Energy storage system optimization operation method, system and equipment based on reconfigurable battery network and medium

Abstract

The invention belongs to the technical field of energy storage system optimization operation, and discloses an energy storage system optimization operation method, system and device based on a reconfigurable battery network, and a medium, and the method comprises the steps: obtaining the module information of the reconfigurable battery network, presetting the system support capacity and a corresponding penalty coefficient, constructing a multifunctional operation potential evaluation model and calculating the multifunctional operation potential of each module so as to select the corresponding module to be flexibly grouped as an energy storage system; constructing a penalty cost objective function and an operation capability optimization constraint condition of the energy storage system; solving an optimal solution of the penalty cost objective function by combining a mixed integer linear optimization method so as to update a multifunctional combination scheme of the energy storage system and obtain a corresponding multifunctional day-ahead clearing plan curve; and obtaining a corresponding energy storage system action curve and realizing optimized operation of the energy storage system based on the curve. The objective of the invention is to achieve an energy storage system optimization operation target which emphasizes the requirements of multifunctional multiplexing and real-time topological optimization of the energy storage system.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage system optimization operation, and specifically relates to an energy storage system optimization operation method, system, equipment and medium based on a reconfigurable battery network. Background Art

[0002] With the clear proposal of the "dual carbon" goal and the rapid development of new power systems, the new energy industry has shown a trend of sustained growth. As a flexible regulating resource, energy storage plays an important role in improving the utilization rate of new energy. At the same time, it provides key functions for the power system, including system backup and auxiliary services. It has a positive impact on promoting the construction of new power systems and modern energy systems. Therefore, its application has received widespread attention.

[0003] Ensuring the safe and efficient operation of energy storage systems is the key to fully realizing their regulation potential, so energy storage systems based on reconfigurable battery networks have emerged. The system discretizes and digitizes the continuous energy flow of each battery cell through a battery network controller and a high-frequency power electronic switch array, and controls the charging and discharging behavior and timing of each battery cell in real time through algorithms.

[0004] It is worth noting that the optimized operation of energy storage systems not only involves their use in different application scenarios, but also includes the integration and coordination of multiple functions. Since different functions have significantly different requirements for energy storage output, it is particularly important to optimize the operation of energy storage systems. However, most of the current optimization methods focus on the optimization of specific functions of fixed-connected energy storage systems, which have two major limitations: first, they fail to fully consider the multi-functional operation mode of the energy storage system, are usually limited to a single functional mode, and adopt a fixed charging and discharging plan, which undoubtedly limits the full realization of the energy storage operation potential; second, there is a lack of dynamic matching strategies between the dynamic topology of the reconfigurable battery network and different functional application scenarios, resulting in the inability to maximize the real-time operation capabilities of energy storage. Summary of the invention

[0005] Based on the above-mentioned shortcomings and deficiencies in the prior art, one of the objects of the present invention is to at least solve one or more of the above-mentioned problems in the prior art. In other words, one of the objects of the present invention is to provide an energy storage system optimization operation method, system, device and medium based on a reconfigurable battery network that meets one or more of the above-mentioned needs, aiming to achieve the goal of optimizing the operation of an energy storage system that gives equal importance to the multifunctional reuse of the energy storage system and the real-time topology optimization requirements.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for optimizing the operation of an energy storage system based on a reconfigurable battery network, comprising the steps of: obtaining module information, preset system support capacity and corresponding penalty coefficient of the reconfigurable battery network; constructing a multifunctional operation potential evaluation model based on the module information, and calculating the multifunctional operation potential of each module in the reconfigurable energy storage system based on the multifunctional operation potential evaluation model to select the corresponding modules to be flexibly grouped as an energy storage system; constructing a penalty cost objective function and operation capacity optimization constraints of the energy storage system based on the preset system support capacity and the corresponding penalty coefficient; solving the optimal solution of the penalty cost objective function based on the operation capacity optimization constraints and in combination with a mixed integer linear optimization method to update the multifunctional combination scheme of the energy storage system and obtain the corresponding multifunctional day-ahead clearing plan curve; obtaining an energy storage system action curve corresponding to the multifunctional day-ahead clearing plan curve, and realizing the optimized operation of the energy storage system based on the energy storage system action curve.

