Energy storage system control method and device, computer device, readable storage medium and program product

Abstract

The application relates to an energy storage system control method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring meteorological prediction information, real-time meteorological information and a plurality of power control instructions configured for an energy storage system; determining a candidate power control instruction matched with the meteorological prediction information from the plurality of power control instructions based on the meteorological prediction information; selecting a target power control instruction meeting a control condition from the plurality of power control instructions based on the real-time meteorological information and the candidate power control instruction; and controlling the energy storage system to work according to the target power control instruction. The method can improve the system operation efficiency.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and in particular to a control method, apparatus, computer equipment, readable storage medium, and program product for an energy storage system. Background Technology

[0002] With the rapid development of smart grid technology, energy storage systems, as key devices for regulating power supply and demand balance and improving grid flexibility, are receiving increasing attention for their operational efficiency and reliability. Power control of energy storage systems needs to comprehensively consider the impact of meteorological conditions on both the generation and load sides in order to achieve the optimal charging and discharging strategy.

[0003] In traditional methods, power control of energy storage systems typically relies on fixed rules or simple threshold judgments, such as triggering charging and discharging commands based on a preset schedule or a single meteorological parameter. However, fixed rules cannot cover the multi-objective optimization needs in complex scenarios, which can easily lead to lag in the response of the energy storage system or overcharging and discharging, thereby reducing the system's operating efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a control method, device, computer equipment, readable storage medium, and program product for energy storage systems that can improve system operating efficiency in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a control method for an energy storage system, including:

[0006] Acquire meteorological forecast information, real-time meteorological information, and multiple power control commands configured for the energy storage system;

[0007] Based on the meteorological forecast information, a candidate power control command that matches the meteorological forecast information is determined from the plurality of power control commands;

[0008] Based on the real-time meteorological information and the candidate power control commands, a target power control command that meets the control conditions is selected from the plurality of power control commands;

[0009] The energy storage system is controlled to operate according to the target power control command.

[0010] In one embodiment, determining a candidate power control command that matches the weather forecast information from the plurality of power control commands based on the weather forecast information includes:

[0011] Based on the meteorological forecast information, scenario prediction is performed to obtain multiple power scenarios predicted for the energy storage system;

[0012] For each power control command, determine the overall cost of the power control command under each power scenario;

[0013] The power control command with the lowest overall cost is selected as the candidate power control command.

[0014] In one embodiment, the step of performing scenario prediction based on the meteorological forecast information to obtain multiple predicted power scenarios for the energy storage system includes:

[0015] Obtain a pre-trained conditional generative adversarial network model;

[0016] The meteorological forecast information is input into the conditional generative adversarial network model to obtain multiple power scenarios predicted for the energy storage system.

[0017] In one embodiment, selecting a target power control command that meets the control conditions from the plurality of power control commands based on the real-time meteorological information and the candidate power control commands includes:

[0018] Based on the real-time meteorological information, the aging cost of the energy storage system is calculated to determine the aging cost of the energy storage system.

[0019] Obtain the power fluctuation penalty information of the energy storage system;

[0020] For each of the power control commands, determine the command tracking error information between the power control command and the candidate power control command;

[0021] Based on the aging cost, the power fluctuation penalty information, and the instruction tracking error information, a target power control instruction that meets the control conditions is selected from the power control instructions.

[0022] In one embodiment, the step of calculating the aging cost of the energy storage system based on the real-time meteorological information to determine the aging cost of the energy storage system includes:

[0023] Based on the real-time meteorological information, an aging acceleration calculation is performed on the energy storage system to obtain the aging acceleration factor of the energy storage system.

[0024] The aging cost of the energy storage system is obtained by calculating the aging cost of the aging acceleration factor.

[0025] In one embodiment, the real-time meteorological information includes real-time current and surface temperature;

[0026] The step of performing aging acceleration calculations on the energy storage system based on the real-time meteorological information to obtain the aging acceleration factor of the energy storage system includes:

[0027] The real-time current and the surface temperature are fused to determine the estimated system core temperature of the energy storage system.

[0028] Based on the estimated core temperature and the real-time current, an aging acceleration calculation is performed to obtain the aging acceleration factor of the energy storage system.

[0029] In one embodiment, the weather forecast information refers to weather parameters predicted over a future period of time using weather models or historical data.

[0030] Secondly, this application also provides an energy storage system control device, comprising:

[0031] The information acquisition module is used to acquire meteorological forecast information, real-time meteorological information, and multiple power control commands configured for the energy storage system.

[0032] A control command matching module is used to determine, based on the meteorological forecast information, a candidate power control command that matches the meteorological forecast information from the plurality of power control commands;

[0033] The target power control command determination module is used to select a target power control command that meets the control conditions from the plurality of power control commands based on the real-time meteorological information and the candidate power control commands.

[0034] The control module is used to control the energy storage system to operate according to the target power control command.

[0035] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0036] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the method described above.

[0037] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described above.

