Household energy storage system adjustable capacity online tracking method, device, equipment and medium

Abstract

The present application relates to the technical field of energy storage control, and discloses a household energy storage system adjustable capacity online tracking method, device, equipment and medium, at the initial moment of the system or the moment when the solver is woken up, the first state of charge of the household energy storage system is acquired to solve a linear programming model, and the optimal charge and discharge boundary solution is obtained; based on the first state of charge and the linear programming model, the sensitivity analysis of the optimal charge and discharge boundary solution relative to the change of the state of charge is carried out, and the corresponding optimal sensitivity parameter is obtained; the second state of charge of the household energy storage system in the real-time operation stage is collected to judge the state of the solver, when it is dormant, the energy storage physical boundary recursion and the grid connection point superposition mapping are carried out based on the optimal sensitivity parameter and the second state of charge, and the adjustable capacity boundary of the household grid connection point is obtained; the high complexity rolling optimization is reduced to low power algebraic operation, combined with the event triggering mechanism, the solver calling frequency is greatly reduced, and the power consumption is significantly reduced on the premise of ensuring the tracking accuracy.

Description

Technical Field

[0001] This invention relates to the field of energy storage control technology, and in particular to a method, apparatus, equipment and medium for online tracking of the adjustable capacity of a residential energy storage system. Background Technology

[0002] With the integration of a high proportion of renewable energy, the safe and stable operation of the power system faces enormous challenges. Against this backdrop, massive amounts of residential distributed resources, such as photovoltaics and energy storage, are increasingly involved in external grid interactions, including demand response, ancillary services, and intraday electricity markets. To achieve effective dispatch of these wide-area resources, the underlying system must accurately and in real-time report the upper and lower limits of its adjustable capacity to the grid.

[0003] Among various residential distributed resources, energy storage systems are the core devices providing bidirectional adjustability. However, accurately assessing the adjustability of energy storage systems faces a severe computational bottleneck. Due to the cross-time-period energy coupling characteristics of energy storage systems, traditional adjustability assessment methods must rely on rolling time-domain methods such as multi-time-period mixed integer linear programming (MILP), but this method incurs huge computational overhead and latency with each state update.

[0004] In practical engineering, residential energy management systems are typically extremely inexpensive, lightweight edge computing terminals with very limited computing power. If, in response to high-frequency interaction demands from the external market, the underlying solver is frequently awakened to handle real-time fluctuations and reassess the power boundaries of energy storage, it will lead to computing power collapse and even paralyze the core underlying device control processes. Therefore, how to achieve extremely rapid online evaluation and tracking of the adjustability of residential energy storage systems while significantly saving computing resources has become a pressing technical problem for those skilled in the art. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and medium for online tracking of the adjustable capacity of residential energy storage systems, solving the problem of how to achieve rapid online assessment and tracking of the adjustable capacity of residential energy storage systems while significantly saving computing resources.

[0006] To address the aforementioned technical problems, the first aspect of this invention provides a method for online tracking of the adjustable capacity of a residential energy storage system, comprising: At the initial moment of the system or the moment the solver is awakened, the first state of charge of the residential energy storage system is obtained to solve the pre-built linear programming model and obtain the optimal charge and discharge boundary solution. Based on the first state of charge and the linear programming model, a sensitivity analysis of the optimal charge-discharge boundary solution with respect to the change of state of charge is performed to obtain the corresponding optimal sensitivity parameters. During the real-time operation phase, the second state of charge of the residential energy storage system is collected, and the state of the solver is determined based on the second state of charge. When the solver is in a dormant state, the energy storage physical boundary is recursively derived and the grid connection point is superimposed and mapped based on the optimal sensitivity parameter and the second state of charge to obtain the adjustable capacity boundary of the household grid connection point.

[0007] A second aspect of the present invention provides an online tracking device for the adjustable capacity of a residential energy storage system, comprising: The model solving module is used to obtain the first state of charge of the residential energy storage system at the initial moment of the system or the moment the solver is awakened, so as to solve the pre-built linear programming model and obtain the optimal charge and discharge boundary solution. The parameter determination module is used to perform sensitivity analysis of the optimal charge-discharge boundary solution relative to the change in charge state based on the first state of charge and the linear programming model, and to obtain the corresponding optimal sensitivity parameters. The state determination module is used to collect the second state of charge of the residential energy storage system during the real-time operation phase, and determine the state of the solver based on the second state of charge. The capacity tracking module is used to perform energy storage physical boundary recursion and grid connection point superposition mapping based on the optimal sensitivity parameter and the second state of charge when the solver is in a dormant state, so as to obtain the adjustable capacity boundary of the household grid connection point.

