Energy storage frequency control method and device based on charge estimation and dynamic scheduling

Abstract

The invention relates to the technical field of energy storage equipment regulation and control, in particular to an energy storage frequency control method and device based on charge estimation and dynamic scheduling, and the method comprises the following steps: initializing an initial energy storage estimated value of an energy storage system; monitoring the power grid frequency in real time and identifying the frequency deviation event type; calculating a current-round global active power regulation target; constructing an energy storage unit response prediction model, and predicting the charge state change process of the energy storage unit under the current power; updating the residual energy storage estimated value of each energy storage system; judging whether a fault critical state of over-limit or under-limit state of charge exists or not; executing the control strategy to enable the charge state of the energy storage system to return to the safety interval; and circulating monitoring is carried out to realize energy storage frequency control. Through combination of charge state estimation, modeling prediction energy storage unit response and an automatic control strategy, self-adaptive and closed-loop frequency regulation control of the heterogeneous energy storage system in a complex power environment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage system regulation and control, and in particular to a method and device for energy storage frequency control based on charge estimation and dynamic scheduling. Background Art

[0002] With the integration of a high proportion of renewable energy into the power system, grid frequency stability faces greater challenges. Energy storage systems, due to their rapid response and flexible regulation, have become a crucial resource for providing primary frequency regulation services (such as frequency containment reserve (FCR)). Within the frequency emergency dead zone, while the grid frequency does not deviate significantly from the nominal value, energy storage systems are still required for fine-tuning to enhance frequency support capabilities and reduce the burden on traditional frequency regulation resources. Currently, technical approaches for energy storage systems to participate in frequency regulation primarily include real-time control based on the P–f characteristic curve, regulation capacity allocation strategies based on SOC (state of charge) feedback, and centralized and distributed coordinated frequency regulation frameworks. These studies rely heavily on real-time communication to obtain the operating status of each energy storage system, which is then combined with grid frequency information for dynamic scheduling. Some advanced controllers have integrated frequency detection, power allocation, and anomaly detection functions, initially realizing a coordinated mechanism for energy storage systems to participate in primary frequency regulation. This has been demonstrated in particular in renewable energy power plants (such as wind and photovoltaic power plants).

[0003] Despite recent technological advances, energy storage systems continue to face numerous challenges as they scale and become increasingly heterogeneous. First, some energy storage systems lack high-frequency measurement devices, making it difficult to obtain state-of-charge (SOC) in real time, resulting in insufficient scheduling support. Second, under conditions of long-term, unidirectional frequency deviation (e.g., sustained over- or under-frequency), the energy storage system as a whole can easily approach its upper or lower SOC limit, reducing its overall responsiveness. Furthermore, existing control strategies often fail to account for SOC differences and safety margins between energy storage systems, leading to imbalances in frequency regulation.

[0004] In summary, there is an urgent need for a method and device for energy storage frequency control based on charge estimation and dynamic scheduling to solve existing problems. Summary of the Invention

[0005] The present invention aims to provide a method and device for energy storage frequency control based on charge estimation and dynamic scheduling. The specific technical solutions are as follows: A method for energy storage frequency control based on charge estimation and dynamic scheduling includes the following steps: S100: Initialize the initial energy storage valuation of the energy storage system. For energy storage units with real-time measurement capabilities, obtain their current state of charge data. For energy storage units without real-time measurement capabilities, estimate their state of charge values based on historical records and statistical models. S200: monitors the grid frequency in real time and identifies the type of frequency deviation event based on the preset dead zone and safety threshold; S300: Calculate the global active power regulation target for this round based on the frequency and power curves. Combined with the current state of charge data of the energy storage units, allocate the target power to each energy storage unit according to the remaining capacity ratio, and calculate the unit-level regulation instructions. S400: Construct an energy storage unit response prediction model and perform short-time series simulation using a small time step to predict the state of charge change process of the energy storage unit at the current power; S500: updating the remaining energy storage estimation of each energy storage system; S600: Determine whether there is a critical fault state of the state of charge exceeding or under the limit; S700: If the SOC exceeds or falls below the limit, the control strategy is automatically executed to adjust the wind turbine output or call for external power to return the SOC of the energy storage system to a safe range. If the SOC does not exceed or fall below the limit, the process proceeds to S800. S800: Repeat S200 to S700 for cyclic monitoring to achieve energy storage frequency control.

