Capacity optimization method, system and device of multi-time scale hybrid energy storage system

Abstract

The application relates to the technical field of power system energy storage and automatic control, and discloses a capacity optimization method, system and device of a multi-time-scale hybrid energy storage system, which comprises the following steps: based on a multi-time-scale decomposition result of new energy power fluctuation, an initial capacity optimization model is established and solved to obtain initial capacity configuration parameters and multi-time-scale division parameters; based on the initial parameters, the hybrid energy storage system is controlled to participate in grid frequency modulation, and operation performance and equipment state data are collected; performance feedback indexes are generated according to the data, and adjustable parameters of the capacity optimization model are dynamically corrected based on the indexes, so that the model is solved again, in the process, the division parameters and the capacity configuration parameters are treated as associated variables to obtain updated parameters; the updated parameters are applied to the system and returned to the frequency modulation operation step to form a closed-loop optimization. The application realizes the collaborative dynamic optimization of energy storage capacity and division strategy, and improves the economy, equipment life and operation robustness of the system.

Description

Technical Field

[0001] This application relates to the field of power system energy storage and automatic control technology, and in particular to a capacity optimization method, system and device for a multi-timescale hybrid energy storage system. Background Technology

[0002] With the increasing penetration of new energy sources such as wind power and photovoltaics into the power system, the randomness and volatility of their output pose a severe challenge to the stability of the power grid frequency. To mitigate power fluctuations from new energy sources and support the power grid frequency, hybrid energy storage systems, by integrating energy storage units with complementary dynamic response characteristics (such as power-type energy storage like flywheels and supercapacitors, and energy-type energy storage like compressed air storage and batteries), have become an important technological direction for improving the flexibility and stability of the power grid by leveraging their adjustment advantages at different time scales.

[0003] The technological development in this field has mainly gone through stages, from single energy storage configurations to multi-type energy storage combinations, from fixed capacity planning to preliminary scenario-adaptive design, and from open-loop control to multi-timescale coordinated control. Early research mainly focused on capacity optimization of single-type energy storage, which was difficult to cope with wide-frequency-domain fluctuations in demand. Subsequently, researchers began to explore hybrid systems that include power-type and energy-type energy storage, and used multi-timescale decomposition techniques to allocate fluctuating power according to frequency bands, initially realizing the division of labor and cooperation of different energy storage media in fast and slow frequency bands. However, most existing methods still adopt a "one-time design, static operation" model, that is, after determining the energy storage capacity and fixed division of labor strategy based on historical or typical data in the planning stage, no adjustments are made during long-term operation. This model has significant limitations: First, static design based on limited scenarios is difficult to adapt to the continuous dynamic changes in renewable energy output and grid frequency regulation demand, which may lead to a disconnect between capacity configuration and actual demand, resulting in wasted investment or insufficient regulation capacity; Second, the fixed division of labor strategy cannot be adaptively adjusted according to the actual operating status of energy storage units (such as state of charge and equipment health), which may easily lead to some units operating under long-term overload and accelerating losses, or an imbalance in the division of labor ratio affecting the overall regulation performance; Third, the lack of a closed-loop mechanism to feed real-time operating data back to the capacity and strategy optimization process means that the system cannot continuously learn and improve itself during operation, limiting its long-term economic efficiency, reliability, and robustness in the face of extreme disturbances.

[0004] Therefore, how to construct a hybrid energy storage system that can dynamically and in a closed loop optimize its capacity configuration and multi-timescale division of labor strategy based on actual operating performance has become a key issue that urgently needs to be addressed in current technological development. Summary of the Invention

[0005] To address the aforementioned technical issues, this application provides a capacity optimization method, system, and apparatus for a multi-timescale hybrid energy storage system, which improves the accuracy of resource allocation and the system's adaptability to dynamic environments.

[0006] In a first aspect, this application provides a capacity optimization method for a multi-timescale hybrid energy storage system, the method comprising:

[0007] Step S1: Based on the multi-timescale decomposition results of new energy power fluctuations, establish and solve the initial capacity optimization model to obtain the initial capacity configuration parameters and initial multi-timescale division of labor parameters of the hybrid energy storage system. The hybrid energy storage system includes a first type of energy storage unit and a second type of energy storage unit with complementary dynamic response characteristics.

