CN-121986433-A - System and method for optimizing life and efficiency of a battery energy storage system

Abstract

An energy storage system includes a Power Conversion System (PCS) and a plurality of energy storage nodes. The energy storage node includes a battery storage element and a control subsystem for receiving battery data from the battery storage element. The energy storage system also includes a control system coupled to the energy storage node and configured to receive or store a desired power flow. The control system is configured to receive or store the desired power flow for electrical applications. The control system is configured to distribute the required power flow across the plurality of energy storage nodes based on at least two of (1) battery cost life of the battery storage element, (2) PCS cost life of the PCS, and (3) operating efficiency of the battery storage element and the PCS.

Inventors

• M. A. Sofla
• I. D. Little Jefferson

Assignees

• 富安能源公司

Dates

Publication Date

20260505

Application Date

20240926

Priority Date

20230928

Claims (20)

1. 1. An energy storage system, comprising: a Power Conversion System (PCS); A plurality of energy storage nodes, wherein the plurality of energy storage nodes comprises: battery storage element, and A control subsystem for receiving battery data from the battery storage element, PCS data from the power conversion system, or a combination thereof, and A control system coupled to the plurality of energy storage nodes and configured to receive or store a desired power flow; Wherein the control system is configured to: Receiving or storing the required power flow for electrical applications, and The desired power flow is distributed across the plurality of energy storage nodes based on at least two of (a) battery cost life of the battery storage element, (b) PCS cost life of the PCS, and (c) operating efficiency of the battery storage element and the PCS.
2. 2. The energy storage system of claim 1, wherein the battery cost lifetime is based on a battery ideal operating profile derived from study-based battery models or experimental data.
3. 3. The energy storage system of claim 2, wherein the battery ideal operating profile includes at least one of a temperature, a current magnitude, a state of charge (SOC), a rate of change of SOC, and a minimum range and a maximum range of DC link voltage.
4. 4. The energy storage system of claim 2, wherein the assigning based on the battery cost lifetime is responsive to a cost function of a degradation variable of the battery storage element to optimize a power command.
5. 5. The energy storage system of claim 1, wherein the PCS cost lifetime of the PCS is a function based on one or more stress factors affecting at least one of a filter capacitor and a switching semiconductor of the PCS.
6. 6. The energy storage system of claim 5 wherein the PCS cost lifetime is based on a PCS ideal operating profile derived from study-based PCS model or experimental data.
7. 7. The energy storage system of claim 6 wherein the PCS ideal operating profile comprises at least one of a minimum range and a maximum range of temperature, current magnitude, and DC link voltage.
8. 8. The energy storage system of claim 1 wherein the operating efficiency is based on a function that maximizes an efficiency curve for the PCS and the battery storage element.
9. 9. A non-transitory computer-readable medium comprising optimized dispatch programming, wherein execution of the optimized dispatch programming by one or more processors configures one or more controllers to: Receiving or storing a desired power flow for an electrical application, and The desired power flow is distributed across a plurality of energy storage nodes based on at least two of (a) battery cost life of a battery storage element, (b) power conversion system cost life of a Power Conversion System (PCS), and (c) operating efficiency of the battery storage element and the PCS.
10. 10. The non-transitory computer-readable medium of claim 9, wherein the battery cost lifetime is based on a battery ideal operating profile derived from study-based battery models or experimental data.
11. 11. The non-transitory computer-readable medium of claim 10, wherein the battery ideal operating profile comprises at least one of a temperature, a current magnitude, a state of charge (SOC), a rate of change of SOC, and a minimum range and a maximum range of DC link voltage.
12. 12. The non-transitory computer-readable medium of claim 10, wherein the assigning based on the battery cost lifetime is responsive to a cost function of a degradation variable of the battery storage element to optimize a power command.
13. 13. The non-transitory computer-readable medium of claim 9, wherein the PCS cost lifetime of the PCS is a function based on one or more stress factors affecting at least one of a filter capacitor and a switching semiconductor of the PCS.
14. 14. The non-transitory computer-readable medium of claim 13, wherein the PCS cost lifetime is based on a PCS ideal operating profile derived from study-based PCS model or experimental data.
15. 15. The non-transitory computer-readable medium of claim 14, wherein the PCS ideal operating profile comprises at least one of a minimum range and a maximum range of temperature, current magnitude, and DC link voltage.
16. 16. The non-transitory computer-readable medium of claim 9, wherein the operating efficiency is based on a function that maximizes an efficiency curve of the PCS and the battery storage element.
17. 17. A method, comprising: Receiving or storing a desired power flow for an electrical application, and The desired power flow is distributed across the plurality of energy storage nodes based on at least two of (a) battery cost life of a battery storage element, (b) power conversion system cost life of a Power Conversion System (PCS), and (c) operating efficiency of the battery storage element and the PCS.
18. 18. The method of claim 17, wherein the battery cost lifetime is based on a battery ideal operating profile derived from study-based battery models or experimental data.
19. 19. The method of claim 18, wherein the battery ideal operating profile comprises at least one of a temperature, a current magnitude, a state of charge (SOC), a rate of change of SOC, and a minimum range and a maximum range of DC link voltage.
20. 20. The method of claim 18, wherein the assigning based on the battery cost lifetime is responsive to a cost function of a degradation variable of the battery storage element to optimize a power command.