[0006] Optionally, in S100, initializing the initial energy storage estimation of the energy storage system includes the following process: S101: Reading on-site measurement data; reading the current state of charge of each energy storage unit. In the early stages of the energy storage system operation, or when it has just taken over the grid frequency regulation task, each energy storage unit is measured once to obtain the battery terminal voltage, current and temperature, and calculate the current state of charge of the energy storage unit; S102: Import historical records, use the end state of the energy storage unit in the previous service cycle as a reference value, obtain the energy storage margin at the end of the previous service cycle from the historical database or scheduling log, and determine the approximate energy storage state of the current energy storage unit; S103: Selecting a probability distribution model, which uses one of binomial distribution, Poisson distribution, or normal distribution, to estimate the percentage of the remaining energy storage capacity of the energy storage unit to the rated capacity; calculating the probability distribution value of the state of charge range based on the historical discharge curve, rated capacity, and intermediate compensation charging record of the energy storage unit; S104: The expected value of the probability distribution in S103 is rounded and mapped to the state of charge value, and then the boundary is eliminated. If it is less than 0, it is recorded as 0, and if it is greater than 100%, it is recorded as 100%, to obtain a normalized probability distribution statistical result; S105: Obtaining the initial state of charge of the energy storage unit during the service cycle and storing the state of charge to initialize the initial energy storage estimation of the energy storage system.

[0007] Optionally, in S200 , the grid frequency is monitored in real time, and the type of frequency deviation event is identified based on a preset dead zone and safety threshold, including the following process: S201: Set a sampling period, set a timer in the controller, trigger a data read when the time expires, and store the measured frequency in a ring buffer; S202: Determine whether the current frequency is in the dead zone, which refers to determining whether the current frequency value is overfrequency, underfrequency, or no deviation based on the nominal frequency and the dead zone range; if the current frequency is greater than the sum of the nominal frequency and the upper limit of the dead zone, it is recorded as overfrequency; if it is less than the difference between the nominal frequency and the lower limit of the dead zone, it is recorded as underfrequency; otherwise, it is recorded as no deviation; S203: Determine a threshold value and set an additional safety threshold at the boundary of the dead zone. If the additional safety threshold is exceeded, initiate a corresponding response in the scheduling; S204: Record the frequency deviation event. The frequency deviation event includes event type, trigger time and frequency value, and insert the frequency deviation event into the event queue.

[0008] Optionally, in S300, calculating and allocating the global active power regulation target for this round based on the frequency and power curve includes the following process: S301: Select a corresponding sub-interval, find the corresponding interval in the curve according to the current frequency value, and read the target active power relationship given in the interval; S302: Calculate the global regulation target. Based on the frequency and power curves of the interval, calculate the overall active power target of this regulation to obtain the global power. S303: Read the current state of charge, traverse the energy storage unit list, and read the state of charge value one by one. If the energy storage system has made scheduling adjustments midway, the most recently updated state of charge value shall prevail; S304: Calculate a proportional factor to distribute the global adjustment target power according to the current state of charge. Calculate a distribution coefficient for each energy storage unit based on the current available capacity and state of charge of the energy storage unit. S305: Forming a unit-level target power, multiplying the global power by the allocation coefficient to determine the allocated power of each energy storage unit. All the allocated powers constitute the global active power regulation target of this round.

