An adaptive dynamic reconfiguration energy storage system integrated control method and device

Abstract

This invention relates to the field of energy storage system control technology, and discloses an adaptive dynamic reconfiguration integrated control method and device for energy storage systems. This invention achieves pooled management of physical energy storage resources by constructing a virtual energy management platform at the software layer. It combines task performance constraints and multi-objective optimization to achieve dynamic configuration of virtual energy storage units. Through the conversion of logical topology to physical action commands, coupled with a safe topology reconfiguration strategy, it realizes autonomous and online reconfiguration of the electrical connections and operating modes of the energy storage system. Simultaneously, relying on closed-loop adaptive adjustment of comprehensive deviation values, it achieves real-time optimization of the system's operating status, effectively solving the problem of refined management of heterogeneous resources, meeting the grid's demand for rapid and seamless multi-task switching, significantly improving the flexibility, intelligence level, and operating economy of the energy storage system, and adapting to the complex operating requirements of high-proportion renewable energy power systems.

Description

Technical Field

[0001] This invention relates to the field of energy storage system control technology, and specifically to an adaptive dynamic reconfiguration integrated control method and device for energy storage systems. Background Technology

[0002] As the penetration rate of renewable energy in the power system continues to increase, the demand for flexible regulation resources in the power grid is growing. Energy storage systems have become a key infrastructure for maintaining system stability, improving power quality, and realizing energy time shifting. Traditional large-scale energy storage power stations adopt fixed electrical topologies and preset control strategies, rigidly binding the functions and output characteristics of energy storage units with physical connections to respond to dispatch commands as a whole. Under complex operating scenarios, this approach exposes inherent defects such as low resource utilization, difficulty in managing heterogeneous units, cumbersome multi-task switching, and poor fault tolerance, making it difficult to adapt to the diversified needs of the power grid.

[0003] Therefore, existing technologies mostly decompose energy storage systems into independently controllable sub-units through modular design, or study flexible interconnection technologies based on power electronic converters to try to improve system control flexibility. However, the above methods all have obvious limitations. Related research focuses on local optimization or control algorithms for specific scenarios, lacking a systematic integrated control method. They cannot autonomously, online, and securely reconstruct the internal electrical connections and operating modes of the system according to high-level instructions, real-time equipment status, and performance requirements, making it difficult to meet the practical application requirements such as rapid switching of multi-tasks in the power grid and refined management of heterogeneous resources. Summary of the Invention

[0004] This invention provides an adaptive dynamic reconfiguration energy storage system integrated control method and device to solve the problem that the existing technology lacks a systematic integrated control method, which is unable to autonomously, online and safely reconfigure the internal electrical connections and operation modes of the system according to high-level instructions, real-time equipment status and performance requirements, and is difficult to meet the practical application requirements such as rapid switching of multi-tasks in the power grid and refined management of heterogeneous resources.

[0005] In a first aspect, the present invention provides an adaptive dynamic reconfiguration integrated control method for an energy storage system, applied to a hardware energy storage system constructed from a physical switching matrix and multiple physical energy storage units, the method comprising: Based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, a virtual energy management platform is constructed, and the virtualization state of all physical energy storage units is mapped through the virtual energy management platform; Based on preset task performance constraints and multiple target optimization conditions, a set of virtualized physical energy storage units are dynamically selected to generate virtual energy storage units. Based on the logical connection topology of the virtual energy storage unit, a sequence of physical action instructions for controlling the physical switching matrix to perform switching operations is determined; By executing the physical action command sequence, the on / off state of the target switch in the physical switching matrix is ​​controlled, and the electrical connection network between each physical energy storage unit is reorganized in real time according to the on / off state of the target switch to generate an initial operating topology. Calculate the comprehensive deviation value between the real-time operating parameters of each physical energy storage unit and the expected performance index of the virtual energy storage unit, and adaptively and dynamically reconstruct the initial operating topology based on the comprehensive deviation value.

[0006] This invention constructs a virtual energy management platform at the software layer to achieve pooled management of physical energy storage resources. Combining task performance constraints and multi-objective optimization, it dynamically configures virtual energy storage units. Through the conversion of logical topology to physical action commands, coupled with a secure topology reconfiguration strategy, it enables autonomous and online reconfiguration of the electrical connections and operating modes of the energy storage system. Simultaneously, relying on closed-loop adaptive adjustment of comprehensive deviation values, it achieves real-time optimization of the system's operating status, effectively solving the problem of refined management of heterogeneous resources, meeting the grid's requirements for rapid and seamless multi-task switching, significantly improving the flexibility, intelligence, and operational economy of the energy storage system, and adapting to the complex operating requirements of high-proportion renewable energy power systems.

[0007] In one optional implementation, the step of constructing a virtual energy management platform based on the physical exchange matrix and the actual connection relationships of each physical energy storage unit, and mapping the virtualization state of all the physical energy storage units through the virtual energy management platform, includes: Based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, a virtual energy management platform is constructed. The virtual energy management platform sends characteristic test signal commands to each physical energy storage unit and collects the corresponding output response data. Based on the output response data, a second-order transfer function model is established for the corresponding physical energy storage unit, and the unit power response speed index and load capacity index of the physical energy storage unit are calculated through the second-order transfer function model. By combining the unit power response speed index, load capacity index, rated parameters and real-time operating status parameters of the physical energy storage unit, the virtualized state of the physical energy storage unit is generated. The virtualized states of all the physical energy storage units are aggregated to generate a resource pool.

[0008] This invention constructs a virtual energy management platform based on actual hardware connections. Through feature testing and modeling identification, it accurately extracts core indicators such as power response and load capacity of energy storage units. Combined with rated and real-time operating parameters, it generates standardized virtual states and constructs resource pools, realizing software abstraction and pooled management of physical energy storage resources. This lays the foundation for subsequent on-demand resource matching and dynamic topology reconfiguration, improving the standardization, refinement, and flexibility of energy storage resource management, and adapting to the unified management and control requirements of heterogeneous energy storage units.

[0009] In one optional implementation, the step of dynamically selecting a set of virtualized physical energy storage units according to preset task performance constraints and multiple target optimization conditions to generate virtual energy storage units includes: Obtain the system's target execution instructions and extract the task performance constraint vector corresponding to the system's target execution instructions; Based on the aforementioned task performance constraint vector, constraints are constructed with total power, total energy, and dynamic response capability as constraint dimensions. With the goals of minimizing resource usage costs, maximizing unit state balance, and maximizing overall performance, multiple objective optimization conditions are generated. Based on the aforementioned task performance constraints and multiple objective optimization conditions, a resource selection optimization model is constructed. The resource selection optimization model is solved, and based on the solution results, a set of virtualized physical energy storage units that meet the constraints and have the best comprehensive evaluation are selected from the resource pool. Logical connection topology is assigned to the virtualized physical energy storage unit to construct the virtual energy storage unit.

[0010] This invention extracts performance constraints from system operation target instructions, constructs multi-dimensional constraints and multi-objective optimization conditions, builds and solves a resource selection optimization model, accurately selects the optimal virtualized physical energy storage unit, and constructs virtual energy storage units by combining task requirement allocation logic topology, thereby achieving optimal matching between task requirements and energy storage resources, improving resource utilization, and taking into account operating costs, unit state balance and overall performance.

[0011] In one optional implementation, determining the sequence of physical action instructions for controlling the physical switching matrix to perform switching operations based on the logical connection topology of the virtual energy storage unit includes: Collect the real-time on / off status of all switches in the physical switching matrix and construct the current topology state matrix; Based on the logical connection topology and mapping rules of the virtual energy storage unit, construct the target topology state matrix; With the optimization objectives of minimizing total reconfiguration time and minimizing switching losses, and with the constraint of maintaining the continuity of critical power paths, a topology switching path planning model is constructed. The topology switching path planning model is solved, and the sequence of physical action instructions for performing switching operations from the current topology state matrix to the target topology state matrix is ​​determined based on the solution results.