[0008] As a preferred solution, the module information includes the number of modules, module capacity, module power and current module capacity; the multifunctional operation potential evaluation model is constructed based on the module information, and the multifunctional operation potential of each module in the reconfigurable energy storage system is calculated based on the multifunctional operation potential evaluation model, so as to select the corresponding modules to be flexibly grouped as the energy storage system, including the steps of: calculating the power output potential of each module in the reconfigurable battery network Charge and discharge capacity and power regulation potential Based on the power output potential of each module Charge and discharge capacity and power regulation potential Calculate the multifunctional operation potential of each module Based on the multifunctional operating potential of each module Modules with great potential are selected and flexibly grouped into energy storage systems.

[0009] As a preferred solution, the power output potential The calculation formula is The charge and discharge capacity The calculation formula is The power regulation potential The calculation formula is The multifunctional operating potential The calculation formula is In the formula, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, represents the historical maximum output power of the nth module in the reconfigurable battery network, P N Indicates the rated power of the energy storage system, DOD NRepresents the maximum discharge depth of the nth module in the reconfigurable battery network, SOH N Indicates the health status of the nth module in the reconfigurable battery network, SOC N represents the current capacity of the nth module in the reconfigurable battery network, Indicates the current capacity reference value under different functions.

[0010] As a preferred solution, the penalty cost objective function is expressed as: In the formula, Δt represents the time step, t represents the time, T represents the optimization period, and α t represents the electricity price at time t, α1 represents the penalty coefficient for energy storage system peak load regulation not meeting the standard, α2 represents the penalty coefficient for energy storage system frequency regulation not meeting the standard, α3 represents the penalty coefficient for energy storage system emergency power support not meeting the standard, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They respectively represent the clearing planned power of peak load regulation, frequency regulation, and emergency power support of the energy storage system at time t.

[0011] As a preferred solution, the operating capacity optimization constraint conditions include:

[0012] in, represents the power of the energy storage system at time t, represents the charging power of the energy storage system at time t, represents the discharge power of the energy storage system at time t, P max represents the maximum output power of the energy storage system, that is, the sum of the maximum output power of all modules in the reconfigurable battery network under the current topology, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, I n represents the topological state of the nth module in the reconfigurable battery network, and I n =0, the module is not selected for integration. n =1, the module is selected to be integrated to form an energy storage system, SOC(t) represents the capacity state of the energy storage system at time t, SOC(t-1) represents the capacity state of the energy storage system at time t-1, η represents the efficiency of the energy storage system, SOC low and SOC high They respectively represent the lower and upper limits of the energy storage system capacity status.

[0013] As a preferred solution, the operating capacity optimization constraint condition also includes:

[0014] in, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They respectively represent the peak load regulation, frequency regulation, and emergency power support flags of the energy storage system at time t.

[0015] As a preferred solution, the optimal solution of the penalty cost objective function is solved based on the operating capacity optimization constraint conditions and combined with a mixed integer linear optimization method to update the multifunctional combination scheme of the energy storage system and obtain the corresponding multifunctional day-ahead clearing plan curve, including the steps of: solving the optimal solution of the penalty cost objective function based on the operating capacity optimization constraint conditions and combined with a mixed integer linear optimization method, if the solution is successful, directly generating a multifunctional day-ahead clearing plan curve, otherwise proceeding to the next step; setting the frequency modulation clearing power and the peak load clearing power to zero, and solving based on the demand for emergency power support to obtain the corresponding multifunctional day-ahead clearing plan curve; repeating the above steps to obtain the multifunctional day-ahead clearing plan curve of the energy storage system.

[0016] In a second aspect, the present invention provides an energy storage system optimization operation system based on a reconfigurable battery network, characterized in that it is used to implement the energy storage system optimization operation method as described in the first aspect, including obtaining module information, preset system support capacity and corresponding penalty coefficient of the reconfigurable battery network; constructing a multifunctional operation potential evaluation model based on the module information, and calculating the multifunctional operation potential of each module in the reconfigurable energy storage system based on the multifunctional operation potential evaluation model to select the corresponding modules to be flexibly grouped as an energy storage system; constructing a penalty cost objective function and operation capacity optimization constraints of the energy storage system based on the preset system support capacity and the corresponding penalty coefficient; solving the optimal solution of the penalty cost objective function based on the operation capacity optimization constraints and in combination with the mixed integer linear optimization method to update the multifunctional combination scheme of the energy storage system and obtain the corresponding multifunctional day-ahead clearing plan curve; obtaining the energy storage system action curve corresponding to the multifunctional day-ahead clearing plan curve, and realizing the optimized operation of the energy storage system based on the energy storage system action curve.

