Communication power supply remote capacity checking method, device and equipment and storage medium

Abstract

The invention provides a communication power supply remote capacity checking method, apparatus and device, and a storage medium. The method comprises the steps of obtaining real-time data related to a storage battery pack of each station in a scheduling area; according to the real-time data, carrying out online updating on a preset battery digital twinborn model, and predicting the power of each station when executing a nuclear capacity test to obtain power data of each station in a future scheduling period; constructing a target function of multi-station collaborative optimization by taking the scheduling state and power of the stations as variables, and setting constraint conditions; and substituting the power data into the target function, solving the target function based on the constraint condition, and determining a scheduling scheme based on the obtained decision. According to the method, on-line updating of digital twinborn model prediction is carried out, a battery digital twinborn model comprises an electrochemical model, a thermal model and an aging model, the equipment safety risk is effectively avoided, meanwhile, multi-station nuclear capacity scheduling is constructed into a collaborative optimization problem, and the scheduling state and power of each station are planned and optimized as a whole.

Description

Technical Field

[0001] This invention belongs to the field of battery management technology, and in particular relates to a method, apparatus, device and storage medium for remote capacity verification of communication power supplies. Background Technology

[0002] Battery banks in communication power systems are crucial backup energy sources for ensuring the uninterrupted operation of communication equipment. Regular capacity discharge testing is essential for maintaining the health of batteries. Traditional capacity testing methods rely heavily on manual on-site operation, which suffers from low efficiency, high cost, and poor safety.

[0003] Existing technologies include several remote capacity control solutions, such as remote discharge devices based on DC / DC converters and battery monitoring systems based on the Internet of Things. While these solutions achieve basic remote control functions, they still suffer from a lack of multi-site coordination capabilities. Specifically, when multiple communication base stations are simultaneously performing capacity control, there is a lack of effective power scheduling algorithms, which can easily lead to localized overloads of the power grid. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, device and storage medium for remote power supply capacity verification of communication power, in order to improve multi-site collaboration capabilities.

[0005] A first aspect of the present invention provides a method for remote capacity verification of a communication power supply, comprising:

[0006] Obtain real-time data related to the battery packs at each site in the dispatch area;

[0007] The preset battery digital twin model is updated online based on the real-time data. The battery digital twin model includes an electrochemical model, a thermal model, and an aging model.

[0008] Using the updated battery digital twin model, the power of each site during the capacity test is predicted to obtain the power data of each site in the future scheduling cycle;

[0009] A multi-site collaborative optimization objective function is constructed using the scheduling status and power of the sites as variables, and constraints are set.

[0010] The power data is substituted into the objective function, the objective function is solved based on the constraints, and the scheduling scheme is determined based on the obtained decision.

[0011] A second aspect of the present invention provides a remote capacity verification device for communication power supplies, comprising:

[0012] The data acquisition module is used to acquire real-time data related to the battery packs at various stations in the scheduling area;

[0013] The model update module is used to update the preset battery digital twin model online based on the real-time data. The battery digital twin model includes an electrochemical model, a thermal model, and an aging model.

[0014] The prediction module is used to predict the power of each site when performing the capacity test using the updated battery digital twin model, so as to obtain the power data of each site in the future scheduling cycle.

[0015] The objective function setting module is used to construct an objective function for multi-site collaborative optimization using the scheduling status and power of the sites as variables, and to set constraints.

[0016] The decision module is used to substitute the power data into the objective function, solve the objective function based on the constraints, and determine the scheduling scheme based on the obtained decision.

[0017] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the remote power supply recalculation method as described in the first aspect above.

[0018] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the remote power supply recalculation method as described in the first aspect above.

[0019] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0020] First, by updating the digital twin model online (covering electrochemical, thermal, and aging multi-dimensional models), accurate perception of battery status and prediction of power demand are achieved, ensuring that the scheduling scheme conforms to the actual operating boundary of the battery and effectively preventing equipment safety risks caused by overcharging, over-discharging, and overheating.

