A method for self-adaptive control of charge and discharge depth of energy storage for power grid peak regulation

Abstract

The application discloses a kind of energy storage charge-discharge depth self-adaptive control methods for power grid peak shaving.The method comprises the following steps: collecting the operating state data of target energy storage system at discrete control time, determining initial operating state vector;Determine target peak shaving gap, dischargeable depth upper limit and chargeable depth upper limit;Based on initial operating state vector, target peak shaving gap, dischargeable depth upper limit and chargeable depth upper limit, determine target charge-discharge depth;Based on target charge-discharge depth, determine target power instruction, determine the actual execution power of energy storage converter;Update the battery state of charge and battery temperature corresponding to target energy storage system, and calculate the life loss increment, feed back the life loss increment to the life impairment coefficient of next control time;Dynamically adjust the weight of target depth optimization function and dynamic state of charge boundary.The application can realize energy storage charge-discharge depth self-adaptive control, meet the efficient control demand of energy storage system in peak shaving scenario.

Description

Technical Field

[0001] This invention relates to the field of energy storage system operation control technology, and in particular to an adaptive control method for energy storage charge and discharge depth oriented towards grid peak shaving. Background Technology

[0002] As the proportion of renewable energy generation continues to increase, the peak-to-valley difference in the power grid's net load curve is widening, and complex operating characteristics such as steep rises and falls, as well as persistent high loads, may occur in certain periods. Energy storage systems, due to their fast response speed, high regulation accuracy, and flexible deployment, have become one of the important technical means for power grid peak shaving.

[0003] Currently, traditional energy storage peak-shaving control methods generally employ fixed upper and lower limits of state of charge, fixed charge / discharge depth thresholds, or rigid execution methods based on daily plans. For example, they simply set the energy storage system to operate at a fixed discharge depth during peak hours and a fixed charge depth during off-peak hours based on experience. However, traditional energy storage peak-shaving control methods are highly dependent on human experience, and the formulation of peak-shaving control strategies is fragmented and lagging, making it difficult to adapt to the dynamic changes in grid peak-shaving demand. This can easily lead to excessive charging and discharging under high load or high temperature scenarios, shortening battery life. Consequently, the objectivity, economy, and robustness of energy storage peak-shaving operation are low, failing to meet the safety, economy, and efficiency control requirements of energy storage systems in peak-shaving scenarios. Summary of the Invention

[0004] This invention provides an adaptive control method for the charge and discharge depth of energy storage for grid peak shaving, which achieves efficient adaptive control of the charge and discharge depth of energy storage for grid peak shaving, reduces reliance on human experience, can adapt to the dynamic changes in grid peak shaving demand, avoids excessive charge and discharge under high load or high temperature scenarios, improves battery life, and thus ensures the objectivity, economy and robustness of energy storage peak shaving operation, meeting the safety, economy and efficiency control requirements of energy storage systems in peak shaving scenarios.

[0005] According to one aspect of the present invention, an adaptive control method for the charging and discharging depth of energy storage for grid peak shaving is provided, the method comprising: At discrete control moments, the operating status data of the target energy storage system is collected, and based on the operating status data, an original operating status vector is generated. The original operating status vector is then normalized to generate an initial operating status vector. Based on the deviation between the net load power in the station and the target smooth curve in the initial operating state vector, the target peak-shaving gap is determined, and the upper limit of the discharge depth and the upper limit of the recharge depth are determined according to the rolling peak-shaving energy index, dynamic state of charge boundary, lifetime constraint and temperature constraint in the future prediction window. Based on the initial operating state vector, the target peak-shaving gap, the upper limit of the discharge depth, and the upper limit of the charge depth, the target depth optimization function is solved to determine the target charge-discharge depth that minimizes the target depth optimization function. The target depth optimization function is obtained by weighted summation of multiple sub-objective functions, including: peak-shaving tracking sub-objective function, retention capability sub-objective function, lifetime loss sub-objective function, and temperature risk sub-objective function. Based on the target charge and discharge depth, the target power command is determined, and the target power command is subject to power ramping constraints and saturation limiting to generate the actual execution power of the energy storage converter. The battery state of charge and battery temperature of the target energy storage system are updated according to the actual power of execution, and the lifetime loss increment is calculated. The lifetime loss increment is then fed back to the lifetime loss coefficient at the next control moment. Based on the peak-shaving effect evaluation index within the preset sliding window, the weights of the target depth optimization function and the dynamic charge state boundary are dynamically adjusted to achieve online updates of control parameters.

[0006] According to another aspect of the present invention, an adaptive control device for the charging and discharging depth of energy storage for grid peak shaving is provided, the device comprising: The state vector generation module is used to collect the operating state data of the target energy storage system at discrete control moments, generate the original operating state vector based on the operating state data, and normalize the original operating state vector to generate the initial operating state vector. The available depth boundary determination module is used to determine the target peak-shaving gap based on the deviation between the net load power in the station and the target smooth curve in the initial operating state vector, and to determine the upper limit of the discharge depth and the upper limit of the chargeable depth based on the rolling peak-shaving energy index, dynamic state of charge boundary, lifetime constraint and temperature constraint within the future prediction window. The target depth determination module is used to solve the target depth optimization function based on the initial operating state vector, the target peak shaving gap, the upper limit of the discharge depth, and the upper limit of the charge depth, and to determine the target charge and discharge depth that minimizes the target depth optimization function. The target depth optimization function is obtained by weighted summation of multiple sub-objective functions, including: peak shaving tracking sub-objective function, retention capacity sub-objective function, lifetime loss sub-objective function, and temperature risk sub-objective function. The execution power determination module is used to determine the target power command based on the target charge and discharge depth, and to perform power ramping constraints and saturation limiting on the target power command to generate the actual execution power of the energy storage converter; The lifetime loss coefficient determination module is used to update the battery state of charge and battery temperature corresponding to the target energy storage system according to the actual execution power, calculate the lifetime loss increment, and feed the lifetime loss increment back to the lifetime loss coefficient at the next control moment. The control parameter update module is used to dynamically adjust the weight of the target depth optimization function and the dynamic charge state boundary according to the peak-shaving effect evaluation index within a preset sliding window, so as to realize the online update of control parameters.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute the adaptive control method for energy storage charging and discharging depth for grid peak shaving as described in any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the adaptive control method for energy storage charge and discharge depth for grid peak shaving as described in any embodiment of the present invention.