[0017] As a preferred solution, the module information includes the number of modules, module capacity, module power and current module capacity; the multifunctional operation potential evaluation model is constructed based on the module information, and the multifunctional operation potential of each module in the reconfigurable energy storage system is calculated based on the multifunctional operation potential evaluation model, so as to select the corresponding modules to be flexibly grouped as the energy storage system, including the steps of: calculating the power output potential of each module in the reconfigurable battery network Charge and discharge capacity and power regulation potential Based on the power output potential of each module Charge and discharge capacity and power regulation potential Calculate the multifunctional operation potential of each module Based on the multifunctional operating potential of each module Modules with great potential are selected and flexibly grouped into energy storage systems.

[0018] As a preferred solution, the power output potential The calculation formula is The charge and discharge capacity The calculation formula is The power regulation potential The calculation formula is The multifunctional operating potential The calculation formula is In the formula, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, represents the historical maximum output power of the nth module in the reconfigurable battery network, P N Indicates the rated power of the energy storage system, DOD N Represents the maximum discharge depth of the nth module in the reconfigurable battery network, SOH N Indicates the health status of the nth module in the reconfigurable battery network, SOC N represents the current capacity of the nth module in the reconfigurable battery network, Indicates the current capacity reference value under different functions.

[0019] As a preferred solution, the penalty cost objective function is expressed as: In the formula, Δt represents the time step, t represents the time, T represents the optimization period, and α t represents the electricity price at time t, α1 represents the penalty coefficient for energy storage system peak load regulation not meeting the standard, α2 represents the penalty coefficient for energy storage system frequency regulation not meeting the standard, α3 represents the penalty coefficient for energy storage system emergency power support not meeting the standard, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They respectively represent the clearing planned power of peak load regulation, frequency regulation, and emergency power support of the energy storage system at time t.

[0020] As a preferred solution, the operating capacity optimization constraint conditions include:

[0021] in, represents the power of the energy storage system at time t, represents the charging power of the energy storage system at time t, represents the discharge power of the energy storage system at time t, Pmax represents the maximum output power of the energy storage system, that is, the sum of the maximum output power of all modules in the reconfigurable battery network under the current topology, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, I n represents the topological state of the nth module in the reconfigurable battery network, and I n =0, the module is not selected for integration. n =1, the module is selected to be integrated to form an energy storage system, SOC(t) represents the capacity state of the energy storage system at time t, SOC(t-1) represents the capacity state of the energy storage system at time t-1, η represents the efficiency of the energy storage system, SOC low and SOC high They respectively represent the lower and upper limits of the energy storage system capacity status.

[0022] As a preferred solution, the operating capacity optimization constraint condition also includes:

[0023] in, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They respectively represent the peak load regulation, frequency regulation, and emergency power support flags of the energy storage system at time t.

[0024] As a preferred solution, the optimal solution of the penalty cost objective function is solved based on the operating capacity optimization constraint conditions and combined with a mixed integer linear optimization method to update the multifunctional combination scheme of the energy storage system and obtain the corresponding multifunctional day-ahead clearing plan curve, including the steps of: solving the optimal solution of the penalty cost objective function based on the operating capacity optimization constraint conditions and combined with a mixed integer linear optimization method, if the solution is successful, directly generating a multifunctional day-ahead clearing plan curve, otherwise proceeding to the next step; setting the frequency modulation clearing power and the peak load clearing power to zero, and solving based on the demand for emergency power support to obtain the corresponding multifunctional day-ahead clearing plan curve; repeating the above steps to obtain the multifunctional day-ahead clearing plan curve of the energy storage system.

[0025] In a third aspect, the present invention provides an electronic device, wherein the computer device includes a memory, a processor, and a computer program, and when the computer program is executed by the processor, the energy storage system optimization operation method as described in the first aspect is implemented.