[0012] This invention constructs a current and target topology state matrix, builds a path planning model with the goal of minimizing reconstruction time and switching losses and the constraint of critical power path continuity, solves and generates ordered switching action commands and performs timing optimization, thus achieving accurate conversion from logical topology to physical action commands. This effectively reduces system disturbances and hardware losses during topology reconstruction and ensures the continuity and efficiency of the reconstruction process.

[0013] In an optional implementation, the method further includes: A digital twin simulation environment is constructed based on the equivalent circuit models corresponding to the physical exchange matrix and the physical energy storage unit, respectively. Import the system's current electrical state and the sequence of physical action commands into the digital twin simulation environment; In the digital twin simulation environment, the physical action instruction sequence is executed step by step, and the equivalent circuit model is driven according to the timing of the physical action instruction sequence and the equivalent circuit model is solved to obtain the voltage data of each key node and the current data of each branch of the equivalent circuit model. The voltage data and the current data are compared with their respective safety thresholds. If the comparison result is greater than either safety threshold, the physical action command sequence is corrected.

[0014] This invention constructs a digital twin simulation environment based on the equivalent circuit model of the physical exchange matrix and energy storage unit. It imports system states and action commands and simulates the solution, obtaining electrical data which is then compared with safety thresholds. Risky command sequences are corrected promptly. By verifying command feasibility in advance through virtual simulation, electrical risks such as overvoltage and overcurrent during topology reconfiguration are avoided at the source, significantly improving the safety of command execution, ensuring the stability and reliability of the physical layer topology reconfiguration process, and preventing damage to hardware devices.

[0015] In one optional implementation, the step of controlling the on / off state of the target switches in the physical switching matrix by executing the physical action command sequence, and real-time reorganizing the electrical connection network between each physical energy storage unit according to the on / off state of the target switches to generate an initial operating topology includes: Adjust the output voltage of the physical energy storage unit to be connected to the target bus, and connect multiple physical energy storage units corresponding to the adjusted output voltage to the target bus. The physical action instruction sequence is divided into multiple sub-action instruction sequences; The sub-action instruction sequence is executed one by one according to the preset execution order to control the on / off state of the target switch in the physical switching matrix, and the seamless switching logic of first closing and then closing is adopted to change the electrical connection network between the physical energy storage units. When all the sub-action instruction sequences have been executed, the circulating current component of the branch of each physical energy storage unit is collected in real time; Determine whether the amplitude of the circulating current component is greater than or equal to a preset rated current threshold. If so, then suppress the circulating component; If not, the current electrical connection network between each of the physical energy storage units is determined as the initial operating topology.

[0016] This invention achieves a smooth and shock-free topology reconfiguration process through voltage pre-synchronization, phased command execution, and a seamless switching strategy of closing before disconnecting, effectively avoiding closing overcurrent and power interruption. Simultaneously, it detects and actively suppresses circulating current in real time, reducing inter-unit internal losses and equipment stress. The entire solution significantly improves system operational stability and safety while ensuring reconfiguration continuity, enabling more efficient collaborative work among energy storage units and ultimately forming a reliable initial operating topology.

[0017] In one optional implementation, the step of calculating the comprehensive deviation value between the real-time operating parameters of each of the physical energy storage units and the expected performance indicators of the virtual energy storage units, and adaptively and dynamically reconstructing the initial operating topology based on the comprehensive deviation value, includes: Collect real-time operating parameters of each physical energy storage unit; The weighted deviation between the real-time operating parameters of the physical energy storage unit and the expected performance indicators of the virtual energy storage unit is calculated to obtain the comprehensive deviation value; Determine whether the overall deviation value is greater than or equal to the preset adaptive trigger threshold; If so, then proceed to the step of dynamically selecting a set of virtualized physical energy storage units according to preset task performance constraints and multiple target optimization conditions, and generating virtual energy storage units, until the comprehensive deviation value is less than the preset adaptive trigger threshold, and the target running topology is obtained.

[0018] This invention achieves accurate evaluation of the actual system performance by collecting operating parameters in real time and calculating weighted comprehensive deviations. When the deviation exceeds the limit, it automatically triggers closed-loop re-optimization, continuously iterating and updating the virtual energy storage units and operating topology to ensure that the system always operates close to the expected performance indicators. This effectively improves the adaptability and robustness of the topology, ensuring that the energy storage system can meet mission requirements stably, efficiently, and accurately in the long term.

[0019] Secondly, the present invention provides an adaptive dynamic reconfiguration energy storage system integrated control device, applied to a hardware energy storage system constructed from a physical switching matrix and multiple physical energy storage units, the device comprising: The mapping module is used to construct a virtual energy management platform based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, and to map the virtualization state of all the physical energy storage units through the virtual energy management platform; The selection module is used to dynamically select a set of virtualized energy storage units according to preset task performance constraints and multiple target optimization conditions, and generate virtual energy storage units. The processing module is used to determine a sequence of physical action instructions for controlling the physical switching matrix to perform switching operations based on the logical connection topology of the virtual energy storage unit. The execution module is used to control the on / off state of the target switch in the physical switching matrix by executing the physical action instruction sequence, and to reorganize the electrical connection network between each physical energy storage unit in real time according to the on / off state of the target switch to generate an initial operating topology. The calculation module is used to calculate the comprehensive deviation value between the real-time operating parameters of each physical energy storage unit and the expected performance index of the virtual energy storage unit, and to adaptively and dynamically reconstruct the initial operating topology based on the comprehensive deviation value.

[0020] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the adaptive dynamic reconfiguration energy storage system integrated control method of the first aspect or any corresponding embodiment described above.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the adaptive dynamic reconfiguration energy storage system integrated control method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1This is a schematic diagram of the first type of integrated control method for adaptive dynamic reconfiguration energy storage system according to an embodiment of the present invention; Figure 2 This is a second flowchart illustrating the adaptive dynamic reconfiguration integrated control method for an energy storage system according to an embodiment of the present invention. Figure 3 This is a structural block diagram of an adaptive dynamic reconfiguration integrated control device for an energy storage system according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] This invention provides an adaptive dynamic reconfiguration integrated control method for energy storage systems. By constructing a virtual energy management platform at the software layer to achieve pooled management of physical energy storage resources, and combining task performance constraints with multi-objective optimization, the method achieves dynamic configuration of virtual energy storage units. Through the conversion of logical topology to physical action commands, coupled with a secure topology reconfiguration strategy, the method enables autonomous and online reconfiguration of the energy storage system's electrical connections and operating modes. Simultaneously, relying on closed-loop adaptive adjustment based on comprehensive deviation values, the method achieves real-time optimization of the system's operating status. This effectively solves the problem of refined management of heterogeneous resources, meets the grid's requirements for rapid and seamless multi-task switching, significantly improves the flexibility, intelligence, and operational economy of the energy storage system, and adapts to the complex operational requirements of high-proportion renewable energy power systems.

[0028] According to an embodiment of the present invention, an embodiment of an adaptive dynamic reconfiguration energy storage system integrated control method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides an adaptive dynamic reconfiguration integrated control method for energy storage systems, applied to hardware energy storage systems constructed from a physical switching matrix and multiple physical energy storage units. Figure 1 This is a flowchart of an adaptive dynamic reconfiguration integrated control method for an energy storage system according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, a virtual energy management platform is constructed, and the virtualized state of all physical energy storage units is mapped through the virtual energy management platform.

[0030] It should be noted that a physical switching matrix refers to a controllable electrical network composed of multiple power semiconductor switches (such as IGBTs and MOSFETs), with its input terminals connected to each physical energy storage unit and its output terminals connected to the DC bus or AC grid interface.

[0031] Physical energy storage units include, but are not limited to, lithium-ion battery modules, supercapacitor banks, and flywheel energy storage devices. Each unit is equipped with a local controller that can collect real-time operating data such as voltage, current, temperature, SOC (state of charge), and SOH (state of health).

[0032] A virtual energy management platform refers to a software abstraction layer built in the memory space of an embedded processor or edge computing node. It establishes data connections with the switching drive circuit of the physical switching matrix and the local controller of each physical energy storage unit through a high-speed communication bus (such as CAN, EtherCAT or industrial Ethernet).