[0009] The technical solution of this invention collects the operating status data of the target energy storage system at discrete control moments, generates an original operating status vector based on the operating status data, and normalizes the original operating status vector to generate an initial operating status vector. This eliminates the numerical dominance of different dimensions in subsequent optimization calculations and provides a stable data foundation for subsequent steps. Based on the deviation between the net load power within the station and the target smooth curve in the initial operating status vector, the target peak-shaving gap is determined. Furthermore, based on the rolling peak-shaving energy index within the future prediction window, the dynamic state of charge boundary, lifetime constraints, and temperature constraints, the upper limits of discharge depth and charge depth are determined. This allows future discharge and charging demands to be incorporated into the current time period control, enabling the energy storage system to meet current peak-shaving tasks while retaining its adjustment capabilities for subsequent time periods. Based on the initial operating state vector, the target peak-shaving gap, the upper limit of the discharge depth, and the upper limit of the charge depth, the target depth optimization function is solved to determine the target charge-discharge depth that minimizes the target depth optimization function. The target depth optimization function is obtained by a weighted sum of multiple sub-objective functions, including: a peak-shaving tracking sub-objective function, a retention capacity sub-objective function, a lifetime loss sub-objective function, and a temperature risk sub-objective function. This can mitigate battery aging and reduce safety risks while ensuring peak shaving and valley filling effects. Based on the target charge-discharge depth, a target power command is determined, and power ramping constraints and saturation limits are applied to the target power command to generate the actual execution power of the energy storage converter. This avoids the impact of sudden power changes on the grid and the energy storage converter, ensuring equipment safety and enhancing project reliability. The battery state of charge and battery temperature corresponding to the target energy storage system are updated according to the actual execution power, and the lifetime loss increment is calculated. The lifetime loss increment is fed back to the lifetime depreciation coefficient at the next control moment, providing accurate state feedback for the next cycle and reflecting the cumulative damage to the battery from each operation in real time, enabling subsequent decisions to automatically avoid high-loss conditions. Based on the peak-shaving effect evaluation index within a preset sliding window, the weights of the target depth optimization function and the dynamic state of charge boundary are dynamically adjusted to achieve online updates of control parameters. This allows for good adaptability to different seasons, load fluctuations, and battery health states, making it highly valuable for widespread application. This invention achieves efficient adaptive control of energy storage charge / discharge depth for grid peak shaving through data acquisition and standardization, peak-shaving gap calculation, adaptive solution of target charge / discharge depth, power allocation execution under depth constraints, and online feedback correction and parameter updates. This reduces reliance on manual experience, adapts to dynamic changes in grid peak-shaving demand, avoids excessive charge / discharge in high-load or high-temperature scenarios, and improves battery lifespan. This ensures the objectivity, economy, and robustness of energy storage peak-shaving operation, meeting the safety, economy, and efficient control requirements of energy storage systems in peak-shaving scenarios.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

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

[0012] Figure 1 This is a flowchart of an adaptive control method for energy storage charging and discharging depth for grid peak shaving, provided according to Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the structure of an adaptive control device for energy storage charge and discharge depth for grid peak shaving provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device that implements the adaptive control method for energy storage charge and discharge depth for grid peak shaving according to an embodiment of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Example 1 Figure 1This is a flowchart illustrating an adaptive control method for the depth of charge and discharge of energy storage for grid peak shaving, provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of adaptive control of the depth of charge and discharge of energy storage for grid peak shaving. This method can be executed by an adaptive control device for the depth of charge and discharge of energy storage for grid peak shaving, which can be implemented in hardware and / or software. This adaptive control device for the depth of charge and discharge of energy storage for grid peak shaving can be configured in an electronic device. For example... Figure 1 As shown, the method includes: S110. At discrete control moments, collect the operating state data of the target energy storage system, generate the original operating state vector based on the operating state data, and normalize the original operating state vector to generate the initial operating state vector.

[0016] In this context, discrete control time refers to the sampling time when the energy storage system controller periodically executes the control algorithm at fixed time intervals (e.g., 15 minutes), denoted as k. The target energy storage system can be the controlled energy storage power station, such as a grid-side lithium iron phosphate energy storage power station. Operating state data refers to data used to record the operating state of the target energy storage system at discrete control times. The original operating state vector can be a multi-dimensional column vector directly composed of operating state data, with its components unnormalized. The initial operating state vector can be the vector obtained by normalizing the components of the original operating state vector to eliminate dimensional influences.

[0017] Specifically, at discrete control time k, operational status data can be collected from the target energy storage system (which may include the power grid dispatching system, load forecasting module, and battery management system). This operational status data is then used to form an initial operational status vector. A normalization formula is then used to map each component of the initial operational status vector to the [0,1] interval, resulting in a dimensionless initial operational status vector. Normalization eliminates the numerical dominance of different dimensions in subsequent optimization calculations, ensuring that all state variables participate fairly in decision-making at the same scale. It also provides a stable data foundation for subsequent steps, improving the algorithm's versatility and numerical stability.

[0018] For example, operational status data includes: power grid dispatching commands, net load power within the station, renewable energy output, battery state of charge, battery temperature, battery health status, rated available capacity, maximum charge and discharge power, real-time lifetime depreciation factor, and future forecast window net load sequence.

[0019] Among these, the grid dispatch power instruction refers to the expected power value issued by the grid dispatch system to the energy storage power station at time k. The net load power within the station refers to the net power value after subtracting the output of new energy sources (such as wind power and photovoltaics) from the actual power load of the node where the energy storage power station is located, reflecting the actual power demand of the grid on that station. New energy output refers to the actual power generation of renewable energy sources such as wind farms or photovoltaic power stations at time k. The battery state of charge (SOC) refers to the percentage of the energy storage battery's current remaining charge relative to its rated capacity. The battery temperature refers to the internal temperature of the energy storage battery at control time k. The battery state of health (SOH) refers to the degree of degradation of the battery's current actual usable capacity relative to its factory rated capacity, reflecting the battery's aging status. The rated usable capacity refers to the maximum usable electrical energy capacity of the energy storage battery under the factory nominal conditions. The maximum charge / discharge power refers to the maximum instantaneous charge / discharge power allowed by the energy storage converter. The real-time lifetime loss factor is a dynamically updated coefficient reflecting the lifetime loss cost corresponding to a unit depth of charge / discharge at the current time. The future forecast window net load sequence can refer to the sequence of predicted net load power within the station over multiple future control cycles obtained through the load forecasting module.

