A dynamic reconfigurable hybrid energy storage optimization scheduling method considering operation life, system, device and storage medium

Abstract

This invention discloses a dynamic reconfigurable hybrid energy storage optimization scheduling method, system, device, and storage medium that considers operational lifetime, belonging to the field of energy storage control technology. The method includes: extracting power commands for energy-type and power-type energy storage using a filtering function; calculating the lifetime loss of energy storage units in the hybrid energy storage; constructing a low-frequency time-scale optimization model using mixed-integer linear programming, and continuously optimizing the switching states of energy-type energy storage in the hybrid energy storage at the low-frequency time scale; constructing a high-frequency time-scale optimization model using mixed-integer linear programming, and continuously optimizing the switching states of power-type energy storage in the hybrid energy storage at the high-frequency time scale; and coordinating the control of energy storage based on the switching states of energy-type and power-type energy storage. This invention, while considering the energy storage lifetime, achieves multiple objectives such as coordinated power output of hybrid energy storage, state-of-charge equalization management, and voltage and current safety limiting.

Description

Technical Field

[0001] This invention belongs to the field of energy storage control technology, specifically relating to a dynamic reconfigurable hybrid energy storage optimization scheduling method, system, device, and storage medium that takes into account the operating life. Background Technology

[0002] Driven by the "dual-carbon" strategic goals, the installed capacity and grid connection ratio of new energy sources such as wind power and photovoltaics are continuously increasing. However, their inherent intermittency and volatility pose challenges to the power balance and safe, stable operation of the power grid. Energy storage systems, due to their flexible power regulation and energy time-shifting capabilities, have become one of the key technologies supporting high-proportion new energy consumption and improving system operational resilience. Among them, hybrid energy storage systems, by integrating the technological advantages of energy-type and power-type energy storage, can achieve rapid power response and continuous energy support across multiple time scales, and have become an important solution for improving the operational performance of new energy power plants.

[0003] However, in actual operation, the large-scale application of energy storage systems still faces several key bottlenecks: On the one hand, the cycle life of electrochemical energy storage such as batteries is significantly affected by the depth and frequency of charge and discharge and the operating conditions. Frequent or deep charge and discharge will accelerate capacity decay, significantly increase the total life cycle cost, and restrict its economic efficiency; on the other hand, the traditional fixed series and parallel energy storage topology is difficult to adapt to the characteristic differences between units, which can easily lead to the bottleneck effect, resulting in a decrease in the overall system efficiency, and the failure of a single unit may trigger a chain reaction, affecting the reliability of the system.

[0004] To address the aforementioned issues, dynamically reconfigurable energy storage technology flexibly connects energy storage units via semiconductor switches, enabling dynamic adjustment of the network topology based on operating conditions. This achieves power balancing, fault isolation, and optimized capacity allocation among units. Existing research has proposed dynamic reconfiguration strategies based on methods such as mixed-integer linear programming, state-of-charge sequencing, or deep learning. However, these methods are mostly designed for single-type energy storage and fail to fully consider the response characteristics of hybrid energy storage at different time scales, nor do they systematically consider the impact of energy storage lifetime degradation on operating strategies. Furthermore, while traditional lifetime modeling methods such as rainflow counting can accurately characterize cyclic aging, their computational complexity and difficulty in directly embedding them into online optimization models limit their application in real-time scheduling.

[0005] Therefore, how to balance power point tracking accuracy, state of charge balance management, operational safety constraints, and energy storage life extension within a multi-timescale collaborative optimization framework remains a key technical problem that urgently needs to be solved in the operation and scheduling of hybrid energy storage systems. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a dynamic reconfigurable hybrid energy storage optimization scheduling method, system, device and storage medium that takes into account the service life, fills the technical gap of dynamic reconfigurable optimization control of hybrid energy storage, and achieves multiple objectives such as hybrid energy storage power collaborative output, state of charge balance management and voltage and current safety limitation while taking into account the service life.

[0007] This invention provides the following technical solution:

[0008] In the first aspect, a dynamic reconfigurable hybrid energy storage optimization scheduling method considering the service life is provided, including: using a filtering function to extract the low-frequency component and high-frequency component in the pre-acquired total power command of hybrid energy storage, and using them as the power command of energy-type energy storage and the power command of power-type energy storage in hybrid energy storage, respectively.

