Energy storage modeling and control

• US 10,574,087 B2
• Filed: 11/30/2018
• Issued: 02/25/2020
• 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:
  US16/205,495
• Publication Number:
  US 20190109492A1
• Time in Patent Office: 452 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 method for controlling operation of an energy storage system to minimize an energy cost of a micro-grid installation comprising a renewable energy generator, an on-site fossil fuel generator and an access to a power grid, the method comprising steps of:

(a) retrieving from a first database, using a computer, first historical data relating to historical energy usage of a user;

(b) retrieving from a second database, using the computer, second historical data relating to historical grid rates;

(c) retrieving from a third database, using the computer, third historical data relating to historical renewable energy generation;

(d) receiving, at the computer, a first physical model relating to a rate of degradation of the energy storage system;

(e) receiving, at the computer, a second physical model relating to a fuel efficiency of the on-site fossil fuel generator;

(f) receiving, at the computer, a third physical model relating to a rate of degradation of the renewable power generator;

(g) receiving, at the computer, pricing schemes for peak and off-peak energy rates, peak demand charges, and net-metering benefits that apply when excess renewable energy is sold back to the power grid;

(h) receiving, at the computer, one or more possible configurations of the energy storage system;

(i) receiving, at the computer, one or more possible operating strategies of the energy storage system to keep a power absorbed from the power grid below a user defined grid absorption limit;

(j) receiving, at the computer, a cost model relating to relative and absolute costs of the energy storage system having different technologically feasible characteristics;

(k) calculating, using the computer, an optimal operating strategy of the energy storage system for a lowest cost dispatch of available resources to meet a user power demand while complying with the user defined grid absorption limit, using at least one of the first historical data, the second historical data, the third historical data, the first physical model, the second physical model, the third physical model, the pricing schemes, the one or more possible configurations of the energy storage system, the one or more possible operating strategies of the energy storage system, and the cost model; and

(l) 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 operating strategy of the energy storage system, wherein the energy storage asset is charged or discharged in accordance with the calculated optimal operating strategy of the energy storage system.

2. The method of claim 1, wherein:

the energy storage system further comprises a battery;

the step (k) comprises;

(i) calculating, using the computer, a load net of the renewable energy generator;

(ii) imposing, using the computer, the user defined grid absorption limit;

(iii) calculating, using the computer, a first optimal dispatch of the battery to shave a peak load, perform a first energy arbitrage between off-peak and peak hours, and perform a second energy arbitrage between generation of the excess renewable energy and the peak hours;

(iv) calculating, using the computer, a second optimal dispatch of the on-site fossil generator to comply with the user defined grid absorption limit and achieve a lowest energy cost during the peak hours; and

(v) specifying, using the computer, maximum and minimum capacities and operational constraints of the energy storage system, and dynamically updating, using the computer, the maximum and minimum capacities and the operational constraints of the energy storage system based on a change in an external or internal condition; and

the step (l) comprises;

(i) directing, using the controller, the battery to charge or discharge a battery energy in accordance with the calculated first optimal dispatch of the battery and the specified and dynamically updated maximum and minimum capacities and operational constraints of the energy storage system; and

(ii) directing, using the controller, the on-site fossil generator to charge or discharge a fossil generated energy in accordance with the calculated second optimal dispatch of the on-site fossil generator and the specified and dynamically updated maximum and minimum capacities and operational constraints of the energy storage system.

3. The method of claim 1, wherein the renewable energy generator comprises a photovoltaic energy generator.

4. The method of claim 1, wherein the on-site fossil fuel generator comprises a diesel generator or a fuel cell.

5. The method of claim 1, wherein the one or more possible configurations of 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.

6. The method of claim 1, wherein the step (k) 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.

7. A system for controlling operation of an energy storage system to minimize an energy cost of a micro-grid installation comprising a renewable energy generator, an on-site fossil fuel generator and an access to a power grid, the system comprising:

a first database;

a second database;

a third database;

a computer that;

(a) retrieves from the first database first historical data relating to historical energy usage of a user;

(b) retrieves from the second database second historical data relating to historical grid rates;

(c) retrieves from the third database third historical data relating to historical renewable energy generation;

(d) receives a first physical model relating to a rate of degradation of the energy storage system;

(e) receives a second physical model relating to a fuel efficiency of the on-site fossil fuel generator;

(f) receives a third physical model relating to a rate of degradation of the renewable power generator;

(g) receives pricing schemes for peak and off-peak energy rates, peak demand charges, and net-metering benefits that apply when excess renewable energy generation is sold back to the power grid;

(h) receives one or more possible configurations of the energy storage system;

(i) receives one or more possible operating strategies of the energy storage system to keep a power absorbed from the power grid below a user defined grid absorption limit;

(j) receives a cost model relating to relative and absolute costs of the energy storage system having different technologically feasible characteristics; and

(k) calculates an optimal operating strategy of the energy storage system for a lowest cost dispatch of available resources to meet a user power demand while complying with the user defined grid absorption limit, using at least one of the first historical data, the second historical data, the third historical data, the first physical model, the second physical model, the third physical model, the pricing schemes, the one or more possible configurations of the energy storage system, the one or more possible operating strategies of the energy storage system, and the cost model; 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 operating strategy of the energy storage system, wherein the energy storage asset is charged or discharged in accordance with the calculated optimal operating strategy of the energy storage system.