[0021] Second, the multi-site capacity scheduling is constructed as a collaborative optimization problem. Through objective functions and constraints, the scheduling status (start-stop) and power of each site are optimized in a coordinated manner, which can reduce the overall electricity cost by participating in demand response and other methods. Attached Figure Description

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

[0023] Figure 1 This is a schematic diagram of a remote capacity verification method for communication power supplies provided in an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of a remote capacity verification device for communication power supply provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the present invention. However, those skilled in the art will recognize that the present application may be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted to avoid unnecessary detail that could obscure the description of the present application.

[0027] The technical solution of the present invention will be illustrated below through specific embodiments.

[0028] Reference Figure 1 The diagram illustrates a remote capacity verification method for communication power supplies provided by an embodiment of the present invention, which may specifically include the following steps:

[0029] S101. Obtain real-time data related to the battery packs of each station in the scheduling area.

[0030] A dispatch area refers to a collection of battery sites that may be geographically dispersed but are controlled by the same central management system. Examples include multiple photovoltaic energy storage power stations or substation energy storage units within a city. The sites are distributed within the dispatch area, meaning that batteries at multiple locations need to be monitored simultaneously.

[0031] Real-time data specifically includes the following types:

[0032] Electrical data: voltage, current, real-time power, remaining charge (SOC), state of health (SOH), internal resistance, etc.

[0033] Thermal data: internal and surface temperatures of the battery pack and ambient temperature.

[0034] Operating status data: charging / discharging status, alarm information (such as overvoltage, overtemperature, insulation fault, etc.).

[0035] Real-time data can provide a real and up-to-date data source for the next update of the digital twin model, ensuring that the virtual model can accurately and in real time reflect the current state of each physical battery.

[0036] S102. Update the preset battery digital twin model online based on real-time data. The battery digital twin model includes an electrochemical model, a thermal model, and an aging model.

[0037] A battery digital twin model is a high-fidelity digital model created in virtual space. It is not just a static model, but a dynamic system that can simulate, analyze, and predict the behavior of a physical entity.

[0038] Model components:

[0039] Electrochemical models describe the complex electrochemical reactions inside a battery. They can more accurately estimate state of charge (SOC), terminal voltage, internal resistance, etc., and are more precise than simple equivalent circuit models.

[0040] Thermal model: Describes the heat generation and dissipation process of a battery. It can predict the temperature changes of a battery under different operating conditions (such as high-current capacity testing) and prevent thermal runaway.

[0041] Aging model: Describes the trend of battery capacity decay and internal resistance growth. It quantifies the battery's state of harmonics (SOH), predicts battery life, and analyzes the impact of different operating strategies (such as depth of charge / discharge and rate) on aging.

[0042] During online updates, the system utilizes real-time data acquired by S101 to dynamically adjust the internal parameters of the aforementioned models through data assimilation or parameter identification algorithms (such as Kalman filtering, least squares method, etc.). For example, the polarization parameters of the electrochemical model are corrected based on real-time voltage and current, the heat dissipation coefficient of the thermal model is corrected based on temperature data, and the decay rate of the aging model is updated based on capacity test data.

[0043] This step enables dynamic mapping and synchronization between the physical battery and the virtual model, ensuring that the digital twin model always remains consistent with the actual aging state and current state of the physical battery, thereby giving its predictive function (S103) extremely high reliability.

[0044] S103. Using the updated battery digital twin model, the power of each site during the capacity test is predicted to obtain the power data of each site in the future scheduling cycle.

[0045] Capacity testing is a routine procedure for battery maintenance, involving a complete charge-discharge cycle to verify whether the battery's actual capacity meets specifications. This process typically consumes significant power and lasts for several hours.

[0046] Power prediction refers to the power data allowed by the battery itself under a given task (capacity testing). Since each battery has different current SOC, SOH, temperature, and internal resistance (this information has been obtained from the updated battery digital twin model), their charge / discharge power and duration of operation when performing the same capacity test will also differ. For example, a battery with slightly lower health and higher temperature may have a lower maximum allowable discharge power and require a longer time to complete the test for safety reasons. A battery in good condition, on the other hand, can complete the test quickly with higher power.

[0047] The power data for the future scheduling period is the output. For each site, the model generates a time series data point, representing the expected charging / discharging power value at each moment during the capacity test within a future period (e.g., the next 24 hours). This constitutes the known input to the optimization problem. For example, the output is the power demand sequence for each site in the future scheduling period {P1(t), P2(t), ..., P...}. N For example, P1(t) represents the power value of station 1 at time t.