[0009] Based on pre-acquired charge and discharge data, the lifetime loss of energy storage units in hybrid energy storage is calculated;

[0010] Based on the power command of energy-type energy storage and the lifetime loss of energy storage units, a low-frequency time-scale optimization model is constructed using mixed integer linear programming with the optimization objective of minimizing the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the lifetime loss. The switching state of energy-type energy storage in hybrid energy storage is continuously optimized at the low-frequency time scale.

[0011] Based on the power command of power-type energy storage, a high-frequency time-scale optimization model is constructed using mixed integer linear programming. The optimization objective is to minimize the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the difference between the power command and the actual power of the power-type energy storage. The switching state of power-type energy storage in the mixed energy storage is continuously optimized at the high-frequency time scale.

[0012] Energy storage is controlled in a coordinated manner based on the switching states of energy-type energy storage and power-type energy storage.

[0013] As an optional technical solution of the present invention, the power command of energy-type energy storage and the power command of power-type energy storage in the hybrid energy storage are respectively expressed as:

[0014] ;

[0015] ;

[0016] in, Indicates the total power command for hybrid energy storage. This indicates a power command for energy storage. Power commands indicating power-type energy storage Indicates time time constant, This represents a complex frequency domain operator.

[0017] As an optional technical solution of the present invention, the life loss is expressed as:

[0018] ;

[0019] ;

[0020] in, Indicates lifespan loss. Indicates the current cycle life. The number of cycles is the rated number of cycles, and DOD is the depth of discharge. Both represent the attenuation coefficient.

[0021] As an optional technical solution of the present invention, the low-frequency time-scale optimization model is expressed as follows:

[0022] ;

[0023] in, This indicates taking the minimum value. Indicates the first Line number State of charge of columnar energy storage on low-frequency timescales Indicates the first Line number Average state of charge of columnar energy storage on low-frequency timescales This indicates the total number of rows in the energy storage arrangement. This indicates the total number of columns in the energy storage arrangement. Indicates lifespan loss. This represents the lifetime loss weighting coefficient.

[0024] As an optional technical solution of the present invention, the low-frequency time-scale optimization model further includes constraints;

[0025] The constraints of the low-frequency time-scale optimization model include current calculation constraints, energy storage state of charge calculation constraints, first power command constraints, current range constraints, and energy storage state of charge range constraints.

[0026] The current calculation constraint is expressed as follows:

[0027] ;

[0028] in, Indicates the first Line number The current of column-type energy storage, This represents the total current of energy storage. This represents a pre-defined auxiliary variable used to represent the first... Line number The switch of the column energy storage type is closed and the first Column total When a switch is closed, there is The current is evenly distributed among the batteries. This indicates the total number of switches for energy storage.

[0029] The constraints for calculating the energy storage state of charge are expressed as follows:

[0030] ;

[0031] in, Indicates time The corresponding number Line number State of charge of columnar energy storage on low-frequency timescales Indicates time The corresponding number Line number State of charge of columnar energy storage on low-frequency timescales This indicates the period of low-frequency timescale rolling. Indicates the first Line number Battery capacity for column-type energy storage;

[0032] The first power command constraint is expressed as:

[0033] ;

[0034] in, Indicates the first Line number Battery voltage for column-type energy storage, This represents the maximum steady-state current of energy storage. Indicates the first Line number The current on / off state of column-type energy storage. Power command indicating energy storage;

[0035] The current range constraint is expressed as follows:

[0036] ;

[0037] The energy storage state of charge range constraint is expressed as follows:

[0038] ;

[0039] in, This represents the minimum state of charge for energy storage at low-frequency timescales. It represents the maximum state of charge of energy storage at low-frequency time scales.

[0040] As an optional technical solution of the present invention, the high-frequency time-scale optimization model is expressed as follows:

[0041] ;

[0042] in, This indicates taking the minimum value. Indicates the first Line number State of charge of column-type energy storage on high-frequency time scales Indicates the first Line number Average state of charge of column-type energy storage on high-frequency time scales This indicates the total number of rows in the power-type energy storage arrangement. This indicates the total number of columns in the power-type energy storage arrangement. Power commands indicating power-type energy storage This indicates the actual power of power-type energy storage. The weighting coefficient represents the difference between the state of charge and the average state of charge of power-type energy storage.

[0043] As an optional technical solution of the present invention, the high-frequency time-scale optimization model further includes constraints;

[0044] The constraints of the high-frequency time-scale optimization model include current calculation constraints, energy storage state of charge calculation constraints, second power command constraints, voltage range constraints, and energy storage state of charge range constraints.

