Energy storage modeling and control

• US 10,170,932 B2
• Filed: 04/19/2016
• Issued: 01/01/2019
• Est. Priority Date: 04/04/2012
• Status: Active Grant

• Current Assignee: IHI Terrasun Solutions Inc. (IHI Corporation)
• Original Assignee: IHI INC. (IHI Corporation)
• Inventors: Carter, W. Craig, Ferrara, Marco, Goroff, Michael, Mann, Maxwell
• Primary Examiner(s): Norton, Jennifer L
• Application Number:
  US15/132,940
• Publication Number:
  US 20160231725A1
• Time in Patent Office: 987 Days
• CPC Class Codes: G05B 13/041 in which a variable is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 19/0428 Safety, monitoring G05B19/0423 takes precedence
• CPC Class Codes: G05B 2219/2639 Energy management, use maximum of cheap power, keep peak load low
• CPC Class Codes: G05F 1/67 to the maximum power available from a generator, e.g. from solar cell
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• 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

Abstract

Systems and methods for optimal planning and real-time control of energy storage systems for multiple simultaneous applications are provided. Energy storage applications can be analyzed for relevant metrics such as profitability and impact on the functionality of the electric grid, subject to system-wide and energy storage hardware constraints. The optimal amount of storage capacity and the optimal operating strategy can then be derived for each application and be prioritized according to a dispatch stack, which can be statically or dynamically updated according to data forecasts. Embodiments can consist of both planning tools and real-time control algorithms.

Claims

1. A computerized method for controlling operation of an energy storage system to shave peak loads at a substation transformer using a computer, the method comprising:

(a) retrieving from a first database, using the computer, first historical data relating to a historical load at the substation transformer;

(b) retrieving from a second database, using the computer, second historical data relating to historical ambient temperature at a location of the substation transformer;

(c) receiving at the computer a physical model of the substation transformer comprising transformer thermodynamic equations;

(d) receiving at the computer information relating to pricing/penalty schemes for accelerated wear of the substation transformer;

(e) receiving at the computer information relating to a premium for deferring upgrade of the substation transformer;

(f) receiving at the computer one or more possible configurations for the energy storage system;

(g) receiving at the computer one or more possible operating strategies of the energy storage system to keep a transformer load at the substation transformer below a maximum transformer rated value;

(h) receiving at the computer a cost model for the energy storage system;

(i) calculating, using the computer, a transformer windings temperature of the substation transformer by solving the transformer thermodynamic equations using at least one of the first historical data, the second historical data, the information relating to pricing/penalty schemes, the information relating to the premium for deferring upgrade of the substation transformer, the one or more possible configurations for the energy storage system, the one or more possible operating strategies of the energy storage system, and the cost model for the energy storage system;

(j) calculating, using the computer, an accelerated wear of windings insulation of the substation transformer using the transformer windings temperature;

(k) quantifying, using the computer, a relative wear for each of peak overloads at the substation transformer;

(l) identifying, using the computer, periods of time when the substation transformer is not overloaded and energy storage system recharge is possible;

(m) identifying, using the computer, idle times when operation of the energy storage system operation is not permitted;

(n) calculating, using the computer, an optimal allocation of available capacity of the energy storage system using at least one of the first historical data, the second historical data, the information relating to pricing/penalty schemes, the information relating to the premium for deferring upgrade of the substation transformer, the one or more possible configurations for the energy storage system, the one or more possible operating strategies of the energy storage system, the cost model for the energy storage system, the accelerated wear, the periods of time, and the idle times, wherein the optimal allocation of available capacity of the energy storage system mitigates the peak overloads at the substation transformer to minimize transformer wear and minimize a cost of operating the energy storage system and the substation transformer; and

(o) directing, using a controller comprising digital electronic circuitry or computer hardware, the energy storage system to charge or discharge an energy storage asset in accordance with the calculated optimal allocation of available capacity of the energy storage system, wherein the energy storage asset is charged or discharged in accordance with the calculated optimal allocation of the available capacity of the energy storage system.