[0038] The aforementioned energy storage system control method, device, computer equipment, readable storage medium, and program product achieve dynamic optimization of energy storage system power control commands by integrating meteorological forecast information and real-time meteorological data. First, based on meteorological forecast information, matching items are selected from multiple candidate commands, enabling the energy storage system to respond in advance to future meteorological trends and avoid control deviations caused by information lag. Second, the applicability of candidate commands is further verified by combining real-time meteorological data, ensuring that the control strategy is highly compatible with current environmental conditions, thereby improving the accuracy of system response. Finally, the target power control command selected through dual information verification can simultaneously consider economic benefits, equipment safety, and grid stability, significantly reducing the operational risks of the energy storage system caused by environmental fluctuations. This method effectively solves the problems of insufficient utilization of meteorological information and poor command adaptability in traditional control methods, providing technical support for the efficient operation of energy storage systems in complex meteorological scenarios and greatly improving system operating efficiency. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is an application environment diagram of an energy storage system control method in one embodiment;

[0041] Figure 2 This is a flowchart illustrating the energy storage system control method in one embodiment;

[0042] Figure 3 This is a flowchart illustrating the energy storage system control method in another embodiment;

[0043] Figure 4 This is a structural block diagram of an energy storage system control device in one embodiment;

[0044] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0046] The energy storage system control method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located on the cloud or other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Specifically, during the control of the energy storage system, the server 104 obtains meteorological forecast information, real-time meteorological information, and multiple power control commands configured for the energy storage system from the terminal 102; based on the meteorological forecast information, it determines candidate power control commands that match the meteorological forecast information from the multiple power control commands; based on the real-time meteorological information and candidate power control commands, it selects the target power control command that meets the control conditions from the multiple power control commands; and controls the energy storage system to work according to the target power control command.

[0047] In one exemplary embodiment, such as Figure 2 As shown, a control method for an energy storage system is provided, which can be applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S202 to S208. Wherein:

[0048] Step S202: Obtain meteorological forecast information, real-time meteorological information, and multiple power control commands configured for the energy storage system.

[0049] Meteorological forecast information refers to meteorological parameters predicted over a future period (e.g., the next 24 hours) using meteorological models or historical data. Examples include meteorological data, weather forecasts, light intensity, wind speed, temperature, and rainfall, used to predict the changing trends of the environment in which the energy storage system operates. Real-time meteorological information refers to current meteorological parameters collected in real-time by sensors or weather stations, reflecting the actual state of the environment in which the energy storage system is currently located. Power control commands are pre-configured operating rules used to control the charging and discharging power of the energy storage system, and may include power adjustment strategies under different meteorological conditions.

[0050] Specifically, the meteorological forecast information for the energy storage system obtained by the server can be an ultra-short-term forecast sequence. and meteorological data and forecasts Real-time meteorological information can include the real-time operating current and surface temperature of the energy storage system. Multiple power control commands configured for the energy storage system are pre-set. For example, the server can pre-configure multiple initial power control commands, and select matching power control commands from these initial commands based on the system characteristics of the energy storage system. Alternatively, multiple power control commands can be pre-matched directly for different energy storage systems.

[0051] Step S204: Based on meteorological forecast information, determine the candidate power control command that matches the meteorological forecast information from multiple power control commands.

[0052] Among them, candidate power control commands refer to power control commands that conform to meteorological forecast trends after preliminary screening, serving as the basis for subsequent real-time adjustments.

[0053] Specifically, the server needs to analyze key parameters in the weather forecast information and iterate through all power control commands to select those that match the predicted weather conditions. Optionally, the server can perform scenario prediction based on the weather forecast information to obtain multiple predicted power scenarios for the energy storage system. For each power control command, the server determines the comprehensive cost of the power control command under each power scenario and selects the power control command with the lowest comprehensive cost as the candidate power control command.

[0054] Optionally, the server can filter candidate power control commands based on their matching degree with predicted weather conditions. For example, if the predicted weather conditions are sunny, and a power control command specifies that "when the light intensity is >700W / m², the energy storage system charges at maximum power," then this command will be selected as a candidate command; while commands for "cloudy days" or "nighttime" will be excluded. This step narrows down the range of commands to be selected, avoids processing irrelevant commands during real-time adjustments, and improves control efficiency.

[0055] Step S206: Based on real-time meteorological information and candidate power control commands, select the target power control command that meets the control conditions from multiple power control commands.

[0056] The target power control command refers to the final selected command that simultaneously conforms to meteorological forecast trends and real-time environmental conditions, and is used to directly control the energy storage system. Control conditions refer to the constraints that the energy storage system must meet to operate, including equipment safety (e.g., battery temperature not exceeding a threshold), grid demand (e.g., power factor requirements), and economic benefits (e.g., peak-valley electricity price differences).

[0057] Specifically, the server combines real-time meteorological information and candidate power control commands to further verify the applicability of the commands. Optionally, the server can perform aging cost calculations on the energy storage system based on the real-time meteorological information to determine the aging cost of the energy storage system, obtain power fluctuation penalty information of the energy storage system, determine the command tracking error information between the power control command and the candidate power control commands for each power control command, and select a target power control command that meets the control conditions from the power control commands based on the aging cost, the power fluctuation penalty information, and the command tracking error information.

[0058] Optionally, the server can also select a target power control command that meets the control conditions from multiple power control commands based on the real-time characteristics of real-time meteorological information and the control information of candidate power control commands. For example, if the real-time battery temperature is close to the upper limit, even with sufficient sunlight, the "full charge" command should be excluded, and the command to "limit charging power to 80%" should be selected instead to meet safety conditions; if the grid requires the energy storage system to discharge to smooth load fluctuations, then commands that include a discharge strategy should be prioritized. Through comprehensive judgment of multiple conditions, the command that best meets the current needs is selected as the target command from the candidate commands.

[0059] Step S208: Control the energy storage system to operate according to the target power control command.

[0060] Specifically, the server needs to adjust the charging and discharging strategy of the energy storage system or coordinate the output power of the power conversion system according to the target power control command to ensure that the energy storage system operates as required by the target power control command. At the same time, the server can also continuously monitor the execution status. If deviations or anomalies occur, it needs to trigger a re-screening command or adjust the control parameters to form a closed-loop control process.