[0045] The second power command constraint is expressed as follows:

[0046] ;

[0047] in, This indicates the maximum voltage of the power storage capacitor. Indicates the first Line number The current of power-type energy storage;

[0048] The voltage range constraint is expressed as follows:

[0049] ;

[0050] in, Indicates the first Line number The current on / off state of column-type energy storage. Indicates the first Line number The current switching state of the column-type energy storage. Indicates the first All switches for the column-type energy storage are in the off state. This indicates the total number of rows in the energy storage arrangement. This indicates the total number of columns in the energy storage arrangement. This represents the average voltage of energy storage. Indicates the safety factor. Indicates the first Line number The voltage of power-type energy storage, Indicates the first Line number The current switching state of the column power type energy storage.

[0051] Secondly, a dynamically reconfigurable hybrid energy storage optimization scheduling system that takes into account operational lifespan is provided, including:

[0052] The power command calculation module is used to extract the low-frequency and high-frequency components from the pre-acquired total power command of the hybrid energy storage using a filtering function, and use them as the power command for energy-type energy storage and power-type energy storage in the hybrid energy storage, respectively.

[0053] The lifetime loss calculation module is used to calculate the lifetime loss of energy storage units in hybrid energy storage based on pre-acquired charge and discharge data.

[0054] The low-frequency time-scale optimization model module is used to construct a low-frequency time-scale optimization model based on the power command of energy-type energy storage and the lifetime loss of energy storage units. It adopts mixed integer linear programming to construct a low-frequency time-scale optimization model with the goal of minimizing the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the lifetime loss. It continuously optimizes the switching state of energy-type energy storage in hybrid energy storage at the low-frequency time scale.

[0055] The high-frequency time-scale optimization model construction module is used to construct a high-frequency time-scale optimization model based on the power command of power-type energy storage. The model aims to minimize the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the difference between the power command and the actual power of the power-type energy storage. The module continuously optimizes the switching state of power-type energy storage in hybrid energy storage at the high-frequency time scale.

[0056] The collaborative control module is used to collaboratively control energy storage based on the switching states of energy-type energy storage and power-type energy storage.

[0057] Thirdly, an apparatus is provided, comprising: a memory, a processor, and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the dynamically reconfigurable hybrid energy storage optimization scheduling method taking into account operational lifetime as described in the first aspect.

[0058] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the dynamically reconfigurable hybrid energy storage optimization scheduling method considering operational lifetime as described in the first aspect.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] This invention provides a dynamic reconfigurable hybrid energy storage optimization scheduling method that considers operational lifespan. It embeds lifespan loss into the multi-timescale optimization scheduling of dynamic reconfigurable hybrid energy storage. By quantifying lifespan loss in real time and using it as an optimization target or constraint, the scheduling strategy is guided to automatically avoid lifespan-damaging conditions such as deep charge-discharge. This significantly extends the actual service life of energy storage such as batteries from the control source and reduces the equivalent operating cost over the entire lifespan. The multi-timescale collaborative optimization framework ensures that low-frequency power is smoothly tracked by energy storage and high-frequency power is rapidly responded to by power storage, achieving precise decoupling and efficient power allocation. This not only improves the overall system operating efficiency and support for fluctuating power sources but also simultaneously extends the service life of both types of energy storage by avoiding high-frequency impacts on batteries and long-term charge-discharge cycles of capacitors, achieving a win-win situation for both performance and economy. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating the dynamic reconfigurable hybrid energy storage optimization scheduling method that takes into account the operational lifespan in an embodiment of the present invention.

[0062] Figure 2 This is a schematic diagram of a dynamically reconfigurable hybrid energy storage system connected to a wind farm in an embodiment of the present invention;

[0063] Figure 3 This is an optimized real-time power curve diagram in an embodiment of the present invention. Detailed Implementation

[0064] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0065] Example 1

[0066] This embodiment provides a dynamically reconfigurable hybrid energy storage optimization scheduling method that takes into account operational lifetime. For example... Figure 1 ,include:

[0067] Step 1: Use a filtering function to extract the low-frequency and high-frequency components from the pre-acquired total power command of the hybrid energy storage, and use them as the power command for energy-type energy storage and power-type energy storage in the hybrid energy storage, respectively.