[0033] Virtualization status refers to a standardized description formed by uniformly encapsulating the rated parameters, dynamic performance indicators, and real-time operating status of physical energy storage units, which is used to realize the abstract management of heterogeneous units.

[0034] In this embodiment of the invention, the heterogeneous characteristics of physical energy storage units are abstracted into a unified virtual resource descriptor by constructing a virtual energy management plane, thereby obtaining virtualized physical energy storage units and realizing software definition and pooling management of resources.

[0035] It is worth mentioning that the virtual energy management plane maintains a topology state table, which records the connection relationship between each physical energy storage unit and the bus at the current moment in the form of an adjacency matrix. For example, for a system containing 4 energy storage units, if units 1 and 3 are connected to the positive bus, and units 2 and 4 are disconnected, the topology state table can be represented as: A_cur=diag(1,0,1,0).

[0036] The switching drive circuit of the physical switching matrix is ​​implemented using FPGA or CPLD, and has microsecond-level response capability.

[0037] Step S102: According to the preset task performance constraints and multiple target optimization conditions, dynamically select a set of virtualized physical energy storage units to generate virtual energy storage units.

[0038] It should be noted that task performance constraints refer to the minimum performance requirements that the system must meet in order to complete a specified task, including hard indicators such as total power, total capacity, and dynamic response speed.

[0039] The target optimization conditions refer to the optimization objectives pursued further under the premise of meeting performance constraints, including multi-dimensional objectives such as minimizing resource usage costs, maximizing the state balance of energy storage units, and optimizing the overall system performance.

[0040] A virtual energy storage unit refers to a unified virtual scheduling unit formed by logically combining multiple physical energy storage units. It presents overall power and energy characteristics to the outside world and can independently participate in system operation and control.

[0041] In this embodiment of the invention, performance requirements such as power, energy and response speed are extracted from the operation instructions of the upper-level energy management system (EMS). Combined with cost, balance and overall efficiency goals, physical energy storage units in each virtualized state are optimally matched. Multiple selected units are combined according to logical relationships to form a virtual energy storage unit with overall operation capability, so that it can directly meet the current task requirements of the system.

[0042] Step S103: Based on the logical connection topology of the virtual energy storage unit, determine the sequence of physical action instructions used to control the physical switching matrix to perform switching operations.

[0043] It should be noted that logical connection topology refers to the logical connection relationship between physical energy storage units within a virtual energy storage unit, and between the unit and the bus.

[0044] A physical action instruction sequence refers to a set of switch control instructions arranged in a timing sequence, used to drive each switch in the physical switching matrix to act sequentially, thereby realizing the mapping from logical topology to physical topology.

[0045] In this embodiment of the invention, the combination connection relationship of virtual energy storage units is converted into the actual action timing of physical switches. By comparing the differences between the current topology and the target topology, the switching sequence of the physical switching matrix is ​​planned and an ordered instruction that can be directly issued and executed is formed, ensuring that the topology switching process is continuous and reliable.

[0046] Step S104: By executing a sequence of physical action instructions, the on / off state of the target switch in the physical switching matrix is ​​controlled, and the electrical connection network between each physical energy storage unit is reorganized in real time according to the on / off state of the target switch to generate the initial operating topology.

[0047] It should be noted that the target switch refers to the controllable power switching device in the physical switching matrix that performs the actions required to achieve a specified logical connection topology.

[0048] An electrical connection network refers to the actual set of electrical paths formed by the switching paths of a physical switching matrix and physical energy storage units.

[0049] The initial operating topology refers to the stable electrical network structure formed by each physical energy storage unit and the physical exchange matrix after the topology reorganization is completed, which meets the operating requirements of the virtual energy storage unit.

[0050] In this embodiment of the invention, the voltage of the physical energy storage unit to be connected to the bus is synchronously adjusted before the instruction is executed. A gradual synchronous switching strategy is adopted to execute the physical action instruction sequence, that is, the topology switching is completed by adopting a phased orderly action and a first-close-then-disconnect method. At the same time, the branch circulating current is detected and suppressed in real time, and finally a stable and reliable initial operating topology structure is formed.

[0051] Step S105: Calculate the comprehensive deviation value between the real-time operating parameters of each physical energy storage unit and the expected performance index of the virtual energy storage unit, and adaptively and dynamically reconstruct the initial operating topology based on the comprehensive deviation value.

[0052] It should be noted that real-time operating parameters refer to the core electrical and status data that dynamically change during the actual operation of each physical energy storage unit.

[0053] Expected performance indicators refer to the overall performance requirements that a virtual energy storage unit must meet to meet the system's operational tasks. These mainly cover key performance benchmarks such as total output power, total energy storage capacity, dynamic response speed, power regulation accuracy, and operational reliability.

[0054] The overall deviation value refers to the quantitative result of the overall difference obtained by assigning weight coefficients to each real-time operating parameter and its corresponding expected performance index and then performing a weighted calculation.

[0055] Adaptive dynamic reconfiguration refers to a system topology and resource iterative optimization process that is automatically triggered based on comprehensive deviation values.

[0056] In this embodiment of the invention, the real-time operating parameters of physical energy storage units, such as power, energy, SOC, and response speed, are synchronously collected through a virtual energy management platform and compared with the expected performance indicators such as power, capacity, and response bandwidth preset by the virtual energy storage units. A comprehensive deviation value is calculated based on multi-dimensional weighting coefficients. When the deviation value exceeds a preset adaptive trigger threshold, the system automatically triggers a resource re-optimization process, re-selects virtualized physical energy storage units, updates logical connection topology, and reorganizes the physical electrical network. This process is iterated until the comprehensive deviation value falls back to the threshold range, ultimately achieving a precise match between the system's operating status and task requirements.

[0057] This embodiment provides an adaptive dynamic reconfiguration integrated control method for energy storage systems, applied to hardware energy storage systems constructed from a physical switching matrix and multiple physical energy storage units. Figure 2 This is a flowchart of an adaptive dynamic reconfiguration integrated control method for an energy storage system according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, a virtual energy management platform is constructed, and the virtualized state of all physical energy storage units is mapped through the virtual energy management platform.

[0058] In some optional implementations, step S201 above includes: Step S2011: Based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, construct a virtual energy management platform.

[0059] It should be noted that the actual connection relationship refers to the actual physical wiring method and connection status between each switching device in the physical switching matrix and the physical energy storage unit and the target bus.

[0060] In this embodiment of the invention, the switch layout, wiring method, and actual connection logic of each physical energy storage unit of the physical switching matrix are sorted out to establish a hardware connection database; then, a software management framework is built to integrate the on / off control logic of the physical switching matrix with the basic information of the physical energy storage units, and core data such as the rated parameters and operating status of each unit are collected synchronously to establish a unified digital management model; by issuing test signals to verify the device response, the digital mapping of physical resources is completed, and virtual management and control of physical hardware is realized, forming a virtual energy management platform that can support resource scheduling, status monitoring, and command issuance.

[0061] In step S2012, characteristic test signal commands are sent to each physical energy storage unit through the virtual energy management platform, and corresponding output response data is collected.

[0062] It should be noted that the characteristic test signal command refers to the standardized set of excitation signals generated by the virtual energy management platform to stimulate the dynamic characteristics of physical energy storage units.

[0063] Output response data refers to the dynamic electrical and status data such as voltage, current, power, and state of charge (SOC) that a physical energy storage unit outputs in real time after receiving a characteristic test signal.

[0064] In this embodiment of the invention, the characteristic test signal is a pseudo-random binary sequence (PRBS) or a swept-frequency sine wave signal with an amplitude not exceeding 5% of the rated voltage of the physical energy storage unit and a duration adjustable from 10ms to 100ms. This signal is used to excite the dynamic characteristics of the physical energy storage unit without affecting its normal operation. The test signal is sent from the virtual energy management plane to the local controller of the target physical energy storage unit. The local controller controls its bidirectional DC / DC converter to generate corresponding voltage disturbances and simultaneously acquires the output current response data y(t) at a sampling frequency of not less than 10kHz.