[0048] S104. Construct an objective function for multi-site collaborative optimization using the scheduling status and power of the sites as variables, and set constraints.

[0049] Decision variables include scheduling status and power.

[0050] The scheduling status is a binary variable (0 or 1) indicating whether a particular site is scheduled to perform a capacity test within a certain time period. For example, X i (t)=1 indicates that the i-th station is scheduled for testing at time t, i.e., it is in the startup / startup state.

[0051] Power is a continuous variable, representing the power value allocated to a specific site during a certain time period.

[0052] An objective function is a metric that needs to be maximized or minimized. The objective function can be set from the following aspects:

[0053] Economic efficiency: Minimize total electricity costs (if the test involves discharging when electricity prices are high and charging when electricity prices are low).

[0054] Efficiency: Maximize the total capacity of test completion, or minimize the total test time.

[0055] Grid friendliness: To make the total power curve (based on power data) of all sites as smooth as possible, so as to avoid impacting the grid.

[0056] Fairness: Prioritize scheduling for sites that most urgently need to undergo capacity testing (such as those with a rapid decrease in SOH).

[0057] Multi-objective optimization: This usually involves a weighted combination of the above objectives.

[0058] Constraints are hard restrictions that must be followed when solving an objective function, and they typically include the following:

[0059] The constraints include task completion constraints, power grid security constraints, and equipment operation constraints.

[0060] The task completion constraint means that the capacity test of each station must be completed within the scheduling cycle; the grid security constraint means that at any given time period, the total power of all operating stations shall not exceed the maximum power that the grid can provide during that time period (which can be predicted or set according to actual conditions), for example, the State of Charge (SOC) must always be kept within a safe window (e.g., 20%~90%). The equipment operation constraint means that each station can only be in one of two states: running or stopped, at any given time period.

[0061] Task completion constraints: Each site Throughout the entire scheduling cycle, it remains in a running state (i.e., The total number of time slots must be exactly equal to the total task duration required by the site. This ensures that tasks such as capacity testing can be completed fully, without being abandoned halfway or needlessly prolonged. For example, if the capacity test for site 1 takes 3 hours ( Therefore, the optimization results must ensure The value is 1 only 3 times in all time periods.

[0062] 2. Power grid security constraints: at any given time period All running sites ( The power of those sites In total, their sum cannot exceed the maximum safe power that the power grid can provide during that period. It prevents the risk of local power grid overload, tripping, or even equipment damage caused by multiple sites conducting high-power tests simultaneously. This may change over time; for example, the grid's capacity may decrease during peak electricity demand periods.

[0063] 3. Equipment operation constraints: For each site i and each time point t: Decision variables for each site in each time period. It can only be 0 (off / not running) or 1 (on / running). A battery pack's capacity test typically cannot be run at half power or percentage power; it's either under test (full power operation) or not. This is a binary decision, making the optimization problem a mixed-integer programming problem. Represented as on state 1 and off state 0, the optimization algorithm's task is to find the "0-1" matrix that optimizes the objective function while satisfying all the rules (the sum of the durations of on states equals the task duration, the sum of each column does not exceed the power limit, and each on state can only be 0 or 1).

[0064] These three constraints together ensure that the scheduling scheme is feasible, safe, and executable.

[0065] S105. Substitute the power data into the objective function, solve the objective function based on the constraints, and determine the scheduling scheme based on the obtained decision.

[0066] The power data substituted here is the power capability that each station may achieve when performing tests at various future times, as predicted in S103. This data is the basis for constructing constraints (such as power limits) and calculating objective functions (such as total power).

[0067] Solving the objective function is a complex mathematical programming problem. Depending on the problem size (number of sites, time granularity) and nature (linear / nonlinear), different optimization algorithms are used, such as mixed integer linear programming, genetic algorithms, particle swarm optimization, etc. The solver will output a set of optimal decision variable values.

[0068] The obtained decision is the output of the solver, specifically manifested as:

[0069] The scheduling plan clearly specifies when each site will begin and end its capacity testing.

[0070] Power instruction set: Specifies the precise power value that each station should execute at each moment during the test.