[0061] The aforementioned energy storage system control method achieves dynamic optimization of power control commands by integrating meteorological forecast information and real-time meteorological data. First, based on meteorological forecast information, matching items are selected from multiple candidate commands, enabling the energy storage system to respond in advance to future meteorological trends and avoid control deviations caused by information lag. Second, the applicability of candidate commands is further verified by combining real-time meteorological data, ensuring that the control strategy is highly compatible with current environmental conditions, thereby improving the accuracy of system response. Finally, the target power control command selected through dual information verification can simultaneously consider economic benefits, equipment safety, and grid stability, significantly reducing the operational risks of the energy storage system caused by environmental fluctuations. This method effectively solves the problems of insufficient utilization of meteorological information and poor command adaptability in traditional control methods, providing technical support for the efficient operation of energy storage systems in complex meteorological scenarios and greatly improving system operating efficiency.

[0062] In an exemplary embodiment, based on meteorological forecast information, candidate power control commands that match the meteorological forecast information are determined from multiple power control commands, including: performing scenario prediction based on the meteorological forecast information to obtain multiple power scenarios predicted for the energy storage system; determining the comprehensive cost of each power control command under each power scenario for each power control command; and determining the power control command with the lowest comprehensive cost as a candidate power control command.

[0063] Scenario prediction involves simulating the operating environment that the energy storage system may face over a future period based on meteorological forecasts (such as sunlight intensity, wind speed, and temperature), generating multiple possible power demand scenarios. A power scenario refers to the possible combinations of charging and discharging power that the energy storage system may experience under different meteorological conditions, such as a "high-sunlight, high-charging scenario" or a "low-sunlight, low-discharging scenario." The overall cost refers to the total cost that must be considered when executing power control commands under a specific power scenario, including electricity costs, equipment loss costs, and grid penalty costs.

[0064] Specifically, in this embodiment, the meteorological forecast information is an ultra-short-term forecast sequence. And the latest meteorological data and forecasts The server can first standardize Ft and Wt, and flatten and concatenate them into a conditional vector ct. The number of scenes to be generated, N (e.g., N=100), is set. For i=1 to N: a random noise vector is sampled from a standard normal distribution. Inputting zi and ct into the pre-trained generator G yields multiple generated power scenes. The generated scene set is obtained. Then, the power mean and standard deviation were used. For each generated scene, denormalize it to convert it back to the original power dimension:

[0065] At this point, {S1,S2,...,SN} is a set of renewable energy power scenarios that can be used for optimization and characterize the current prediction uncertainty. Each scenario Si is a power sequence of length T.

[0066] Next, assuming we are currently in a rolling optimization window, the optimization time domain is the future T time periods (e.g., T=16, representing the next 4 hours, with each time period lasting 15 minutes). Let t represent the time period index (t=1,2,...,T), and s represent the scenario index (s=1,2,...,N, generated by CGAN in claim 2). The decision variable is the charging and discharging power Pess(t) of the energy storage system in each time period (discharging is positive, charging is negative).

[0067] The objective function is the total cost J, where J is the expected value (average) of the sum of costs across all scenarios s and all time periods t, i.e., the comprehensive cost, expressed mathematically as follows:

[0068]

[0069] in, The mathematical expectation, in discrete scenarios, is approximated by averaging:

[0070]

[0071] in: For scenario-based expectation deviation assessment costs, Indicates revenue from ancillary services. This represents the estimated battery aging cost based on average current and average SOC.

[0072] 1) This is a scenario-based expected deviation assessment fee, designed to penalize the deviation between the actual total output power of the power station and the planned / contracted power in each scenario. The specific expression is:

[0073]

[0074] in: and These are the predicted values ​​of photovoltaic power and wind power under scenario s, respectively. and These represent the output power of energy storage during time period t and the planned power generation power or market contract power of the power station, respectively.

[0075] is the linear deviation penalty coefficient, representing a penalty resource of one megawatt per deviation. is the quadratic deviation penalty coefficient; the quadratic term is introduced to impose a more severe penalty on large deviations, enhancing the robustness of the control. Typically... This encourages energy storage systems to charge and discharge to mitigate fluctuations in new energy forecasts, ensuring that total output power is as close as possible to the planned value in all possible scenarios.

[0076] 2) This represents ancillary service revenue, intended to quantify the resource gains that energy storage obtains through participation in ancillary service markets such as frequency regulation and reserve. This is a negative cost (i.e., resource gain). Taking spinning reserve service as an example:

[0077]

[0078] in: Let be the decision variable, representing the energy storage capacity reserved for ancillary services during time period t. This variable must satisfy . i.e., operating power With reserve capacity The sum cannot exceed the maximum energy storage capacity. ; The price for reserve capacity represents the resources paid for reserved capacity. The price of standby energy reflects the electrical energy resources paid when standby is actually used. The energy expected to be called up. In scenario s, the actual power used in time period t may depend on system state (such as frequency deviation).

[0079] 3) This is a battery aging cost estimate based on average current and average SOC, with the goal of monetizing battery cycle aging losses, avoiding overcharging and discharging, and extending battery life. The calculation follows these steps:

[0080] Step 1: Calculate the average current ;

[0081]

[0082] in: Indicates the rated voltage of the energy storage battery pack; The charge / discharge efficiency is a function of power.

[0083] Step 2: Calculate the average state of charge ;

[0084] First, the SOC trajectory is calculated based on the energy conservation model.

[0085]

[0086] in: Let SOC be the initial value at the beginning of the time period, and let SOC be a known quantity. The duration of the time period (usually 15 minutes); This refers to the rated capacity of the energy storage system. Indicates the overall efficiency of the charge-discharge cycle, and Similarly, it can also be expressed as a function of power.

