Application US20190206000

BUILDING ENERGY STORAGE SYSTEM WITH MULTIPLE DEMAND CHARGE COST OPTIMIZATION
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US20190206000

1. A building energy system configured to serve energy loads of a building or campus, the system comprising: (9) (2)

equipment configured to consume, produce, store, or discharge one or more energy resources, at least one of the energy resources purchased from a utility supplier;

a controller configured to: obtain representative loads and rates for the building or campus for each of a plurality of scenarios;

generate a cost function comprising a risk attribute and multiple demand charges, each of the demand charges corresponding to a demand charge period and defining a cost based on a maximum amount of at least one of the energy resources purchased from the utility supplier during any time step within the corresponding demand charge period;

determine, for each of the multiple demand charges, a peak demand target for the corresponding demand charge period by performing a first optimization of the risk attribute over the plurality of the scenarios;

allocate, to each of a plurality of time steps within an optimization period, an amount of the one or more energy resources to be consumed, produced, stored, or discharged by the building equipment by performing a second optimization of the cost function over the optimization period subject to one or more constraints based on the peak demand target for each of the multiple demand charges; and

operate the equipment to consume, produce, store, or discharge the one or more energy resources at each of the plurality of time steps in accordance with a result of the second optimization.

2. The building energy system of claim 1, wherein the controller is configured to 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 resource purchased from the utility during any time steps that occur outside the corresponding demand charge period when calculating a value for the demand charge. (0)

3. The building energy system of claim 1, wherein the risk attribute of the modified cost function comprises at least one of a conditional value at risk, a value at risk, or an expected cost. (0)

4. The building energy system of claim 1, wherein performing the second optimization comprises using each peak demand target to implement a peak demand constraint that limits a maximum purchase of the energy resource subject to the demand charge during the corresponding demand period. (0)

5. The building energy system of claim 1, wherein the cost function comprises a revenue term that accounts for revenue generated by operating the equipment to participate in an incentive-based demand response program. (0)

6. The building energy system of claim 1, wherein the controller is configured to obtain the representative loads and rates by: (0) (2)

7. The building energy system of claim 1, wherein the controller is configured to obtain the representative loads and rates by: (0) (2)

8. The building energy system of claim 1, wherein the controller is configured to obtain the representative loads and rates by: (1) (2)

storing a history of past scenarios comprising actual values for historical loads and rates; and

at least one of: sampling the representative loads and rates from the history of past scenarios; or

generating an estimated distribution based on the history of past scenarios and sampling the representative loads and rates from the estimated distribution.

9. The building energy system of claim 8, wherein: (0) (2)

10. The building energy system of claim 1, wherein the cost function comprises a nonlinear maximum value function for each of the multiple demand charges and the controller is configured to linearize the cost function by replacing each nonlinear maximum value function with an auxiliary demand charge variable. (0)

11. The building energy system of claim 1, 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. (1) (0)

12. The building energy system of claim 11, wherein the controller is configured to calculate each weighting factor by: (0) (3)

13. A method for managing a building energy system, comprising: (6) (3)

operating equipment to consume, store, or discharge one or more energy resources purchased from a utility supplier;

determining an allocation of the energy resources across the equipment over an optimization period by: obtaining representative loads and rates for the building or campus for each of a plurality of scenarios;

generating a cost function comprising a risk attributed and 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 at least one energy resource purchased from the utility supplier during any time step within the corresponding demand charge period;

determining, for each of the multiple demand charges, a peak demand target for the corresponding demand charge period by performing a first optimization of the risk attribute over the plurality of scenarios; and

allocating, to each of a plurality of time steps within an optimization period, an amount of the one or more energy resources to be consumed, produced, stored, or discharged by the building equipment by performing a second optimization of the cost function over the optimization period subject to one or more constraints based on the peak demand target for each of the multiple demand charges; and

controlling the equipment to store or discharge the amount of energy allocated for a current time step of the plurality of time steps.

14. The method of claim 13, comprising modifying 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 resource purchased from the utility during any time steps that occur outside the corresponding demand charge period when calculating a value for the demand charges. (0)

15. The method of claim 13, wherein the risk attribute of the modified cost function comprises at least one of a conditional value at risk, a value at risk, or an expected cost. (0)

16. The method of claim 13, wherein optimizing the modified cost function comprises using each peak demand target to implement a peak demand constraint that limits a maximum purchase of the energy resource subject to the demand charge during the corresponding demand period. (0)

17. The method of claim 13, wherein obtaining the representative loads and rates comprises: (0) (2)

18. The method of claim 13, wherein obtaining the representative loads and rates comprises: (0) (2)

19. The method of claim 13, wherein obtaining the representative loads and rates comprises: (1) (2)

storing a history of past scenarios comprising actual values for historical loads and rates; and

at least one of: sampling the representative loads and rates from the history of past scenarios; or

generating an estimated distribution based on the history of past scenarios and sampling the representative loads and rates from the estimated distribution.

20. The method of claim 19, wherein: (0) (2)

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Application US20190206000

BUILDING ENERGY STORAGE SYSTEM WITH MULTIPLE DEMAND CHARGE COST OPTIMIZATION

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