[0071] The above decision results are transformed into specific control instructions that can be issued to the battery management systems of each site for execution. The system will automatically and orderly start and stop the capacity test tasks of each site according to this plan, thus obtaining the scheduling plan.

[0072] The remote capacity verification method for communication power supplies provided in this invention includes: acquiring real-time data related to battery packs at various stations in a scheduling area; updating a preset battery digital twin model online based on the real-time data, the battery digital twin model including an electrochemical model, a thermal model, and an aging model; using the updated battery digital twin model to predict the power of each station during capacity verification testing, obtaining power data for each station in future scheduling cycles; constructing a multi-site collaborative optimization objective function using the station's scheduling status and power as variables, and setting constraints; substituting the power data into the objective function, solving the objective function based on the constraints, and determining a scheduling scheme based on the obtained decision. Firstly, by updating the digital twin model online (covering electrochemical, thermal, and aging multi-dimensional models), accurate perception of battery status and prediction of power demand are achieved, ensuring that the scheduling scheme conforms to the actual operating boundaries of the battery, effectively preventing equipment safety risks caused by overcharging, over-discharging, and overheating. Second, the multi-site capacity scheduling is constructed as a collaborative optimization problem. Through objective functions and constraints, the scheduling status (start-stop) and power of each site are optimized in a coordinated manner, which can reduce the overall electricity cost by participating in demand response and other methods.

[0073] In an optional embodiment, an objective function for multi-site collaborative optimization is constructed using the site's decision as a variable and incorporating power data, including:

[0074] The predicted power of each station in each time period is obtained based on the power data; the sum of the predicted power of all stations in all time periods is calculated to obtain the total predicted power; the ratio of the total predicted power to the total number of time periods is calculated to obtain the global target average power; the first optimization component of the objective function is constructed, which is configured as the deviation between the total power of all stations in each time period and the global target average power within the scheduling cycle.

[0075] The power data here comes directly from step S103 of the process. That is, for each site i in each future time period t, we have predicted, using a digital twin model, the power P it could achieve if a capacity test were performed. i (t). P i (t) can be viewed as the maximum or potential power of station i in time period t. It is the basis for subsequent optimization calculations.

[0076] The total predicted power is obtained by summing the predicted power of each site across all time periods throughout the entire scheduling cycle. This value represents the total electrical energy consumed (or generated) if all sites simultaneously and at full capacity for testing. It is a theoretical total energy value.

[0077] Global target average power = total predicted power / total number of time periods;

[0078] The global target average power means that if the total energy involved in the test tasks of all stations throughout the entire scheduling cycle is evenly distributed to each time period, then how much power should be allocated to each time period.

[0079] The first optimization component is configured to measure the deviation between the total power of all stations and the global target average power in each time period during the scheduling cycle.

[0080] Optionally, to more severely penalize large deviations, this deviation can be represented using a squared term. Therefore, the typical mathematical expression for the first optimization component, Obj1, is:

[0081] Obj1=Σ[(ΣP_i(t)X_i(t))-Global target average power]²;

[0082] This value is a constant that an ideal, perfectly smooth power curve should achieve. Where: X_i(t) is the decision variable (0 or 1) mentioned in S104, indicating whether station i is scheduled during time period t. ΣP_i(t)X_i(t)) is the sum of the total power of all actually scheduled stations during time period t (i.e., the actual total power).

[0083] (Actual total power - global target average power) is the deviation at time t, and Σ is the sum of the deviations at all time periods t.

[0084] The first optimization component is an effective strategy for achieving multi-site collaborative optimization. It introduces a global average power as the objective and correlates the independent behaviors of multiple sites through optimization algorithms, forcing them to cooperate and ultimately outputting a more grid-friendly and better overall scheduling scheme. In practical applications, this component is usually combined with other objectives (such as economy and battery health) in a weighted summation to form the final overall objective function.

[0085] When optimizing the objective function, minimizing Obj1 means that the optimization algorithm will strive to make the actual total power in each time period as close as possible to the global target average power. This avoids scheduling tests of many high-power sites in the same time period (preventing power peaks) and also avoids having no test tasks in certain time periods (preventing power troughs). It will attempt to stagger and combine high-power and low-power test tasks to flatten the total power curve.