[0020] For example, at discrete control moments Collect power dispatch commands from the power grid. Net load power within the station New energy power output Battery state of charge Battery temperature Battery health status Rated available capacity Maximum charge and discharge power Real-time lifespan depletion factor and the net load sequence within the future forecast window ,in , For rolling prediction step size.

[0021] The original operating state vector of the energy storage system can be defined. for: in, , .

[0022] For example, the normalization formula is: in, The first normalized original running state vector is the first normalized running state vector. One portion, The j-th component of the original running state vector, Let j be the lower bound of the engineering process for the j-th component of the original running state vector. The upper bound of the j-th component of the original running state vector. It is a very small positive number, used to prevent the denominator from being 0.

[0023] S120. Based on the deviation between the net load power in the station and the target smooth curve in the initial operating state vector, determine the target peak-shaving gap, and determine the upper limit of the discharge depth and the upper limit of the recharge depth according to the rolling peak-shaving energy index, dynamic state of charge boundary, lifetime constraint and temperature constraint in the future prediction window.

[0024] The terms "Net Load Power" and "Target Smoothing Curve" refer to the net power output of the energy storage power station node minus the actual load of the new energy source, reflecting the real-time power demand of the grid on the station. "Target Smoothing Curve" refers to the ideal trajectory of the net load expected by the grid, usually provided by the dispatching department and used as a peak-shaving benchmark. "Target Peak-Shaving Gap" refers to the deviation between the net load power and the target smoothing curve; a positive value indicates peak-shaving demand, and a negative value indicates valley-filling demand. "Future Forecast Window" refers to the pre-set rolling forecast time length. "Rolling Peak-Shaving Energy Index" refers to the cumulative energy demand within the future window when the net load is higher or lower than the target smoothing curve. "Dynamic State of Charge Boundary" refers to the dynamically adjusted state of charge based on future peak-shaving demand and battery health. "Lifetime Constraint" refers to the conditions limiting the depth of charge and discharge, determined by the battery life loss coefficient and the nonlinear exponent of deep loss. "Temperature Constraint" refers to the conditions limiting the depth of charge and discharge, determined by the current temperature, the influence coefficient of the depth of charge and discharge on temperature rise, and the maximum allowable temperature. "Upper Limit of Dischargeable Depth" refers to the maximum allowable depth of discharge at the current moment. "Upper Limit of Rechargeable Depth" refers to the maximum allowable depth of charge at the current moment.

[0025] Specifically, the difference between the net load power within the station and the target smoothing curve in the initial operating state vector is determined, i.e., the target peak-shaving gap. Positive values ​​represent peak-shaving demand (discharging), and negative values ​​represent valley-filling demand (charging). Based on the predicted net load within the future forecast window, a rolling peak-shaving energy index is calculated, representing the total energy demand for future discharging and charging, respectively. Based on the rolling peak-shaving energy index, dynamic state of charge boundary, lifetime constraints, and temperature constraints, the upper limits of discharge depth and charge depth are determined. By incorporating future peak-shaving demand into the current boundary calculation, energy storage can meet current tasks while retaining subsequent adjustment capabilities. The dynamic boundary automatically narrows as the state of charge (SOH) decays, effectively preventing overcharging and discharging of aging batteries. Lifetime and temperature constraints directly limit the upper limit of charge depth, avoiding high-risk operations from the source.

[0026] For example, the target peak shaving gap It can be defined as: in, This refers to the net load power within the station. For the target smooth curve, when This indicates that the system is in a peak-shaving demand state, and energy storage is prioritized for discharge; when This indicates that the system is in a valley-filling demand state, and energy storage is prioritized for charging.

[0027] For example, the rolling peak-shaving energy index includes: the potential discharge peak-shaving energy demand within the future window and the potential charging valley-filling energy demand within the future window. The calculation formula for the rolling peak-shaving energy index is as follows: in, To meet the potential discharge peak-shaving energy demand within the future window. For the potential energy demand for charging and filling valleys within the future window, H is the predicted window length. This represents the predicted net load value at time k+i. Let be the target smooth curve value at time k+i. To control the cycle duration.

[0028] For example, the formula for calculating the dynamic charge state boundary is: in, This is the boundary of the dynamic minimum charged state. This is the boundary of the dynamic maximum charge state. Based on the lower safety limit, Basic security limit, 、 、 、 For boundary correction coefficients, For battery health status, To meet the potential discharge peak-shaving energy demand within the future window. To meet potential charging and valley energy demand within the future window, This is the rated available capacity.

[0029] For example, the formulas for calculating the upper limit of discharge depth and the upper limit of charge depth are as follows: in, Upper limit of discharge depth This represents the upper limit of the rechargeable depth. The battery is in its state of charge. This is the boundary of the dynamic minimum charged state. This represents the boundary of the dynamic maximum charge state. Maximum permissible depth of discharge under lifetime constraints This represents the maximum permissible depth of charge under lifetime constraints. This represents the maximum permissible depth of discharge under temperature constraints. This represents the maximum permissible depth of charge under temperature constraints.

[0030] S130. Based on the initial operating state vector, the target peak shaving gap, the upper limit of the discharge depth, and the upper limit of the charge depth, the target depth optimization function is solved to determine the target charge and discharge depth that minimizes the target depth optimization function. The target depth optimization function is obtained by weighted summation of multiple sub-objective functions, including: peak shaving tracking sub-objective function, retention capacity sub-objective function, lifetime loss sub-objective function, and temperature risk sub-objective function.