[0008] Step S2: Based on the initial capacity configuration parameters and the initial multi-timescale division of labor parameters, control the hybrid energy storage system to participate in grid frequency regulation, and collect performance data and equipment status data during operation;

[0009] Step S3: Based on the performance data and equipment status data, generate performance feedback indicators, and dynamically correct the adjustable parameters of the capacity optimization model according to the performance feedback indicators, and re-solve the corrected capacity optimization model. In the re-solve process, the multi-timescale division of labor parameters and the capacity configuration parameters are processed as correlation variables to obtain the updated capacity configuration parameters and updated multi-timescale division of labor parameters for the next running cycle.

[0010] Step S4: Apply the updated capacity configuration parameters and the updated multi-timescale division of labor parameters to the hybrid energy storage system, and return to execute step S2 to form a closed-loop optimization process.

[0011] Secondly, this application provides a capacity optimization system for a multi-timescale hybrid energy storage system, the system comprising:

[0012] The initial configuration module is used to establish and solve the initial capacity optimization model based on the multi-timescale decomposition results of new energy power fluctuations, and obtain the initial capacity configuration parameters and initial multi-timescale division of labor parameters of the hybrid energy storage system. The hybrid energy storage system includes a first type of energy storage unit and a second type of energy storage unit with complementary dynamic response characteristics.

[0013] The frequency regulation operation and data acquisition module is used to control the hybrid energy storage system to participate in grid frequency regulation based on the initial capacity configuration parameters and the initial multi-time scale division of labor parameters, and to collect performance data and equipment status data during operation.

[0014] The closed-loop feedback and reconfiguration module is used to generate performance feedback indicators based on the performance data and equipment status data, dynamically correct the adjustable parameters of the capacity optimization model according to the performance feedback indicators, and re-solve the corrected capacity optimization model. In the re-solving process, the multi-timescale division of labor parameters and the capacity configuration parameters are processed as correlation variables to obtain the updated capacity configuration parameters and updated multi-timescale division of labor parameters for the next running cycle.

[0015] The application and iteration module is used to apply the updated capacity configuration parameters and the updated multi-timescale division of labor parameters to the hybrid energy storage system, and return to execute the frequency regulation operation and data acquisition module to form a closed-loop optimization process.

[0016] A third aspect of this application provides a capacity optimization device for a multi-timescale hybrid energy storage system, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the capacity optimization device for the multi-timescale hybrid energy storage system to execute the aforementioned capacity optimization method for the multi-timescale hybrid energy storage system.

[0017] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0018] 1. The technical solution provided in this application, by constructing a closed-loop feedback and reconfiguration mechanism, transforms real-time frequency regulation performance data and equipment status data into a basis for correcting the capacity optimization model. This enables the system to periodically or based on event triggers to collaboratively update capacity configuration parameters and task allocation parameters, breaking the limitations of traditional static planning. This allows the "hardware capabilities" and "task allocation strategies" of the energy storage system to adaptively match according to the actual operating conditions of the power grid and the fluctuation characteristics of new energy sources, significantly improving the accuracy of resource allocation and the system's adaptability to dynamic environments.

[0019] 2. This application introduces performance feedback indicators (such as frequency deviation and equipment wear count) and links them to the objective function and safety constraints of the optimization model. During the optimization process, it automatically balances frequency regulation performance, investment costs, and equipment lifespan loss. The optimization results can guide the system to avoid overload operation and deep cycling of specific energy storage units. For example, it reduces frequent deep charging and discharging of flywheel energy storage and maintains the pressure of compressed air energy storage within a safer range. This effectively reduces the operating stress and lifespan loss of key equipment, extends the overall service life of the system, and optimizes the total lifespan cost while ensuring frequency regulation performance.

[0020] 3. This application integrates modules such as intelligent triggering, update verification, and performance root cause analysis, enabling the system to have the capabilities of self-sensing, self-diagnosis, self-decision-making, and self-optimization. It can flexibly initiate optimization based on preset conditions or external commands, perform safety verification on new configurations, and accurately locate the source of performance deviations through root cause analysis, and implement targeted corrections. This intelligent closed-loop architecture not only improves the system's robustness in dealing with complex operating conditions and extreme disturbances, but also ensures the smoothness and safety of the configuration update process, ultimately enabling the hybrid energy storage system to operate stably as a highly reliable, adaptive, and sustainably evolving intelligent frequency regulation resource. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the steps of the capacity optimization method for a multi-timescale hybrid energy storage system in the embodiments of this application.