[0086] In an optional embodiment, it further includes:

[0087] A second optimization component is constructed for the objective function, which is configured to calculate the magnitude of change in the scheduling status of each site over consecutive time periods.

[0088] X_i(t)=1: This indicates that during time period t, site i is scheduled to perform a capacity test (i.e., it is in the startup state). X_i(t)=0: This indicates that during time period t, site i is not scheduled to perform a capacity test (i.e., it is in the idle / shutdown state). The change in the scheduling status of each site between consecutive time periods is essentially a quantification of whether the scheduling status of a site has changed between two adjacent time periods.

[0089] Optionally, the mathematical expression for the second optimization component is to calculate the absolute value of the state change:

[0090] |X_i(t)-X_i(t-1)|.

[0091] If X_i(t) = X_i(t-1) (i.e., the state remains unchanged):

[0092] Whether working continuously or resting continuously, the calculation result is 0, indicating no change, which is a "good" situation.

[0093] If X_i(t) ≠ X_i(t-1) (i.e., the state changes):

[0094] Switching from rest to work (0 to 1) results in a value of 1; switching from work to rest (1 to 0) also results in a value of 1. This indicates that a state transition has occurred, which is a "bad" situation that the optimizer needs to penalize.

[0095] The second optimization component, Obj2, summarizes the state change magnitudes of all sites and all adjacent time periods, typically as follows:

[0096] Obj2=ΣΣ|X_i(t)-X_i(t-1)|;

[0097] The first Σ sums over all stations i, and the second Σ sums over time t from the second time period to the last time period (because each change requires two adjacent time periods).

[0098] When solving the objective function, the optimizer attempts to minimize Obj2. Minimizing Obj2 means that the optimizer will tend to generate longer, continuous work batches for each site, rather than fragmented, frequently start-stop plans, resulting in fewer site switches and smoother operation. For example, it will be more likely to test a site continuously from hour 1 to hour 5, rather than testing it intermittently in hours 1, 3, and 5.

[0099] The synergy and trade-offs between the first and second optimization components, optionally, the objective function when the two components are combined, is expressed as:

[0100] Objective function = w1×Obj1 + w2×Obj2;

[0101] w1 and w2 are weighting coefficients, representing the relative importance that decision-makers attach to the two objectives of "power smoothing" and "state stability".

[0102] In pursuit of ultimate total power smoothness (minimum Obj1), the optimizer may need to finely adjust the combination of sites participating in the test in each time period. This may cause some sites to need to be started and stopped frequently, thus increasing Obj2. Solution: By adjusting the weights w1 and w2, a compromise can be found.

[0103] If grid stability is the primary objective, then w1 should be set greater than w2. The system will prioritize ensuring smooth total power output and can tolerate a certain degree of site state switching. If extending equipment lifespan and reducing operational complexity are the primary objectives, then w2 should be set greater than w1. The system will prioritize ensuring continuous operation of each site and can accept slightly larger fluctuations in the total power output curve.

[0104] Optionally, the expression for the objective function is:

[0105] Where T is the total number of time steps, N is the number of stations, and P is the number of stations. i (t) represents the predicted power of station i in time period t, x i (t) represents the scheduling state, indicating whether site i performs capacity approval during time period t, x i The value of (t) is 0 or 1, λ is the smoothing coefficient, and P avg The average power of the global target.

[0106] In an optional embodiment, the remote capacity verification method for communication power supplies further includes:

[0107] After executing the scheduling plan, obtain the corresponding execution results; evaluate the scheduling plan based on the execution results.

[0108] Execution content: The dispatch center will issue the final plan determined by S105 (when to start the test at which site, and what power curve to use) to the battery management system of each site.

[0109] Data Acquisition: During and after the implementation of the plan, the system needs to collect detailed actual operational data. This data is similar to the real-time data in S101, but focuses more on comparison with forecasts and plans. This mainly includes:

[0110] Actual power curve: The actual measured charging / discharging power at each site is compared with the predicted power of S103.

[0111] Actual SOC change: The actual power consumption / replenishment of the battery.

[0112] Actual temperature data: The temperature change of the battery during the test is compared with the predicted temperature of the thermal model.

[0113] Status change log: The actual start and stop times of the site, and whether they are consistent with the planned times.