[0008] A third aspect of the present invention provides an electronic device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the online tracking method for the adjustable capacity of a residential energy storage system as described above.

[0009] A fourth aspect of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the online tracking method for the adjustable capacity of a residential energy storage system as described above.

[0010] Compared with the prior art, the beneficial effects of the embodiments of the present invention are as follows: Instead of relying on a high-performance solver to repeatedly solve online in every control cycle, this system employs an "intermittent wake-up + sleep recursion" mechanism. The linear programming solver is invoked only once at the initial moment or when specific conditions are met. For most of the subsequent timeframes, simple linear recursion and algebraic mapping are used, significantly reducing computational power and memory usage. By introducing the finite difference method to calculate the sensitivity parameter of the "deviation between the optimal charge / discharge boundary and the state of charge," the system accurately characterizes the local influence of state of charge fluctuations on the optimal boundary. This allows the system to dynamically correct the initial optimal solution based on the current real-time state of charge even during solver sleep periods, avoiding the conservatism or risk of exceeding limits associated with fixed boundaries and ensuring the accuracy of boundary tracking. The system judges the solution based on real-time changes in the state of charge. The solver can be put into sleep mode or woken up. When the state of charge fluctuates within the normal operating range, it remains in sleep mode to improve efficiency. At the same time, it uses algebraic recursion with optimal sensitivity to replace the complex underlying MILP rolling solution. This transforms the high-energy-consuming optimization problem in most stable periods into extremely low-complexity algebraic addition, subtraction, multiplication, and division operations, with a single update time of milliseconds. When the state of charge fluctuates significantly, the solver is woken up to update the baseline solution and sensitivity. This ensures the effectiveness of linear approximation under small perturbations and avoids the accumulation of errors under large perturbations, achieving seamless switching between the two modes. The high-complexity rolling optimization is reduced to low-computational-power algebraic operations. Combined with an event-triggered mechanism, the frequency of solver calls is greatly reduced, significantly reducing the computing power consumption of edge terminals while ensuring tracking accuracy. Attached Figure Description

[0011] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of an online tracking method for the adjustable capacity of a residential energy storage system provided in a certain embodiment of the present invention; Figure 2 This is a schematic diagram of the actual operating trajectory of a residential energy storage system (SOC) according to a certain embodiment of the present invention; Figure 3 This is a comparison chart of the effects of an optimization scheme provided in a certain embodiment of the present invention; Figure 4 This is a distribution diagram of the all-day event trigger times and the wake-up frequency of the underlying optimization solver provided in a certain embodiment of the present invention; Figure 5 This is a structural diagram of an online tracking device for the adjustable capacity of a residential energy storage system according to a certain embodiment of the present invention; Figure 6This is a structural diagram of an electronic device provided in a certain embodiment of the present invention; Figure label: Among them, 10 is the model solving module; 20 is the parameter determination module; 30 is the state judgment module; 40 is the capability tracking module; 5000 is the electronic device; 5001 is the processor; 5002 is the bus; 5003 is the memory; and 5004 is the transceiver. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings and examples. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0014] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0015] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0016] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0017] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0018] To address the pain points of limited computing power in existing residential edge terminals, which make it difficult to support high-frequency traditional rolling optimization evaluation, this invention provides an online tracking method for the adjustable capacity of residential energy storage based on optimal sensitivity. This method aims to break through the computing bottleneck and achieve real-time power boundary tracking of energy storage with extremely low computing power overhead.