[0009] Optionally, in S400, predicting a change in the state of charge of the energy storage unit at the current power includes the following process: S401: calling a response model and inputting parameters, wherein the parameters include the current state of charge, allocated power, temperature parameters, and power limit of each energy storage unit; S402: Construct a corresponding unit state structure for each energy storage unit. The unit state structure includes the current state of charge, allocated power, and charge and discharge efficiency. The unit state structure is submitted to the modeling interface. S403: Time series simulation, simulating the actual charging and discharging behavior of the energy storage unit in segments, and predicting the final state of charge change of the energy storage unit; S404: Calculate the new state of charge. After the time series simulation is completed, obtain the state of charge value of the energy storage unit when the current round of adjustment is completed, and return it to the controller; S405: If the state of charge of the energy storage unit after the response exceeds 100% or is lower than 0, a correction is performed and a prompt is given that the current energy storage unit cannot meet the allocated power, and the energy storage unit is marked.

[0010] Optionally, in S500 , updating the estimated remaining energy storage of each energy storage system includes the following process: S501: Create an update table for each energy storage unit, use the energy storage unit SOC value in S404 as the current SOC, and attach an updated timestamp; S502: Storing information about the adjustment process, storing data on changes in the state of charge during the process; S503: If the grid frequency is still fluctuating, return to S200 for the next round of monitoring; if the grid frequency stops fluctuating, enter the standby state.

[0011] Optionally, in S600, determining whether there is a critical fault state of the state of charge exceeding or under a limit includes the following process: S601: Read the limit range of the frequency modulation service and record the upper and lower limits of the state of charge; S602: Writing the state of charge upper limit and state of charge lower limit into the controller, and using a fault determination module to determine whether the state of charge exceeds the limit; S603: Traverse the state of charge of all energy storage units. If the state of charge of any unit reaches or exceeds the upper limit, or falls below the lower limit, then the energy storage unit is determined to be in a critical fault state. S604: When it is detected that the energy storage unit is in a critical fault state, it is marked that the energy storage system has a fault sign after the current frequency adjustment, and the sign is recorded.

[0012] Optionally, in S700, the control strategy is as follows: S701: Determine the fault type, which includes overcharge fault and over-discharge fault. S702: For overcharge faults, a power reduction command is issued. The power output is reduced through internal scheduling commands, reducing power transmission from the main grid to the energy storage system, or causing the energy storage system to reverse power to the grid until the state of charge of all energy storage systems returns to a safe range below the upper limit of the state of charge. For over-discharge faults, the power command is increased, increasing the output of the wind turbines or purchasing external power to charge the energy storage system until the state of charge of all energy storage systems returns to a safe range above the lower limit of the state of charge. S703: Re-estimate the state of charge, return to step S400, update the state of charge of each energy storage system, and substitute the new power allocation into the calculation until the state of charge no longer exceeds the limit; S704: Record the fault recovery event, where the recorded recovery event includes the fault type, fault trigger time, fault contact time, and operating power.

[0013] Optionally, in S800, the cyclic monitoring process is as follows: S801: Continuously monitor the grid frequency. When a new frequency deviation event occurs, return to step S200 until the service period ends. S802: After the service cycle ends, the final state of charge of each energy storage unit in the service cycle is summarized as a reference for the next service cycle.

[0014] In addition, the present invention also includes an energy storage frequency control device based on charge estimation and dynamic scheduling, including a memory and a controller: The memory is used to store a computer program that can be run on the controller; The controller is used to implement the steps of the energy storage frequency control method as described above when executing the computer program.

[0015] The application of the technical solution of the present invention has the following beneficial effects: The present invention provides a method and device for energy storage frequency control based on charge estimation and dynamic scheduling. By estimating the initial state of charge, monitoring the grid frequency in real time, and identifying the frequency deviation, the regulation target is generated and distributed to each energy storage system according to the remaining capacity. The present invention also simulates the response process of the energy storage unit, updates the state of charge, determines whether it exceeds the limit, and triggers power regulation measures if it exceeds the limit to ensure that the system continues to have frequency regulation capabilities. The present invention introduces a state of charge estimation mechanism based on historical information and statistical models, a short-term prediction simulation model, and a dynamic capacity-aware scheduling strategy to achieve automatic closed-loop control of frequency regulation tasks when some energy storage units cannot be monitored. It has the advantages of low engineering deployment threshold, high regulation accuracy, and wide adaptability.