[0023] Figure 2 This is a schematic diagram of the multi-timescale decomposition results of wind farm power in the embodiments of this application;

[0024] Figure 3 This is a schematic diagram illustrating the power distribution and safe redistribution of flywheel energy storage and compressed air energy storage in an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of the frequency modulation tracking error distribution in the embodiments of this application;

[0026] Figure 5 This is a structural diagram of the capacity optimization system of the multi-timescale hybrid energy storage system in the embodiments of this application. Detailed Implementation

[0027] This application provides a method, system, and apparatus for capacity optimization of a multi-timescale hybrid energy storage system. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings 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 the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "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.

[0028] Example 1:

[0029] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 The capacity optimization method for a multi-timescale hybrid energy storage system in this application includes:

[0030] Step S1: Based on the multi-timescale decomposition results of new energy power fluctuations, establish and solve the initial capacity optimization model to obtain the initial capacity configuration parameters and initial multi-timescale division of labor parameters of the hybrid energy storage system. The hybrid energy storage system includes a first type of energy storage unit and a second type of energy storage unit with complementary dynamic response characteristics.

[0031] Step S1 further includes: the first type of energy storage unit is flywheel energy storage or supercapacitor energy storage; the second type of energy storage unit is compressed air energy storage, flow battery or hydrogen energy storage; the multi-timescale decomposition results include high-frequency components, mid-frequency components and low-frequency components; the multi-timescale division of labor parameters are used to define the power allocation ratio of the first type of energy storage unit and the second type of energy storage unit for power components of different time scales; the solution results of the capacity optimization model are configured as follows: the high-frequency component is mainly undertaken by the first type of energy storage unit, the low-frequency component is mainly undertaken by the second type of energy storage unit, and the mid-frequency component is jointly undertaken by the first type of energy storage unit and the second type of energy storage unit according to the multi-timescale division of labor parameters.

[0032] Specifically, this embodiment aims to explain in detail how to complete the initial capacity and division of labor strategy configuration of a hybrid energy storage system, so as to solve the technical problems in the prior art where the planning of energy storage system is out of sync with the actual multi-timescale power fluctuation characteristics of the power grid, and relies on one-time static design and cannot adapt to future operating needs.

[0033] Specifically, in one embodiment of the present invention, the initial configuration begins with preprocessing and analyzing the historical power output data of the target renewable energy power plant, such as a wind farm with an installed capacity of 100 MW. First, the empirical mode decomposition method is used to process the fluctuation sequence of the plant's net power output. The principle of this method is to adaptively decompose the non-stationary, nonlinear power signal into a series of intrinsic mode function components arranged from high frequency to low frequency, and a residual trend term. In this application scenario, the decomposition process effectively separates the rapid disturbances at the second level, the medium fluctuations at the minute level, and the slow trends at the ten-minute level or longer contained in the power fluctuations. The first intrinsic mode function component typically corresponds to the high-frequency component, with a timescale between 1 and 10 seconds, mainly reflecting the instantaneous power surge caused by wind turbulence or rapid cloud movement. The subsequent components together constitute the mid-frequency component, with a timescale extending to 10 seconds to 5 minutes, reflecting the medium-term power fluctuation characteristics. The final residual trend term characterizes the low-frequency component, with a timescale of more than 5 minutes, corresponding to the long-term power output change trend guided by weather system changes. Through this decomposition, the distribution characteristics of grid frequency regulation demand in the frequency domain are clearly quantified, providing a direct input basis for subsequent allocation of energy storage regulation tasks according to different time scales.

[0034] To more intuitively demonstrate the multi-timescale decomposition of signals in actual energy storage, Figure 2 A schematic diagram of the multi-timescale decomposition results of wind farm power is shown. This embodiment performs a similar decomposition on the fluctuation sequence within a typical operating cycle. The decomposition results intuitively reveal the distribution characteristics of pressure fluctuations at different time scales, further verifying the effectiveness of the multi-timescale decomposition method in the state analysis and task allocation of hybrid energy storage systems.