2. The computerized method of claim 1, wherein the step (j) of calculating an accelerated wear of windings insulation of the substation transformer comprises solving an Arrhenius activation equation.

3. The computerized method of claim 1, wherein the one or more possible configurations for the energy storage system comprise at least one of capacity information, power rating information, charge rate information, discharge rate information, efficiency loss information, and useful life information.

4. The computerized method of claim 1, wherein the step (n) of calculating an optimal allocation of available capacity of the energy storage system comprises using at least one of a list comparison, a multivariate regression technique, a local optimization technique, linear programming, non-linear programming, a Monte Carlo optimization, a machine learning technique, a regression fitting technique, and a multivariate optimization technique.

5. The computerized method of claim 1, further comprising calculating, using the computer, at least one of an optimal energy storage system capacity, an optimal energy storage system operating strategy, and a financial metric for the energy storage system using simulation of the one or more possible configurations for the energy storage system and the one or more possible operating strategies of the energy storage system.

6. A system for controlling operation of an energy storage system to shave peak loads at a substation transformer, the system comprising:

a first database;

a second database;

a computer that;

retrieves from the first database first historical data relating to a historical load at the substation transformer;

retrieves from a second database second historical data relating to historical ambient temperature at a location of the substation transformer;

receives a physical model of the substation transformer comprising transformer thermodynamic equations;

receives information relating to pricing/penalty schemes for accelerated wear of the substation transformer;

receives information relating to a premium for deferring upgrade of the substation transformer;

receives one or more possible configurations for the energy storage system;

receives one or more possible operating strategies of the energy storage system to keep a transformer load at the substation transformer below a maximum transformer rated value;

receives a cost model for the energy storage system;

calculates a transformer windings temperature of the substation transformer by solving the transformer thermodynamic equations using at least one of the first historical data, the second historical data, the information relating to pricing/penalty schemes, the information relating to the premium for deferring upgrade of the substation transformer, the one or more possible configurations for the energy storage system, the one or more possible operating strategies of the energy storage system, and the cost model for the energy storage system;

calculates an accelerated wear of windings insulation of the substation transformer using the transformer windings temperature;

quantifies a relative wear for each of peak overloads at the substation transformer;

identifies periods of time when the substation transformer is not overloaded and energy storage system recharge is possible;

identifies idle times when operation of the energy storage system operation is not permitted; and

calculates an optimal allocation of available capacity of the energy storage system using at least one of the first historical data, the second historical data, the information relating to pricing/penalty schemes, the information relating to the premium for deferring upgrade of the substation transformer, the one or more possible configurations for the energy storage system, the one or more possible operating strategies of the energy storage system, the cost model for the energy storage system, the accelerated wear, the periods of time, and the idle times, wherein the optimal allocation of available capacity of the energy storage system mitigates the peak overloads at the substation transformer to minimize transformer wear and minimize a cost of operating the energy storage system and the substation transformer; and

a controller comprising digital electronic circuitry or computer hardware and directing the energy storage system to charge or discharge an energy storage asset in accordance with the calculated optimal allocation of available capacity of the energy storage system, wherein the energy storage asset is charged or discharged in accordance with the calculated optimal allocation of the available capacity of the energy storage system.

7. The system of claim 6, wherein the computer further calculates the accelerated wear of windings insulation of the substation transformer by solving an Arrhenius activation equation.

8. The system of claim 6, wherein the one or more possible configurations for the energy storage system comprise at least one of capacity information, power rating information, charge rate information, discharge rate information, efficiency loss information, and useful life information.

9. The system of claim 6, wherein the computer further calculates the optimal allocation of available capacity of the energy storage system by using at least one of a list comparison, a multivariate regression technique, a local optimization technique, linear programming, non-linear programming, a Monte Carlo optimization, a machine learning technique, a regression fitting technique, and a multivariate optimization technique.