[0026] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for optimizing operation of an energy storage system as described in the first aspect is implemented.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] By obtaining the module information of the reconfigurable battery network, the preset system support capacity and the corresponding penalty coefficient, a multifunctional operation potential evaluation model, a penalty cost objective function and operation capacity optimization constraints are constructed. The multifunctional operation potential of each module in the reconfigurable energy storage system can be calculated, and the corresponding modules can be flexibly grouped into an energy storage system to meet the multifunctional operation requirements of the energy storage system; the multifunctional combination scheme of the energy storage system can also be updated and the corresponding multifunctional day-ahead clearing plan curve and the corresponding energy storage system action curve can be obtained. The optimized operation of the energy storage system is realized based on the energy storage system action curve, providing guidance and assistance for the safe and efficient operation of the energy storage system based on the reconfigurable battery network.

[0029] Further or more detailed beneficial effects will be described in detail in conjunction with specific examples in the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 It is a flow chart of the energy storage system optimization operation method provided by an embodiment of the present invention.

[0032] Figure 2 is a structural diagram of the electronic device provided in an embodiment of the present invention.

[0033] Figure Number:

[0034] 200. Electronic equipment;

[0035] 201, processor; 202, communication bus; 203, user interface; 204, network interface; 205, memory. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0037] In the following description, multiple embodiments of the present invention are provided, and different embodiments may be replaced or combined, so the present invention may also be considered to include all possible combinations of the same and / or different embodiments described. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments containing one or more of A, B, C, and D, all other possible combinations, even though the embodiment may not be clearly described in the following text.

[0038] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present invention. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the described order, and various steps may be added, omitted or combined. In addition, the features described in some examples may be combined in other examples.

[0039] In order to facilitate a better understanding of the embodiments of the present invention, before explaining the specific implementation modes of the present invention in detail, its application scenarios are first described.

[0040] The energy storage system optimization operation method described in the embodiments of this specification is applied to industrial production, power system management, and renewable energy utilization processes. In these scenarios, the application of the energy storage system optimization operation method is intended to achieve both the multifunctional operation requirements of the energy storage system and the dynamic multifunctional potential evaluation and flexible grouping of the reconfigurable energy storage system, thereby providing guidance and assistance for the safe and efficient operation of the energy storage system based on the reconfigurable battery network.

[0041] The following is a brief explanation of the reconfigurable battery network, multifunctional operation potential, and multifunctional day-ahead clearing plan curve involved in multiple embodiments of this specification:

[0042] A reconfigurable battery network is a battery system that can dynamically adjust the connection mode and operation mode of battery modules according to different operating requirements and conditions. By changing the connection relationship between battery modules, the network can achieve multiple operation modes, such as parallel, series or mixed connection, to meet different energy storage and discharge requirements.

[0043] Multifunctional operation potential refers to the ability of energy storage systems to provide auxiliary services such as frequency regulation, voltage support, peak-valley load balancing, etc. while meeting basic energy storage functions. This versatility enables energy storage systems to play a greater role in the power system and improve the stability and economy of the entire system.

[0044] The multifunctional day-ahead clearing plan curve refers to the operation plan of the energy storage system in different time periods, which is formulated in advance based on the predicted load demand and renewable energy generation in the electricity market. The plan curve takes into account the multifunctional potential of the energy storage system and aims to optimize the operation of the energy storage system to minimize costs and maximize benefits.

[0045] Embodiment 1:

[0046] like Figure 1 As shown, this embodiment provides a method for optimizing the operation of an energy storage system based on a reconfigurable battery network, comprising the steps of: obtaining module information, preset system support capacity and corresponding penalty coefficient of the reconfigurable battery network; constructing a multifunctional operation potential evaluation model based on the module information, and calculating the multifunctional operation potential of each module in the reconfigurable energy storage system based on the multifunctional operation potential evaluation model to select the corresponding modules to be flexibly grouped as an energy storage system; constructing a penalty cost objective function and operation capacity optimization constraints of the energy storage system based on the preset system support capacity and the corresponding penalty coefficient; solving the optimal solution of the penalty cost objective function based on the operation capacity optimization constraints and in combination with a mixed integer linear optimization method to update the multifunctional combination scheme of the energy storage system and obtain the corresponding multifunctional day-ahead clearing plan curve; obtaining an energy storage system action curve corresponding to the multifunctional day-ahead clearing plan curve, and realizing the optimized operation of the energy storage system based on the energy storage system action curve.