[0019] In one embodiment, such as Figure 1 As shown, the first aspect of the present invention provides a method for online tracking of the adjustable capacity of a residential energy storage system, comprising: S1. At the initial moment of the system or the moment the solver is awakened, the first state of charge of the residential energy storage system is obtained to solve the pre-built linear programming model and obtain the optimal charge and discharge boundary solution. Specifically, the residential energy storage system includes a photovoltaic array, a battery pack (rated voltage 48V), a bidirectional energy storage converter (PCS), and a local energy management controller (EMC). The point of connection (PCC) connects to the grid and the residential load. The EMC is pre-loaded with a linear programming model, which is a mixed integer linear programming (MILP) model with the optimization objective of maximizing the energy storage charging and discharging power of the residential energy storage system at the current moment. The model is constrained by nodal power balance constraints, energy storage timing recursion and charging / discharging mutual exclusion constraints, and grid connection and power purchase / sale mutual exclusion constraints. To evaluate the maximum / minimum grid connection adjustment boundary that the system can provide at the current moment, the adjustment potential is maximized by driving the energy storage SOC towards an extreme value. When solving for the upward adjustable capability (maximizing the consumption of grid power), the objective function of this model is set as maximizing the sum of SOC at all times within the time window. In the formula, t0 is the current time; N is the length of the look-ahead time window; SOC t This represents the state of charge (SOC) data of a residential energy storage system at time t.

[0020] When solving for down-adjustability (maximizing power injected into the grid), the objective function is set as minimizing the sum of the State of Charge (SOC) within the time window: Among them, the node power balance constraint requires that the power purchased and sold at the grid connection point must maintain a strict real-time balance with the power within the microgrid: In the formula, , Let t be the power purchase and sales power variables of the grid connection point; , Let t be the charging and discharging power variables of the residential energy storage system at time t; The photovoltaic output at time t; Let t be the load power at time t.

[0021] Energy storage timing recursion and charge / discharge mutual exclusion constraints include SOC recursion relationship, SOC safety range (SOC always stays between 0 and 1 or the upper and lower limits specified by the manufacturer), charge / discharge mutual exclusion (using a binary variable of 0-1 to constrain simultaneous charging and discharging to avoid physical dead zones), and power limit constraint (charge / discharge power does not exceed the rated maximum value of the equipment), which are expressed by the following formula: In the formula, The state of charge at time t-1; For time step; Rated capacity of residential energy storage system; and These are the upper limits of the physical rated maximum charging and discharging power of the residential energy storage system, respectively. and These are 0-1 binomial mutually exclusive variables (lowercase) representing the charging and discharging states of the residential energy storage system at time t, to prevent confusion in the charging and discharging dead zones.

[0022] The mutual exclusion constraint between grid-connected power purchase and sales prevents confusion between power purchase and sales states by introducing 0-1 state variables: In the formula, This represents the maximum permissible limit of interactive power at the grid connection point. and Let be 0-1 binomial mutually exclusive variables representing the microgrid's power purchase and sale status from the main grid at time t.

[0023] At the system's initial moment (e.g., midnight each day) or when the solver is awake, the first state of charge (initial state of charge) of the battery packs in the residential energy storage system is collected at the current moment. This initial state of charge is used as the initial condition to call the embedded solver (which can be open-source GLPK or commercial Cplex / Gurobi, etc.) to solve the linear programming model. The solution process takes the first state of charge, rated energy storage capacity, rated maximum charging power, rated maximum discharging power; the predicted photovoltaic output and load power for each period within the look-ahead time window (it should be noted that these predicted values ​​can be obtained by fitting historical data, or predicted using a machine learning model trained on historical data, or by other methods); the maximum interactive power limit at the grid connection point; the time step and the look-ahead window length as inputs, and the energy storage charging power, discharging power, state of charge, grid-connected power purchase, power sale, charging / discharging mutual exclusion 0-1 variables, and purchasing / selling mutual exclusion 0-1 variables for each period as decision variables. Then, the optimal charging / discharging boundary solution of the residential energy storage at the current moment is obtained, which includes the optimal charging power boundary solution. Boundary solution with optimal discharge power , as the baseline truth value.

[0024] S2. Based on the first state of charge and the linear programming model, perform sensitivity analysis of the optimal charge-discharge boundary solution relative to the change in state of charge to obtain the corresponding optimal sensitivity parameters; In one embodiment, step S2 includes: A preset perturbation is applied to the first state of charge to obtain a perturbed state of charge, which is then used to solve the linear programming model to obtain the perturbed charge-discharge boundary solution. Based on the perturbation charge-discharge boundary solution and the optimal charge-discharge boundary solution, the optimal sensitivity parameter is determined by the finite difference method.

[0025] Specifically, a very small positive preset perturbation is applied to the first state of charge (the perturbation amount needs to take into account both linear accuracy and numerical stability, and is usually taken as 0.1%-1% of the rated capacity, for example, 0.001 pu), to obtain the perturbed state of charge (its value is the sum of the first state of charge and the preset perturbation); keeping all other conditions such as photovoltaic, load, and rated parameters unchanged, the perturbed state of charge is used as the new initial condition, and the solver is called again to solve the linear programming model of step S1 to obtain the perturbed charge and discharge boundary solution.