10. The system of claim 6, wherein the computer further calculates at least one of an optimal energy storage system capacity, an optimal energy storage system operating strategy, and a financial metric for the energy storage system using simulation of the one or more possible-configurations for the energy storage system and the one or more possible operating strategies of the energy storage system.

11. A non-transitory computer readable medium having stored thereon executable code which causes a computer to perform a method for controlling operation of an energy storage system to shave peak loads at a substation transformer, the method comprising:

(a) retrieving from a first database, using the computer, first historical data relating to a historical load at the substation transformer;

(b) retrieving from a second database, using the computer, second historical data relating to historical ambient temperature at a location of the substation transformer;

(c) receiving at the computer a physical model of the substation transformer comprising transformer thermodynamic equations;

(d) receiving at the computer information relating to pricing/penalty schemes for accelerated wear of the substation transformer;

(e) receiving at the computer information relating to a premium for deferring upgrade of the substation transformer;

(f) receiving at the computer one or more possible configurations for the energy storage system;

(g) receiving at the computer one or more possible operating strategies of the energy storage system to keep a transformer load at the substation transformer below a maximum transformer rated value;

(h) receiving at the computer a cost model for the energy storage system;

(i) calculating, using the computer, a transformer windings temperature of the substation transformer by solving the transformer thermodynamic equations using at least one of the first historical data, the second historical data, the information relating to pricing/penalty schemes, the information relating to the premium for deferring upgrade of the substation transformer, the one or more possible configurations for the energy storage system, the one or more possible operating strategies of the energy storage system, and the cost model for the energy storage system;

(j) calculating, using the computer, an accelerated wear of windings insulation of the substation transformer using the transformer windings temperature;

(k) quantifying, using the computer, a relative wear for each of peak overloads at the substation transformer;

(l) identifying, using the computer, periods of time when the substation transformer is not overloaded and energy storage system recharge is possible;

(m) identifying, using the computer, idle times when operation of the energy storage system operation is not permitted;

(n) calculating, using the computer, an optimal allocation of available capacity of the energy storage system using at least one of the first historical data, the second historical data, the information relating to pricing/penalty schemes, the information relating to the premium for deferring upgrade of the substation transformer, the one or more possible configurations for the energy storage system, the one or more possible operating strategies of the energy storage system, the cost model for the energy storage system, the accelerated wear, the periods of time, and the idle times, wherein the optimal allocation of available capacity of the energy storage system mitigates the peak overloads at the substation transformer to minimize transformer wear and minimize a cost of operating the energy storage system and the substation transformer; and

(o) directing, using a controller comprising digital electronic circuitry or computer hardware, the energy storage system to charge or discharge an energy storage asset in accordance with the calculated optimal allocation of available capacity of the energy storage system, wherein the energy storage asset is charged or discharged in accordance with the calculated optimal allocation of the available capacity of the energy storage system.

12. The computer readable medium of claim 11, wherein the step (j) of calculating an accelerated wear of windings insulation of the substation transformer comprises solving an Arrhenius activation equation.

13. The computer readable medium of claim 11, wherein the one or more possible configurations for the energy storage system comprise at least one of capacity information, power rating information, charge rate information, discharge rate information, efficiency loss information, and useful life information.

14. The computer readable medium of claim 11, wherein the step (n) of calculating the optimal allocation of available capacity of the energy storage system comprises using at least one of a list comparison, a multivariate regression technique, a local optimization technique, linear programming, non-linear programming, a Monte Carlo optimization, a machine learning technique, a regression fitting technique, and a multivariate optimization technique.

15. The computer readable medium of claim 11, wherein the method further comprises calculating, using the computer, at least one of an optimal energy storage system capacity, an optimal energy storage system operating strategy, and a financial metric for the energy storage system using simulation of the one or more possible configurations for the energy storage system and the one or more possible operating strategies of the energy storage system.