[0114] Alarms and Abnormal Events: Are there any unplanned events such as overvoltage, overcurrent, or overtemperature?

[0115] Test results: The actual battery capacity obtained from the core capacity test was compared with the capacity predicted by the aging model.

[0116] To clearly describe the remote capacity verification method for communication power supplies provided by this invention, the following example is used for illustration. The remote capacity verification method for communication power supplies includes:

[0117] 1. Data acquisition and preprocessing stage.

[0118] The system first synchronously collects real-time operating parameters such as voltage, current, temperature, and internal resistance from the multi-mode sensor arrays of the battery banks at each site. Simultaneously, it obtains external information such as regional load data and electricity price signals from the power grid dispatch center. The data preprocessing module employs wavelet denoising technology to eliminate measurement noise and uses the 3σ criterion for outlier detection and removal to ensure the quality and reliability of the input data.

[0119] 2. Digital twin model updated online.

[0120] Based on preprocessed real-time data, the cloud-based digital twin engine updates parameters and estimates the state of the battery models at each site. Specifically, this includes:

[0121] Battery equivalent circuit model parameters are identified online using the recursive least squares method.

[0122] An extended Kalman filter algorithm is used to estimate the internal states of the battery, such as SOC and SOH.

[0123] Update the parameters of the thermal model and aging model to ensure consistency between the model and the actual battery state.

[0124] 3. Accurate power demand forecasting.

[0125] Using an updated digital twin model, rolling forecasts of power demand during capacity approval processes at each site are performed. The forecasting algorithm combines physical models with data-driven methods.

[0126] Mechanism prediction based on battery model provides the basic power curve

[0127] Using a time-series neural network to capture the dynamic characteristics of load changes

[0128] Output the power demand sequence of each station in the future scheduling period {P1(t), P2(t), ..., P...} N (t)}.

[0129] 4. Optimize model construction.

[0130] The multi-site capacity scheduling problem is constructed as a mixed-integer linear programming model, and constraints are set.

[0131] 5. Model solution and feasibility analysis.

[0132] The branch-and-bound algorithm is used to solve the above MILP problem, including: relaxing integer constraints to solve the linear programming problem; processing fractional solutions through branch operations to gradually approach the optimal integer solution; and obtaining the optimal solution or an approximate optimal solution that meets engineering requirements within a specified time. If the problem is infeasible, the system enters the constraint relaxation phase, appropriately relaxing the constraints according to a preset priority to ensure a feasible scheduling scheme is obtained.

[0133] 6. Implementation and closed-loop correction of the scheme.

[0134] After generating the optimal scheduling scheme, the system sends control commands to each field device via a secure communication link. During execution:

[0135] The system monitors the deviation between the actual discharge power and the planned value at each site in real time; it uses a model predictive control framework for rolling optimization and feedback correction; and it initiates an online rescheduling mechanism for large deviations to ensure the safe and stable operation of the system.

[0136] 7. Generate capacity report.

[0137] After the capacity control task is completed, the system automatically generates a comprehensive report containing the following:

[0138] Battery capacity test results and health status assessment at each site; statistical analysis of dispatch plan execution; power grid impact assessment and quantitative analysis of optimization effects; equipment operation anomaly records and maintenance recommendations.

[0139] Among them, a security mechanism design was also established.

[0140] Establish a multi-layered security protection system:

[0141] 1. Transport layer security: Employs TLS 1.3 protocol and supports forward secrecy;

[0142] 2. Identity Authentication: A two-way authentication mechanism based on X.509 digital certificates;

[0143] 3. Data integrity: SHA-256 hash algorithm and digital signature are used;

[0144] 4. Access Control: A permission management system based on the RBAC model.

[0145] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0146] Reference Figure 2 The diagram illustrates a remote capacity verification device for communication power supplies provided in an embodiment of the present invention, which may specifically include the following modules:

[0147] Data acquisition module 21 is used to acquire real-time data related to the battery packs of each station in the scheduling area;

[0148] The model update module 22 is used to update the preset battery digital twin model online according to the real-time data. The battery digital twin model includes an electrochemical model, a thermal model, and an aging model.

[0149] Prediction module 23 is used to predict the power of each site when performing the capacity test using the updated battery digital twin model, so as to obtain the power data of each site in the future scheduling cycle.