[0026] Based on the charge-discharge boundary solutions before and after the disturbance, the optimal sensitivity parameters of the energy storage boundary are obtained using finite difference calculus. This process is expressed by the following equation: In the formula, and These are the optimal sensitivity parameters for charging and discharging (i.e., the partial derivatives of the boundary with respect to the state variables). Preset disturbance; and These are the optimal charge and discharge boundary solutions output by the residential energy storage system at time t.

[0027] This invention eliminates the need to derive the KKT conditional expressions for the linear programming model at the optimal vertex, and also eliminates the need to calculate the inverse of the high-dimensional Jacobian matrix, completely avoiding complex mathematical derivations and making engineering implementation extremely easy. By actually resolving the model, it captures the nonlinear / piecewise linear effects at the constraint boundary, which is much more accurate than relying solely on first-order Taylor expansion without recalculation. The computational cost is decoupled from the wake-up cycle, the overhead is fixed and predictable, and the sensitivity direction automatically conforms to the economically optimal path.

[0028] S3. During the real-time operation phase, the second state of charge of the household energy storage system is collected, and the state of the solver is determined based on the second state of charge. Specifically, during the real-time operation phase of the residential energy storage system, the following monitoring and judgment are periodically performed at a fixed time resolution (typically 5-15 minutes): The actual state of charge (SOC) of the residential energy storage system at the current moment is collected in real time as the second SOC, and the deviation between the second SOC and the SOC at the previous trigger moment (i.e., the second SOC at the previous moment) is calculated; subsequently, the solver's state is determined based on the relationship between the deviation and a preset safety threshold. In one embodiment, determining the solver's state based on the second SOC includes: Calculate the deviation between the second states of charge at adjacent time points, and compare the deviation with a preset safety threshold; When the deviation is not greater than the preset safety threshold, the solver is determined to be in a dormant state. When the deviation exceeds the preset safety threshold, the solver is determined to be in a state of wake-up.

[0029] Specifically, the deviation between the second states of charge at adjacent times is calculated using the following formula: In the formula, This is the deviation value.

[0030] When the absolute value of the deviation is not greater than a preset safety threshold, the solver is determined to be in a dormant state; when the absolute value of the deviation is greater than the preset safety threshold, it indicates that the accumulated error exceeds the safe range, and the solver is determined to be in a wake-up state. The preset safety threshold is tuned as follows: it is determined based on the linearity of the optimal sensitivity parameter within a preset SOC disturbance range. Specifically, during step S2, the fluctuation of the power boundary rate of change within a ±5% SOC disturbance range is simultaneously evaluated. If the fluctuation of the power boundary rate of change is less than 2%, it indicates that the sensitivity has good linearity within this range, and the preset safety threshold is set to 5%; if the fluctuation of the power boundary rate of change is large (≥2%), the threshold is appropriately reduced to 2%-3%. This tuning method ensures that the error of linear recursion within the dormant interval remains within an acceptable range.

[0031] This invention directly reflects the current net rate of energy change of the energy storage system by measuring the charge deviation between adjacent time points. When the deviation is small, it indicates that the photovoltaic output and load power consumption are in a relatively stable state, and the original optimal boundary is still effective. When the deviation suddenly increases (such as a sudden increase in high-power load or a sudden change in strong sunlight), the solver is immediately awakened for re-optimization. This strategy, driven by "disturbance intensity" rather than "time", ensures control accuracy under severe disturbances and avoids performing a large number of invalid solutions during stable periods. Using the deviation between adjacent time points can effectively filter out the effects of slow drift and DC bias, and can also shield small random fluctuations, significantly improving the stability of solver state switching and reducing computational overhead.

[0032] S4. When the solver is in a dormant state, the energy storage physical boundary is recursively deduced and the grid connection point superimposed mapping is performed based on the optimal sensitivity parameter and the second state of charge to obtain the adjustable capacity boundary of the household grid connection point. In one embodiment, step S4 includes: The final charge / discharge power boundary is obtained by recursively extrapolating the energy storage physical boundary using the optimal sensitivity parameter, the disturbed state of charge, and the second state of charge. The final charge / discharge power boundary is algebraically decoupled and superimposed with the photovoltaic output data of the residential energy storage system during the real-time operation phase and the adjustable range of the flexible load to obtain the adjustable capacity boundary of the residential grid connection point.