[0087] Then, calculate the average SOC within a short time window to smooth out the impact of instantaneous fluctuations on aging. For example, use the arithmetic mean of the previous and current time periods:

[0088]

[0089] Step 3: Establish an aging cost model. Aging costs are directly proportional to the energy throughput and aging acceleration factors.

[0090]

[0091] in: This is the battery aging cost coefficient; This represents the absolute value of energy throughput within time period t; The aging acceleration factor is a coefficient greater than or equal to 1, representing the rate of aging relative to ideal conditions at a specific current and state of charge (SOC). Its expression is typically an empirical function, usually obtained by fitting battery cycle life test data.

[0092] It can be a polynomial model:

[0093] Or an exponential model:

[0094] in: These are parameters fitted from experimental data. The characteristics of this function are: The larger the value (higher rate charge / discharge), the larger the AF. The greater the deviation from 50% (too high or too low), the larger the AF usually is.

[0095] After determining the overall cost of each power control command in various power scenarios using the above method, the power control command with the lowest overall cost can be identified as the candidate power control command.

[0096] In this embodiment, by using scenario prediction and comprehensive cost assessment, candidate instructions can be matched with future weather conditions in advance, avoiding the failure of control strategies due to sudden weather changes, while balancing economy and equipment lifespan and reducing operational risks.

[0097] In an exemplary embodiment, scenario prediction is performed based on meteorological forecast information to obtain multiple power scenarios predicted for the energy storage system, including: obtaining a pre-trained conditional generative adversarial network model; inputting meteorological forecast information into the conditional generative adversarial network model to obtain multiple power scenarios predicted for the energy storage system.

[0098] The Conditional Generative Adversarial Network (CGAN) is a deep learning model consisting of a generator and a discriminator. Through adversarial training, it generates realistic data samples to simulate power scenarios under complex weather conditions. The input data for the CGAN model is weather forecast information, which serves as its input features. The output data consists of multiple generated power scenarios, each containing the predicted charging and discharging power of the energy storage system at different time points.

[0099] Specifically, this embodiment uses a pre-trained CGAN model to generate power scenarios. First, meteorological forecast information is input into the generator of the CGAN model. Based on the relationship between historical meteorological and power data, the generator outputs multiple possible power scenarios. The discriminator then evaluates the realism of the generated scenarios and iteratively optimizes them to approximate the actual possible power distribution. The final output scenario set covers the uncertainties of meteorological forecasts, providing a more comprehensive reference for subsequent instruction selection.

[0100] Furthermore, in this embodiment, historical data is primarily used to train a CGAN, enabling its generator to learn the conditional probability distribution. Where X is the actual power sequence and C is the conditional information. The training process of the CGAN model includes the following steps:

[0101] First, historical ultra-short-term power forecast data, historical actual power data, and historical meteorological data were collected. The specific data are as follows: This is a historical ultra-short-term power prediction data series with a time resolution of [missing information]. (Usually 15 minutes). The data sequence length is T. ,in This represents a prediction of power in the future time period t. This corresponds to the historical actual power data sequence. ,in This represents the historical actual power data for time period t. For the corresponding historical meteorological data, including wind speed ,wind direction Irradiance ,temperature Etc. Each sample is ,in It is the meteorological vector at time t, represented as .

[0102] Next, construct the condition vector C and the target vector X. Align and concatenate the predicted sequence and the meteorological sequence along the time dimension. For each sample i, its condition Ci is a high-dimensional vector, flattened from Fi and Wi:

[0103]

[0104] at this time, Dimensions for: 4 represents the four selected meteorological variables. The objective is the actual power sequence Xi, and its dimension... To prevent excessively large numerical differences from causing training difficulties, Z-score normalization is applied to all features. For power data... (Including values ​​in F and X):

[0105] in: The mean of the power data in the training set; The standard deviation of the power data in the training set.

[0106] For meteorological data m:

[0107] in: and These correspond to the mean and standard deviation of the meteorological variables on the training set, respectively.

[0108] Next, the CGAN model structure is defined. CGAN contains two core neural networks: a generator G and a discriminator D.

[0109] First, construct a generator G(z,c), where z is random noise sampled from a standard normal distribution. Its dimension is N. To ensure the diversity of generated scenes, N can generally be chosen as 100. The condition vector c is Ci after standardization and flattening. Concatenate z and c: inputG=[z,c], and perform upsampling and nonlinear transformation through multiple fully connected layers. A standardized power scene can then be obtained. .

[0110] Secondly, a discriminator D(x,c) is constructed, where x is the input power sequence with dimension T, which can be real data X or generated data. The conditional vector is c, with dimension dim(c). x and c are concatenated: inputD=[x,c], and features are extracted through multiple fully connected layers. This yields a scalar. And there are , which represents the probability that the discriminator considers the input sequence x to be "true" under given condition c.

[0111] Then, CGAN is trained with the objective of a binary minimax game, whose value function... for:

[0112]

[0113] in: This represents the expectation of the actual data distribution; This represents the expectation of the prior distribution of noise.

[0114] The mini-batch stochastic gradient descent training algorithm involves the following two processes for each training iteration:

[0115] Process 1: Under the premise of fixing G, train the discriminator D, which includes the following steps: draw a mini-batch of real sample pairs of size m from the training set. From the noise prior Sample m noise vectors Fake samples are generated using a generator: Calculate the loss function LD of the discriminator: .

[0116] Process 2: Under the premise of fixing D, train the generator G, which includes the following steps: start from the noise prior again. Sample m noise vectors Calculate the generator's loss function LG: Repeat the above steps until the model converges.