[0150] Objective function setting module 24 is used to construct an objective function for multi-site collaborative optimization with the scheduling status and power of the site as variables, and to set constraints.

[0151] The decision module 25 is used to substitute the power data into the objective function, solve the objective function based on the constraints, and determine the scheduling scheme based on the obtained decision.

[0152] Optionally, the electrochemical model is an improved Dennavi equivalent circuit model, the thermal model is a three-dimensional temperature field model established based on the finite element method, and the aging model is a capacity decay model based on preset aging parameters.

[0153] Optionally, the objective function setting module 24 is used for:

[0154] The predicted power of each station in each time period is obtained based on the power data;

[0155] Calculate the sum of the predicted power for all stations in all time periods to obtain the total predicted power;

[0156] The ratio of the total predicted power to the total number of time periods is calculated to obtain the global target average power;

[0157] A first optimization component is constructed for the objective function, which is configured to be the deviation between the total power of all stations and the global target average power in each time period within the scheduling cycle.

[0158] Optionally, the objective function setting module 24 is further configured to:

[0159] A second optimization component is constructed for the objective function, which is configured to calculate the magnitude of change in the scheduling status of each site over consecutive time periods.

[0160] Optionally, the expression for the objective function is:

[0161] ;

[0162] Where T is the total number of time steps, N is the number of stations, and P is the number of stations. i (t) represents the predicted power of station i in time period t, x i (t) represents the scheduling state, indicating whether site i performs capacity approval during time period t, x i The value of (t) is 0 or 1, λ is the smoothing coefficient, and P avg The global target average power is given.

[0163] Optionally, the constraints include task completion constraints, power grid security constraints, and equipment operation constraints;

[0164] The task completion constraint means that the capacity test for each site must be completed within the scheduling cycle;

[0165] The power grid security constraint means that, at any given time, the total power of all operating stations does not exceed the maximum power that the power grid can provide during that time period;

[0166] The device operation constraints indicate that each station can only be in one of two states: running or stopped, at any given time period.

[0167] Optionally, the remote capacity verification device for communication power supplies also includes an analysis module, which is used for:

[0168] After executing the scheduling scheme, obtain the corresponding execution result;

[0169] The scheduling scheme is evaluated based on the execution results.

[0170] The device of this invention adopts a "cloud-edge-device" collaborative architecture, comprising three layers: on-site core capacity device, edge computing nodes, and cloud-based digital twin platform.

[0171] Core hardware specifications of this device:

[0172] 1. Main control chip: STM32H743VIT6, supports double-precision floating-point operations, meeting the needs of complex algorithms.

[0173] 2. Data Acquisition: ADS131M088 channel synchronous sampling ΔΣ ADC, sampling rate 32kSPS, precision 24 bits.

[0174] 3. Power supply module: pre-amplifier PFC circuit (efficiency > 95%), post-amplifier bidirectional DC / DC converter (efficiency > 97%)

[0175] 4. Load Unit: Employs an IGBT + power resistor combination, with a maximum discharge current of 200A and supports dynamic adjustment.

[0176] 5. Communication Interface: Supports 4G / 5G wireless communication and Gigabit Ethernet, with dual-mode redundancy backup.

[0177] The present invention provides a remote capacity verification device for communication power supplies. By using the remote capacity verification device for communication power supplies, the various steps in the aforementioned embodiments of the remote capacity verification method for communication power supplies can be implemented.

[0178] It should be noted that the module division in the various communication power supply remote capacity devices provided in the above embodiments is illustrative and only represents one logical functional division. In actual implementation, other division methods may also be used. Furthermore, the functional modules in the various embodiments of this invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0179] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of the embodiments of the present invention can be embodied in the form of a computer program product, which is stored in a computer storage medium and includes several instructions to cause an electronic device or processor to execute all or part of the steps of the methods in the various embodiments of the present invention. The aforementioned computer storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0180] Furthermore, the communication power supply remote capacity verification device and the communication power supply remote capacity verification method provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0181] Reference Figure 3 The diagram illustrates an electronic device according to an embodiment of the present invention. Figure 3 As shown, the electronic device in this embodiment of the invention includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-described embodiment of the remote capacity verification method for communication power supplies. Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-described embodiment of the remote capacity verification device for communication power supplies.