[0033] Specifically, when the system is determined to be in a dormant state, the complex MILP solution containing various 0-1 mutually exclusive variables is skipped. Instead, the physical limits of charging and discharging of the current energy storage are directly derived using sensitivity algebra, and a physical capacity cutoff is applied.

[0034] In one embodiment, the step of recursively deriving the final charge / discharge power boundary by using the optimal sensitivity parameter, the perturbed state of charge, and the second state of charge includes: The relative fluctuation amount is determined based on the second state of charge and the disturbed state of charge. Based on the optimal sensitivity parameter and the relative fluctuation, the energy storage physical boundary is recursively deduced through linear mapping to obtain preliminary recursive results. Apply a physical limit truncation correction to the preliminary recursive result to obtain the final charge / discharge power boundary.

[0035] Specifically, the difference between the second state of charge and the disturbed state of charge is taken as the relative fluctuation quantity. Combined with the calculated optimal sensitivity parameter, the energy storage physical boundary is recursively derived through linear mapping to obtain the preliminary recursive result. Then, a physical limit truncation correction is applied to it to obtain the final charge and discharge power boundary. This process is shown in the following equation: In the formula, , This represents the final charge and discharge power boundary values ​​of the residential energy storage system at time t. This represents the relative fluctuation.

[0036] After the above recursion and truncation, the accurate final charge and discharge power boundary of the energy storage system at the current moment can be obtained. The process reduces the original exponential complexity of MILP rolling solution to O(1) complexity basic algebraic operations (only including addition, subtraction, multiplication and division), which greatly reduces the computing power consumption of edge terminals while ensuring accuracy.

[0037] After obtaining the accurate energy storage boundary, it is substituted into the overall power balance equation of the microgrid. The energy storage boundary is then decoupled and superimposed with the real-time photovoltaic output data and the upper and lower adjustment limits of the household flexible load. Finally, the upper and lower envelopes of the adjustable capacity of the household grid connection point are obtained, which is the adjustable capacity boundary of the household grid connection point. This process is shown in the following formula: In the formula, , Let t be the upper and lower boundary envelopes of the adjustable capacity of the household grid connection point at time t, with positive values ​​flowing to the grid and negative values ​​drawn from the grid. The photovoltaic output data at time t; , Let t be the upper and lower limits of the household flexible load at time t.

[0038] It should be noted that in the above grid connection point mapping, the energy storage boundary uses a recursive value, while the photovoltaic output and load power must use real-time measurement values ​​under the same time section to ensure that the adjustable capacity reported to the grid is real-time effective. After the adjustable capacity boundary of the household grid connection point is reported, the assessment is determined to be completed at this moment, and the system waits for the next sampling moment and returns to step S3 to continue the cyclic monitoring.

[0039] This invention replaces complex online planning with a linear recursive formula (multiplication and addition), enabling the physical boundary of energy storage to be updated instantaneously with each frame of change in the state of charge. The physical boundary and the scheduling boundary are clearly decoupled, facilitating hierarchical control and fault isolation, and significantly improving the robustness and maintainability of the control. By incorporating the adjustable range of flexible loads into the superposition formula, the grid connection point boundary is no longer a rigid limit of energy storage alone, but rather an "equivalent virtual energy storage" boundary that integrates the flexibility of the load side, thus broadening the overall grid connection point adjustment range.

[0040] In one embodiment, after determining the state of the solver based on the second state of charge, the method further includes: When the solver is in the wake-up state, the solver is woken up, and the second charge state is used as the updated initial condition to resolve the linear programming model, so as to obtain the updated optimal charge-discharge boundary solution to update the optimal sensitivity parameter; The solver is controlled to switch to sleep mode, and the energy storage physical boundary recursion and grid connection point superposition mapping process are performed according to the updated optimal sensitivity parameters to obtain the adjustable capacity boundary of the household grid connection point as the output of the adjustable capacity online tracking result of the household energy storage system.

[0041] Specifically, when the solver is in the wake-up state, it is determined that the error of the original linear programming model under the current operating conditions has accumulated beyond the safe range and needs to be corrected.