[0031] The target charge / discharge depth optimization function can be a weighted sum of four sub-objectives: peak tracking, retention capacity, lifespan loss, and temperature risk. The optimal charge / discharge depth is found by minimizing this function. The target charge / discharge depth can be defined as the charge / discharge depth that minimizes the target depth optimization function. The peak tracking sub-objective function measures the degree of matching between the current charge / discharge power and the actual peak-shaving gap; a smaller value indicates better peak-shaving performance. The retention capacity sub-objective function measures the degree of matching between the remaining energy after the current decision and the predicted future peak-shaving demand, ensuring the system retains its subsequent adjustment capability. The lifespan loss sub-objective function is a sub-objective calculated based on the depth loss nonlinearity exponent and lifespan reduction coefficient, characterizing the damage to battery life caused by the current charge / discharge cycle. The temperature risk sub-objective function characterizes the risk of the battery temperature exceeding the allowable upper limit during charge / discharge; it is positive when the limit is exceeded and zero otherwise.

[0032] Specifically, the direction of the peak-shaving gap can be determined based on the target peak-shaving gap, and the decision to discharge or charge can be made based on the direction of the peak-shaving gap. A target depth optimization function containing four sub-objective functions is constructed: the peak-shaving tracking term measures the matching degree between the actual power and the gap; the retention capacity term measures the difference between the current discharge depth plus the dynamic lower boundary and the future discharge demand; the lifetime loss term is the depth raised to the power of γ multiplied by the loss coefficient; and the temperature risk term is the penalty for exceeding the upper limit of temperature rise. Combining the initial operating state vector, the target peak-shaving gap, the upper limit of the discharge depth, and the upper limit of the charge depth, the target depth optimization function is solved to obtain the target charge-discharge depth that minimizes the target depth optimization function. Through multi-objective weighted summation, the optimal trade-off between peak-shaving effect, lifetime extension, thermal safety, and future adjustment margin is achieved. The retention capacity term replaces the fixed reserve capacity, enabling the system to intelligently reserve future energy while serving the current task. The nonlinear lifetime term and the temperature penalty term effectively suppress excessive depth or excessive temperature rise.

[0033] For example, the target depth optimization function is expressed as: in, Let k be the value of the comprehensive objective function at control time k. To compensate for peak tracking error, As a reserve capacity margin, As a result of lifespan loss, As a penalty for temperature risk, , , , The target weight is, and satisfies ; when When using discharge decision-making, the terms of the target depth optimization function are defined as follows: when When charging decisions are adopted, the terms of the objective depth optimization function are defined as follows: in, To address the peak-shaving gap. For the depth of discharge to be optimized, Rated available capacity, To control the cycle duration, To meet the potential discharge peak-shaving energy demand within the future window. This is the boundary of the dynamic minimum charged state. This is the real-time lifespan reduction factor. This is a nonlinear exponent for deep loss. To control the battery temperature at time k, This is the coefficient representing the effect of discharge depth on temperature rise. For the highest permissible battery temperature, For the charging depth to be optimized, To meet potential charging and valley energy demand within the future window, This is the boundary of the dynamic maximum charge state. This is the coefficient representing the effect of depth of charge on temperature rise.

[0034] Accordingly, the target depth satisfies the following constraints: When making a discharge decision, take When making a charging decision, take in, For the depth of discharge to be optimized, For the charging depth to be optimized, This represents the upper limit of the discharge depth. This represents the upper limit of the rechargeable depth. Target depth of charge / discharge.

[0035] S140. Based on the target charge and discharge depth, determine the target power command, and apply power ramping constraints and saturation limits to the target power command to generate the actual execution power of the energy storage converter.

[0036] The target power command can refer to the raw power value obtained by mapping the target charge / discharge depth, without ramping or limiting processing. The power conversion system (PCS) is the core power electronic device in the energy storage system that connects the battery pack to the grid, responsible for executing power commands and realizing bidirectional power conversion between the energy storage battery and the grid. The actual executed power can refer to the power value finally sent to the PCS after power ramping constraints and saturation limiting processing.

[0037] Specifically, the target charge / discharge depth is multiplied by the rated capacity and divided by the control cycle to obtain the theoretical power value. This theoretical power value is then divided by the minimum of the maximum power and multiplied by the sign function of the target peak-shaving gap (positive for discharging, negative for charging) to obtain the target power command. A power ramp constraint is then applied: the power change rate between two adjacent cycles must not exceed the product of the maximum power change slope and the control cycle. If it does, the limit is applied to the boundary value. Finally, a saturation function restricts the command within a preset range, outputting the actual executed power. This simple and efficient linear mapping from depth to power facilitates interface integration with the PCS. The ramp constraint avoids the impact of sudden power changes on the power grid and PCS, ensuring equipment safety. The saturation limit ensures the command does not exceed hardware limits, enhancing engineering reliability.

[0038] For example, based on the obtained target charge / discharge depth Generate the target power command for the current time period. The mapping relationship from depth to power is defined as follows: in, For target power command, For symbolic functions, To address the peak-shaving gap. For maximum charge and discharge power, For the target depth of charge and discharge, Rated available capacity, To control the cycle, when hour, Indicates discharge; when hour, This indicates that the device is charging.

[0039] To prevent power surges caused by depth changes, a power ramp-up constraint is set: in, The slope of the maximum power change. It could refer to the historical power command from the previous control moment.

[0040] Furthermore, the actual execution power of PCS Recorded as: in, This represents the actual power output. It is a saturation function. This represents the maximum charging and discharging power.

[0041] S150. Update the battery state of charge and battery temperature corresponding to the target energy storage system according to the actual execution power, calculate the lifetime loss increment, and feed the lifetime loss increment back to the lifetime loss coefficient at the next control moment.

[0042] Here, "battery state of charge" can refer to the percentage of the energy storage battery's current remaining capacity relative to its rated capacity. "Battery temperature" can refer to the current internal temperature of the energy storage battery. "Lifetime degradation increment" can refer to the cumulative damage to the battery's lifespan caused by a single charge-discharge operation. "Lifetime degradation factor" can refer to a coefficient reflecting the cost per unit depth of degradation under the current aging state of the battery.