[0065] Step S2013: Based on each output response data, establish a second-order transfer function model for the corresponding physical energy storage unit, and calculate the unit power response speed index and load-carrying capacity index of the physical energy storage unit through the second-order transfer function model.

[0066] It should be noted that the second-order transfer function model refers to a mathematical model used to describe the dynamic relationship between the input (characteristic test signal) and output (response data) of a physical energy storage unit.

[0067] The unit power response speed index refers to the speed at which the output power of a physical energy storage unit adjusts from the initial state to the target state after receiving a control command, obtained by solving a second-order transfer function model.

[0068] The load-carrying capacity index refers to the output characteristics calculated based on the second-order transfer function model, which reflects the maximum power that the physical energy storage unit can continuously output and its long-term load-carrying stability under stable operating conditions.

[0069] In this embodiment of the invention, based on the output response data, a parameter identification algorithm is used to identify the equivalent mathematical model of the physical energy storage unit. By solving the characteristic parameters of the model, the unit's power response speed and load-carrying capacity are quantitatively calculated. Specifically, the key parameter vector of the model is obtained by solving the following optimization problem. ;

[0070] in, Let be the measured current response at the t-th sampling point. Second-order transfer function model The predicted output, This is the length of the data window (usually 1000 sampling points). is the regularization coefficient (0.01 in this example) used to prevent overfitting. The optimization problem is solved iteratively using the Levenberg-Marquardt algorithm, with a convergence accuracy set to . .

[0071] Among them, the key parameter vector The physical meaning is: This represents the DC gain of the physical energy storage unit, expressed in A / V. , It is a time constant, measured in seconds, reflecting the dynamic response speed of the physical energy storage unit.

[0072] The core dynamic performance indicators of this physical energy storage unit, including bandwidth, were calculated based on key parameters. This indicator directly determines the types of control tasks that the physical energy storage unit can participate in. For example, if a physical energy storage unit's... s, then its bandwidth Hz, suitable for high-speed frequency tuning tasks; if s, then Hz is only suitable for slow energy balance tasks.

[0073] Step S2014: Combine the unit power response speed index, load capacity index, rated parameters and real-time operating status parameters of the physical energy storage unit to generate the virtualized state of the physical energy storage unit.

[0074] It should be noted that rated parameters refer to the inherent parameters calibrated by the physical energy storage unit at the factory, including rated power, rated capacity, rated voltage, etc., which are the basic constraints for equipment operation.

[0075] Real-time operating status parameters refer to the dynamic data collected in real time during the operation of the physical energy storage unit, including state of charge (SOC), operating temperature, and current output power.

[0076] In this embodiment of the invention, the unit power response speed index (bandwidth) and load capacity index, along with the rated parameters such as rated power and rated capacity of the physical energy storage unit, as well as the operating status parameters such as real-time SOC and operating temperature, are standardized and encapsulated to form virtualized status data in a unified format.

[0077] Specifically, physical energy storage units iThe static parameters and core dynamic performance indicators are encapsulated to form a virtual resource descriptor. This is known as virtual stateification. Among them, Rated power (kW). Rated capacity (kWh) The current state of charge (%) Current health status (%) The internal equivalent resistance (mΩ) is obtained through parameter identification. The maximum permissible operating temperature (°C).

[0078] Step S2015: Collect the virtualized states of all physical energy storage units to generate a resource pool.

[0079] It should be noted that the resource pool refers to a structured data set composed of the virtualized states of all physical energy storage units.

[0080] In this embodiment of the invention, the virtual resource descriptors of all physical energy storage units are stored in the memory of the virtual energy management plane in the form of a hash table to form a virtual energy storage resource pool, which facilitates rapid retrieval and matching in the future.

[0081] Step S202: According to the preset task performance constraints and multiple target optimization conditions, dynamically select a set of virtualized physical energy storage units to generate virtual energy storage units.

[0082] In some optional implementations, step S202 above includes: Step S2021: Obtain the system running target instruction and extract the task performance constraint vector corresponding to the system running target instruction.

[0083] It should be noted that the system operation target instruction refers to the instruction issued by the upper-level energy management system or dispatch center.

[0084] The task performance constraint vector refers to a multi-dimensional vector formed after quantifying and parsing the system's target instructions. It includes hard constraint indicators such as total output power, total energy storage capacity, minimum dynamic response bandwidth, and operational reliability.

[0085] In this embodiment of the invention, the system operation target instruction is issued by the upper-level energy management system (EMS) or the power grid dispatch center. Its format is JSON or XML, which includes the task type (such as "primary frequency regulation", "peak shaving and valley filling", "reactive power support") and the task performance requirement constraint vector. Fields such as [e.g., ]. Among them, Power required for the task (kW). Energy required for the mission (kWh). Minimum response bandwidth (Hz) required for the task. Voltage regulation accuracy requirement (%).

[0086] Analyze the system's target instructions and extract the performance requirement constraint vector. For example, for a frequency modulation task, it might require... kW, Hz, for energy There are no strict requirements; however, for peak shaving and valley filling tasks, there are requirements. kWh, kW, Hz.

[0087] Step S2022: Based on the task performance constraint vector, construct constraint conditions with total power, total energy, and dynamic response capability as constraint dimensions.

[0088] It should be noted that the total power constraint refers to the minimum / maximum output power range that the virtual energy storage unit needs to provide to the outside world.

[0089] Total energy constraint refers to the minimum energy storage capacity that a virtual energy storage unit must possess.

[0090] Dynamic response capability constraints refer to the minimum bandwidth that a virtual energy storage unit must meet.

[0091] In this embodiment of the invention, based on the task performance constraint vector, quantitative constraints are established from three core dimensions: total output power, total energy storage capacity, and dynamic response bandwidth. This transforms task requirements into mathematical constraints that can be directly used for resource optimization and matching. The constraints are as follows:

[0092] in, For the set of candidate cell indices, Choose a binary variable. For the unit's available energy, For unit i bandwidth, and These are the upper and lower limits of the allowable SOC (10% and 90% in this embodiment). For unit i Real-time temperature This is the maximum permissible temperature.

[0093] Step S2023: With the goals of minimizing resource usage costs, maximizing unit state balance, and maximizing overall performance, generate multiple objective optimization conditions.

[0094] It should be noted that minimizing resource usage costs means reducing the overall costs of unit scheduling, losses, and operation and maintenance to the lowest level while meeting task performance constraints, through optimizing the selection and combination of physical energy storage units.

[0095] Maximizing the balance of unit states refers to minimizing the differences in operating state parameters such as state of charge (SOC) and state of health (SOH) among the units by rationally selecting physical energy storage units.

[0096] Maximizing overall performance means optimizing the comprehensive performance of the selected virtual energy storage units in terms of power support, response speed, and load stability.

[0097] In this embodiment of the invention, the objective function is to minimize the cost. State imbalance And maximize performance adaptability. The goal is to minimize resource usage cost, maximize unit state balance, and maximize overall performance, generating multiple objective optimization conditions. The specific calculation method for the objective function is as follows: Resource usage cost objective: ,in The cost weighting is based on the unit's state of equilibrium (SOH) and degree of aging. , The aging penalty coefficient is set to 0.5.

[0098] Performance compatibility target: This product form ensures that the unit i Maximum power ,bandwidth Health A balanced match among the three.

[0099] State imbalance objective: ,in This represents the average State of Charge (SOC) of the selected cells. A smaller value indicates a more balanced state among the cells, which is beneficial for extending the overall lifespan.

[0100] Step S2024: Based on task performance constraints and multiple objective optimization conditions, construct a resource selection optimization model.

[0101] It should be noted that the resource selection optimization model refers to a mathematical optimization model that integrates constraints and multi-objective optimization functions.

[0102] In this embodiment of the invention, the task performance constraints are used as the feasible region boundary of the model, and the multi-objective optimization function is used as the optimization guide of the model. This integrates to form a resource selection optimization model with the selection of physical energy storage units as decision variables, constraints as boundaries, and multi-objective optimization as the guide.