[0047] Specifically, this embodiment provides a preferred implementation method, wherein the module information includes the number of modules, module capacity, module power and current module capacity; the multifunctional operation potential evaluation model is constructed based on the module information, and the multifunctional operation potential of each module in the reconfigurable energy storage system is calculated based on the multifunctional operation potential evaluation model, so as to select the corresponding modules to be flexibly grouped as the energy storage system, including the steps of: calculating the power output potential of each module in the reconfigurable battery network; Charge and discharge capacity and power regulation potential Based on the power output potential of each module Charge and discharge capacity and power regulation potential Calculate the multifunctional operation potential of each module Based on the multifunctional operating potential of each module Modules with great potential are selected and flexibly grouped into energy storage systems.

[0048] Specifically, this embodiment provides a preferred implementation method, the power output potential The calculation formula is The charge and discharge capacity The calculation formula is The power regulation potential The calculation formula is The multifunctional operating potential The calculation formula is In the formula, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, represents the historical maximum output power of the nth module in the reconfigurable battery network, P N Indicates the rated power of the energy storage system, DOD N Represents the maximum discharge depth of the nth module in the reconfigurable battery network, SOH N Indicates the health status of the nth module in the reconfigurable battery network, SOC N represents the current capacity of the nth module in the reconfigurable battery network, It indicates the current capacity reference value under different functions. It can be understood that the power output potential The larger the value, the stronger the power output capability of the system. When SOH decreases, the DOD discharge depth must increase to meet the discharge demand. Therefore, when the charge and discharge capacity capability The larger the value, the lower the charge and discharge capacity of the module. N is the current capacity of the nth reconfigurable energy storage system module, The SOC reference value under different functions. When executing multiple functions, the power regulation potential Different Taking the average value, the power regulation potential represents the functional regulation potential of the nth reconfigurable energy storage system module, the power regulation potential The larger the value, the stronger the module's ability to perform functions.

[0049] Specifically, this embodiment provides a preferred implementation, and the expression of the penalty cost objective function is: In the formula, Δt represents the time step, t represents the time, T represents the optimization period, and α t represents the electricity price at time t, α1 represents the penalty coefficient for energy storage system peak load regulation not meeting the standard, α2 represents the penalty coefficient for energy storage system frequency regulation not meeting the standard, α3 represents the penalty coefficient for energy storage system emergency power support not meeting the standard, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They respectively represent the clearing planned power of peak load regulation, frequency regulation, and emergency power support of the energy storage system at time t. The actual output consistency rate of the function can be calculated through the ratio of operating power to clearing power.

[0050] Specifically, this embodiment provides a preferred implementation, and the operating capacity optimization constraint conditions include: in, represents the power of the energy storage system at time t, represents the charging power of the energy storage system at time t, represents the discharge power of the energy storage system at time t, P max represents the maximum output power of the energy storage system, that is, the sum of the maximum output power of all modules in the reconfigurable battery network under the current topology, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, I n represents the topological state of the nth module in the reconfigurable battery network, and I n =0, the module is not selected for integration. n =1, the module is selected to be integrated to form an energy storage system, SOC(t) represents the capacity state of the energy storage system at time t, SOC(t-1) represents the capacity state of the energy storage system at time t-1, η represents the efficiency of the energy storage system, SOC low and SOC high They respectively represent the lower and upper limits of the energy storage system capacity status.

[0051] Specifically, this embodiment provides a preferred implementation, wherein the operating capacity optimization constraint condition further includes: in, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They represent the peak load regulation, frequency regulation, and emergency power support flags of the energy storage system at time t. Since multiple functions cannot be performed at the same time, the peak load regulation, frequency regulation, and emergency power support flags are used. To constrain the energy storage function at each moment, the operating power of peak load regulation, frequency regulation, and emergency power support at each moment Do not exceed the maximum power of the energy storage system.