[0043] Specifically, after executing the actual power, the SOC update equation is used to calculate the SOC at the next moment, taking into account the impact of SOH decay on the actual capacity; the temperature update equation takes into account power heating, deep additional heating, and heat exchange with the environment to calculate the battery temperature at the next moment; the lifetime loss increment is determined and added to the lifetime loss coefficient as the lifetime cost input for the next moment. Through accurate SOC and temperature updates, accurate state feedback can be provided for the next cycle. The lifetime loss increment can reflect the cumulative damage to the battery from each operation in real time and is fed back to the optimization function, enabling subsequent decisions to automatically avoid high-loss conditions.

[0044] For example, after power execution, the energy storage state is updated. The state of charge update equation is: in, This could refer to the battery state of charge at the next control moment. This could refer to the battery state of charge at the current control moment. This represents the actual power output. To control the cycle, Rated available capacity, Battery health status.

[0045] Considering the effects of thermal inertia, the temperature update equation can be expressed as: in, The battery temperature at the next moment. The current battery temperature. This represents the actual power output. For the target depth of charge and discharge, The power-heat effect coefficient, The depth thermal effect coefficient, For heat dissipation coefficient, The ambient temperature.

[0046] Incremental lifespan loss It can be represented as: in, For the increase in lifespan loss, For the target depth of charge and discharge, The current battery temperature. This serves as the baseline coefficient for lifespan loss. For temperature sensitivity coefficient, Use the reference temperature. Feedback to the lifespan depreciation factor This yields an estimate of the life cost at the next moment.

[0047] S160. Based on the peak-shaving effect evaluation index within the preset sliding window, dynamically adjust the weight of the target depth optimization function and the dynamic charge state boundary to achieve online updating of control parameters.

[0048] The preset sliding window can refer to a fixed-length historical time period used to evaluate the recent control effect. The peak-shaving effect evaluation index can refer to a weighted index that combines peak-shaving tracking error, average lifetime loss, and alarm frequency, used to quantify the peak-shaving effect within the preset sliding window.

[0049] Specifically, a preset sliding window of length M can be set to calculate the peak-shaving effect evaluation index. When the peak-shaving effect evaluation index exceeds the reference threshold, the objective function weights are automatically adjusted: for example, increasing the lifetime and temperature penalty weights and decreasing the peak-shaving weights, while tightening the dynamic SOC boundary to compress the available depth window, enabling online updates of control parameters. This allows the control strategy to adapt to seasonal changes, battery aging, and load fluctuations, prioritizing safety and lifetime without requiring manual parameter readjustment, significantly improving the method's generalization ability and long-term operational stability.

[0050] For example, to adapt to different seasons, different battery aging stages, and different peak-shaving fluctuation intensities, an online adaptive correction mechanism can be set. The window length is defined as... Evaluation indicators of peak shaving effect: in, M is the sliding window length, used as an evaluation index for peak-shaving effect. , , To evaluate the weights, The target peak shaving gap for historical moment m. The actual execution power at historical moment m. This represents the lifetime loss increment at historical moment m. To evaluate the number of alarms triggered when the temperature or charge state exceeds the limit within the window.

[0051] when Update the weight parameters as needed: And simultaneously tighten the dynamic boundaries: in, As a reference threshold for effect evaluation, , To adjust the weights of the tracking sub-targets at the current and next time steps, , Let be the weight values ​​of the lifetime loss sub-objective at the current time and the next time step. , Let the weights of the temperature risk sub-objectives be the weights at the current and next time steps. , , , This allows for online step size updates. This improves the penalty for lifespan and temperature risks when peak-shaving tracking deviations are large, and automatically reduces the available depth window when safety alarms increase.

[0052] It should be noted that the energy storage charging and discharging depth adaptive control method for grid peak shaving of the present invention is deployed in the Energy Management System (EMS) controller. The EMS receives data from the scheduling system and load forecasting module, and after completing the target depth calculation, issues a power command to the PCS. At the same time, it obtains state variables such as SOC(k), SOH(k), and T(k) from the Battery Management System (BMS) to form a closed-loop control link of "peak shaving demand identification - depth solution - power execution - feedback correction".

[0053] In this embodiment, by collecting the operating status data of the target energy storage system at discrete control moments, generating an original operating status vector based on the operating status data, and normalizing the original operating status vector to generate an initial operating status vector, the numerical dominance of different dimensions in subsequent optimization calculations can be eliminated, while providing a stable data foundation for subsequent steps. Based on the deviation between the net load power within the station and the target smooth curve in the initial operating status vector, the target peak-shaving gap is determined. Furthermore, based on the rolling peak-shaving energy index within the future prediction window, the dynamic state of charge boundary, lifetime constraints, and temperature constraints, the upper limits of the discharge depth and charge depth are determined. This allows future discharge and charging demands to be incorporated into the current time period control, enabling the energy storage system to meet the current peak-shaving task while retaining its adjustment capability for subsequent time periods. Based on the initial operating state vector, target peak-shaving gap, upper limit of discharge depth, and upper limit of charge depth, the target depth optimization function is solved to determine the target charge-discharge depth that minimizes the target depth optimization function. This target depth optimization function is obtained by a weighted sum of multiple sub-objective functions, including: peak-shaving tracking sub-objective function, retention capacity sub-objective function, lifetime loss sub-objective function, and temperature risk sub-objective function. This can mitigate battery aging and reduce safety risks while ensuring peak shaving and valley filling effects. Based on the target charge-discharge depth, the target power command is determined, and power ramping constraints and saturation limits are applied to the target power command to generate the actual execution power of the energy storage converter. This avoids the impact of sudden power changes on the grid and the energy storage converter, ensuring equipment safety and enhancing project reliability. The battery state of charge and battery temperature corresponding to the target energy storage system are updated according to the actual execution power, and the lifetime loss increment is calculated. The lifetime loss increment is fed back to the lifetime depreciation coefficient at the next control moment, providing accurate state feedback for the next cycle and reflecting the cumulative damage to the battery from each operation in real time, enabling subsequent decisions to automatically avoid high-loss conditions. Based on the peak-shaving effect evaluation index within a preset sliding window, the weights of the target depth optimization function and the dynamic state of charge boundary are dynamically adjusted to achieve online updates of control parameters. This invention demonstrates good adaptability to different seasons, load fluctuations, and battery health states, making it highly valuable for widespread application. Through operational data acquisition and standardization, peak-shaving gap calculation, adaptive solution of target charge / discharge depth, power allocation execution under depth constraints, and online feedback correction and parameter updates, this invention achieves efficient adaptive control of energy storage charge / discharge depth for grid peak shaving. This reduces reliance on manual experience, adapts to dynamic changes in grid peak-shaving demand, avoids excessive charge / discharge in high-load or high-temperature scenarios, and improves battery lifespan. This ensures the objectivity, economy, and robustness of energy storage peak-shaving operation, meeting the safety, economy, and efficient control requirements of energy storage systems in peak-shaving scenarios.

