battery during the time step by optimizing the modified cost function, wherein optimizing the modified cost function comprises performing an optimization process to determine optimal values of the decision variables that optimize the cost defined by the modified cost function.

20. A method for allocating a battery asset in an energy storage system, the method comprising:

generating a cost function comprising multiple demand charges, each of the demand charges corresponding to a demand charge period and defining a cost based on a maximum amount of electrical energy purchased from a utility during any time step within the corresponding demand charge period, wherein the cost is a function of one or more decision variables representing an amount of the electrical energy to store in a battery or discharge from the battery during each time step of an optimization period;

applying a weighting factor to each of the multiple demand charges in the cost function, each weighting factor scaling the corresponding demand charge to the optimization period;

calculating each weighting factor by:

determining a first number of time steps that occur within both the optimization period and the corresponding demand charge period;

determining a second number of time steps that occur within the corresponding demand charge period but not within the optimization period; and

calculating a ratio of the first number of time steps to the second number of time steps;

modifying the cost function by applying a demand charge mask to each of the multiple demand charges;

calculating a value for each of the multiple demand charges in the modified cost function, wherein each demand charge mask causes the electrical energy purchased from the utility during any time steps that occur outside the corresponding demand charge period to be disregarded when calculating the value for the corresponding demand charge;

allocating, to each of a plurality of time steps within the optimization period, an optimal amount of electrical energy to store in the battery or discharge from the battery during the time step by optimizing the modified cost function, wherein optimizing the modified cost function comprises performing an optimization process to determine optimal values of the decision variables that optimize the cost defined by the modified cost function; and

operating the battery to store electrical energy purchased from the utility and discharge the stored electrical energy based on the optimal amounts of electrical energy allocated to each time step.

Assignments (3)

Cited By (11)

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