IP Library › Granted Patent US 11,962,156
Granted Patent B2
US 11,962,156 · App. 17/406,687 · Granted Apr 16, 2024

Systems and methods for constrained optimization of a hybrid power system that accounts for asset maintenance and degradation

Inventors: Suresh Baddam Reddy (Peoria, IL); Dimple Raja Prathapaneni (Samanathnam, IN)
Assignee: Caterpillar Inc.
H02J3/381G05B19/042H02J13/00002G05B2219/2639H02J2300/24H02J2310/12
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 11,962,156
App. No.
17/406,687
Granted
Apr 16, 2024
Kind
B2
Abstract

Systems and methods for operating a hybrid power system are disclosed. A controller may perform operations, including: obtaining load data for the hybrid power system; obtaining power availability data and energy cost data for each power asset in each power asset group of a plurality of power asset groups; and determining active power commands for each power asset by performing at least one optimization, such that the determined active power commands optimize a total operating cost, wherein: the at least one optimization is based on at least one cost function that accounts for asset degradation, asset maintenance cost, asset operation efficiency cost, and the energy cost data; and the at least one optimization is constrained by a plurality of constraints based on the load data, the power availability data, and characteristics of the power assets; and operating each power asset based on the determined active power commands.

Claims (83)

1. A method of operating a hybrid power system, comprising:

obtaining load data for the hybrid power system;

obtaining power availability data and energy cost data for each power asset in each power asset group of a plurality of power asset groups, the plurality of power asset groups including a genset group having a plurality of gensets; and

determining active power commands for each power asset by performing at least one optimization, such that the determined active power commands optimize a total operating cost of the hybrid power system, wherein:

the determined active power commands include one or more of a power on or start command, a power off or stop command, a continue command, an idle command, a charge command, or a discharge command;

the at least one optimization is based on at least one cost function that accounts for asset degradation, asset maintenance cost, asset operation efficiency cost, and the energy cost data; and

the at least one optimization is constrained by a plurality of constraints based on the load data, the power availability data, and characteristics of the power assets; and

operating each power asset based on the determined active power commands, wherein operating a respective genset in the genset group based on the determined active power commands includes:

determining whether an operating condition of the hybrid power system has been stable for a predetermined threshold period; and

upon determining that the hybrid power system has been stable for the predetermined threshold period:

waiting for a predetermined period of time; and

after waiting for the predetermined period of time, starting or stopping operation of the respective genset.

2. The method of claim 1 , wherein the plurality of power asset groups further includes one or more of, an energy storage system group, a photovoltaic group, or a power grid connection.

3. The method of claim 2 , wherein asset degradation includes calendar aging and cycling aging of the energy storage system group.

4. The method of claim 2 , wherein the asset maintenance cost for each genset in the genset group is based on an operation time of the genset.

5. The method of claim 2 , wherein the operation efficiency cost includes one or more of a state-of-charge balance factor between energy storage systems in the energy storage system group, or a cumulative state-of-charge change for each energy storage system in the energy storage system group.

6. The method of claim 1 , wherein the at least one optimization includes:

at least one prospective optimization that is based on forecasts in the load data, the power availability data, and the energy cost data; and

at least one on-line optimization that is based on on-line data in the load data, the power availability data, and the energy cost data.

7. The method of claim 6 , wherein each of the at least one prospective optimization and the at least one on-line optimization respectively include

a group optimization that determines active power commands for each asset power group on a group level; and

an individual asset optimization that determines active power commands for each power asset within each power asset group, based on the active power commands for the power asset group.

8. The method of claim 1 , wherein:

the hybrid power system includes a power grid connection; and

the hybrid power system is configured such that an imbalance between an on-line load of the hybrid power system and power generated by the plurality of power asset groups is fed into or out from the power grid connection, respectfully.

9. The method of claim 8 , wherein the at least one cost function is further configured to account for import costs and export costs for feeding power into and out from the power grid connection, respectfully.

10. The method of claim 1 , wherein:

the hybrid power system is a power system of a vehicle; and

the plurality of power asset groups further includes an energy storage system group and one or more of an internal combustion group or a power grid connection group.

11. The method of claim 1 , wherein:

the hybrid power system is a power system of a building or facility; and

the plurality of power asset groups further includes a power grid connection and an energy storage system group.