[0068] The power command for energy-type energy storage and the power command for power-type energy storage in the hybrid energy storage are respectively expressed as follows:

[0069] ;

[0070] ;

[0071] in, Indicates the total power command for hybrid energy storage. This indicates a power command for energy storage. Power commands indicating power-type energy storage Indicates time time constant, This represents a complex frequency domain operator.

[0072] Step 2: Based on the pre-acquired charge and discharge data, calculate the lifespan loss of the energy storage units in the hybrid energy storage system.

[0073] A lifetime loss model for energy storage is constructed to calculate the cycle life decay of energy storage units in real time. The model employs either the rainflow counting method or an equivalent cycle life model to quantify the impact of charge / discharge depth and cycle count on the energy storage lifetime. The lifetime loss is expressed as:

[0074] ;

[0075] ;

[0076] in, Indicates lifespan loss. Indicates the current cycle life. The number of cycles is the rated number of cycles, and DOD is the depth of discharge. Both represent the attenuation coefficient.

[0077] Step 3: Based on the power command of energy-type energy storage and the lifetime loss of energy storage units, a low-frequency time-scale optimization model is constructed using mixed integer linear programming. The optimization objective is to minimize the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the lifetime loss. The switching state of energy-type energy storage in hybrid energy storage is then continuously optimized at the low-frequency time scale.

[0078] At low-frequency time scales, the switching states of energy storage are optimized using mixed-integer linear programming to synchronously track low-frequency power commands, balance the state of charge of each unit, and minimize lifetime loss. The low-frequency time-scale optimization model is expressed as follows:

[0079] in, This indicates taking the minimum value. Indicates the first Line number State of charge of columnar energy storage on low-frequency timescales Indicates the first Line number Average state of charge of columnar energy storage on low-frequency timescales This indicates the total number of rows in the energy storage arrangement. This indicates the total number of columns in the energy storage arrangement. Indicates lifespan loss. This represents the lifetime loss weighting coefficient.

[0080] The low-frequency time-scale optimization model also includes constraints, which include current calculation constraints, energy storage state of charge calculation constraints, first power command constraints, current range constraints, and energy storage state of charge range constraints.

[0081] The current calculation constraint is expressed as follows:

[0082] ;

[0083] in, Indicates the first Line number The current of column-type energy storage, This represents the total current of energy storage. This represents a pre-defined auxiliary variable used to represent the first... Line number The switch of the column energy storage type is closed and the first Column total When a switch is closed, there is The current is evenly distributed among the batteries. This indicates the total number of switches for energy storage.

[0084] The constraints for calculating the energy storage state of charge are expressed as follows:

[0085] ;

[0086] in, Indicates time The corresponding number Line number State of charge of columnar energy storage on low-frequency timescales Indicates time The corresponding number Line number State of charge of columnar energy storage on low-frequency timescales This indicates the period of low-frequency timescale rolling. Indicates the first Line number Battery capacity for column-type energy storage.

[0087] The first power command constraint is expressed as:

[0088] ;

[0089] in, Indicates the first Line number The battery voltage of columnar energy storage is estimated from the state information measured. This represents the maximum steady-state current of energy storage. Indicates the first Line number The current on / off state of column-type energy storage. This indicates the power command for energy storage.

[0090] The current range constraint is expressed as follows:

[0091] .

[0092] The energy storage state of charge range constraint is expressed as follows:

[0093] ;

[0094] in, This represents the minimum state of charge for energy storage at low-frequency timescales. It represents the maximum state of charge of energy storage at low-frequency time scales.

[0095] Step 4: Based on the power command of power-type energy storage, a high-frequency time-scale optimization model is constructed using mixed integer linear programming. The optimization objective is to minimize the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the difference between the power command and the actual power of the power-type energy storage. The switching state of power-type energy storage in the hybrid energy storage is then continuously optimized on a high-frequency time scale.

[0096] At high-frequency time scales, the switching states of power-type energy storage are optimized based on mixed-integer linear programming, synchronously tracking high-frequency power commands and balancing the state of charge, while also incorporating lifetime loss constraints. The high-frequency time scale optimization model is expressed as follows:

[0097] in, This indicates taking the minimum value. Indicates the first Line number State of charge of column-type energy storage on high-frequency time scales Indicates the first Line number Average state of charge of column-type energy storage on high-frequency time scales This indicates the total number of rows in the power-type energy storage arrangement. This indicates the total number of columns in the power-type energy storage arrangement. Power commands indicating power-type energy storage This indicates the actual power of power-type energy storage. The weighting coefficient represents the difference between the state of charge and the average state of charge of power-type energy storage.