[0042] First, the underlying MILP optimization solver is activated, and the second state of charge at the current moment is used as the initial state. The actual load data and photovoltaic data collected at the current moment are used as inputs to re-execute the complete model solution process of step S1, and output the accurate energy storage charging and discharging power boundary at the current moment. Then, the actual state of charge at the current moment is updated to the new first state of charge, and the newly solved power boundary is updated to the new reference power boundary. Based on the new reference state, the optimal sensitivity parameters under the current operating conditions are re-extracted and updated to provide accurate local linear approximation parameters for the linear recursion of the subsequent dormancy period.

[0043] After updating the optimal sensitivity parameters, the control solver switches to sleep mode and re-executes the energy storage physical boundary recursion and grid connection point superposition mapping process based on the updated optimal sensitivity parameters to obtain the adjustable capacity boundary of the residential grid connection point as the output of the online tracking result of the adjustable capacity of the residential energy storage system.

[0044] When the state of charge changes significantly, this invention forces a reset of the linearization reference point (i.e., replaces the old disturbance point with the current true state) and recalculates the precise sensitivity at the new reference point. This "fixed-point zeroing" mechanism ensures that the system always operates near the latest optimal solution, fundamentally eliminating the risk of divergence in open-loop recursion. The re-solution process automatically captures changes in the external environment and has full-condition adaptive capability. The strict state machine design of "wake-up-sleep" balances accuracy and low power consumption, and the updated recursion starting point is seamlessly connected to the new boundary to prevent control command jumps.

[0045] Online evaluation and verification were conducted based on actual data from a real user system (15-minute time resolution, 96 time slots per day), with a small perturbation factor assumed. The safety trigger threshold is 0.02. A schematic diagram of the actual operating trajectory of the residential energy storage system's State of Charge (SOC) is shown below. Figure 2 As shown in the figure, the comparison of the effects of the optimization scheme is as follows: Figure 3 As shown in the figure, the distribution of event trigger times and underlying optimization solver wake-up frequency throughout the day is as follows: Figure 4 As shown. Please refer to [the original text]. Figures 2-4Verification results show that the upper and lower bound envelopes calculated using the sensitivity tracking method of this invention highly coincide with the envelopes optimized by the actual MILP. Regarding efficiency improvements: the traditional rolling time-domain evaluation method requires 192 solver calls per day, with a total solution time of 77.94 seconds; while the method of this invention only triggers 6 solver wake-ups per day, drastically reducing the total time to 7.15 seconds. This invention completely avoids the computational disaster caused by high-frequency MILP solutions and is extremely suitable for deployment on lightweight edge terminals.

[0046] This invention addresses the problem of achieving rapid online assessment and tracking of the adjustability of residential energy storage systems while significantly reducing computational resources. It designs an online tracking scheme for the adjustability of residential energy storage systems, employing a closed-loop rolling online tracking framework of "accurate benchmark solution + normal linear recursion + event-triggered correction." Only at a few critical nodes is a high-computing-power mixed-integer linear programming solver called to obtain accurate benchmark values ​​and sensitivity parameters; for the vast majority of the time, low-complexity algebraic linear recursion replaces complex optimization solutions; and the SOC deviation threshold is used as a "switch" to dynamically control the solver's rest. The process involves sleep and wake-up, specifically: during the initialization phase, the solver performs the first accurate solution to the linear programming model, extracting the optimal sensitivity of the power boundary to the State of Charge (SOC); during the normal operation phase, at each sampling time, the event trigger judgment in step S3 is executed; if the SOC deviation does not exceed the threshold, the solver remains asleep, executes step S4 to complete the rapid recursion and outputs the result, then returns to step S3 to enter the next round of monitoring; during the correction and update phase, if step S3 determines that the SOC deviation exceeds the limit, the solver is awakened, the model is re-solved accurately, and the process returns to step S2 to refresh the sensitivity parameters, before returning to the normal operation loop. The entire process progresses over time, dynamically switching between "low-computing-power recursion" and "high-precision correction," achieving continuous online tracking with adjustable capabilities, significantly reducing the computing power overhead of edge terminals while ensuring evaluation accuracy.