12. A controller for a hybrid power system, comprising:

at least one memory storing instructions; and

at least one processor operatively connected to the memory, and configured to execute the instructions to perform operations, including:

obtaining load data for the hybrid power system;

obtaining power availability data and energy cost data for each power asset in each power asset group of a plurality of power asset groups, the plurality of power asset groups including a genset group having a plurality of gensets; and

determining active power commands for each power asset by performing at least one optimization, such that the determined active power commands optimize a total operating cost of the hybrid power system, wherein:

the determined active power commands include one or more of a power on or start command, a power off or stop command, a continue command, an idle command, a charge command, or a discharge command;

the at least one optimization is based on at least one cost function that accounts for asset degradation, asset maintenance cost, asset operation efficiency cost, and the energy cost data; and

the at least one optimization is constrained by a plurality of constraints based on the load data, the power availability data, and characteristics of the power assets; and

operating each power asset based on the determined active power commands, wherein operating a respective genset in the genset group based on the determined active power commands includes:

determining whether an operating condition of the hybrid power system has been stable for a predetermined threshold period; and

upon determining that the hybrid power system has been stable for the predetermined threshold period:

waiting for a predetermined period of time; and

after waiting for the predetermined period of time, starting or stopping operation of the respective genset.

13. The controller of claim 12 , wherein the plurality of power asset groups further includes one or more of a photovoltaic group or a power grid connection.

14. The controller of claim 12 , wherein:

the at least one optimization includes:

at least one prospective optimization that is based on forecasts in the load data, the power availability data, and the energy cost data; and

at least one on-line optimization that is based on on-line data in the load data, the power availability data, and the energy cost data; and

each of the at least one prospective optimization and the at least one on-line optimization respectively include

a group optimization that determines active power commands for each asset power group on a group level; and

an individual asset optimization that determines active power commands for each power asset within each power asset group, based on the active power commands for the power asset group.

15. The controller of claim 12 , wherein:

the hybrid power system includes a power grid connection;

the hybrid power system is configured such that an imbalance between an on-line load of the hybrid power system and power generated by the plurality of power asset groups is fed into or out from the power grid connection, respectfully; and

the at least one cost function is further configured to account for import costs and export costs for feeding power into and out form the power grid connection, respectfully.

16. The controller of claim 12 , wherein either:

(i) the hybrid power system is a power system of a vehicle, and the plurality of power asset groups further includes an energy storage system group and one or more of an internal combustion group or a power grid connection group; or

(ii) the hybrid power system is a power system of a building or facility, and the plurality of power asset groups further includes a power grid connection, and an energy storage system group.

17. A hybrid power system, comprising:

a plurality of power asset groups that includes a genset group and one or more of an energy storage system group, a photovoltaic group, or a power grid connection; and

a controller that includes:

at least one memory storing instructions; and

at least one processor operatively connected to the memory, and configured to execute the instructions to perform operations, including:

obtaining load data for the hybrid power system;

obtaining power availability data and energy cost data for each power asset in each power asset group of the plurality of power asset groups; and

determining active power commands for each power asset by performing at least one optimization, such that the determined active power commands optimize a total operating cost of the hybrid power system, wherein:

the determined active power commands include one or more of a power on or start command, a power off or stop command, a continue command, an idle command, a charge command, or a discharge command;

the at least one optimization is based on at least one cost function that accounts for asset degradation, asset maintenance cost, asset operation efficiency cost, and the energy cost data; and

the at least one optimization is constrained by a plurality of constraints based on the load data, the power availability data, and characteristics of the power assets; and

operating each power asset based on the determined active power commands, wherein operating a respective genset in the genset group based on the determined active power commands includes:

determining whether an operating condition of the hybrid power system has been stable fora predetermined threshold period; and

upon determining that the hybrid power system has been stable for the predetermined threshold period:

 waiting for a predetermined period of time; and

 after waiting for the predetermined period of time, starting or stopping operation of the respective genset.

18. The hybrid power system of claim 17 , wherein:

the hybrid power system is a power system of a vehicle; and

the plurality of power asset groups further includes an energy storage system group and one or more of an internal combustion or a power grid connection group.

19. The hybrid power system of claim 17 , wherein:

the hybrid power system is a power system of a building or facility; and

the plurality of power asset groups further includes a power grid connection and an energy storage system group.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 15, 2021
From: REDDY, SURESH BADDAM; PRATHAPANENI, DIMPLE RAJA
To: CATERPILLAR INC.
Reel/Frame 057493/0939 →
Continuity (1)
Related Publication 20230060100A1 · Feb 23, 2023