[0016] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flowchart of the steps of the energy storage frequency control method based on charge estimation and dynamic scheduling in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, this embodiment provides a method for energy storage frequency control based on charge estimation and dynamic scheduling, including the following steps: S100: Initialize the initial energy storage valuation of the energy storage system. For energy storage units with real-time measurement capabilities, obtain their current state of charge data. For energy storage units without real-time measurement capabilities, estimate their state of charge values based on historical records and statistical models. S200: monitors the grid frequency in real time and identifies the type of frequency deviation event based on the preset dead zone and safety threshold; S300: Calculate the global active power regulation target for this round based on the frequency and power curves. Combined with the current state of charge data of the energy storage units, allocate the target power to each energy storage unit according to the remaining capacity ratio, and calculate the unit-level regulation instructions. S400: Construct an energy storage unit response prediction model and perform short-time series simulation using a small time step to predict the state of charge change process of the energy storage unit at the current power; S500: updating the remaining energy storage estimation of each energy storage system; S600: Determine whether there is a critical fault state of the state of charge exceeding or under the limit; S700: If the SOC exceeds or falls below the limit, the control strategy is automatically executed to adjust the wind turbine output or call for external power to return the SOC of the energy storage system to a safe range. If the SOC does not exceed or fall below the limit, the process proceeds to S800. S800: Repeat S200 to S700 for cyclic monitoring to achieve energy storage frequency control.

[0021] Optionally, in S100, initializing the initial energy storage estimation of the energy storage system includes the following process: S101: Reading on-site measurement data; reading the current state of charge of each energy storage unit. In the early stages of the energy storage system's operation, or when it has just taken over the grid frequency regulation task, each energy storage unit is measured to obtain the battery terminal voltage, current, and temperature. The current state of charge of the energy storage unit is calculated using the BMS algorithm. The BMS algorithm is a battery management system algorithm that can calculate the state of charge based on relevant battery information. S102: Import historical records, use the end state of the energy storage unit in the previous service cycle as a reference value, obtain the energy storage margin at the end of the previous service cycle from the historical database or scheduling log, and determine the approximate energy storage state of the current energy storage unit; S103: Select a probability distribution model. The probability distribution model uses one of the binomial distribution, Poisson distribution, or normal distribution to estimate the percentage of the remaining energy storage capacity of the energy storage unit to the rated capacity; calculate the probability distribution value of the state of charge range based on the historical discharge curve, rated capacity, and intermediate compensation charging record of the energy storage unit; It should be noted that in this embodiment, in order to facilitate the unified scheduling of different energy storage units in subsequent steps, the statistical results need to be normalized to the range of 0-100%. In this embodiment, the expected value of the estimated probability distribution is rounded and mapped to the charge state value, and then the boundary is eliminated. If it is less than 0, it is recorded as 0, and if it is greater than 100%, it is recorded as 100%. S104: The expected value of the probability distribution in S103 is rounded and mapped to the state of charge value, and then the boundary is eliminated. If it is less than 0, it is recorded as 0, and if it is greater than 100%, it is recorded as 100%, to obtain a normalized probability distribution statistical result; S105: Obtain the initial state of charge of the energy storage unit in this service cycle and store it to initialize the initial energy storage estimation of the energy storage system. It should be noted that the fields of the initial state of charge include the energy storage unit ID, initial charge state and rated capacity.

[0022] Optionally, in S200 , the grid frequency is monitored in real time, and the type of frequency deviation event is identified based on a preset dead zone and safety threshold, including the following process: S201: Set a sampling period (e.g., sampling every 100–200 ms), set a timer in the controller, trigger a data read upon expiration, and store the measured frequency in a ring buffer; S202: Determining whether the current frequency is in the dead zone. This refers to determining whether the current frequency value is overfrequency, underfrequency, or no deviation based on the nominal frequency (the frequency specified by the power network, specifically 50 Hz or 60 Hz) and the dead zone range (in this embodiment, the range is plus or minus 0.5 Hz). If the current frequency is greater than the sum of the nominal frequency and the upper limit of the dead zone, it is recorded as overfrequency; if it is less than the difference between the nominal frequency and the lower limit of the dead zone, it is recorded as underfrequency; otherwise, it is recorded as no deviation. S203: Determine a threshold value and set an additional safety threshold value at the boundary of the dead zone range (the additional safety threshold value in this embodiment is set to +0.2 Hz and -0.2 Hz). If the additional safety threshold value is exceeded, initiate a corresponding response in the scheduling; S204: Record the frequency deviation event. The frequency deviation event includes event type, trigger time and frequency value, and insert the frequency deviation event into the event queue.