[0054] For example, a grid-side lithium iron phosphate energy storage power station can be used as an application scenario for illustration. The rated power of this energy storage power station is... Rated capacity is The control cycle is The rolling prediction window is That is, the future The system's fundamental state of charge safety boundary is set to... , The maximum permissible temperature is The reference temperature is .

[0055] Preferably, the parameter values ​​are as follows: At 18:00 on a certain day, the dispatch center requested energy storage to participate in peak shaving during the evening rush hour. The measured net load power within the station at that time was... The smoothed reference power (target smoothed curve) is Therefore, by equation We can obtain: This indicates that the system is in a state of significant peak shaving demand, and discharge control should be prioritized.

[0056] Meanwhile, the BMS reports the current battery status as follows: future Within this context, the potential discharge peak-shaving energy demand calculated by the load forecasting module is as follows: There is no obvious demand for valley filling at this moment, so it is acceptable: Substitute the above data into the dynamic lower boundary formula: in, Then we have: Similarly, substitute the data into the dynamic upper boundary formula: get: Therefore, the upper limit of the real-time discharge depth at this moment is mainly determined by the charge state boundary as follows: That is, without triggering other constraints, the maximum depth of discharge is approximately .

[0057] Based on peak shaving requirements, the current decision is to discharge. First, consider the depth required to completely cover the peak shaving gap within a single cycle: If the peak-shaving shortfall in the current 15-minute cycle is entirely covered by energy storage, then the corresponding depth of discharge is... .

[0058] Furthermore, calculate each component of the comprehensive objective function (objective depth optimization function). Select candidate depths. Then the peak shaving error term is: The retention rate is as follows: If the current lifespan reduction factor is taken Then the life loss term is: Temperature risk items are: Therefore, the overall objective function value is: Take another candidate depth Then we have: but: The comparison shows that the candidate depth The overall objective function value is significantly smaller, therefore the optimal discharge depth (target discharge depth) is chosen as: Substitute the optimal target depth into the power mapping relationship: Therefore, the discharge power command (target power command) issued by the energy storage system in this cycle is: .

[0059] If the execution power of the previous cycle was Then the change in power is: The upper limit of the ramp constraint is: because Therefore, the power command satisfies the ramp constraint and can be executed directly.

[0060] After execution, the state of charge will be updated to the following at the next moment: Temperature updated to: It is evident that the temperature remained significantly lower than the upper limit after execution. .

[0061] The increment of lifespan loss is: This invention can adaptively select a more reasonable depth of charge and discharge based on the intensity of peak shaving demand and battery status, thereby reducing lifespan loss and thermal risks while ensuring peak shaving effect.

[0062] Example 2 Figure 2 This is a schematic diagram of a device for adaptive control of energy storage charge and discharge depth for grid peak shaving, provided in Embodiment 2 of the present invention. Figure 2 As shown, the device includes: a state vector generation module 210, an available depth boundary determination module 220, a target depth determination module 230, an execution power determination module 240, a lifetime loss coefficient determination module 250, and a control parameter update module 260; The state vector generation module 210 is used to collect the operating state data of the target energy storage system at discrete control moments, generate an original operating state vector based on the operating state data, and normalize the original operating state vector to generate an initial operating state vector. The available depth boundary determination module 220 is used to determine the target peak-shaving gap based on the deviation between the net load power in the station and the target smooth curve in the initial operating state vector, and to determine the upper limit of the discharge depth and the upper limit of the chargeable depth based on the rolling peak-shaving energy index, dynamic state of charge boundary, lifetime constraint and temperature constraint in the future prediction window. The target depth determination module 230 is used to solve the target depth optimization function based on the initial operating state vector, the target peak shaving gap, the upper limit of the discharge depth, and the upper limit of the charge depth, and to determine the target charge and discharge depth that minimizes the target depth optimization function. The target depth optimization function is obtained by weighted summation of multiple sub-objective functions, including: peak shaving tracking sub-objective function, retention capacity sub-objective function, lifetime loss sub-objective function, and temperature risk sub-objective function. The execution power determination module 240 is used to determine the target power command based on the target charge and discharge depth, and to perform power ramping constraints and saturation limiting on the target power command to generate the actual execution power of the energy storage converter; The lifetime loss coefficient determination module 250 is used to update the battery state of charge and battery temperature corresponding to the target energy storage system according to the actual execution power, calculate the lifetime loss increment, and feed the lifetime loss increment back to the lifetime loss coefficient at the next control moment. The control parameter update module 260 is used to dynamically adjust the weight of the target depth optimization function and the dynamic charge state boundary according to the peak-shaving effect evaluation index within a preset sliding window, so as to realize the online update of control parameters.