[0035] Based on the time series of power components in each frequency band obtained from the above decomposition, this embodiment constructs an initial capacity optimization model. This capacity optimization model is a multi-objective constrained optimization problem that comprehensively considers technical feasibility, economy, and reliability. Its core principle is to seek the optimal balance between the system's total life cycle cost and frequency regulation support performance under the premise of satisfying a series of physical and engineering hard constraints. The solution objective of the capacity optimization model directly corresponds to the initial configuration parameters that need to be determined, mainly including: the rated power and rated energy storage capacity of the first type of energy storage unit, such as flywheel energy storage; the rated power and rated energy storage capacity of the second type of energy storage unit, such as compressed air energy storage; and a set of multi-time-scale division of labor parameters. These division of labor parameters are used to quantitatively define the power sharing ratio of the two types of energy storage units in different frequency bands. For example, if a parameter α is set to represent the power sharing ratio undertaken by flywheel energy storage in a certain mid-frequency band, then the power sharing ratio undertaken by compressed air energy storage is 1-α. For the high-frequency band and low-frequency band obtained from the decomposition, a clear division of labor relationship can be pre-set according to the energy storage characteristics. For example, high-frequency fluctuations are mainly responded to by flywheel energy storage, and low-frequency fluctuations are mainly suppressed by compressed air energy storage.

[0036] The objective function of this capacity optimization model is typically set to minimize the system's equivalent annualized cost. This cost integrates investment costs related to power and capacity, estimated operation and maintenance costs, and depreciation costs estimated from expected cycle life losses. Simultaneously, to ensure basic frequency regulation performance, a penalty term is introduced into the objective function to minimize the cumulative deviation energy between the actual energy storage output and demand power in each frequency band during the evaluation period. The constraint system of the capacity optimization model is key to its close integration with actual power system engineering, mainly including: First, power balance constraints, ensuring that at each calculation moment, the total regulation output of energy storage for each frequency band can cover the required power. The constraints include: first, the power fluctuation requirements of the frequency band; second, the physical and safety constraints of the energy storage unit itself, such as the instantaneous output of flywheel energy storage not exceeding the power limit of its power electronic converter, and the energy stored must be maintained between the minimum energy corresponding to the safe speed and the rated capacity. Compressed air energy storage must simultaneously meet the power limit of its compressor / expander and the upper and lower pressure safety limits of the gas tank. The pressure limit is directly related to its usable energy boundary through the gas state equation; third, the constraints on the range of values ​​of the division parameters; and fourth, the systemic performance constraints that meet the requirements of the grid connection technical specifications, such as the response time, regulation rate, and allowable range of steady-state frequency deviation for primary frequency regulation.

[0037] After clarifying the objective function and constraints, this embodiment employs mature optimization algorithms for numerical solution. For example, a non-dominated sorting genetic algorithm with an elitist strategy can be used to handle multi-objective optimization problems, obtaining a set of non-degraded Pareto optimal solutions for decision-makers to choose according to their preferences; or, after determining the weight coefficients of each sub-objective, a nonlinear programming algorithm such as sequential quadratic programming can be used to obtain a single optimal solution. The solution process requires specific cost parameters, such as unit power cost, unit capacity cost, equipment efficiency parameters, and lifetime model parameters, with the power demand time series of each frequency band obtained from the above empirical mode decomposition as the core input data. After the solution is completed, the algorithm outputs specific numerical results, which constitute the initial operating configuration scheme of the system; for example, for The initial configuration that can be obtained for the aforementioned 100 MW wind farm is as follows: a flywheel energy storage capacity of 20 MW and a usable energy of 1 MWh; a compressed air energy storage capacity of 15 MW and a stored energy of 30 MWh; and it is clearly stated that the mid-frequency fluctuation component is shared by the flywheel and compressed air in a pre-calculated ratio, such as 70% and 30% respectively. This configuration scheme quantifies the division of responsibilities and capacity boundaries of various energy storage types in the initial state of the system, laying a scientific and executable starting point for subsequent real-time frequency regulation operation and feedback-based closed-loop capacity optimization. The entire modeling and solution process deeply integrates mathematical optimization theory with the physical mechanism of power system frequency regulation, the characteristics of energy storage equipment, and the economic requirements of the project, ensuring the technical rationality and engineering feasibility of the initial configuration scheme.