[0023] Optionally, in S300, calculating and allocating the global active power regulation target for this round based on the frequency and power curve includes the following process: S301: Select the corresponding sub-interval, find the corresponding interval in the curve according to the current frequency value, and read the target active power relationship given in the interval; the active power relationship in this embodiment is P(f) = k Δf, where P(f) is the active power, k is the frequency modulation coefficient, and Δf is the frequency deviation value.

[0024] S302: Calculate the global regulation target. Based on the frequency and power curves of the interval, calculate the overall active power target of this regulation to obtain the global power. S303: Read the current state of charge, traverse the energy storage unit list, and read the state of charge value one by one. If the energy storage system has made scheduling adjustments midway, the most recently updated state of charge value shall prevail; S304: Calculate the proportional factor and distribute the global adjustment target power according to the current state of charge. The distribution coefficient for each energy storage unit is calculated based on the current available capacity and state of charge of the energy storage unit. The distribution coefficient is calculated as follows: the current available capacity of each energy storage unit = 1 - state of charge (in discharge mode) or + state of charge (in charge mode), and then normalize to obtain the distribution coefficient α_i for each unit.

[0025] S305: A unit-level target power is generated. The global power is multiplied by the allocation coefficient to determine the allocated power for each energy storage unit. All allocated powers constitute the global active power regulation target for this round. Specifically, the allocated power P_i is calculated as P_i = P_total × α_i, where P_total is the global power, also known as the total system frequency regulation power. If the allocated power exceeds the total system frequency regulation power, the allocated power is clipped.

[0026] Optionally, in S400, predicting a change in the state of charge of the energy storage unit at the current power includes the following process: S401: calling a response model and inputting parameters, wherein the parameters include the current state of charge, allocated power, temperature parameters, and power limit of each energy storage unit; S402: Construct a corresponding unit state structure for each energy storage unit. The unit state structure includes the current state of charge, allocated power, and charge and discharge efficiency. The unit state structure is submitted to the modeling interface. S403: Time series simulation, which simulates the actual charging and discharging behavior of the energy storage unit in segments and predicts the final state of charge change of the energy storage unit. Specifically, for a small step size Δt, iteratively update SOC(t+Δt) = SOC(t)±(P_i × Δt / E_rated) every Δt, where E_rated is the rated capacity of the energy storage unit, SOC(t+Δt) is the predicted state of charge, and SOC(t) represents the current state of charge. S404: Calculate the new state of charge. After the time series simulation is completed, obtain the state of charge value of the energy storage unit when the current round of adjustment is completed, and return it to the controller; S405: If the state of charge of the energy storage unit after the response exceeds 100% or is lower than 0, a correction is performed and a prompt is given that the current energy storage unit cannot meet the allocated power, and the energy storage unit is marked.

[0027] Optionally, in S500 , updating the estimated remaining energy storage of each energy storage system includes the following process: S501: Create an update table for each energy storage unit, use the energy storage unit SOC value in S404 as the current SOC, and attach an updated timestamp; S502: Storing information about the adjustment process, including data on changes in the state of charge during the process; the information about the adjustment process includes at least the start time, end time, and actual power curve; S503: If the grid frequency is still fluctuating, return to S200 for the next round of monitoring; if the grid frequency stops fluctuating, enter the standby state.