[0117] In this embodiment, the power scenario generated by the CGAN model can capture the complex nonlinear relationships in weather forecasts, improve scenario diversity, make the candidate instruction selection closer to the actual operating conditions, and enhance the robustness of the control strategy.

[0118] In an exemplary embodiment, based on real-time meteorological information and candidate power control commands, a target power control command that meets the control conditions is selected from multiple power control commands. This includes: calculating the aging cost of the energy storage system based on real-time meteorological information to determine the aging cost of the energy storage system; obtaining power fluctuation penalty information of the energy storage system; determining the command tracking error information between the power control command and the candidate power control commands for each power control command; and selecting the target power control command that meets the control conditions from each power control command based on the aging cost, power fluctuation penalty information, and command tracking error information.

[0119] Among these, aging cost is the economic cost of equipment lifespan loss caused by real-time operating conditions (such as current and temperature) in the energy storage system. Power fluctuation penalty information is the penalty cost imposed by the grid on the output power fluctuations of the energy storage system (such as frequent charge-discharge switching), reflecting the grid stability requirements. Command tracking error information is the deviation between the current power control command and the candidate power control command, measuring the smoothness of command switching.

[0120] Specifically, in real-time optimization control, for each power control command All of these require instantaneous assessment of the resulting aging costs. The basic form of the cost item remains the throughput energy multiplied by an aging acceleration factor and a cost coefficient, as shown below:

[0121]

[0122] The aging acceleration factor AF is defined as a function that includes electrical stress and thermal stress, and the modeling process is as follows:

[0123] (1) Modeling of aging acceleration factor AF:

[0124] Step 1: Determine the basic aging model based on electro-thermal stress. The Arrhenius-power law equation, which is widely used in battery life modeling, is adopted as the basis. This equation can simultaneously capture the strong influence of current (rate) and temperature on the aging rate.

[0125]

[0126] in: The pre-aging factor or aging rate benchmark value. It represents the theoretical aging rate at a reference current and infinite temperature. It is a scaling factor obtained by fitting a large amount of aging experimental data; This refers to the real-time operating current. This is the reference current.

[0127] n is a dimensionless power-law exponent, representing the sensitivity of the aging rate to the current (rate). n > 0; the larger the value, the more severe the damage to lifespan caused by high-current operation. The apparent activation energy (Ea) reflects the energy barrier height that dominates the aging process and is a key parameter for measuring the temperature sensitivity of aging. The larger the Ea value, the more sensitive the aging rate is to temperature. For the ideal gas constant; This provides a real-time estimated temperature inside the battery cell. This temperature value is not directly measured, but rather estimated online using the electrothermal coupling reduced-order model and state estimation algorithm (such as Kalman filtering) described in claim 5. It serves as a bridge between this model and thermal safety constraints.

[0128] Step 2: Introduce a SOC correction factor. Since the aging rate is also affected by the SOC operating window (e.g., prolonged operation at extremely high or low SOC will accelerate aging), a SOC correction factor needs to be introduced into the basic model. .

[0129]

[0130] A common form can be a quadratic function:

[0131]

[0132] in: For reference SOC, it is usually set to 50%, which is the point where aging is slowest. The SOC stress coefficient (dimensionless) is obtained by fitting experimental data.

[0133] Step 3: Introduce a State of Health (SOH) degradation factor. To make the model applicable throughout the battery's entire lifespan, a degradation factor related to SOH can be introduced. This indicates that batteries may become more sensitive to stress after aging.

[0134]

[0135] It can be a simple linear function, such as , where α is the fitting parameter.

[0136] (2) Battery aging cost coefficient Calibration.

[0137] It is a key coefficient that converts physical aging into monetary costs, and its expression is as follows: ,in: This indicates the initial capital cost of battery assets, including battery cells, BMS, PCS, etc. This represents the total energy throughput expected of the battery before the end of its lifespan, a value that can be obtained through accelerated aging tests and model predictions.

[0138] (3) Within each real-time control cycle (e.g., seconds), the optimization algorithm performs the following steps to evaluate aging costs:

[0139] 1) Input the candidate energy storage power command obtained from the optimization algorithm. Input the real-time operating current I, input the current SOC estimated by the ampere-hour integration method or Kalman filtering, and input the core temperature estimated by the thermal model of claim 5. Online estimates are available.

[0140] 2) Calculate the aging acceleration factor AF, and then calculate the instantaneous aging cost. .

[0141] 3) This The total objective function for real-time optimization is formed by adding factors such as command tracking error and power fluctuation penalties. The optimizer automatically selects the optimal power command that best protects battery life by weighing these costs. .

[0142] Furthermore, the thermal safety constraint is based on an electrothermal coupling reduced-order model and a Kalman filter algorithm, which estimates the highest internal temperature of the battery online and constrains it to be less than a set safety threshold.

[0143] Offline preparation is possible—establishment and parameter identification of the electrothermal coupling reduced-order model. The goal of this step is to obtain a state-space model that accurately describes the heat generation and heat transfer dynamics of the battery while minimizing computational complexity. Specifically, it consists of the following steps: using a second-order RC equivalent thermal network, the state-space equations of the model are established, achieving a good balance between accuracy and computational complexity. The continuous-time state-space equations are as follows:

[0144]

[0145] in: Since the core temperature of the battery cell cannot be directly measured, it is a state variable to be estimated. This allows for direct measurement of the cell surface temperature using a temperature sensor. The measurable ambient temperature; Thermal resistance from core to surface; Core heat capacity; The thermal resistance from the surface to the environment; Surface heat capacity; This is the heat generated inside the battery.