[0103] Step S2025: Solve the resource selection optimization model, and select a set of virtualized physical energy storage units that meet the constraints and have the best comprehensive evaluation from the resource pool based on the solution results.

[0104] It should be noted that optimal comprehensive evaluation refers to the optimal combination of unit states that achieves the best balance in three dimensions—resource usage cost, unit state balance, and overall performance—after a multi-objective optimization algorithm weighs the factors that meet the task performance constraints.

[0105] In this embodiment of the invention, the NSGA-II multi-objective genetic algorithm is used to solve the resource selection optimization model. The population size is set to 100, the number of iterations is 200, the crossover probability is 0.9, and the mutation probability is 0.1. The algorithm outputs a set of Pareto front solutions. The virtual energy management plane selects the optimal solution based on current preferences (such as prioritizing performance or prioritizing cost reduction), and selects the optimal subset of physical units (i.e., a set of virtualized physical energy storage units) from the resource pool according to the optimal solution.

[0106] Step S2026: Assign logical connection topology to the virtualized physical energy storage unit to construct the virtual energy storage unit.

[0107] In this embodiment of the invention, a virtual energy storage unit can be constructed by assigning a logical connection topology to a virtualized physical energy storage unit. The logical topology allocation rule is as follows: if the task is a high-power demand type (… kW and If the frequency is Hz, a parallel topology is adopted, that is, all selected units are connected in parallel to the same bus through a physical exchange matrix. The output voltage of the virtual energy storage unit is equal to the unit voltage, and the total current is the sum of the currents of each unit. If the task is high energy demand type ( kWh and If the voltage is Hz, a series topology is used, where units are connected in series sequentially. The output voltage of the virtual energy storage unit is the sum of the voltages of all units, and the current is the same. If the task has mixed requirements, a hybrid topology is used, where several branches are first connected in parallel, and then the branches are connected in series. The topology allocation result is represented by a logical connection matrix. It means that among them This indicates that unit i and unit j are logically directly connected; otherwise, it is 0. This logical connection matrix, as a core attribute of the virtual energy storage unit, is stored in the memory of the virtual energy management plane.

[0108] Step S203: Based on the logical connection topology of the virtual energy storage unit, determine the sequence of physical action instructions used to control the physical switching matrix to perform switching operations.

[0109] In some optional implementations, step S203 above includes: Step S2031: Collect the real-time on / off status of all switches in the physical switching matrix and construct the current topology state matrix.

[0110] It should be noted that the real-time on / off status refers to the current on or off state of each switching device in the physical switching matrix.

[0111] The current topology state matrix refers to a matrix that numerically represents the real-time on / off state of each switch in the physical switching matrix.

[0112] In this embodiment of the invention, the real-time on / off status of all switches in the physical switching matrix is ​​obtained, and a topology state matrix describing the current system connection relationship is generated accordingly to obtain the current topology state matrix.

[0113] Step S2032: Construct the target topology state matrix based on the logical connection topology and mapping rules of the virtual energy storage unit.

[0114] It should be noted that the mapping rule refers to the corresponding conversion rule between the logical connection topology and the physical switching matrix switches.

[0115] The target topology state matrix refers to the numerical matrix that characterizes the ideal switching states of the physical exchange matrix required for the operation of the virtual energy storage unit.

[0116] In this embodiment of the invention, based on the logical connection topology defined by the virtual energy storage unit, a target topology state matrix of the desired system is generated through mapping rules. The mapping rules are as follows: if the logical topology is parallel, the switches between the corresponding nodes of each unit and the bus node are set to 1, and the switches between units are set to 0; if it is series, the switches between adjacent unit nodes are set to 1, and the switches between the first and last units and the bus are set to 1; if it is a mixed connection, sub-matrix blocks are set accordingly based on the series and parallel relationships. For example, if units 1, 2, and 3 are connected in series to the bus, then... middle The remaining related elements are 0.

[0117] Step S2033: With the optimization objectives of minimizing total reconfiguration time and minimizing switching losses, and with the constraint of maintaining the continuity of critical power paths, a topology switching path planning model is constructed.

[0118] It should be noted that minimizing the total reconstruction time refers to shortening the overall time spent in the topology switching process and improving the system's response speed and dynamic adaptability.

[0119] Minimizing switching losses refers to reducing the number and frequency of switching actions, thereby reducing device losses and extending the lifespan of the physical switching matrix.

[0120] Critical power path continuity refers to maintaining the main power path uninterrupted during topology switching to ensure system power supply and operational stability.

[0121] Topology switching path planning model refers to an optimization model that uses switching action as the decision variable, switching time and switching loss as objectives, and power path continuity as a constraint.

[0122] In this embodiment of the invention, the difference between the current topology state matrix and the target topology state matrix is ​​used as the basis for path planning. Switching action timing and path maintenance constraints are set to establish an optimization model that balances switching efficiency and device lifetime. Specifically, the goal is to minimize the total reconfiguration time and the number of switching actions (minimizing switching losses), and the path planning proceeds from... arrive The objective function for the switching path is expressed as:

[0123] in, Total reconstruction time The time interval for the k-th step operation (not less than 10ms to ensure reliable switch operation); This represents the number of times the master switch has been activated. This represents the number of switches that operate simultaneously in the k-th step. The predicted peak power transient is obtained through simulation calculation, with weighting coefficients... This is used to penalize excessive power surges. The optimization problem can be modeled as a shortest path problem, where the state space is a matrix of all possible topologies, and the edge weights are the costs of performing a single switching action (considering time, number of actions, and surge).

[0124] Step S2034: Solve the topology switching path planning model and determine the sequence of physical action instructions for performing switching operations from the current topology state matrix to the target topology state matrix based on the solution results.

[0125] In this embodiment of the invention, a graph search algorithm is used to solve the topology switching path planning model to generate a preliminary instruction sequence containing a series of ordered switching actions from the current topology state to the target topology state, thus obtaining the physical action instruction sequence.

[0126] Specifically, the A* graph search algorithm is used, with the heuristic function set to the Hamming distance from the current topology to the target topology (i.e., the number of switches to be changed). The algorithm outputs a series of topology migration instructions, each with the following format: ,in For the execution time, This is a set of switch numbers to be operated. For example, an instruction. This means that switches 3 and 7 are closed simultaneously at 50 milliseconds.

[0127] Step S204: Based on the equivalent circuit models corresponding to the physical exchange matrix and the physical energy storage unit, a digital twin simulation environment is constructed.

[0128] It should be noted that the equivalent circuit model refers to a circuit topology composed of resistors, inductors, capacitors and controlled sources, which characterizes the electrical external characteristics of the physical energy storage unit and the physical exchange matrix.

[0129] A digital twin simulation environment refers to a virtual simulation platform that maps to a physical system in real time.

[0130] In this embodiment of the invention, the equivalent circuit model of each physical energy storage unit is jointly networked and mapped with the equivalent switching path model of the physical switching matrix, and real-time operating parameters and topology parameters are synchronously imported to establish a virtual simulation operating environment that corresponds one-to-one with the physical entity.

[0131] Step S205: Import the current electrical state and physical action command sequence of the system into the digital twin simulation environment.

[0132] It should be noted that the current electrical status of the system refers to the real-time electrical operating parameters of the physical switching matrix and physical energy storage units before the switching command is issued, including voltage, current, power, state of charge (SOC), and operating temperature.

[0133] In this embodiment of the invention, the current electrical state of the system and the sequence of physical action instructions are imported into a digital twin simulation environment based on an equivalent circuit model through a data interface.

[0134] Step S206: In the digital twin simulation environment, execute the physical action command sequence step by step, drive the equivalent circuit model according to the timing of the physical action command sequence, and solve the equivalent circuit model to obtain the voltage data of each key node and the current data of each branch of the equivalent circuit model.

[0135] It should be noted that the voltage data of critical nodes refers to the real-time voltage values ​​of the core electrical nodes (such as bus nodes and unit interface nodes) in the equivalent circuit model that play a decisive role in the system's operating state.

[0136] The current data of a branch refers to the real-time current value of each electrical branch (such as a switch branch or an energy storage unit output branch) in the equivalent circuit model.