[0028] Optionally, in S600, determining whether there is a critical fault state of the state of charge exceeding or under a limit includes the following process: S601: Read the limit range of the frequency modulation service and record the upper and lower limits of the state of charge; S602: Writing the state of charge upper limit and state of charge lower limit into the controller, and using a fault determination module to determine whether the state of charge exceeds the limit; S603: Traverse the state of charge of all energy storage units. If the state of charge of any unit reaches or exceeds the upper limit, or falls below the lower limit, then the energy storage unit is determined to be in a critical fault state. S604: When it is detected that the energy storage unit is in a critical fault state, it is marked that the energy storage system has a fault sign after the current frequency adjustment, and the sign is recorded.

[0029] Optionally, in S700, the control strategy is as follows: S701: Determine the fault type, which includes overcharge fault and over-discharge fault. S702: For overcharge faults, a power reduction command is issued. The power output is reduced through internal scheduling commands, reducing power transmission from the main grid to the energy storage system, or causing the energy storage system to reverse power to the grid until the state of charge of all energy storage systems returns to a safe range below the upper limit of the state of charge. For over-discharge faults, the power command is increased, increasing the output of the wind turbines or purchasing external power to charge the energy storage system until the state of charge of all energy storage systems returns to a safe range above the lower limit of the state of charge. S703: Re-estimate the state of charge, return to step S400, update the state of charge of each energy storage system, and substitute the new power allocation into the calculation until the state of charge no longer exceeds the limit; S704: Record the fault recovery event, where the recorded recovery event includes the fault type, fault trigger time, fault contact time, and operating power.

[0030] Optionally, in S800, the cyclic monitoring process is as follows: S801: Continuously monitor the grid frequency. When a new frequency deviation event occurs, return to step S200 until the service period ends. S802: After the service cycle ends, the final state of charge of each energy storage unit in the service cycle is summarized as a reference for the next service cycle.

[0031] Unlike existing energy storage system frequency control methods, this embodiment's method does not rely on system-wide real-time communication and a unified state feedback mechanism. It is particularly suitable for distributed deployments, edge grid access, or scenarios where some energy storage units lack state measurement capabilities. By combining state-of-charge estimation, modeling and predictive modeling of energy storage unit responses, and automatic control strategies, this embodiment achieves adaptive, robust, closed-loop frequency regulation and control for heterogeneous energy storage systems in complex power environments, demonstrating strong promotional value and engineering practicality.

[0032] In addition, the present invention also includes an energy storage frequency control device based on charge estimation and dynamic scheduling, including a memory and a controller: The memory is used to store a computer program that can be run on the controller; The controller is used to implement the steps of the energy storage frequency control method as described above when executing the computer program.

[0033] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0034] The memory can be used to store the computer programs and / or modules. The processor implements the computer programs by running or executing the computer programs and / or modules stored in the memory and accessing data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0035] In addition, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned energy storage frequency control method based on charge estimation and dynamic scheduling are implemented.

[0036] This embodiment provides a method for energy storage frequency control based on charge estimation and dynamic scheduling. The method of this embodiment adopts a step-by-step integral iterative calculation method to accurately simulate the gas reservoir inflation and deflation processes, thereby improving the calculation accuracy. The present invention also introduces a numerical simulation calculation method to accurately calculate the temperature changes of the gas reservoir during the idle process between inflation and deflation.

[0037] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0038] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for controlling energy storage frequency based on charge estimation and dynamic scheduling, characterized in that: The steps include: S100: Initialize the initial energy storage valuation of the energy storage system. For energy storage units with real-time measurement capabilities, obtain their current state of charge data. For energy storage units without real-time measurement capabilities, estimate their state of charge values based on historical records and statistical models. S200: monitors the grid frequency in real time and identifies the type of frequency deviation event based on the preset dead zone and safety threshold; S300: Calculate the global active power regulation target for this round based on the frequency and power curves. Combined with the current state of charge data of the energy storage units, allocate the target power to each energy storage unit according to the remaining capacity ratio, and calculate the unit-level regulation instructions. S400: Construct an energy storage unit response prediction model and perform short-time series simulation using a small time step to predict the state of charge change process of the energy storage unit at the current power; S500: updating the remaining energy storage estimation of each energy storage system; S600: Determine whether there is a critical fault state of the state of charge exceeding or under the limit; S700: If the SOC exceeds or falls below the limit, the control strategy is automatically executed to adjust the wind turbine output or call for external power to return the SOC of the energy storage system to a safe range. If the SOC does not exceed or fall below the limit, the process proceeds to S800. S800: Repeat S200 to S700 for cyclic monitoring to achieve energy storage frequency control.