8. The system of claim 7, wherein:

the energy storage system further comprises a battery;

the computer calculates the optimal operating strategy of the energy storage system by;

(i) calculating a load net of the renewable energy generator;

(ii) imposing the user defined grid absorption limit;

(iii) calculating a first optimal dispatch of the battery to shave a peak load, perform a first energy arbitrage between off-peak and peak hours, and perform a second energy arbitrage between generation of the excess renewable energy and the peak hours;

(iv) calculating a second optimal dispatch of the on-site fossil generator to comply with the user defined grid absorption limit and achieve a lowest energy cost during the peak hours; and

(v) specifying maximum and minimum capacities and operational constraints of the energy storage system, and dynamically updating the maximum and minimum capacities and the operational constraints of the energy storage system based on a change in an external or internal condition; and

the controller directs the energy storage system to charge or discharge the energy storage asset by;

(i) directing the battery to charge or discharge a battery energy in accordance with the calculated first optimal dispatch of the battery and the specified and dynamically updated maximum and minimum capacities and operational constraints of the energy storage system; and

(ii) directing the on-site fossil generator to charge or discharge a fossil generated energy in accordance with the calculated second optimal dispatch of the on-site fossil generator and the specified and dynamically updated maximum and minimum capacities and operational constraints of the energy storage system.

9. The system of claim 7, wherein the renewable energy generator comprises a photovoltaic energy generator.

10. The system of claim 7, wherein the on-site fossil fuel generator comprises a diesel generator or a fuel cell.

11. The system of claim 7, wherein the one or more possible configurations of 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.

12. The system of claim 7, wherein the computer calculates the optimal operating strategy 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.

13. 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 minimize an energy cost of a micro-grid installation comprising a renewable energy generator, an on-site fossil fuel generator and an access to a power grid, the method comprising steps of:

(a) retrieving from a first database, using the computer, first historical data relating to historical energy usage of a user;

(b) retrieving from a second database, using the computer, second historical data relating to historical grid rates;

(c) retrieving from a third database, using the computer, third historical data relating to historical renewable energy generation;

(d) receiving, at the computer, a first physical model relating to a rate of degradation of the energy storage system;

(e) receiving, at the computer, a second physical model relating to a fuel efficiency of the on-site fossil fuel generator;

(f) receiving, at the computer, a third physical model relating to a rate of degradation of the renewable power generator;

(g) receiving, at the computer, pricing schemes for peak and off-peak energy rates, peak demand charges, and net-metering benefits that apply when excess renewable energy is sold back to the power grid;

(h) receiving, at the computer, one or more possible configurations of the energy storage system;

(i) receiving, at the computer, one or more possible operating strategies of the energy storage system to keep a power absorbed from the power grid below a user defined grid absorption limit;

(j) receiving, at the computer, a cost model relating to relative and absolute costs of the energy storage system having different technologically feasible characteristics;

(k) calculating, using the computer, an optimal operating strategy of the energy storage system for a lowest cost dispatch of available resources to meet a user power demand while complying with the user defined grid absorption limit, using at least one of the first historical data, the second historical data, the third historical data, the first physical model, the second physical model, the third physical model, the pricing schemes, the one or more possible configurations of the energy storage system, the one or more possible operating strategies of the energy storage system, and the cost model; and

(l) 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 operating strategy of the energy storage system, wherein the energy storage asset is charged or discharged in accordance with the calculated optimal operating strategy of the energy storage system.

14. The computer readable medium of claim 13, wherein:

the energy storage system further comprises a battery;

the step (k) comprises;

(i) calculating, using the computer, a load net of the renewable energy generator;

(ii) imposing, using the computer, the user defined grid absorption limit;

(iii) calculating, using the computer, a first optimal dispatch of the battery to shave a peak load, perform a first energy arbitrage between off-peak and peak hours, and perform a second energy arbitrage between generation of the excess renewable energy and the peak hours;

(iv) calculating, using the computer, a second optimal dispatch of the on-site fossil generator to comply with the user defined grid absorption limit and achieve a lowest energy cost during the peak hours; and

(v) specifying, using the computer, maximum and minimum capacities and operational constraints of the energy storage system, and dynamically updating, using the computer, the maximum and minimum capacities and the operational constraints of the energy storage system based on a change in an external or internal condition; and

the step (l) comprises;

(i) directing, using the controller, the battery to charge or discharge a battery energy in accordance with the calculated first optimal dispatch of the battery and the specified and dynamically updated maximum and minimum capacities and operational constraints of the energy storage system; and

(ii) directing, using the controller, the on-site fossil generator to charge or discharge a fossil generated energy in accordance with the calculated second optimal dispatch of the on-site fossil generator and the specified and dynamically updated maximum and minimum capacities and operational constraints of the energy storage system.

15. The computer readable medium of claim 13, wherein the renewable energy generator comprises a photovoltaic energy generator.

16. The computer readable medium of claim 13, wherein the on-site fossil fuel generator comprises a diesel generator or a fuel cell.

17. The computer readable medium of claim 13, wherein the one or more possible configurations of 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.

18. The computer readable medium of claim 13, wherein the step (k) 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.