[0047] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0048] In another embodiment, such as Figure 5 As shown, a second aspect of the present invention provides an online tracking device for the adjustable capacity of a residential energy storage system, comprising: The model solving module 10 is used to obtain the first state of charge of the residential energy storage system at the initial moment of the system or the moment the solver is awakened, so as to solve the pre-built linear programming model and obtain the optimal charge and discharge boundary solution. The parameter determination module 20 is used to perform sensitivity analysis of the optimal charge-discharge boundary solution relative to the change in charge state based on the first state of charge and the linear programming model, and to obtain the corresponding optimal sensitivity parameters. The state judgment module 30 is used to collect the second state of charge of the household energy storage system during the real-time operation phase, and to judge the state of the solver based on the second state of charge. The capacity tracking module 40 is used to perform energy storage physical boundary recursion and grid connection point superposition mapping based on the optimal sensitivity parameter and the second state of charge when the solver is in a dormant state, so as to obtain the adjustable capacity boundary of the household grid connection point.

[0049] It should be noted that each module in the aforementioned adjustable capacity online tracking device for a residential energy storage system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module. For specific limitations regarding the adjustable capacity online tracking device for a residential energy storage system, please refer to the limitations of the adjustable capacity online tracking method for a residential energy storage system described above; both have the same function and role, and will not be repeated here.

[0050] A third aspect of the present invention provides an electronic device comprising: Processor, memory, and bus; The bus is used to connect the processor and the memory; The memory is used to store operation instructions; The processor is configured to execute the operation instructions by calling the operation instructions, thereby causing the processor to perform the operation corresponding to the online tracking method for the adjustable capacity of a residential energy storage system as shown in the first aspect of this application.

[0051] In one alternative embodiment, an electronic device is provided, such as Figure 6 As shown, Figure 6 The illustrated electronic device 5000 includes a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, for example, via a bus 5002. Optionally, the electronic device 5000 may also include a transceiver 5004. It should be noted that in practical applications, the transceiver 5004 is not limited to one type, and the structure of this electronic device 5000 does not constitute a limitation on the embodiments of this application.

[0052] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 5001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0053] Bus 5002 may include a path for transmitting information between the aforementioned components. Bus 5002 may be a PCI bus or an EISA bus, etc. Bus 5002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0054] The memory 5003 may be a ROM or other type of static storage device capable of storing static information and instructions, RAM or other type of dynamic storage device capable of storing information and instructions, or it may be an EEPROM, CD-ROM or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0055] The memory 5003 is used to store application code that executes the scheme of this application, and its execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the foregoing method embodiments.

[0056] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers.

[0057] The fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the online tracking method for the adjustable capacity of a residential energy storage system as shown in the first aspect of this application.

[0058] Another embodiment of this application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0059] Furthermore, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0060] In summary, this invention relates to the field of energy storage control technology, and discloses a method, device, equipment, and medium for online tracking of the adjustable capacity of a residential energy storage system. At the system initial moment or the solver wake-up moment, the first state of charge of the residential energy storage system is acquired to solve the linear programming model, obtaining the optimal charge / discharge boundary solution. Based on the first state of charge and the linear programming model, sensitivity analysis is performed on the optimal charge / discharge boundary solution relative to the state of charge change, obtaining the corresponding optimal sensitivity parameters. The second state of charge of the residential energy storage system during real-time operation is collected to determine the solver's state. When the solver is in sleep mode, the energy storage physical boundary is recursively deduced and the grid connection point is superimposed and mapped based on the optimal sensitivity parameters and the second state of charge, obtaining the adjustable capacity boundary of the residential grid connection point. The high-complexity rolling optimization is reduced to low-computational-power algebraic operations, and combined with an event-triggered mechanism, the solver call frequency is significantly reduced, significantly reducing computational overhead while ensuring tracking accuracy.

[0061] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0062] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method for online tracking of the adjustable capacity of a residential energy storage system, characterized in that, include: At the initial moment of the system or the moment the solver is awakened, the first state of charge of the residential energy storage system is obtained to solve the pre-built linear programming model and obtain the optimal charge and discharge boundary solution. Based on the first state of charge and the linear programming model, a sensitivity analysis of the optimal charge-discharge boundary solution with respect to the change of state of charge is performed to obtain the corresponding optimal sensitivity parameters. During the real-time operation phase, the second state of charge of the residential energy storage system is collected, and the state of the solver is determined based on the second state of charge. When the solver is in a dormant state, the energy storage physical boundary is recursively derived and the grid connection point is superimposed and mapped based on the optimal sensitivity parameter and the second state of charge to obtain the adjustable capacity boundary of the household grid connection point.