[0038] Step S2: Based on the initial capacity configuration parameters and the initial multi-timescale division of labor parameters, control the hybrid energy storage system to participate in grid frequency regulation, and collect performance data and equipment status data during operation.

[0039] The hierarchical coordination control strategy for controlling the hybrid energy storage system to participate in grid frequency regulation includes: the upper-level coordinator generates a total frequency regulation power command and a power allocation command for each energy storage unit based on the grid frequency deviation, frequency change rate, and the real-time state margin of the first and second types of energy storage units; the lower-level actuator performs rapid tracking control on the power allocation command; when the upper-level coordinator determines that the real-time state margin of any type of energy storage unit is lower than the safety threshold, it initiates the power command reallocation logic and reallocates the portion of the power command that the energy storage unit with the real-time state margin lower than the safety threshold cannot safely bear to another type of energy storage unit or backup resources.

[0040] Specifically, this embodiment aims to explain in detail how to control a hybrid energy storage system based on an initial configuration to safely and efficiently perform grid frequency regulation tasks and collect key operational data, in order to solve the technical problems in the prior art where the operation control of hybrid energy storage is disconnected from the safety status of the equipment, and there is a lack of adaptive protection and redistribution mechanisms, which affects the long-term reliable operation of the system and the validity of the data.

[0041] Specifically, in one embodiment, after the system obtains the initial capacity configuration parameters and multi-timescale division of labor parameters, it immediately enters the frequency regulation operation and data acquisition stage. The core of this stage is to execute a hierarchical coordination control strategy. The upper-level coordinator in the strategy is essentially a real-time decision-making algorithm module running at the millisecond level. This real-time decision-making algorithm module continuously receives real-time measurement signals from the power grid, mainly including the frequency value f(t) at the grid connection point and its first derivative df / dt, i.e., the rate of frequency change. At the same time, it obtains the real-time state margin of the first type of energy storage unit, such as flywheel energy storage (FESS), and the second type of energy storage unit, such as compressed air energy storage (CAES), through the internal communication bus. For flywheel energy storage, its state margin is usually characterized by the state of charge or equivalent speed percentage, defined as the ratio of the current available energy to the maximum available energy. Therefore, the multi-timescale division of labor + safety protection control logic proposed in this application can effectively constrain the working range of flywheel energy storage during frequency regulation operation, avoid deep loop and out-of-bounds risks, thereby ensuring the safety and reliability of equipment operation and lifespan.

[0042] For compressed air energy storage, its state margin is mainly determined by the position of the real-time pressure value of the air tank relative to the upper and lower limits of the safe operating pressure, which can be quantified as a pressure margin coefficient between 0 and 1. The upper-level coordinator first calculates the total frequency regulation power command P_ref_total(t) required by the system to support the grid frequency based on the frequency deviation Δf, for example, the difference between the current frequency and the rated frequency of 50.00Hz and the frequency change rate, through a unified algorithm model that combines virtual inertia and droop characteristics. The principle of this unified algorithm model is to simulate the frequency response characteristics of a synchronous generator, and its output can simultaneously provide fast inertia support and steady-state frequency recovery power.