[0098] The high-frequency time-scale optimization model also includes constraints, which include current calculation constraints, energy storage state of charge calculation constraints, second power command constraints, voltage range constraints, and energy storage state of charge range constraints.

[0099] The constraints for current calculation, energy storage state of charge calculation, and energy storage state of charge range in the high-frequency time-scale optimization model are similar to those in the low-frequency time-scale optimization model, and will not be elaborated here.

[0100] The second power command constraint is expressed as follows:

[0101] ;

[0102] in, This indicates the maximum voltage of the power storage capacitor. Indicates the first Line number The current of power-type energy storage.

[0103] The voltage range constraint is expressed as follows:

[0104] ;

[0105] in, Indicates the first Line number The current on / off state of column-type energy storage. Indicates the first Line number The current switching state of the column-type energy storage. Indicates the first All switches for the column-type energy storage are in the off state. This indicates the total number of rows in the energy storage arrangement. This indicates the total number of columns in the energy storage arrangement. This represents the average voltage of energy storage. Indicates the safety factor. Indicates the first Line number The voltage of power-type energy storage, Indicates the first Line number The current switching state of the column power type energy storage.

[0106] Step 5: Coordinate the control of energy storage based on the switching states of energy-type energy storage and power-type energy storage.

[0107] Through multi-timescale coordinated control, the hybrid energy storage system achieves comprehensive optimization in terms of power response, lifetime protection, and operational safety. In this embodiment, the low-frequency timescale has a period of more than 10 seconds, and the high-frequency timescale has a period of less than 1 second.

[0108] Example 2

[0109] This embodiment provides a set of simulation experiments based on Embodiment 1.

[0110] Figure 2 This is a schematic diagram of the structure of the dynamic reconfigurable hybrid energy storage simulation model for accessing a wind farm provided in this embodiment of the invention. This embodiment operates based on the dynamic reconfigurable hybrid energy storage simulation model, and the parameters of the dynamic reconfigurable hybrid energy storage simulation model are shown in Table 1:

[0111] Table 1 Main System Parameters

[0112] Parameter name numerical values Single battery capacity 206Ah Rated voltage of a single battery 3.2V Number of batteries connected in series in a single energy storage unit 16 strings Number of energy storage units 4 in 6 strings single supercapacitor capacitance value 3000F Single supercapacitor withstand voltage 2.7V Number of series and parallel capacitors in a single power energy storage unit 25 strings of 10 Number of power-type energy storage units 2 in parallel 4 strings Inverter capacity 125kW Number of reconfigurable energy storage units in each container 4 Number of containers in the energy storage station 20 wind farm rated capacity 100MW

[0113] Table 2 shows a comparison of the solution speed between the proposed optimization method and the nonlinear method. The average computation time of the nonlinear method is 10.953 s, while that of the proposed method is only 0.0876 s, demonstrating a significant advantage in computation speed and meeting the requirements of online optimization.

[0114] Table 2 Comparison of Optimized Solution Time

[0115] Serial Number Nonlinear optimization The proposed method 1 10.3s 0.066s 2 12.8s 0.075s 3 11.3s 0.065s 4 9.50s 0.115s 5 15.6s 0.106s 6 10.9s 0.103s 7 7.34s 0.094s 8 13.6s 0.110s 9 7.89s 0.056s 10 10.3s 0.086s

[0116] Optimize the real-time power curve as follows: Figure 3 As shown in the figure, this method decouples the energy storage power output across multiple time scales and energy storage forms, ensuring that the actual power closely tracks the command value. This avoids the high-frequency charging and discharging of energy-type energy storage and the long-term charging and discharging of power-type energy storage. Calculations show that the battery charging and discharging frequency in this scenario is reduced by 32% compared to single-type energy storage, thus improving the overall lifespan and safety of the energy storage.

[0117] In summary, this method integrates the advantages of hybrid energy storage, extends the lifespan of energy storage, avoids the bottleneck effect, and also ensures operational safety.

[0118] Example 3

[0119] This embodiment provides a dynamically reconfigurable hybrid energy storage optimization scheduling system that takes into account operational lifespan, including:

[0120] The power command calculation module is used to extract the low-frequency and high-frequency components from the pre-acquired total power command of the hybrid energy storage using a filtering function, and use them as the power command for energy-type energy storage and power-type energy storage in the hybrid energy storage, respectively.