2. The method for online tracking of the adjustable capacity of a residential energy storage system according to claim 1, characterized in that, The linear programming model aims to maximize the energy storage charging and discharging power of the residential energy storage system at the current moment, and uses node power balance constraints, energy storage timing recursion and charging and discharging mutual exclusion constraints, and grid connection and power purchase and sale mutual exclusion constraints as constraints.

3. The method for online tracking of the adjustable capacity of a residential energy storage system according to claim 1, characterized in that, The sensitivity analysis of the optimal charge / discharge boundary solution relative to the change in charge state, based on the first state of charge and the linear programming model, yields the corresponding optimal sensitivity parameters, including: A preset perturbation is applied to the first state of charge to obtain a perturbed state of charge, which is then used to solve the linear programming model to obtain the perturbed charge-discharge boundary solution. Based on the perturbation charge-discharge boundary solution and the optimal charge-discharge boundary solution, the optimal sensitivity parameter is determined by the finite difference method.

4. The method for online tracking of the adjustable capacity of a residential energy storage system according to claim 1, characterized in that, The step of determining the solver's state based on the second state of charge includes: Calculate the deviation between the second states of charge at adjacent time points, and compare the deviation with a preset safety threshold; When the deviation is not greater than the preset safety threshold, the solver is determined to be in a dormant state. When the deviation exceeds the preset safety threshold, the solver is determined to be in a state of wake-up.

5. The method for online tracking of the adjustable capacity of a residential energy storage system according to claim 3, characterized in that, The process of recursively elucidating the physical boundary of energy storage and superimposing the grid connection point based on the optimal sensitivity parameter and the second state of charge to obtain the adjustable capacity boundary of the household grid connection point includes: The final charge / discharge power boundary is obtained by recursively extrapolating the energy storage physical boundary using the optimal sensitivity parameter, the disturbed state of charge, and the second state of charge. The final charge / discharge power boundary is algebraically decoupled and superimposed with the photovoltaic output data of the residential energy storage system during the real-time operation phase and the adjustable range of the flexible load to obtain the adjustable capacity boundary of the residential grid connection point.

6. The method for online tracking of the adjustable capacity of a residential energy storage system according to claim 5, characterized in that, The step of recursively deriving the energy storage physical boundary using the optimal sensitivity parameter, the perturbed state of charge, and the second state of charge to obtain the final charge / discharge power boundary includes: The relative fluctuation amount is determined based on the second state of charge and the disturbed state of charge. Based on the optimal sensitivity parameter and the relative fluctuation, the energy storage physical boundary is recursively deduced through linear mapping to obtain preliminary recursive results. Apply a physical limit truncation correction to the preliminary recursive result to obtain the final charge / discharge power boundary.

7. The method for online tracking of the adjustable capacity of a residential energy storage system according to claim 4, characterized in that, After determining the solver's state based on the second state of charge, the method further includes: When the solver is in the wake-up state, the solver is woken up, and the second charge state is used as the updated initial condition to resolve the linear programming model, so as to obtain the updated optimal charge-discharge boundary solution to update the optimal sensitivity parameter; The solver is controlled to switch to sleep mode, and the energy storage physical boundary recursion and grid connection point superposition mapping process are performed according to the updated optimal sensitivity parameters to obtain the adjustable capacity boundary of the household grid connection point as the output of the adjustable capacity online tracking result of the household energy storage system.

8. A device for online tracking of the adjustable capacity of a residential energy storage system, characterized in that, include: The model solving module is used to obtain the first state of charge of the residential energy storage system at the initial moment of the system or the moment the solver is awakened, so as to solve the pre-built linear programming model and obtain the optimal charge and discharge boundary solution. The parameter determination module is used to perform sensitivity analysis of the optimal charge-discharge boundary solution relative to the change in charge state based on the first state of charge and the linear programming model, and to obtain the corresponding optimal sensitivity parameters. The state determination module is used to collect the second state of charge of the residential energy storage system during the real-time operation phase, and determine the state of the solver based on the second state of charge. The capacity tracking module is used to perform energy storage physical boundary recursion and grid connection point superposition mapping based on the optimal sensitivity parameter and the second state of charge when the solver is in a dormant state, so as to obtain the adjustable capacity boundary of the household grid connection point.

9. An electronic device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the online tracking method for the adjustable capacity of a residential energy storage system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the online tracking method for the adjustable capacity of a residential energy storage system as described in any one of claims 1 to 7.

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