Description

System and method for optimizing life and efficiency of a battery energy storage system Cross Reference to Related Applications The present application claims priority from U.S. provisional patent application No. 63/541,154 entitled "SYSTEMS AND Methods for Optimizing LIFETIME AND EFFICIENCY of a Battery Energy Storage System (systems and methods for optimizing the life and efficiency of battery energy storage systems)" filed on month 9 of 2023, the entire disclosure of which is incorporated herein by reference. Technical Field The present subject matter relates to an energy storage system including a plurality of energy storage nodes. The present subject matter also contemplates controlling the dispatch of energy storage nodes using an optimized dispatch based on battery cost life of a battery storage element, PCS cost life of a Power Conversion System (PCS), or operating efficiency of the battery storage element and PCS. Background Energy storage systems, such as Battery Energy Storage Systems (BESS), may be arranged in a distributed manner to meet safety and economic considerations. The energy storage system typically includes associated components such as a number of energy storage nodes and a power conversion system each including a housing within which a number of batteries are housed. Typically, the energy storage system includes a control system that monitors the energy storage nodes. BESS are typically composed of large, often expensive components designed to perform the task of generating, storing, or deploying energy as efficiently as possible. Downtime of these components can incur excessive costs and, therefore, over the cost of parts and labor in repair and replacement, emergency fees may also be paid for maintenance and replacement of degraded components. Accordingly, operators of energy storage systems wish to operate their energy storage systems as efficiently as possible, at least in part taking into account maintenance and replacement costs. However, determining efficient use is a difficult task. Typically, operators are primarily concerned with meeting energy requirements within their contractual terms, either to provide a certain kilowatt-hour for a certain period of time, or to provide a certain kilowatt-hour during a certain span of time. However, existing energy storage system control does not take into account the operating efficiency of the PCS (e.g., inverter) and the battery. This lack of consideration may lead to inefficiency in implementation when there is flexibility in the distribution of power delivered under the control of the energy storage system. After meeting contractual obligations, the determination of efficiency within the boundaries of contractual obligations is less well defined. In some cases, it is efficient to have components run at low but constant demand, but some components (such as inverters) are maximally efficient at 75% to 80% capacity. However, when the inverter is operating at this maximally efficient capacity, the connected energy storage nodes may be operating at an inefficient capacity and charging or discharging outside of an optimal range. In addition, energy efficiency is not necessarily related to life efficiency of the component. It may be better to run certain components, such as the inverter, at low operating efficiency and consume energy as heat but extend the life or maintenance time of the inverter. End of life (EOL) of a battery in an energy storage system is significantly affected by a power control strategy. Numerous studies have shown that EOL can be improved when using optimized methods to control batteries. The overall operator profit of an energy storage system depends on the EOL of its battery. EOL and costly maintenance of the inverter are affected by stress factors defined by the power conversion system. Thus, excessive stress affects EOL of the inverter and may require costly part replacement. Meeting these opposing demands for contract requirements and maximizing operational and life efficiency across different classes of components is difficult. Particularly when the different components coupled together have different operating capacities that are maximally efficient, and the particular components have different capacities that maximize operating or life efficiency. Currently, power control is performed without considering degradation factors of the battery. Current methods of commanding a PCS (e.g., inverter) also ignore stress factors on the inverter. Faced with these multiple variables of current power control tools, some operators can only guess at high operating capacities and hope that they will not pay much or lose excess energy as heat dissipation and that their components will not be subjected to excessive stress requiring advanced maintenance or replacement. Disclosure of Invention In a first example, an energy storage system 101 includes a Power Conversion System (PCS) 104 and a plurality of energy storage nodes 105