[0063] In this embodiment, by collecting the operating status data of the target energy storage system at discrete control moments, and generating an original operating status vector based on the operating status data, and then normalizing the original operating status vector to generate an initial operating status vector, the numerical dominance of different dimensions in subsequent optimization calculations can be eliminated, while providing a stable data foundation for subsequent steps. Based on the deviation between the net load power within the station and the target smooth curve in the initial operating status vector, the target peak-shaving gap is determined. Furthermore, based on the rolling peak-shaving energy index within the future prediction window, the dynamic state of charge boundary, lifetime constraints, and temperature constraints, the upper limits of discharge depth and charge depth are determined. This allows future discharge and charging demands to be incorporated into the current time period control, enabling the energy storage system to meet the current peak-shaving task while retaining its adjustment capability for subsequent time periods. Based on the initial operating state vector, the target peak-shaving gap, the upper limit of the discharge depth, and the upper limit of the charge depth, the target depth optimization function is solved to determine the target charge-discharge depth that minimizes the target depth optimization function. The target depth optimization function is obtained by a weighted sum of multiple sub-objective functions, including: a peak-shaving tracking sub-objective function, a retention capacity sub-objective function, a lifetime loss sub-objective function, and a temperature risk sub-objective function. This can mitigate battery aging and reduce safety risks while ensuring peak shaving and valley filling effects. Based on the target charge-discharge depth, a target power command is determined, and power ramping constraints and saturation limits are applied to the target power command to generate the actual execution power of the energy storage converter. This avoids the impact of sudden power changes on the grid and the energy storage converter, ensuring equipment safety and enhancing project reliability. The battery state of charge and battery temperature corresponding to the target energy storage system are updated according to the actual execution power, and the lifetime loss increment is calculated. The lifetime loss increment is fed back to the lifetime depreciation coefficient at the next control moment, providing accurate state feedback for the next cycle and reflecting the cumulative damage to the battery from each operation in real time, enabling subsequent decisions to automatically avoid high-loss conditions. Based on the peak-shaving effect evaluation index within a preset sliding window, the weights of the target depth optimization function and the dynamic state of charge boundary are dynamically adjusted to achieve online updates of control parameters. This allows for good adaptability to different seasons, load fluctuations, and battery health states, making it highly valuable for widespread application. This invention achieves efficient adaptive control of energy storage charge / discharge depth for grid peak shaving through data acquisition and standardization, peak-shaving gap calculation, adaptive solution of target charge / discharge depth, power allocation execution under depth constraints, and online feedback correction and parameter updates. This reduces reliance on manual experience, adapts to dynamic changes in grid peak-shaving demand, avoids excessive charge / discharge in high-load or high-temperature scenarios, and improves battery lifespan. This ensures the objectivity, economy, and robustness of energy storage peak-shaving operation, meeting the safety, economy, and efficient control requirements of energy storage systems in peak-shaving scenarios.

[0064] Optionally, the operating status data includes: power grid dispatching command, net load power within the station, output of new energy sources, battery state of charge, battery temperature, battery health status, rated available capacity, maximum charge and discharge power, real-time lifespan depreciation factor, and future prediction window net load sequence.

[0065] Optionally, the normalization formula is: in, The first normalized original running state vector One portion, The j-th component of the original running state vector, Let j be the lower bound of the engineering process for the j-th component of the original running state vector. The upper bound of the j-th component of the original running state vector. It is a very small positive number, used to prevent the denominator from being 0.

[0066] Optionally, the rolling peak-shaving energy index includes: potential discharge peak-shaving energy demand within the future window and potential charging valley-filling energy demand within the future window. The calculation formula for the rolling peak-shaving energy index is as follows: in, To meet the potential discharge peak-shaving energy demand within the future window. For the potential energy demand for charging and filling valleys within the future window, H is the predicted window length. This represents the predicted net load value at time k+i. Let be the target smooth curve value at time k+i. To control the cycle duration.

[0067] Optionally, the calculation formula for the dynamic charge state boundary is: in, This is the boundary of the dynamic minimum charged state. This is the boundary of the dynamic maximum charge state. Based on the lower safety limit, Basic security limit, , , , For boundary correction coefficients, For battery health status, To meet the potential discharge peak-shaving energy demand within the future window. To meet potential charging and valley energy demand within the future window, This is the rated available capacity.

[0068] Optionally, the formulas for calculating the upper limit of discharge depth and the upper limit of chargeable depth are as follows: in, Upper limit of discharge depth This represents the upper limit of the rechargeable depth. The battery is in its state of charge. This is the boundary of the dynamic minimum charged state. This represents the boundary of the dynamic maximum charge state. Maximum permissible depth of discharge under lifetime constraints This represents the maximum permissible depth of charge under lifetime constraints. This represents the maximum permissible depth of discharge under temperature constraints. This represents the maximum permissible depth of charge under temperature constraints.

[0069] Optionally, the target depth optimization function is expressed as: in, Let k be the value of the comprehensive objective function at control time k. To compensate for peak tracking error, As a reserve capacity margin, As a result of lifespan loss, As a penalty for temperature risk, , , , The target weight is, and satisfies ; when When using discharge decision-making, the terms of the target depth optimization function are defined as follows: when When charging decisions are adopted, the terms of the objective depth optimization function are defined as follows: in, To address the target peak-shaving gap. For the depth of discharge to be optimized, Rated available capacity, To control the cycle duration, To meet the potential discharge peak-shaving energy demand within the future window. This is the boundary of the dynamic minimum charged state. This is the real-time lifespan reduction factor. This is a nonlinear exponent for deep loss. To control the battery temperature at time k, This is the coefficient representing the effect of discharge depth on temperature rise. For the highest permissible battery temperature, For the charging depth to be optimized, To meet potential charging and valley energy demand within the future window, This is the boundary of the dynamic maximum charge state. This is the coefficient representing the effect of depth of charge on temperature rise.

[0070] The above-mentioned device can execute the adaptive control method for energy storage charge and discharge depth for grid peak shaving provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the adaptive control method for energy storage charge and discharge depth for grid peak shaving.

[0071] Example 3 Figure 3 This is a schematic diagram of an electronic device implementing the adaptive control method for energy storage charge and discharge depth for grid peak shaving according to embodiments of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0072] like Figure 3As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0073] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0074] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the energy storage charge-discharge depth adaptive control method for grid peak shaving.

[0075] In some embodiments, the adaptive control method for the depth of charge and discharge of energy storage for grid peak shaving can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the adaptive control method for the depth of charge and discharge of energy storage for grid peak shaving described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the adaptive control method for the depth of charge and discharge of energy storage for grid peak shaving by any other suitable means (e.g., by means of firmware).