2. The energy storage frequency control method based on charge estimation and dynamic scheduling according to claim 1 is characterized in that: In S100, the initial energy storage estimation of the energy storage system is initialized, including the following process: S101: Reading on-site measurement data; reading the current state of charge of each energy storage unit. In the early stages of the energy storage system operation, or when it has just taken over the grid frequency regulation task, each energy storage unit is measured once to obtain the battery terminal voltage, current and temperature, and calculate the current state of charge of the energy storage unit; S102: Import historical records, use the end state of the energy storage unit in the previous service cycle as a reference value, obtain the energy storage margin at the end of the previous service cycle from the historical database or scheduling log, and determine the approximate energy storage state of the current energy storage unit; S103: Selecting a probability distribution model, which uses one of binomial distribution, Poisson distribution, or normal distribution, to estimate the percentage of the remaining energy storage capacity of the energy storage unit to the rated capacity; calculating the probability distribution value of the state of charge range based on the historical discharge curve, rated capacity, and intermediate compensation charging record of the energy storage unit; S104: The expected value of the probability distribution in S103 is rounded and mapped to the state of charge value, and then the boundary is eliminated. If it is less than 0, it is recorded as 0, and if it is greater than 100%, it is recorded as 100%, to obtain a normalized probability distribution statistical result; S105: Obtaining the initial state of charge of the energy storage unit during the service cycle and storing the state of charge to initialize the initial energy storage estimation of the energy storage system.

3. The energy storage frequency control method based on charge estimation and dynamic scheduling according to claim 2 is characterized in that: In S200, the grid frequency is monitored in real time, and the type of frequency deviation event is identified based on the preset dead zone and safety threshold, including the following process: S201: Set a sampling period, set a timer in the controller, trigger a data read when the time expires, and store the measured frequency in a ring buffer; S202: Determine whether the current frequency is in the dead zone, which refers to determining whether the current frequency value is overfrequency, underfrequency, or no deviation based on the nominal frequency and the dead zone range; if the current frequency is greater than the sum of the nominal frequency and the upper limit of the dead zone, it is recorded as overfrequency; if it is less than the difference between the nominal frequency and the lower limit of the dead zone, it is recorded as underfrequency; otherwise, it is recorded as no deviation; S203: Determine a threshold value and set an additional safety threshold at the boundary of the dead zone. If the additional safety threshold is exceeded, initiate a corresponding response in the scheduling; S204: Record the frequency deviation event. The frequency deviation event includes event type, trigger time and frequency value, and insert the frequency deviation event into the event queue.

4. The energy storage frequency control method based on charge estimation and dynamic scheduling according to claim 3 is characterized in that: In S300, the global active power regulation target for this round is calculated and allocated based on the frequency and power curves, including the following processes: S301: Select a corresponding sub-interval, find the corresponding interval in the curve according to the current frequency value, and read the target active power relationship given in the interval; S302: Calculate the global regulation target. Based on the frequency and power curves of the interval, calculate the overall active power target of this regulation to obtain the global power. S303: Read the current state of charge, traverse the energy storage unit list, and read the state of charge value one by one. If the energy storage system has made scheduling adjustments midway, the most recently updated state of charge value shall prevail; S304: Calculate a proportional factor to distribute the global adjustment target power according to the current state of charge. Calculate a distribution coefficient for each energy storage unit based on the current available capacity and state of charge of the energy storage unit. S305: Forming a unit-level target power, multiplying the global power by the allocation coefficient to determine the allocated power of each energy storage unit. All the allocated powers constitute the global active power regulation target of this round.