[0182] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which can be used to describe the execution process of the computer program in the electronic device.

[0183] The electronic device may be a desktop computer, a cloud server, or other computing device. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 3 This is merely one example of an electronic device and does not constitute a limitation on the electronic device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0184] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0185] The memory can be an internal storage unit of the electronic device, such as a hard drive or RAM. Alternatively, it can be an external storage device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory can include both internal and external storage units. The memory is used to store the computer program and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output.

[0186] This invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the remote power supply verification method as described in the foregoing embodiments.

[0187] This invention also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the remote power supply capacity verification method as described in the foregoing embodiments.

[0188] This invention also discloses a computer program product that, when run on a computer, causes the computer to execute the remote power supply verification method described in the foregoing embodiments.

[0189] The embodiments described above are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for remote capacity verification of communication power supplies, characterized in that, include: Obtain real-time data related to the battery packs at each site in the dispatch area; The preset battery digital twin model is updated online based on the real-time data. The battery digital twin model includes an electrochemical model, a thermal model, and an aging model. Using the updated battery digital twin model, the power of each site during the capacity test is predicted to obtain the power data of each site in the future scheduling cycle; A multi-site collaborative optimization objective function is constructed using the scheduling status and power of the sites as variables, and constraints are set. The power data is substituted into the objective function, the objective function is solved based on the constraints, and the scheduling scheme is determined based on the obtained decision.

2. The method according to claim 1, characterized in that, The electrochemical model is an improved Denonwei equivalent circuit model, the thermal model is a three-dimensional temperature field model established based on the finite element method, and the aging model is a capacity decay model based on preset aging parameters.

3. The method according to claim 1, characterized in that, The objective function for multi-site collaborative optimization, which uses the site's decision as a variable and combines the power data, includes: The predicted power of each station in each time period is obtained based on the power data; Calculate the sum of the predicted power for all stations in all time periods to obtain the total predicted power; The ratio of the total predicted power to the total number of time periods is calculated to obtain the global target average power; A first optimization component is constructed for the objective function, which is configured to be the deviation between the total power of all stations and the global target average power in each time period within the scheduling cycle.

4. The method according to claim 3, characterized in that, Also includes: A second optimization component is constructed for the objective function, which is configured to calculate the magnitude of change in the scheduling status of each site over consecutive time periods.

5. The method according to claim 4, characterized in that, The expression for the objective function is: ； Where T is the total number of time steps, N is the number of stations, and P is the number of stations. i (t) represents the predicted power of station i in time period t, x i (t) represents the scheduling state, indicating whether site i performs capacity approval during time period t, x i The value of (t) is 0 or 1, λ is the smoothing coefficient, and P avg The global target average power is given.

6. The method according to any one of claims 1-5, characterized in that, The constraints include task completion constraints, power grid security constraints, and equipment operation constraints. The task completion constraint means that the capacity test for each site must be completed within the scheduling cycle; The power grid security constraint means that, at any given time, the total power of all operating stations does not exceed the maximum power that the power grid can provide during that time period; The device operation constraints indicate that each station can only be in one of two states: running or stopped, at any given time period.

7. The method according to any one of claims 1-5, characterized in that, Also includes: After executing the scheduling scheme, obtain the corresponding execution result; The scheduling scheme is evaluated based on the execution results.

8. A remote capacity verification device for communication power supplies, characterized in that, include: The data acquisition module is used to acquire real-time data related to the battery packs at various stations in the scheduling area; The model update module is used to update the preset battery digital twin model online based on the real-time data. The battery digital twin model includes an electrochemical model, a thermal model, and an aging model. The prediction module is used to predict the power of each site when performing the capacity test using the updated battery digital twin model, so as to obtain the power data of each site in the future scheduling cycle. The objective function setting module is used to construct an objective function for multi-site collaborative optimization using the scheduling status and power of the sites as variables, and to set constraints. The decision module is used to substitute the power data into the objective function, solve the objective function based on the constraints, and determine the scheduling scheme based on the obtained decision.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the remote capacity verification method for communication power supply as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the remote capacity verification method for communication power supply as described in any one of claims 1-7.

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