[0137] In this embodiment of the invention, a sequence of physical action instructions is executed step by step in a digital twin simulation environment. The voltage and current changes of each key node and branch are simulated by numerical calculation during the reconstruction process to obtain the voltage data of each key node and the current data of each branch.

[0138] Step S207: Compare the voltage data and current data with the corresponding safety thresholds respectively. If the comparison result is greater than any safety threshold, then correct the physical action command sequence.

[0139] It should be noted that the safety threshold refers to the upper limit of electrical parameters preset based on the hardware performance and operational safety requirements of the physical energy storage unit and the switching matrix, including voltage safety threshold and current safety threshold.

[0140] In this embodiment of the invention, based on preset electrical safety limits, all voltage and current data obtained from simulation are verified for compliance. If the verification finds potential risk of exceeding the limit (i.e., voltage or current data exceeds the corresponding safety threshold), the original instruction sequence is corrected by adjusting the instruction timing or inserting transition instructions, and the simulation is re-verified until a safe and executable final physical action instruction sequence is generated.

[0141] It is worth mentioning that by introducing a digital twin simulation verification step before physical execution, a closed-loop safety assurance mechanism of "prediction-verification-correction" is constructed, fundamentally avoiding potential electrical risks in topology reconfiguration. This mechanism can accurately simulate the dynamic trajectories of voltage and current under the timing of each switch action in a virtual environment, proactively identify potential over-limit risks, and resolve risks in advance through fine-tuning of command timing or insertion of transitional commands. This avoids problems such as equipment overvoltage, overcurrent impacts, and protection malfunctions caused by traditional "trial and error" reconfiguration. This strategy transforms safety verification from passive post-event protection to proactive pre-event optimization, significantly improving the robustness and executability of reconfiguration commands. It enables smooth implementation of complex topology switching while ensuring electrical safety, greatly reducing stress damage to power devices, extending the overall system lifespan, and providing reliable technical support for high-frequency dynamic reconfiguration, enhancing the flexibility and availability of energy storage systems in response to changes in operating conditions.

[0142] Step S208: By executing a sequence of physical action instructions, the on / off state of the target switch in the physical switching matrix is ​​controlled, and the electrical connection network between each physical energy storage unit is reorganized in real time according to the on / off state of the target switch to generate an initial operating topology.

[0143] In some optional implementations, step S208 includes: Step S2081: Adjust the output voltage of the physical energy storage unit to be connected to the target bus, and connect multiple physical energy storage units corresponding to the adjusted output voltage to the target bus.

[0144] It should be noted that the target bus refers to the core electrical node in the system that undertakes the functions of power transmission and distribution, and it needs to be connected to a physical energy storage unit to supplement power and stabilize voltage.

[0145] Output voltage refers to the voltage value when a physical energy storage unit outputs electrical energy.

[0146] In this embodiment of the invention, before starting the switch operation, the voltage control loop pre-adjusts the amplitude and phase of the output voltage of the physical energy storage unit that is about to be connected to the new bus, so that the voltage of the physical energy storage unit is synchronized with the electrical parameters of the target bus, and then the adjusted physical energy storage unit is connected to the target bus.

[0147] Step S2082: Divide the physical action instruction sequence into multiple sub-action instruction sequences.

[0148] It should be noted that a sub-action instruction sequence refers to a subset of instructions that are separated from a complete physical action instruction sequence according to the switching stage or functional logic, and that correspond to a specific sub-task or sub-state.

[0149] In this embodiment of the invention, the complete sequence of physical action instructions is divided into several stages that are executed sequentially, generating multiple sub-action instruction sequences.

[0150] Step S2083: Execute each sub-action instruction sequence one by one according to the preset execution order, control the on / off state of the target switch in the physical switching matrix, and adopt the seamless switching logic of first closing and then closing to change the electrical connection network between each physical energy storage unit.

[0151] It should be noted that the preset execution order refers to the order in which the sequence of sub-action instructions is executed in advance to ensure a safe and orderly topology switchover.

[0152] A target switch refers to a specific controllable power switch in the physical switching matrix that is specified by a sequence of action instructions to perform on / off operations and is used to change electrical connections.

[0153] The seamless switching logic of "connect first, disconnect later" refers to the safe operation logic adopted during topology switching. That is, the new electrical path is connected first, and the original path is disconnected after the path is stable, so as to avoid abnormal situations such as power interruption and electrical shock.

[0154] In this embodiment of the invention, each sub-action instruction sequence is executed one by one according to a preset execution order to control the on / off state of the target switch in the physical switching matrix. After completing the switching operation of each stage, the execution of the next stage is temporarily suspended, and the key electrical quantities of the system are monitored. After confirming that the system has entered a steady state, subsequent instructions are triggered. In the specific operation involving connection changes in each stage, a seamless switching logic of closing first and then opening is adopted to ensure that the power supply to the load is not interrupted. Before disconnecting the original electrical connection, the new target path is connected first to avoid power interruption or electrical impact during the switching process. The electrical connection relationship between each physical energy storage unit is smoothly changed to form an actual electrical network consistent with the logical connection topology of the virtual energy storage unit.

[0155] Step S2084: When all sub-action command sequences have been executed, the circulating current component of the branch of each physical energy storage unit is collected in real time.

[0156] It should be noted that the circulating current component refers to the current component that appears in the branches of each physical energy storage unit after the topology switch is completed. This component does not participate in the transmission of active power in the system and only circulates between the units.

[0157] In this embodiment of the invention, when all sub-action instruction sequences have been executed, the circulating current component of the branch of each physical energy storage unit is monitored in real time using high-frequency detection technology.

[0158] Step S2085: Determine whether the amplitude of the circulating current component is greater than or equal to the preset rated current threshold.

[0159] It should be noted that the preset rated current threshold refers to the upper limit of the circulating current that is preset based on the hardware rated performance and safe operation requirements of the physical energy storage unit, power switching device and bus.

[0160] In this embodiment of the invention, the real-time collected circulating current component values ​​of each branch are quantitatively compared with the preset rated current threshold to accurately determine whether the circulating current is within a safe and controllable range.

[0161] Step S2086: If yes, then suppress the circulating component.

[0162] In this embodiment of the invention, if the amplitude of the circulating current component is greater than or equal to a preset rated current threshold, a reverse compensation component is injected through the power converter to actively dampen and suppress the circulating current.

[0163] Step S2087: If not, determine the current electrical connection network between each physical energy storage unit as the initial operating topology.

[0164] In this embodiment of the invention, when the amplitude of the circulating current component is determined to be less than the preset rated current threshold, it indicates that the current topology switch has been completed and the system is operating stably without the risk of excessive circulating current. At this time, the current electrical connection network formed by each physical energy storage unit through the physical exchange matrix is ​​formally determined as the initial operating topology of the virtual energy storage unit.

[0165] It is worth mentioning that, through the synergistic effect of voltage pre-synchronization, phased execution, seamless switching logic and active circulating current suppression, key problems such as high inrush current, power interruption and circulating current loss in traditional topology reconfiguration are fundamentally solved. The voltage pre-synchronization mechanism suppresses the closing inrush current to an extremely low level, significantly reducing the electrical stress on power devices and extending the service life of switching devices and energy storage units. The phased execution and steady-state confirmation mechanism isolates the reconfiguration risk within a single phase, enabling precise fault location and rapid rollback, greatly improving reconfiguration power and system fault tolerance, making online incremental reconfiguration possible, and enhancing operational flexibility in multi-task parallel scenarios. The seamless switching logic of closing before disconnecting completely eliminates power interruption, and bus voltage transients are controlled within a very small range, fully meeting the stringent requirements of critical loads for power continuity, while effectively reducing power fluctuations during switching. The high-frequency circulating current active damping technology overcomes the circulating current problem in parallel reconfiguration. Through real-time detection and rapid compensation, the circulating current amplitude is reduced to an extremely low level, avoiding charging and discharging conflicts and thermal imbalances between units, and significantly improving system efficiency and energy storage asset utilization without additional hardware modifications.