[0146] When used in digital control, it is discretized using the zero-order hold method, with a discretization time step of [value missing]. (Consistent with the real-time control cycle). The discrete state-space equations are obtained:

[0147]

[0148] Where: state vector Input vector Output vector (observations) , , and All of these are discretized system matrices.

[0149] Using the simplified Bernardi heat production equation, neglecting the reversible entropy heat term:

[0150]

[0151] in: The current is in real time (A). ; Terminal voltage; This is the open-circuit voltage, a function of SOC (calibrated experimentally). It is the battery internal resistance, SOC, and average temperature. The function is calibrated as a two-dimensional lookup table by electrochemical impedance spectroscopy or pulse testing.

[0152] Then, the thermal model parameters were fitted using experimental data: a known power pulse (or actual charge / discharge conditions) was applied to the battery, and high-precision sensors were used to record the data. and The changes are observed, and the surface temperature field is measured using a thermal imager. Then, the thermal parameters are adjusted using the system identification toolbox or optimization algorithm. , , and This makes the model output The predicted curve matches the experimental measurement curve best.

[0153] Then, in each control cycle, the model predictions and temperature sensor measurements are fused to obtain the optimal result. The estimation consists of the following steps: First, an extended Kalman filter is constructed. Since the heat generation model or thermal model may be nonlinear, an extended Kalman filter is used here.

[0154] Equations of state: ,in It is process noise.

[0155] Observation equation: ,in It is observation noise.

[0156] Noise assumption: , , and These are the covariance matrices for process noise and observation noise, respectively, and require parameter tuning.

[0157] Furthermore, the implementation of the EKF recursive algorithm (executed in each control cycle) consists of the following steps:

[0158] First, calculate the Jacobian matrix:

[0159] Secondly, prior state estimation is performed:

[0160] Finally, prior error covariance estimation is performed:

[0161] On the other hand, firstly, calculate the Kalman gain:

[0162] Secondly, obtain the current surface temperature sensor measurement value.

[0163] Finally, posterior state estimation (data fusion) is performed:

[0164] At this point, the estimated system kernel temperature can be obtained: The updated post-hoc error covariance is .

[0165] In real-time optimization control, thermal safety constraints are applied according to the following steps: First, an absolute safety temperature threshold is set. This value is based on the battery chemistry system and is typically provided by the cell manufacturer, with a margin. The estimated temperature is then transformed into a constraint, adding a nonlinear inequality constraint regarding the future temperature to the real-time optimization problem of S2. A single-step prediction method is used, based on the current state... And candidate control variables, predict the next core package temperature: ,in, This is the state transition function of the thermal model. The constraints are: ,in, This is a safety margin used to handle estimation errors and dynamic uncertainties. Then, the real-time optimization controller solves for the optimal power command. At this time, the above temperature constraints will be strictly guaranteed. The solver (such as IPOPT (Interior Point OPTimizer) or Sequential Quadratic Programming (SQP)) will evaluate whether each candidate solution will cause the predicted temperature to exceed the limit. If so, the solution will be eliminated, and finally an optimal instruction will be found that satisfies the tracking and smoothing objectives while absolutely ensuring that the internal temperature of the battery is within a safe range. Finally, the nonlinear objective function and constraints in the real-time optimization control model are linearized or quadratically approximated at the current operating point, transforming it into a standard quadratic programming or linear programming problem.

[0166] For example, fast solution is achieved by calling the high-efficiency convex optimization solver pre-built in the embedded system (such as OSQP (Operator Splitting Quadratic Program) or qpOASES (Online Active Set Strategy Implementation)) to ensure that the computation delay meets the time limit requirements of real-time control.

[0167] In this embodiment, through multi-objective optimization (aging cost, power grid stability, command smoothness), the target command can dynamically adapt to the real-time environment, avoiding short-term economic optimization from damaging equipment life or power grid stability, and maximizing long-term operating benefits.

[0168] In an exemplary embodiment, the aging cost of the energy storage system is calculated based on real-time meteorological information to determine the aging cost of the energy storage system, including: performing an aging acceleration calculation on the energy storage system based on real-time meteorological information to obtain an aging acceleration factor for the energy storage system; and performing an aging cost calculation on the aging acceleration factor to obtain the aging cost of the energy storage system.

[0169] The aging acceleration factor AF is defined as a function that includes electrical stress and thermal stress, and the modeling process is as follows:

[0170] First, a basic aging model based on electro-thermal stress is established. The Arrhenius-power law equation, which is widely used in battery life modeling, is adopted as the basis. This equation can simultaneously capture the strong influence of current (rate) and temperature on the aging rate.

[0171]

[0172] in: The pre-aging factor or aging rate benchmark value. It represents the theoretical aging rate at a reference current and infinite temperature. It is a scaling factor obtained by fitting a large amount of aging experimental data; This refers to the real-time operating current. This is the reference current.

[0173] n is a dimensionless power-law exponent, representing the sensitivity of the aging rate to the current (rate). n > 0; the larger the value, the more severe the damage to lifespan caused by high-current operation. The apparent activation energy (Ea) reflects the energy barrier height that dominates the aging process and is a key parameter for measuring the temperature sensitivity of aging. The larger the Ea value, the more sensitive the aging rate is to temperature. For the ideal gas constant; This provides a real-time estimated temperature inside the battery cell. This temperature value is not directly measured, but rather estimated online using a reduced-order electrothermal coupling model and a state estimation algorithm (such as Kalman filtering). It serves as a bridge between this model and thermal safety constraints. A State of Charge (SOC) correction factor is introduced. Since the aging rate is also affected by the SOC operating window (e.g., long-term operation at extremely high or low SOCs accelerates aging), a SOC correction factor needs to be introduced into the basic model. .