[0043] Subsequently, the coordinator decomposes the total command according to preset multi-timescale division parameters: specifically, it internally maintains a digital filter bank corresponding to the decomposed frequency band in step S1, decomposing P_ref_total(t) into high-frequency, mid-frequency, and low-frequency components in real time; according to the division parameters, the high-frequency components are all directed to flywheel energy storage, the low-frequency components are all directed to compressed air energy storage, and the mid-frequency components are proportionally assigned, for example, 70% to flywheel energy storage and 30% to compressed air energy storage, thereby generating the preliminary power allocation command P_FESS_pre(t) for each energy storage unit. And P_CAES_pre(t); however, this is only an ideal allocation. Before the command is issued, the coordinator will check this preliminary command against the real-time state margin of each energy storage unit. The safety threshold is a pre-set boundary value. For example, the lower limit of safe operation of flywheel energy storage SOC is set to 20%, and the lower limit of safe pressure of compressed air energy storage is a specific value. The coordinator's algorithm includes a real-time logic judgment unit. If it detects that the state margin of a certain energy storage unit is lower than its safety threshold, or if it predicts that its state will soon exceed the limit according to the preliminary command, the power command reallocation logic is immediately started. The core of this logic is to recalculate the power allocation. For example, when the flywheel SOC is close to the 20% lower limit, the algorithm will reduce the medium frequency and even part of the high frequency power command originally allocated to it at a preset smooth rate, and dynamically add this reduction amount ΔP(t) to the command of compressed air energy storage. If compressed air energy storage also has no margin, the excess part can be marked as "unmet power" and the standby resources can be triggered. This redistribution process uses a slope limiter to ensure smooth power command changes and avoid secondary impacts on the power grid; to visually demonstrate the effects of the above power allocation and safe redistribution process, such as Figure 3 The embodiment shown records flywheel energy storage over a typical time period. Compressed air energy storage By comparing the changes in power commands, it can be seen that at a specific moment, such as when the flywheel energy storage margin is insufficient, the system automatically triggers power redistribution, safely and smoothly transferring part of the power commands from the flywheel energy storage to the compressed air energy storage, ensuring the overall operational safety of the system and the continuous execution of the frequency regulation task; after safety verification and redistribution, the final power commands P_FESS_cmd(t) and P_CAES_cmd(t) are sent to the lower-level actuators.

[0044] The lower-level actuators directly drive the power conversion devices of each energy storage unit, such as converters and compressors / expanders. They typically employ a dual-closed-loop control structure based on proportional-integral regulation. The outer loop receives power commands from the upper-level coordinator, while the inner loop performs rapid current tracking control to ensure accurate and fast execution of power commands, with response times down to the millisecond level. Throughout operation, the system synchronously and frequently records two types of data: performance data, including frequency deviation Δf, frequency change rate, and the tracking error between the actual total frequency regulation output and the required power; and equipment status data, including real-time SOC and speed of flywheel energy storage, real-time tank pressure and temperature of compressed air energy storage, and records of each power redistribution event triggered due to insufficient state margin and the amount of energy transferred. This detailed operational data is cached and packaged, providing an indispensable data foundation for subsequent performance evaluation and closed-loop optimization.

[0045] Step S3: Based on performance data and equipment status data, generate performance feedback indicators, and dynamically correct the adjustable parameters of the capacity optimization model according to the performance feedback indicators. Then, re-solve the corrected capacity optimization model. In the re-solution process, the multi-timescale division of labor parameters and capacity configuration parameters are treated as correlation variables, and the updated capacity configuration parameters and updated multi-timescale division of labor parameters for the next running cycle are obtained.

[0046] The dynamic correction of the adjustable parameters of the capacity optimization model includes adjusting at least one of the following parameters: the weight coefficients of each sub-objective function related to frequency performance, investment cost, and equipment life loss in the capacity optimization model; the boundary conditions of the operational safety constraints of the first type of energy storage unit and / or the second type of energy storage unit in the capacity optimization model; and the initial or reference values ​​of the multi-timescale division parameters used in the re-solution process.

[0047] Specifically, this embodiment aims to explain in detail how to dynamically correct the capacity optimization model and generate an updated configuration based on the actual effect of frequency regulation operation and equipment status, so as to solve the core technical problem in the prior art that the energy storage capacity configuration is fixed and cannot be adaptively adjusted according to the operating performance and equipment loss, resulting in long-term performance degradation and economic loss of the system.

[0048] In one specific embodiment, after the system completes a predetermined cycle of frequency regulation operation and collects complete performance data and equipment status data, a closed-loop feedback and reconfiguration process is initiated. First, the system performs statistical processing on the collected raw time-series data to generate quantified performance feedback indicators. These performance feedback indicators include: the integral of the absolute value of frequency deviation reflecting the stability of the power grid frequency, for example, accumulating the absolute value of frequency deviation per second to obtain the cumulative value for the whole day; the peak value of the frequency change rate reflecting the severity of the impact on the power grid; the energy of unmet frequency regulation requirements reflecting the completion of the frequency regulation task, i.e., the total energy that needs to be stored but could not be output due to power or energy limitations; and equipment loss indicators reflecting the health of the equipment, such as the number of deep cycles of flywheel energy storage throughout the day, defined as the number of times the state of charge (SOC) crosses a preset depth threshold, such as counting the process of dropping from 80% to 20% and then rising again as one cycle; and the frequency of compressed air energy storage pressure exceeding the limit, defined as the number of times the pressure of the storage tank falls below the safety lower limit threshold. These performance feedback indicators comprehensively characterize the operating effect of the previous cycle from three dimensions: system performance, task completion, and equipment durability.

