Building energy storage system with peak load contribution cost optimization

• US 10,324,483 B2
• Filed: 01/12/2017
• Issued: 06/18/2019
• Est. Priority Date: 01/12/2017
• Status: Active Grant

• Current Assignee: Tyco Fire & Security GmbH (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: ElBsat, Mohammad N.
• Primary Examiner(s): Laughlin, Nathan L
• Application Number:
  US15/405,234
• Publication Number:
  US 20180196456A1
• Time in Patent Office: 887 Days
• CPC Class Codes: G05B 13/026 using a predictor
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2614 HVAC, heating, ventillation, climate control
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05F 1/66 Regulating electric power
• 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 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/32 using batteries with converting means
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: H02J 7/0068 Battery or charger load switching, e.g. concurrent charging and load supply H02J7/0013 takes precedence
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/12 Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
• CPC Class Codes: Y04S 10/30 State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 20/12 : Energy storage units, uninterruptible power supply [UPS] systems or standby or emergency generators, e.g. in the last power distribution stages
• CPC Class Codes: Y04S 40/20 : Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

An energy storage system for a building includes a battery and an energy storage controller. The battery is configured to store electrical energy purchased from a utility and to discharge stored electrical energy for use in satisfying a building energy load. The energy storage controller is configured to generate a cost function including a peak load contribution (PLC) term. The PLC term represents a cost based on electrical energy purchased from the utility during coincidental peak hours in an optimization period. The controller is configured to modify the cost function by applying a peak hours mask to the PLC term. The peak hours mask identifies one or more hours in the optimization period as projected peak hours and causes the energy storage controller to disregard the electrical energy purchased from the utility during any hours not identified as projected peak hours when calculating a value for the PLC term.

Claims

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 an energy cost term and a separate peak load contribution (PLC) term, wherein the energy cost term represents a first cost based on an amount of the electrical energy purchased from the utility during each time step of a total number of time steps in an optimization period, wherein the PLC term represents a second cost based on an amount of the electrical energy purchased from the utility during coincidental peak (CP) hours in the optimization period, the CP hours comprising a subset of the total number of time steps in the optimization period, wherein both the first cost and the second cost are functions 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 the optimization period;

modify the cost function by applying a peak hours mask to the PLC term, wherein the peak hours mask identifies one or more hours in the optimization period as projected peak hours and causes the energy storage controller to assign zero cost, in the PLC term, to the electrical energy purchased from the utility during any hours not identified as projected peak hours; and

allocate, to each of the 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 a total cost defined by the modified cost function.

2. The energy storage system of claim 1, wherein the CP hours comprise one or more hours of the optimization period during which regional demand peaks occur for a geographic region in which the building is located.

3. The energy storage system of claim 1, wherein:

the CP hours are unknown prior to the optimization period; and

the projected peak hours comprise an estimate of the CP hours and are identified prior to the optimization period.

4. The energy storage system of claim 1, wherein the peak hours mask comprises a vector of binary values, each of the binary values corresponding to an hour of the optimization period and indicating whether the corresponding hour is a projected peak hour.

5. The energy storage system of claim 1, wherein the energy storage controller is configured to create the peak hours mask based on user input defining the projected peak hours.

6. The energy storage system of claim 1, wherein the energy storage controller is configured to:

predict regional demand peaks for a geographic region in which the building is located;

define the projected peak hours based on the predicted regional demand peaks; and

create the peak hours mask using the projected peak hours.

7. The energy storage system of claim 6, wherein the energy storage controller is configured to predict the regional demand peaks based on at least one of load history data and weather data for the geographic region in which the building is located.

8. The energy storage system of claim 1, wherein:

the PLC term comprises a rate factor defining a cost per unit of the electrical energy purchased from the utility during the CP hours; and

the energy storage controller is configured to;

identify a geographic region in which the building is located; and

calculate the rate factor based on one or more parameters that depend on the identified geographic region.

9. The energy storage system of claim 8, wherein the energy storage controller is configured to:

identify the geographic region in which the building is located as a Pennsylvania, Jersey, Maryland (PJM) region; and

calculate the rate factor based on at least one of a capacity loss factor for the PJM region and a PLC rate for the PJM region.

10. The energy storage system of claim 8, wherein the energy storage controller is configured to:

identify the geographic region in which the building is located as an Ontario region; and

calculate the rate factor based on at least one of a region-wide global adjustment cost for the Ontario region and a region-wide peak load estimate for the Ontario region.

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

generating a cost function comprising an energy cost term and a separate peak load contribution (PLC) term, wherein the energy cost term represents a first cost based on an amount of electrical energy purchased from a utility during each time step of a total number of time steps in an optimization period, wherein the PLC term represents a second cost based on an amount of the electrical energy purchased from the utility during coincidental peak (CP) hours in the optimization period, the CP hours comprising a subset of the total number of time steps in the optimization period, wherein both the first cost and the second cost are functions 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 the optimization period;

modifying the cost function by applying a peak hours mask to the PLC term, wherein the peak hours mask identifies one or more hours in the optimization period as projected peak hours and causes the electrical energy purchased from the utility during any hours not identified as projected peak hours to be assigned zero cost in the PLC term;

allocating, to each of the time steps within the optimization period, an optimal amount of electrical energy to store in a 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 a total 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.

12. The method of claim 11, wherein the CP hours comprise one or more hours of the optimization period during which regional demand peaks occur for a geographic region in which the building is located.

13. The method of claim 11, wherein:

the CP hours are unknown prior to the optimization period; and

the projected peak hours comprise an estimate of the CP hours and are identified prior to the optimization period.

14. The method of claim 11, wherein the peak hours mask comprises a vector of binary values, each of the binary values corresponding to an hour of the optimization period and indicating whether the corresponding hour is a projected peak hour.

15. The method of claim 11, further comprising:

predicting regional demand peaks for a geographic region in which the building is located;

defining the projected peak hours based on the predicted regional demand peaks; and

creating the peak hours mask using the projected peak hours.

16. The method of claim 15, wherein the regional demand peaks are predicted based on at least one of load history data and weather data for the geographic region in which the building is located.

17. The method of claim 11, wherein the PLC term comprises a rate factor defining a cost per unit of the electrical energy purchased from the utility during the CP hours;

the method further comprising;

identifying a geographic region in which the building is located; and

calculating the rate factor based on one or more parameters that depend on the identified geographic region.

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

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

a controller configured to;

generate a cost function comprising an energy cost term and a separate peak load contribution (PLC) term, wherein the energy cost term represents a first cost based on an amount of the electrical energy purchased from the utility during each time step of a total number of time steps in an optimization period, wherein the PLC term represents a second cost based on an amount of the electrical energy purchased from the utility during coincidental peak (CP) hours in the optimization period, the CP hours comprising a subset of the total number of time steps in the optimization period, wherein both the first cost and the second cost are functions 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 the optimization period;

modify the cost function by applying a peak hours mask to the PLC term, wherein the peak hours mask identifies one or more hours in the optimization period as projected peak hours and causes the controller to assign zero cost, in the PLC term, to the electrical energy purchased from the utility during any hours not identified as projected peak hours; and

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

19. The energy cost optimization system of claim 18, wherein the peak hours mask comprises a vector of binary values, each of the binary values corresponding to an hour of the optimization period and indicating whether the corresponding hour is a projected peak hour.

20. The energy cost optimization system of claim 18, wherein:

the PLC term comprises a rate factor defining a cost per unit of the energy purchased from the utility during the CP hours; and

the controller is configured to;

identify a geographic region in which the building is located; and

calculate the rate factor based on one or more parameters that depend on the identified geographic region.