[0174]

[0175] A common form can be a quadratic function:

[0176]

[0177] in: For reference SOC, it is usually set to 50%, which is the point where aging is slowest. The SOC stress coefficient (dimensionless) is obtained by fitting experimental data.

[0178] Next, a State of Health (SOH) degradation factor is introduced. To make the model applicable throughout the entire battery lifespan, a degradation factor related to SOH can be introduced. This indicates that batteries may become more sensitive to stress after aging.

[0179]

[0180] It can be a simple linear function, such as , where α is the fitting parameter.

[0181] After obtaining the aging acceleration factor, the aging cost can be calculated by applying the aging acceleration factor according to the method in the above embodiment to obtain the aging cost of the energy storage system, which will not be elaborated here.

[0182] In this embodiment, by quantifying real-time aging costs, the control strategy can proactively avoid high-loss operating conditions (such as high-current charging and discharging at high temperatures), extend equipment lifespan, and reduce the total lifespan maintenance cost.

[0183] In an exemplary embodiment, the real-time meteorological information includes real-time current and surface temperature; the aging acceleration calculation of the energy storage system based on the real-time meteorological information to obtain the aging acceleration factor of the energy storage system includes: data fusion of real-time current and surface temperature to determine the estimated value of the system core temperature of the energy storage system; and aging acceleration calculation based on the estimated core temperature and real-time current to obtain the aging acceleration factor of the energy storage system.

[0184] Specifically, in each control cycle, the system core temperature estimate is obtained by fusing model predictions and temperature sensor measurements. The process consists of the following steps: First, an extended Kalman filter is constructed. Since the heat generation model or thermal model may be nonlinear, an extended Kalman filter is used here.

[0185] Equations of state: ,in It is process noise.

[0186] Observation equation: ,in It is observation noise.

[0187] Noise assumption: , , and These are the covariance matrices for process noise and observation noise, respectively, and require parameter tuning.

[0188] The implementation of the EKF (Extended Kalman Filter Recursive Algorithm) recursive algorithm (executed in each control cycle) consists of the following steps:

[0189] a. Prediction step.

[0190] First, calculate the Jacobian matrix:

[0191] Secondly, prior state estimation is performed:

[0192] Finally, prior error covariance estimation is performed:

[0193] b. Update step.

[0194] First, calculate the Kalman gain:

[0195] Secondly, obtain the current surface temperature sensor measurement value.

[0196] Finally, posterior state estimation (data fusion) is performed:

[0197] At this point, the estimated system kernel temperature can be obtained: The updated post-hoc error covariance is .

[0198] After obtaining the estimated core temperature of the system, the aging acceleration calculation can be performed based on the estimated core temperature and real-time current according to the method described in the above embodiment to obtain the aging acceleration factor of the energy storage system, which will not be elaborated here.

[0199] In this embodiment, by accurately estimating the core temperature and quantifying the current-temperature coupling effect, the calculation of the aging acceleration factor is closer to the actual loss mechanism, providing a reliable basis for aging cost assessment and improving the accuracy of the control strategy.

[0200] In one embodiment, weather forecast information refers to weather parameters predicted over a future period of time using weather models or historical data.

[0201] In a specific embodiment, such as Figure 3 As shown, a system control method is also provided, including:

[0202] Step S301: Obtain meteorological forecast information, real-time meteorological information, and multiple power control commands configured for the energy storage system.

[0203] Step S302: Obtain the pre-trained conditional generative adversarial network model;

[0204] Step S303: Input meteorological forecast information into the conditions to generate an adversarial network model and obtain multiple power scenarios for the energy storage system.

[0205] Step S304: For each power control command, determine the comprehensive cost of the power control command under each power scenario;

[0206] Step S305: The power control command with the lowest overall cost is determined as the candidate power control command;

[0207] The real-time meteorological information includes real-time current and surface temperature;

[0208] Step S306: Perform data fusion on real-time current and surface temperature to determine the estimated value of the system core temperature of the energy storage system;

[0209] Step S307: Based on the estimated core temperature and real-time current, perform aging acceleration calculations to obtain the aging acceleration factor of the energy storage system.

[0210] Step S308: Calculate the aging cost of the aging acceleration factor to obtain the aging cost of the energy storage system.

[0211] Step S309: Obtain power fluctuation penalty information of the energy storage system;

[0212] Step S310: For each power control command, determine the command tracking error information between the power control command and the candidate power control command;

[0213] Step S311: Based on aging cost, power fluctuation penalty information and tracking error information of each instruction, select the target power control instruction that meets the control conditions from each power control instruction;

[0214] Step S312: Control the energy storage system to work according to the target power control command.

[0215] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0216] Based on the same inventive concept, this application also provides an energy storage system control device for implementing the energy storage system control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more energy storage system control device embodiments provided below can be found in the limitations of the energy storage system control method described above, and will not be repeated here.

[0217] In one exemplary embodiment, such as Figure 4 As shown, an energy storage system control device 400 is provided, including: an information acquisition module 402, a control command matching module 404, a target power control command determination module 406, and a control module 408, wherein:

[0218] The information acquisition module 402 is used to acquire meteorological forecast information, real-time meteorological information, and multiple power control commands configured for the energy storage system.

[0219] The control command matching module 404 is used to determine, based on meteorological forecast information, a candidate power control command that matches the meteorological forecast information from multiple power control commands.

[0220] The target power control command determination module 406 is used to select a target power control command that meets the control conditions from multiple power control commands based on real-time meteorological information and candidate power control commands.

[0221] The control module 408 is used to control the operation of the energy storage system according to the target power control command.