[0166] Step S209: Calculate the comprehensive deviation value between the real-time operating parameters of each physical energy storage unit and the expected performance index of the virtual energy storage unit, and adaptively and dynamically reconstruct the initial operating topology based on the comprehensive deviation value.

[0167] In some optional implementations, step S209 above includes: Step S2091: Collect real-time operating parameters of each physical energy storage unit.

[0168] In this embodiment of the invention, real-time operating parameters of each physical energy storage unit are collected, with a focus on collecting core data such as output power, voltage stability, state of charge (SOC), and operating temperature. The real-time operating status of the physical switching matrix is ​​synchronously correlated to ensure that the collected parameters are accurately matched with the current topology switching progress and system operation requirements.

[0169] Step S2092: Calculate the weighted deviation between the real-time operating parameters of the physical energy storage unit and the expected performance indicators of the virtual energy storage unit to obtain the comprehensive deviation value.

[0170] In this embodiment of the invention, corresponding weights are assigned according to the importance of the real-time operating parameters of each physical energy storage unit. The real-time operating parameters such as voltage, power, and response speed are compared one by one with the expected performance indicators preset by the virtual energy storage unit. The deviation value of each parameter is calculated and assigned a corresponding weight. The comprehensive deviation value is obtained by weighted summation, which is used to judge the degree of fit between the actual operating state and the expected target.

[0171] Step S2093: Determine whether the overall deviation value is greater than or equal to the preset adaptive trigger threshold.

[0172] It should be noted that the preset adaptive trigger threshold refers to the deviation threshold set in advance based on the system's safe operation requirements, hardware performance limitations, and expected operating goals.

[0173] In this embodiment of the invention, the comprehensive deviation value is compared with a preset adaptive trigger threshold to determine whether the comprehensive deviation value is greater than or equal to the preset adaptive trigger threshold.

[0174] If so, proceed to step S2094, and execute the step of dynamically selecting a set of virtualized physical energy storage units according to preset task performance constraints and multiple target optimization conditions, and generating virtual energy storage units until the comprehensive deviation value is less than the preset adaptive trigger threshold, and obtain the target running topology.

[0175] It should be noted that the target operating topology refers to the actual physical connection structure that meets the operating requirements of the virtual energy storage unit and corresponds to the logical connection topology.

[0176] In this embodiment of the invention, if the overall deviation value is greater than or equal to the preset adaptive trigger threshold, the process jumps to step S202 and immediately starts online rolling re-optimization to adaptively adjust the virtual resource selection, virtual topology allocation and switching action sequence until the overall deviation value is less than the preset adaptive trigger threshold, thereby realizing the adaptive dynamic reconstruction of the control closed loop and obtaining the target operating topology.

[0177] This embodiment also provides an adaptive dynamic reconfiguration energy storage system integrated control device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0178] This embodiment provides an integrated control device for an adaptive dynamic reconfiguration energy storage system, such as... Figure 3 As shown, this device includes: The mapping module 301 is used to construct a virtual energy management platform based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, and to map the virtualized state of all physical energy storage units through the virtual energy management platform; The selection module 302 is used to dynamically select a set of virtualized energy storage units according to preset task performance constraints and multiple target optimization conditions, and generate virtual energy storage units. Processing module 303 is used to determine a sequence of physical action instructions for controlling the physical switching matrix to perform switching operations based on the logical connection topology of the virtual energy storage unit. The execution module 304 is used to control the on / off state of the target switch in the physical switching matrix by executing a sequence of physical action instructions, and to reorganize the electrical connection network between each physical energy storage unit in real time according to the on / off state of the target switch to generate an initial operating topology. The calculation module 305 is used to calculate the comprehensive deviation value between the real-time operating parameters of each physical energy storage unit and the expected performance index of the virtual energy storage unit, and to adaptively and dynamically reconstruct the initial operating topology based on the comprehensive deviation value.

[0179] In some alternative implementations, the mapping module 301 includes: The platform building unit is used to construct a virtual energy management platform based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit; The distribution unit is used to send characteristic test signal commands to each physical energy storage unit through the virtual energy management platform and collect the corresponding output response data; Establish a unit to build a second-order transfer function model for the corresponding physical energy storage unit based on each output response data, and calculate the unit power response speed index and load capacity index of the physical energy storage unit through the second-order transfer function model. The combination unit is used to combine the unit power response speed index, load capacity index, rated parameters and real-time operating status parameters of the physical energy storage unit to generate the virtual state of the physical energy storage unit. The aggregation unit is used to aggregate the virtualized states of all physical energy storage units to generate a resource pool.

[0180] In some alternative implementations, the selection module 302 includes: The acquisition unit is used to acquire the system's target instructions and extract the task performance constraint vector corresponding to the system's target instructions. Construct constraint units to build constraint conditions based on the task performance constraint vector, with total power, total energy, and dynamic response capability as constraint dimensions; Construct optimization target units to generate multiple target optimization conditions with the objectives of minimizing resource usage costs, maximizing unit state balance, and maximizing overall performance. The model building unit is used to build a resource selection optimization model based on task performance constraints and multiple objective optimization conditions. The screening unit is used to solve the resource selection optimization model and select a set of virtualized physical energy storage units from the resource pool that meet the constraints and have the best comprehensive evaluation based on the solution results. The allocation unit is used to allocate logical connection topologies to virtualized physical energy storage units to construct virtual energy storage units.

[0181] In some alternative implementations, the processing module 303 includes: The data acquisition state unit is used to acquire the real-time on / off status of all switches in the physical switching matrix and construct the current topology state matrix; The matrix construction unit is used to construct the target topology state matrix based on the logical connection topology and mapping rules of the virtual energy storage unit. The planning model building unit is used to construct a topology switching path planning model with the optimization objectives of minimizing total reconfiguration time and minimizing switching losses, and with the constraint of maintaining the continuity of critical power paths. The solving unit is used to solve the topology switching path planning model and determine the sequence of physical action instructions for performing switching operations from the current topology state matrix to the target topology state matrix based on the solution results.

[0182] In some alternative embodiments, the device includes: Sub-units are constructed to build a digital twin simulation environment based on the equivalent circuit models corresponding to the physical exchange matrix and the physical energy storage unit, respectively. The import sub-unit is used to import the current electrical state and physical action command sequence of the system into the digital twin simulation environment; The solution sub-unit is used to execute the physical action instruction sequence step by step in the digital twin simulation environment, and drive the equivalent circuit model according to the timing of the physical action instruction sequence and solve the equivalent circuit model to obtain the voltage data of each key node and the current data of each branch of the equivalent circuit model. The correction subunit is used to compare the voltage data and current data with the corresponding safety thresholds respectively. If the comparison result is greater than any safety threshold, the physical action command sequence is corrected.

[0183] In some alternative implementations, execution module 304 includes: The adjustment unit is used to adjust the output voltage of the physical energy storage unit to be connected to the target bus, and connect multiple physical energy storage units corresponding to the adjusted output voltage to the target bus. A partitioning unit is used to divide a physical action instruction sequence into multiple sub-action instruction sequences; The control unit is used to execute the sequence of sub-action instructions one by one according to the preset execution order, control the on / off state of the target switch in the physical switching matrix, and adopt a seamless switching logic of first closing and then closing to change the electrical connection network between each physical energy storage unit. The component acquisition unit is used to acquire the circulating current component of the branch of each physical energy storage unit in real time after all sub-action command sequences have been executed. The judgment unit is used to determine whether the amplitude of the circulating current component is greater than or equal to the preset rated current threshold. Suppression unit, used to suppress the circulating component if so; Determine the initial structural unit, which, if not, determines the current electrical connection network between the physical energy storage units as the initial operating topology.

[0184] In some alternative implementations, the computing module 305 includes: The parameter acquisition unit is used to collect the real-time operating parameters of each physical energy storage unit; The calculation unit is used to calculate the weighted deviation between the real-time operating parameters of the physical energy storage unit and the expected performance indicators of the virtual energy storage unit, and obtain the comprehensive deviation value. The trigger threshold determination unit is used to determine whether the overall deviation value is greater than or equal to the preset adaptive trigger threshold. The jump unit is used to jump to execute the steps of dynamically selecting a set of virtualized physical energy storage units according to preset task performance constraints and multiple target optimization conditions, and generating virtual energy storage units until the comprehensive deviation value is less than the preset adaptive trigger threshold, and the target running topology is obtained.