5. The energy storage frequency control method based on charge estimation and dynamic scheduling according to claim 4 is characterized in that: In S400, the state of charge change of the energy storage unit at the current power is predicted, including the following process: S401: calling a response model and inputting parameters, wherein the parameters include the current state of charge, allocated power, temperature parameters, and power limit of each energy storage unit; S402: Construct a corresponding unit state structure for each energy storage unit. The unit state structure includes the current state of charge, allocated power, and charge and discharge efficiency. The unit state structure is submitted to the modeling interface. S403: Time series simulation, simulating the actual charging and discharging behavior of the energy storage unit in segments, and predicting the final state of charge change of the energy storage unit; S404: Calculate the new state of charge. After the time series simulation is completed, obtain the state of charge value of the energy storage unit when the current round of adjustment is completed, and return it to the controller; S405: If the state of charge of the energy storage unit after the response exceeds 100% or is lower than 0, a correction is performed and a prompt is given that the current energy storage unit cannot meet the allocated power, and the energy storage unit is marked.

6. The energy storage frequency control method based on charge estimation and dynamic scheduling according to claim 5 is characterized in that: In S500, the remaining energy storage estimates of each energy storage system are updated, including the following process: S501: Create an update table for each energy storage unit, use the energy storage unit SOC value in S404 as the current SOC, and attach an updated timestamp; S502: Storing information about the adjustment process, storing data on changes in the state of charge during the process; S503: If the grid frequency is still fluctuating, return to S200 for the next round of monitoring; if the grid frequency stops fluctuating, enter the standby state.

7. The energy storage frequency control method based on charge estimation and dynamic scheduling according to claim 6 is characterized in that: In S600, determining whether there is a critical fault state of the state of charge exceeding or under the limit includes the following process: S601: Read the limit range of the frequency modulation service and record the upper and lower limits of the state of charge; S602: Writing the state of charge upper limit and state of charge lower limit into the controller, and using a fault determination module to determine whether the state of charge exceeds the limit; S603: Traverse the state of charge of all energy storage units. If the state of charge of any unit reaches or exceeds the upper limit, or falls below the lower limit, then the energy storage unit is determined to be in a critical fault state. S604: When it is detected that the energy storage unit is in a critical fault state, it is marked that the energy storage system has a fault sign after the current frequency adjustment, and the sign is recorded.

8. The energy storage frequency control method based on charge estimation and dynamic scheduling according to claim 7 is characterized in that: In S700, the control strategy is as follows: S701: Determine the fault type, which includes overcharge fault and over-discharge fault. S702: For overcharge faults, a power reduction command is issued. The power output is reduced through internal scheduling commands, reducing power transmission from the main grid to the energy storage system, or causing the energy storage system to reverse power to the grid until the state of charge of all energy storage systems returns to a safe range below the upper limit of the state of charge. For over-discharge faults, the power command is increased, increasing the output of the wind turbines or purchasing external power to charge the energy storage system until the state of charge of all energy storage systems returns to a safe range above the lower limit of the state of charge. S703: Re-estimate the state of charge, return to step S400, update the state of charge of each energy storage system, and substitute the new power allocation into the calculation until the state of charge no longer exceeds the limit; S704: Record the fault recovery event, where the recorded recovery event includes the fault type, fault trigger time, fault contact time, and operating power.

9. The energy storage frequency control method based on charge estimation and dynamic scheduling according to claim 8 is characterized in that: In S800, the cyclic monitoring process is as follows: S801: Continuously monitor the grid frequency. When a new frequency deviation event occurs, return to step S200 until the service period ends. S802: After the service cycle ends, the final state of charge of each energy storage unit in the service cycle is summarized as a reference for the next service cycle.

10. An energy storage frequency control device based on charge estimation and dynamic scheduling, characterized in that: Including memory and controller: The memory is used to store a computer program that can be run on the controller; The controller is configured to implement the steps of the energy storage frequency control method according to any one of claims 1 to 9 when executing the computer program.

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