[0222] In one exemplary embodiment, the control command matching module 404 includes:

[0223] The scenario prediction unit is used to predict scenarios based on meteorological forecast information to obtain multiple power scenarios for the energy storage system.

[0224] The comprehensive cost determination unit is used to determine the comprehensive cost of each power control command under various power scenarios.

[0225] The candidate power control command determination unit is used to determine the power control command with the lowest overall cost as the candidate power control command.

[0226] In one exemplary embodiment, the scene prediction unit is specifically used for:

[0227] Obtain a pre-trained conditional generative adversarial network model;

[0228] By inputting meteorological forecast information into a generative adversarial network model, multiple power scenarios for energy storage system predictions are obtained.

[0229] In one exemplary embodiment, the target power control command determination module 406 includes:

[0230] The aging cost determination unit is used to calculate the aging cost of the energy storage system based on real-time meteorological information and determine the aging cost of the energy storage system.

[0231] The power fluctuation penalty information acquisition unit is used to acquire power fluctuation penalty information of the energy storage system.

[0232] The instruction tracking error information determination unit is used to determine the instruction tracking error information between the power control instruction and the candidate power control instruction for each power control instruction.

[0233] The target power control command determination unit is used to select the target power control command that meets the control conditions from each power control command based on aging cost, power fluctuation penalty information and tracking error information of each command.

[0234] In one exemplary embodiment, the aging cost determination unit includes:

[0235] The aging acceleration factor determination component is used to perform aging acceleration calculations on the energy storage system based on real-time meteorological information to obtain the aging acceleration factor of the energy storage system.

[0236] The aging cost calculation component is used to calculate the aging cost of the energy storage system based on the aging acceleration factor.

[0237] In one exemplary embodiment, the real-time meteorological information includes real-time current and surface temperature. In this embodiment, the aging acceleration factor determination component is specifically used for:

[0238] Based on real-time meteorological information, aging acceleration calculations are performed on the energy storage system to obtain the aging acceleration factor of the energy storage system, including:

[0239] By fusing real-time current and surface temperature data, the estimated core temperature of the energy storage system is determined.

[0240] The aging acceleration factor of the energy storage system is obtained by performing aging acceleration calculations based on the core temperature estimate and real-time current.

[0241] Each module in the aforementioned energy storage system control device 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 in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0242] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements an energy storage system control method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0243] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0244] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0245] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0246] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0247] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0248] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0249] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0250] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A control method for an energy storage system, characterized in that, The method includes: Acquire meteorological forecast information, real-time meteorological information, and multiple power control commands configured for the energy storage system; Based on the meteorological forecast information, a candidate power control command that matches the meteorological forecast information is determined from the plurality of power control commands; Based on the real-time meteorological information and the candidate power control commands, a target power control command that meets the control conditions is selected from the plurality of power control commands; The energy storage system is controlled to operate according to the target power control command.

2. The method according to claim 1, characterized in that, The step of determining a candidate power control command that matches the weather forecast information from the plurality of power control commands based on the weather forecast information includes: Based on the meteorological forecast information, scenario prediction is performed to obtain multiple power scenarios predicted for the energy storage system; For each power control command, determine the overall cost of the power control command under each power scenario; The power control command with the lowest overall cost is selected as the candidate power control command.

3. The method according to claim 2, characterized in that, The process of performing scenario prediction based on the meteorological forecast information yields multiple predicted power scenarios for the energy storage system, including: Obtain a pre-trained conditional generative adversarial network model; The meteorological forecast information is input into the conditional generative adversarial network model to obtain multiple power scenarios predicted for the energy storage system.

4. The method according to claim 1, characterized in that, The step of selecting a target power control command that meets the control conditions from the plurality of power control commands based on the real-time meteorological information and the candidate power control commands includes: Based on the real-time meteorological information, the aging cost of the energy storage system is calculated to determine the aging cost of the energy storage system. Obtain the power fluctuation penalty information of the energy storage system; For each of the power control commands, determine the command tracking error information between the power control command and the candidate power control command; Based on the aging cost, the power fluctuation penalty information, and the instruction tracking error information, a target power control instruction that meets the control conditions is selected from the power control instructions.

5. The method according to claim 4, characterized in that, The step of calculating the aging cost of the energy storage system based on the real-time meteorological information to determine the aging cost of the energy storage system includes: Based on the real-time meteorological information, an aging acceleration calculation is performed on the energy storage system to obtain the aging acceleration factor of the energy storage system. The aging cost of the energy storage system is obtained by calculating the aging cost of the aging acceleration factor.

6. The method according to claim 5, characterized in that, The real-time meteorological information includes real-time current and surface temperature; The step of performing aging acceleration calculations on the energy storage system based on the real-time meteorological information to obtain the aging acceleration factor of the energy storage system includes: The real-time current and the surface temperature are fused to determine the estimated system core temperature of the energy storage system. Based on the estimated core temperature and the real-time current, an aging acceleration calculation is performed to obtain the aging acceleration factor of the energy storage system.

7. The method according to claim 1, characterized in that, The meteorological forecast information refers to meteorological parameters predicted for a future period of time based on meteorological models or historical data.

8. A control device for an energy storage system, characterized in that, The device includes: The information acquisition module is used to acquire meteorological forecast information, real-time meteorological information, and multiple power control commands configured for the energy storage system. A control command matching module is used to determine, based on the meteorological forecast information, a candidate power control command that matches the meteorological forecast information from the plurality of power control commands; The target power control command determination module is used to select a target power control command that meets the control conditions from the plurality of power control commands based on the real-time meteorological information and the candidate power control commands. The control module is used to control the energy storage system to operate according to the target power control command.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images