[0121] The lifetime loss calculation module is used to calculate the lifetime loss of energy storage units in hybrid energy storage based on pre-acquired charge and discharge data.

[0122] The low-frequency time-scale optimization model module is used to construct a low-frequency time-scale optimization model based on the power command of energy-type energy storage and the lifetime loss of energy storage units. It adopts mixed integer linear programming to construct a low-frequency time-scale optimization model with the goal of minimizing the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the lifetime loss. It continuously optimizes the switching state of energy-type energy storage in hybrid energy storage at the low-frequency time scale.

[0123] The high-frequency time-scale optimization model construction module is used to construct a high-frequency time-scale optimization model based on the power command of power-type energy storage. The model aims to minimize the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the difference between the power command and the actual power of the power-type energy storage. The module continuously optimizes the switching state of power-type energy storage in hybrid energy storage at the high-frequency time scale.

[0124] The collaborative control module is used to collaboratively control energy storage based on the switching states of energy-type energy storage and power-type energy storage.

[0125] Example 4

[0126] This embodiment provides an apparatus, including: a memory, a processor, and a computer program;

[0127] The computer program is stored in the memory and configured to be executed by the processor to implement the dynamic reconfigurable hybrid energy storage optimization scheduling method taking into account operational lifetime as described in Embodiment 1.

[0128] Example 5

[0129] This embodiment provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the dynamically reconfigurable hybrid energy storage optimization scheduling method considering operational lifetime described in Embodiment 1.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimized scheduling of dynamically reconfigurable hybrid energy storage considering operational lifetime, characterized in that, include: The low-frequency and high-frequency components in the pre-acquired total power command of hybrid energy storage are extracted using a filtering function and used as the power command for energy-type energy storage and power-type energy storage in hybrid energy storage, respectively. Based on pre-acquired charge and discharge data, the lifetime loss of energy storage units in hybrid energy storage is calculated; Based on the power command of energy-type energy storage and the lifetime loss of energy storage units, a low-frequency time-scale optimization model is constructed using mixed integer linear programming with the optimization objective of minimizing the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the lifetime loss. The switching state of energy-type energy storage in hybrid energy storage is continuously optimized at the low-frequency time scale. Based on the power command of power-type energy storage, a high-frequency time-scale optimization model is constructed using mixed integer linear programming. The optimization objective is to minimize the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the difference between the power command and the actual power of the power-type energy storage. The switching state of power-type energy storage in the mixed energy storage is continuously optimized at the high-frequency time scale. Energy storage is controlled in a coordinated manner based on the switching states of energy-type energy storage and power-type energy storage.

2. The dynamic reconfigurable hybrid energy storage optimization scheduling method considering operational lifespan according to claim 1, characterized in that: The power command for energy-type energy storage and the power command for power-type energy storage in the hybrid energy storage are respectively expressed as follows: ； ； in, Indicates the total power command for hybrid energy storage. This indicates a power command for energy storage. Power commands indicating power-type energy storage Indicates time time constant, This represents a complex frequency domain operator.

3. The dynamically reconfigurable hybrid energy storage optimization scheduling method considering operational lifespan according to claim 1, characterized in that, The lifespan loss is expressed as: ； ； in, Indicates lifespan loss. Indicates the current cycle life. The number of cycles is the rated number of cycles, and DOD is the depth of discharge. Both represent the attenuation coefficient.

4. The dynamically reconfigurable hybrid energy storage optimization scheduling method considering operational lifespan according to claim 1, characterized in that, The low-frequency time-scale optimization model is expressed as follows: ； in, This indicates taking the minimum value. Indicates the first Line number State of charge of columnar energy storage on low-frequency timescales Indicates the first Line number Average state of charge of columnar energy storage on low-frequency timescales This indicates the total number of rows in the energy storage arrangement. This represents the total number of columns in the energy storage arrangement. Indicates lifespan loss. This represents the lifetime loss weighting coefficient.