[0052] Specifically, this embodiment provides a preferred implementation method, which is based on the operating capacity optimization constraint condition and combined with the mixed integer linear optimization method to solve the optimal solution of the penalty cost objective function to update the multifunctional combination scheme of the energy storage system and obtain the corresponding multifunctional day-ahead clearing plan curve, including the steps of: based on the operating capacity optimization constraint condition and combined with the mixed integer linear optimization method to solve the optimal solution of the penalty cost objective function, if the solution is successful, directly generate the multifunctional day-ahead clearing plan curve, otherwise proceed to the next step; set the frequency modulation clearing power and the peak regulation clearing power to zero, and solve based on the demand for emergency power support to obtain the corresponding multifunctional day-ahead clearing plan curve; repeat the above steps to obtain the multifunctional day-ahead clearing plan curve of the energy storage system.

[0053] Embodiment 2:

[0054] The present embodiment provides an energy storage system optimization operation system based on a reconfigurable battery network, which is used to implement the energy storage system optimization operation method as described in Example 1, including obtaining module information, preset system support capacity and corresponding penalty coefficient of the reconfigurable battery network; constructing a multifunctional operation potential evaluation model based on the module information, and calculating the multifunctional operation potential of each module in the reconfigurable energy storage system based on the multifunctional operation potential evaluation model to select the corresponding modules to be flexibly grouped as the energy storage system; constructing a penalty cost objective function and operation capacity optimization constraint conditions of the energy storage system based on the preset system support capacity and the corresponding penalty coefficient; solving the optimal solution of the penalty cost objective function based on the operation capacity optimization constraint conditions and in combination with the mixed integer linear optimization method to update the multifunctional combination scheme of the energy storage system and obtain the corresponding multifunctional day-ahead clearing plan curve; obtaining the energy storage system action curve corresponding to the multifunctional day-ahead clearing plan curve, and realizing the optimized operation of the energy storage system based on the energy storage system action curve.

[0055] Specifically, this embodiment provides a preferred implementation method, wherein the module information includes the number of modules, module capacity, module power and current module capacity; the multifunctional operation potential evaluation model is constructed based on the module information, and the multifunctional operation potential of each module in the reconfigurable energy storage system is calculated based on the multifunctional operation potential evaluation model, so as to select the corresponding modules to be flexibly grouped as the energy storage system, including the steps of: calculating the power output potential of each module in the reconfigurable battery network; Charge and discharge capacity and power regulation potential Based on the power output potential of each module Charge and discharge capacity and power regulation potential Calculate the multifunctional operation potential of each module Based on the multifunctional operating potential of each module Modules with great potential are selected and flexibly grouped into energy storage systems.

[0056] Specifically, this embodiment provides a preferred implementation method, the power output potential The calculation formula is The charge and discharge capacity The calculation formula is The power regulation potential The calculation formula is The multifunctional operating potential The calculation formula is In the formula, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, represents the historical maximum output power of the nth module in the reconfigurable battery network, P N Indicates the rated power of the energy storage system, DOD N Represents the maximum discharge depth of the nth module in the reconfigurable battery network, SOH N Indicates the health status of the nth module in the reconfigurable battery network, SOC N represents the current capacity of the nth module in the reconfigurable battery network, Indicates the current capacity reference value under different functions.

[0057] Specifically, this embodiment provides a preferred implementation, and the expression of the penalty cost objective function is: In the formula, Δt represents the time step, t represents the time, T represents the optimization period, and α t represents the electricity price at time t, α1 represents the penalty coefficient for energy storage system peak load regulation not meeting the standard, α2 represents the penalty coefficient for energy storage system frequency regulation not meeting the standard, α3 represents the penalty coefficient for energy storage system emergency power support not meeting the standard, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They respectively represent the clearing planned power of peak load regulation, frequency regulation, and emergency power support of the energy storage system at time t.

[0058] Specifically, this embodiment provides a preferred implementation, and the operating capacity optimization constraint conditions include: in, represents the power of the energy storage system at time t, represents the charging power of the energy storage system at time t, represents the discharge power of the energy storage system at time t, P max represents the maximum output power of the energy storage system, that is, the sum of the maximum output power of all modules in the reconfigurable battery network under the current topology, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, I n represents the topological state of the nth module in the reconfigurable battery network, and I n =0, the module is not selected for integration. n =1, the module is selected to be integrated to form an energy storage system, SOC(t) represents the capacity state of the energy storage system at time t, SOC(t-1) represents the capacity state of the energy storage system at time t-1, η represents the efficiency of the energy storage system, SOC low and SOC high They respectively represent the lower and upper limits of the energy storage system capacity status.