[0185] The adaptive dynamic reconfiguration energy storage system integrated control device provided in this embodiment of the invention can execute the adaptive dynamic reconfiguration energy storage system integrated control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0186] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0187] The following is a detailed reference. Figure 4This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0188] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0189] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the adaptive dynamic reconfiguration energy storage system integrated control method of the embodiments of the present invention.

[0190] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0191] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the adaptive dynamic reconfiguration energy storage system integrated control method shown in the above embodiments is implemented.

[0192] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An integrated control method for an adaptive dynamic reconfigurable energy storage system, characterized in that, A hardware energy storage system applied to a physical switching matrix and multiple physical energy storage units, the method comprising: Based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, a virtual energy management platform is constructed, and the virtualization state of all physical energy storage units is mapped through the virtual energy management platform; Based on preset task performance constraints and multiple target optimization conditions, a set of virtualized physical energy storage units are dynamically selected to generate virtual energy storage units. Based on the logical connection topology of the virtual energy storage unit, a sequence of physical action instructions for controlling the physical switching matrix to perform switching operations is determined; By executing the physical action command sequence, the on / off state of the target switch in the physical switching matrix is ​​controlled, and the electrical connection network between each physical energy storage unit is reorganized in real time according to the on / off state of the target switch to generate an initial operating topology. Calculate the comprehensive deviation value between the real-time operating parameters of each physical energy storage unit and the expected performance index of the virtual energy storage unit, and adaptively and dynamically reconstruct the initial operating topology based on the comprehensive deviation value.

2. The method according to claim 1, characterized in that, The virtual energy management platform is constructed based on the physical exchange matrix and the actual connection relationships of each physical energy storage unit, and the virtualized state of all the physical energy storage units is mapped through the virtual energy management platform, including: Based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, a virtual energy management platform is constructed. The virtual energy management platform sends characteristic test signal commands to each physical energy storage unit and collects the corresponding output response data. Based on the output response data, a second-order transfer function model is established for the corresponding physical energy storage unit, and the unit power response speed index and load capacity index of the physical energy storage unit are calculated through the second-order transfer function model. By combining the unit power response speed index, load capacity index, rated parameters and real-time operating status parameters of the physical energy storage unit, the virtualized state of the physical energy storage unit is generated. The virtualized states of all the physical energy storage units are aggregated to generate a resource pool.

3. The method according to claim 2, characterized in that, The process of dynamically selecting a set of virtualized physical energy storage units according to preset task performance constraints and multiple target optimization conditions to generate virtual energy storage units includes: Obtain the system's target execution instructions and extract the task performance constraint vector corresponding to the system's target execution instructions; Based on the aforementioned task performance constraint vector, constraints are constructed with total power, total energy, and dynamic response capability as constraint dimensions. With the goals of minimizing resource usage costs, maximizing unit state balance, and maximizing overall performance, multiple objective optimization conditions are generated. Based on the aforementioned task performance constraints and multiple objective optimization conditions, a resource selection optimization model is constructed. The resource selection optimization model is solved, and based on the solution results, a set of virtualized physical energy storage units that meet the constraints and have the best comprehensive evaluation are selected from the resource pool. Logical connection topology is assigned to the virtualized physical energy storage unit to construct the virtual energy storage unit.

4. The method according to claim 1, characterized in that, Based on the logical connection topology of the virtual energy storage unit, a sequence of physical action instructions for controlling the physical switching matrix to perform switching operations is determined, including: Collect the real-time on / off status of all switches in the physical switching matrix and construct the current topology state matrix; Based on the logical connection topology and mapping rules of the virtual energy storage unit, construct the target topology state matrix; With the optimization objectives of minimizing total reconfiguration time and minimizing switching losses, and with the constraint of maintaining the continuity of critical power paths, a topology switching path planning model is constructed. The topology switching path planning model is solved, and the sequence of physical action instructions for performing switching operations from the current topology state matrix to the target topology state matrix is ​​determined based on the solution results.

5. The method according to claim 1, characterized in that, The method further includes: A digital twin simulation environment is constructed based on the equivalent circuit models corresponding to the physical exchange matrix and the physical energy storage unit, respectively. Import the system's current electrical state and the sequence of physical action commands into the digital twin simulation environment; In the digital twin simulation environment, the physical action instruction sequence is executed step by step, and the equivalent circuit model is driven according to the timing of the physical action instruction sequence and the equivalent circuit model is solved to obtain the voltage data of each key node and the current data of each branch of the equivalent circuit model. The voltage data and the current data are compared with their respective safety thresholds. If the comparison result is greater than either safety threshold, the physical action command sequence is corrected.

6. The method according to claim 1, characterized in that, The process of controlling the on / off state of target switches in the physical switching matrix by executing the physical action command sequence, and real-time reorganizing the electrical connection network between each physical energy storage unit according to the on / off state of the target switches to generate an initial operating topology includes: Adjust the output voltage of the physical energy storage unit to be connected to the target bus, and connect multiple physical energy storage units corresponding to the adjusted output voltage to the target bus. The physical action instruction sequence is divided into multiple sub-action instruction sequences; The sub-action instruction sequence is executed one by one according to the preset execution order to control the on / off state of the target switch in the physical switching matrix, and the seamless switching logic of first closing and then closing is adopted to change the electrical connection network between the physical energy storage units. When all the sub-action instruction sequences have been executed, the circulating current component of the branch of each physical energy storage unit is collected in real time; Determine whether the amplitude of the circulating current component is greater than or equal to a preset rated current threshold. If so, then suppress the circulating component; If not, the current electrical connection network between each of the physical energy storage units is determined as the initial operating topology.

7. The method according to claim 1, characterized in that, The calculation of the comprehensive deviation between the real-time operating parameters of each physical energy storage unit and the expected performance indicators of the virtual energy storage unit, and the adaptive dynamic reconstruction of the initial operating topology based on the comprehensive deviation, includes: Collect real-time operating parameters of each physical energy storage unit; The weighted deviation between the real-time operating parameters of the physical energy storage unit and the expected performance indicators of the virtual energy storage unit is calculated to obtain the comprehensive deviation value; Determine whether the overall deviation value is greater than or equal to the preset adaptive trigger threshold; If so, then proceed to the step of dynamically selecting a set of virtualized physical energy storage units according to preset task performance constraints and multiple target optimization conditions, and generating virtual energy storage units, until the comprehensive deviation value is less than the preset adaptive trigger threshold, and the target running topology is obtained.

8. An integrated control device for an adaptive dynamic reconfiguration energy storage system, characterized in that, A hardware energy storage system applied to a physical switching matrix and multiple physical energy storage units, the device comprising: The mapping module is used to construct a virtual energy management platform based on the physical exchange matrix and the actual connection relationship of each physical energy storage unit, and to map the virtualization state of all the physical energy storage units through the virtual energy management platform; The selection module is used to dynamically select a set of virtualized energy storage units according to preset task performance constraints and multiple target optimization conditions, and generate virtual energy storage units. The processing module is used to determine a sequence of physical action instructions for controlling the physical switching matrix to perform switching operations based on the logical connection topology of the virtual energy storage unit. The execution module is used to control the on / off state of the target switch in the physical switching matrix by executing the physical action instruction sequence, and to reorganize the electrical connection network between each physical energy storage unit in real time according to the on / off state of the target switch to generate an initial operating topology. The calculation module is used to calculate the comprehensive deviation value between the real-time operating parameters of each physical energy storage unit and the expected performance index of the virtual energy storage unit, and to adaptively and dynamically reconstruct the initial operating topology based on the comprehensive deviation value.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the adaptive dynamic reconfiguration energy storage system integrated control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the adaptive dynamic reconfiguration energy storage system integrated control method according to any one of claims 1 to 7.

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