5. The dynamically reconfigurable hybrid energy storage optimization scheduling method considering operational lifespan according to claim 4, characterized in that, The low-frequency time-scale optimization model also includes constraints. The constraints of the low-frequency time-scale optimization model include current calculation constraints, energy storage state of charge calculation constraints, first power command constraints, current range constraints, and energy storage state of charge range constraints. The current calculation constraint is expressed as follows: ； in, Indicates the first Line number The current of column-type energy storage, This represents the total current of energy storage. This represents a pre-defined auxiliary variable used to represent the first... Line number The switch of the column energy storage type is closed and the first Column total When a switch is closed, there is The current is evenly distributed among the batteries. This indicates the total number of switches for energy storage. The constraints for calculating the energy storage state of charge are expressed as follows: ； in, Indicates time The corresponding number Line number State of charge of columnar energy storage on low-frequency timescales Indicates time The corresponding number Line number State of charge of columnar energy storage on low-frequency timescales This indicates the period of low-frequency timescale rolling. Indicates the first Line number Battery capacity for column-type energy storage; The first power command constraint is expressed as: ； in, Indicates the first Line number Battery voltage for column-type energy storage, This represents the maximum steady-state current of energy storage. Indicates the first Line number The current on / off state of column-type energy storage. Power command indicating energy storage; The current range constraint is expressed as follows: ； The energy storage state of charge range constraint is expressed as follows: ； in, This represents the minimum state of charge for energy storage at low-frequency timescales. It represents the maximum state of charge of energy storage at low-frequency time scales.

6. The dynamically reconfigurable hybrid energy storage optimization scheduling method considering operational lifespan according to claim 1, characterized in that, The high-frequency time-scale optimization model is expressed as follows: ； in, This indicates taking the minimum value. Indicates the first Line number State of charge of power-type energy storage at high-frequency time scales Indicates the first Line number Average state of charge of column-type energy storage on high-frequency time scales This indicates the total number of rows in the power-type energy storage arrangement. This indicates the total number of columns in the power-type energy storage arrangement. Power commands indicating power-type energy storage This indicates the actual power of power-type energy storage. The weighting coefficient represents the difference between the state of charge and the average state of charge of power-type energy storage.

7. The dynamically reconfigurable hybrid energy storage optimization scheduling method considering operational lifespan according to claim 6, characterized in that, The high-frequency time-scale optimization model also includes constraints; The constraints of the high-frequency time-scale optimization model include current calculation constraints, energy storage state of charge calculation constraints, second power command constraints, voltage range constraints, and energy storage state of charge range constraints. The second power command constraint is expressed as follows: ； in, This indicates the maximum voltage of the power storage capacitor. Indicates the first Line number The current of power-type energy storage; The voltage range constraint is expressed as follows: ； in, Indicates the first Line number The current on / off state of column-type energy storage. Indicates the first Line number The current switching state of the column-type energy storage. Indicates the first All switches for the column-type energy storage are in the off state. This indicates the total number of rows in the energy storage arrangement. This represents the total number of columns in the energy storage arrangement. This represents the average voltage of energy storage. Indicates the safety factor. Indicates the first Line number The voltage of power-type energy storage, Indicates the first Line number The current switching state of the column power type energy storage.

8. A dynamically reconfigurable hybrid energy storage optimization scheduling system considering operational lifespan, characterized in that, include: The power command calculation module is used to extract the low-frequency and high-frequency components from the pre-acquired total power command of the hybrid energy storage using a filtering function, and use them as the power command for energy-type energy storage and power-type energy storage in the hybrid energy storage, respectively. The lifetime loss calculation module is used to calculate the lifetime loss of energy storage units in hybrid energy storage based on pre-acquired charge and discharge data. The low-frequency time-scale optimization model module is used to construct a low-frequency time-scale optimization model based on the power command of energy-type energy storage and the lifetime loss of energy storage units. It adopts mixed integer linear programming to construct a low-frequency time-scale optimization model with the goal of minimizing the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the lifetime loss. It continuously optimizes the switching state of energy-type energy storage in hybrid energy storage at the low-frequency time scale. The high-frequency time-scale optimization model construction module is used to construct a high-frequency time-scale optimization model based on the power command of power-type energy storage. The model aims to minimize the sum of the difference between the state of charge and the average state of charge of each energy storage unit and the difference between the power command and the actual power of the power-type energy storage. The module continuously optimizes the switching state of power-type energy storage in hybrid energy storage at the high-frequency time scale. The collaborative control module is used to collaboratively control energy storage based on the switching states of energy-type energy storage and power-type energy storage.

9. An apparatus, characterized in that, include: Memory, processor, and computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the dynamic reconfigurable hybrid energy storage optimization scheduling method taking into account the operational lifetime as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the dynamic reconfigurable hybrid energy storage optimization scheduling method taking into account the operating lifetime as described in any one of claims 1-7.

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