[0076] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0077] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0078] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0079] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0080] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0081] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0082] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0083] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0084] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An adaptive control method for the charging and discharging depth of energy storage for grid peak shaving, characterized in that, include: At discrete control moments, the operating status data of the target energy storage system is collected, and based on the operating status data, an original operating status vector is generated. The original operating status vector is then normalized to generate an initial operating status vector. Based on the deviation between the net load power in the station and the target smooth curve in the initial operating state vector, the target peak-shaving gap is determined, and the upper limit of the discharge depth and the upper limit of the recharge depth are determined according to the rolling peak-shaving energy index, dynamic state of charge boundary, lifetime constraint and temperature constraint in the future prediction window. Based on the initial operating state vector, the target peak-shaving gap, the upper limit of the discharge depth, and the upper limit of the charge depth, the target depth optimization function is solved to determine the target charge-discharge depth that minimizes the target depth optimization function. The target depth optimization function is obtained by weighted summation of multiple sub-objective functions, including: peak-shaving tracking sub-objective function, retention capability sub-objective function, lifetime loss sub-objective function, and temperature risk sub-objective function. Based on the target charge and discharge depth, the target power command is determined, and the target power command is subject to power ramping constraints and saturation limiting to generate the actual execution power of the energy storage converter. The battery state of charge and battery temperature of the target energy storage system are updated according to the actual power of execution, and the lifetime loss increment is calculated. The lifetime loss increment is then fed back to the lifetime loss coefficient at the next control moment. Based on the peak-shaving effect evaluation index within the preset sliding window, the weights of the target depth optimization function and the dynamic charge state boundary are dynamically adjusted to achieve online updates of control parameters.

2. The method according to claim 1, characterized in that, The operational status data includes: power grid dispatching commands, net load power within the station, renewable energy output, battery state of charge, battery temperature, battery health status, rated available capacity, maximum charge and discharge power, real-time lifespan depreciation coefficient, and future forecast window net load sequence.

3. The method according to claim 1, characterized in that, The normalization formula is: ； in, The first normalized original running state vector is the first normalized running state vector. One portion, The j-th component of the original running state vector, Let j be the lower bound of the engineering process for the j-th component of the original running state vector. The upper bound of the j-th component of the original running state vector. It is a very small positive number, used to prevent the denominator from being 0.

4. The method according to claim 1, characterized in that, The rolling peak-shaving energy index includes: the potential discharge peak-shaving energy demand within the future window and the potential charging valley-filling energy demand within the future window. The calculation formula for the rolling peak-shaving energy index is as follows: ； ； in, To meet the potential discharge peak-shaving energy demand within the future window. For the potential energy demand for charging and filling valleys within the future window, H is the predicted window length. This represents the predicted net load value at time k+i. Let be the target smooth curve value at time k+i. To control the cycle duration.

5. The method according to claim 1, characterized in that, The formula for calculating the dynamic charge state boundary is: ； ； in, This is the boundary of the dynamic minimum charged state. This is the boundary of the dynamic maximum charge state. Based on the lower safety limit, Basic security limit, 、 、 、 For boundary correction coefficients, For battery health status, To meet the potential discharge peak-shaving energy demand within the future window. To meet potential charging and valley energy demand within the future window, This is the rated available capacity.

6. The method according to claim 1, characterized in that, The formulas for calculating the upper limit of discharge depth and the upper limit of chargeable depth are as follows: ； ； in, Upper limit of discharge depth This represents the upper limit of the rechargeable depth. The battery is in its state of charge. This is the boundary of the dynamic minimum charged state. This represents the boundary of the dynamic maximum charge state. Maximum permissible depth of discharge under lifetime constraints This represents the maximum permissible depth of charge under lifetime constraints. This represents the maximum permissible depth of discharge under temperature constraints. This represents the maximum permissible depth of charge under temperature constraints.

7. The method according to claim 1, characterized in that, The target depth optimization function is expressed as: ； in, Let k be the value of the comprehensive objective function at control time k. To compensate for peak tracking error, To improve retention capabilities, As a result of lifespan loss, As a penalty for temperature risk, , , , The target weight is, and satisfies ; when When using discharge decision-making, the terms of the target depth optimization function are defined as follows: ； ； ； ； when When charging decisions are adopted, the terms of the objective depth optimization function are defined as follows: ； ； ； ； in, To address the peak-shaving gap. For the depth of discharge to be optimized, Rated available capacity, To control the cycle duration, To meet the potential discharge peak-shaving energy demand within the future window. This is the boundary of the dynamic minimum charged state. This is the real-time lifespan reduction factor. This is a nonlinear exponent for deep loss. To control the battery temperature at time k, This is the coefficient representing the effect of discharge depth on temperature rise. For the highest permissible battery temperature, For the charging depth to be optimized, To meet potential charging and valley energy demand within the future window, This is the boundary of the dynamic maximum charge state. This is the coefficient representing the effect of depth of charge on temperature rise.

8. An adaptive control device for the charging and discharging depth of energy storage for grid peak shaving, characterized in that, include: The state vector generation module is used to collect the operating state data of the target energy storage system at discrete control moments, generate the original operating state vector based on the operating state data, and normalize the original operating state vector to generate the initial operating state vector. The available depth boundary determination module is used to determine the target peak-shaving gap based on the deviation between the net load power in the station and the target smooth curve in the initial operating state vector, and to determine the upper limit of the discharge depth and the upper limit of the chargeable depth based on the rolling peak-shaving energy index, dynamic state of charge boundary, lifetime constraint and temperature constraint within the future prediction window. The target depth determination module is used to solve the target depth optimization function based on the initial operating state vector, the target peak shaving gap, the upper limit of the discharge depth, and the upper limit of the charge depth, and to determine the target charge and discharge depth that minimizes the target depth optimization function. The target depth optimization function is obtained by weighted summation of multiple sub-objective functions, including: peak shaving tracking sub-objective function, retention capacity sub-objective function, lifetime loss sub-objective function, and temperature risk sub-objective function. The execution power determination module is used to determine the target power command based on the target charge and discharge depth, and to perform power ramping constraints and saturation limiting on the target power command to generate the actual execution power of the energy storage converter; The lifetime loss coefficient determination module is used to update the battery state of charge and battery temperature corresponding to the target energy storage system according to the actual execution power, calculate the lifetime loss increment, and feed the lifetime loss increment back to the lifetime loss coefficient at the next control moment. The control parameter update module is used to dynamically adjust the weight of the target depth optimization function and the dynamic charge state boundary according to the peak-shaving effect evaluation index within a preset sliding window, so as to realize the online update of control parameters.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the adaptive control method for energy storage charge and discharge depth for grid peak shaving as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the adaptive control method for energy storage charge and discharge depth for grid peak shaving as described in any one of claims 1-7.

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