[0059] Specifically, this embodiment provides a preferred implementation, wherein the operating capacity optimization constraint condition further includes: in, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They respectively represent the peak load regulation, frequency regulation, and emergency power support flags of the energy storage system at time t.

[0060] Specifically, this embodiment provides a preferred implementation method, which is based on the operating capacity optimization constraint condition and combined with the mixed integer linear optimization method to solve the optimal solution of the penalty cost objective function to update the multifunctional combination scheme of the energy storage system and obtain the corresponding multifunctional day-ahead clearing plan curve, including the steps of: based on the operating capacity optimization constraint condition and combined with the mixed integer linear optimization method to solve the optimal solution of the penalty cost objective function, if the solution is successful, directly generate the multifunctional day-ahead clearing plan curve, otherwise proceed to the next step; set the frequency modulation clearing power and the peak regulation clearing power to zero, and solve based on the demand for emergency power support to obtain the corresponding multifunctional day-ahead clearing plan curve; repeat the above steps to obtain the multifunctional day-ahead clearing plan curve of the energy storage system.

[0061] Embodiment three:

[0062] like Figure 2 As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0063] The communication bus can be used to realize the connection and communication among the above-mentioned components.

[0064] The user interface may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0065] The network interface may include but is not limited to a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0066] Among them, the processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but may be implemented separately through a chip.

[0067] Among them, the memory may include RAM or ROM. Optionally, the memory includes a non-transient computer-readable medium. The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory may also be at least one storage device located away from the aforementioned processor. The memory as a computer storage medium may include an operating system, a network communication module, a user interface module, and an optimized operation application. The processor can be used to call the optimized operation application stored in the memory and execute the optimized operation method mentioned in the aforementioned embodiment.

[0068] Embodiment 4:

[0069] This embodiment provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is executed on a computer or a processor, the computer or the processor executes the above-mentioned Figure 1 One or more steps in the illustrated embodiment. If the components of the electronic device described above are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0070] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0071] A person of ordinary skill in the art can understand that all or part of the processes in the method of the first embodiment can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes. In the absence of conflict, the technical features in this embodiment and the implementation scheme can be combined arbitrarily.

[0072] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0073] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0074] The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention. That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the specification and practicing the disclosure here, it will be easy for those skilled in the art to think of the implementation scheme of the present invention. The present invention is intended to cover any modification, use or adaptation of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not recorded in the present invention. The description and examples are only regarded as exemplary, and the scope and spirit of the present invention are defined by the claims.

Claims

1. A method for optimizing the operation of an energy storage system based on a reconfigurable battery network, characterized in that: Includes steps: Obtain module information of the reconfigurable battery network, preset system support capacity and corresponding penalty coefficient; A multifunctional operation potential evaluation model is constructed based on the module information, and the multifunctional operation potential of each module in the reconfigurable energy storage system is calculated based on the multifunctional operation potential evaluation model, so as to select corresponding modules to flexibly group them into an energy storage system; Based on the preset system support capacity and the corresponding penalty coefficient, construct a penalty cost objective function and operating capacity optimization constraint condition of the energy storage system; Solving the optimal solution of the penalty cost objective function based on the operating capacity optimization constraint condition and in combination with a mixed integer linear optimization method to update the multifunctional combination scheme of the energy storage system and obtain a corresponding multifunctional day-ahead clearing plan curve; An energy storage system action curve corresponding to the multifunctional day-ahead clearing plan curve is obtained, and optimal operation of the energy storage system is achieved based on the energy storage system action curve.

2. The method for optimizing operation of an energy storage system based on a reconfigurable battery network according to claim 1, characterized in that: The module information includes the number of modules, module capacity, module power and current capacity of the module; The method of constructing a multifunctional operation potential evaluation model based on the module information, and calculating the multifunctional operation potential of each module in the reconfigurable energy storage system based on the multifunctional operation potential evaluation model, so as to select corresponding modules to flexibly group into an energy storage system, comprises the following steps: Calculating the power output potential of each module in a reconfigurable battery network Charge and discharge capacity and power regulation potential Based on the power output potential of each module Charge and discharge capacity and power regulation potential Calculate the multifunctional operation potential of each module Based on the multifunctional operation potential of each module Modules with great potential are selected and flexibly grouped into energy storage systems.

3. The method for optimizing operation of an energy storage system based on a reconfigurable battery network according to claim 2, characterized in that: The power output potential The calculation formula is The charge and discharge capacity The calculation formula is The power regulation potential The calculation formula is The multifunctional operating potential The calculation formula is In the formula, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, represents the historical maximum output power of the nth module in the reconfigurable battery network, P N Indicates the rated power of the energy storage system, DOD N Represents the maximum discharge depth of the nth module in the reconfigurable battery network, SOH N Indicates the health status of the nth module in the reconfigurable battery network, SOC N represents the current capacity of the nth module in the reconfigurable battery network, Indicates the current capacity reference value under different functions.

4. The method for optimizing operation of an energy storage system based on a reconfigurable battery network according to claim 3 is characterized in that: The expression of the penalty cost objective function is: In the formula, Δt represents the time step, t represents the time, T represents the optimization period, and α t represents the electricity price at time t, α1 represents the penalty coefficient for energy storage system peak load regulation not meeting the standard, α2 represents the penalty coefficient for energy storage system frequency regulation not meeting the standard, α3 represents the penalty coefficient for energy storage system emergency power support not meeting the standard, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They respectively represent the clearing planned power of peak load regulation, frequency regulation, and emergency power support of the energy storage system at time t.

5. The method for optimizing operation of an energy storage system based on a reconfigurable battery network according to claim 4 is characterized in that: The operating capacity optimization constraints include: in, represents the power of the energy storage system at time t, represents the charging power of the energy storage system at time t, represents the discharge power of the energy storage system at time t, P max represents the maximum output power of the energy storage system, that is, the sum of the maximum output power of all modules in the reconfigurable battery network under the current topology, N represents the total number of modules in the reconfigurable battery network, n represents the module number in the reconfigurable battery network, I n represents the topological state of the nth module in the reconfigurable battery network, and I n =0, the module is not selected for integration. n =1, the module is selected to be integrated to form an energy storage system, SOC(t) represents the capacity state of the energy storage system at time t, SOC(t-1) represents the capacity state of the energy storage system at time t-1, η represents the efficiency of the energy storage system, SOC low and SOC high They respectively represent the lower and upper limits of the energy storage system capacity status.

6. The method for optimizing operation of an energy storage system based on a reconfigurable battery network according to claim 5, characterized in that: The operating capacity optimization constraints also include: in, represents the power of the energy storage system at time t, They represent the operating power of the energy storage system performing peak load regulation, frequency regulation, and emergency power support at time t, respectively. They respectively represent the peak load regulation, frequency regulation, and emergency power support flags of the energy storage system at time t.

7. The method for optimizing operation of an energy storage system based on a reconfigurable battery network according to claim 6, characterized in that: The method of solving the optimal solution of the penalty cost objective function based on the operating capacity optimization constraint condition and in combination with a mixed integer linear optimization method to update the multifunctional combination scheme of the energy storage system and obtain the corresponding multifunctional day-ahead clearing plan curve includes the following steps: Based on the operating capacity optimization constraint condition and in combination with the mixed integer linear optimization method, the optimal solution of the penalty cost objective function is solved. If the solution is successful, a multifunctional day-ahead clearing plan curve is directly generated, otherwise, the next step is entered; The frequency regulation clearing power and peak regulation clearing power are set to zero, and the demand for emergency power support is solved to obtain the corresponding multifunctional day-ahead clearing plan curve; Repeat the above steps to obtain the multifunctional day-ahead clearing schedule curve of the energy storage system.

8. An energy storage system optimization operation system based on a reconfigurable battery network, characterized in that: Used to implement the energy storage system optimization operation method as described in any one of claims 1 to 7.

9. A computer device, comprising a memory, a processor and a computer program, characterized in that: When the computer program is executed by a processor, the energy storage system optimization operation method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the energy storage system optimization operation method according to any one of claims 1 to 7 is implemented.

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