[0049] Another key and intuitive evaluation indicator for the completion of frequency regulation tasks is the "power point tracking error," which is the real-time deviation between the actual total output of the hybrid energy storage system and the total frequency regulation power demand command calculated by the upper-level coordinator. To analyze the statistical characteristics of this error, this embodiment performs distribution statistics on the tracking error sequence within the selected operating cycle. The results are as follows: Figure 4 The diagram shown illustrates the distribution of frequency modulation (FM) tracking errors. The horizontal axis represents the tracking error, and the vertical axis represents the frequency or probability density of each error value within the statistical period. Figure 4 As shown, the tracking error is mainly concentrated in a very narrow range near zero, for example, ±0.02MW, exhibiting an approximately normal distribution. Furthermore, the mean and standard deviation of the absolute error values ​​are both at extremely low levels. This error distribution characteristic clearly indicates that, under the current configuration, the comprehensive regulation capability of the hybrid energy storage system can closely follow the frequency regulation requirements brought about by wind power fluctuations, achieving high-precision power tracking within the limited rated power and energy boundaries. This error distribution map quantifies the system's tracking capability boundary in a statistical sense and serves as an important visual basis for assessing whether the current capacity configuration is sufficient and whether the division of labor strategy is effective. It also provides direct performance feedback for possible subsequent model corrections.

[0050] Subsequently, based on these feedback indicators, the system dynamically corrects the adjustable parameters of the initial capacity optimization model. This correction process follows preset mapping rules. For example, if statistics show that the number of deep cycles of flywheel energy storage exceeds the designed daily reference value, it is considered that the flywheel is undertaking an excessive transient regulation task, accelerating its lifespan loss. In this case, the system will automatically increase the weight coefficient of the equipment lifespan loss sub-objective function in the capacity optimization model, for example, increasing its weight from the initial 0.2 to 0.3. This means that in the next round of optimization calculations, the algorithm will be more inclined to choose a configuration scheme that reduces the number of flywheel cycles, even if this may slightly increase investment costs or relax frequency performance. Similarly, if the frequency deviation integral value is significantly higher than the allowable value of the power grid assessment, the system will correspondingly increase the weight coefficient of the frequency performance sub-objective function. Another correction method is to adjust the operational safety constraint boundary conditions of the model. For example, if the pressure... The frequent occurrence of pressure approaching the lower limit during operation of compressed air energy storage indicates that the safety margin reserved by the minimum allowable operating pressure set in the current model is insufficient. Therefore, this constraint will be automatically tightened, increasing the lower pressure limit value in the next round of optimization by a safety margin increment, thereby guiding the new configuration scheme to reserve more energy buffer space for compressed air energy storage. Furthermore, the initial values ​​of multi-timescale division of labor parameters can also be used as adjustable parameters. For example, if analysis reveals a large frequency tuning tracking error in the mid-frequency band and frequent deep flywheel circulation, the system can infer that the current mid-frequency division of labor ratio, such as flywheel:compressed air = 70%:30%, may be unreasonable, causing the flywheel to undertake too much mid-term energy regulation task that should be borne by compressed air. Therefore, before the next optimization solution, the initial reference value of the mid-frequency division of labor parameters can be adjusted towards the compressed air side, for example, preset to 60%:40%, providing a better search starting point for the optimization algorithm.

[0051] After correcting the aforementioned adjustable parameters, the system will invoke the optimization solver to re-solve the corrected capacity optimization model. A key improvement in this process is the collaborative optimization of multi-timescale task allocation parameters and capacity configuration parameters as a set of correlated variables. This means that when searching for the optimal solution, the optimization algorithm simultaneously considers the two coupled problems of "how to allocate tasks" and "how much capacity to configure," automatically seeking the optimal combination that matches the task allocation strategy with the equipment's capacity. For example, the algorithm might discover that under new conditions of increased lifetime loss weight and tightened pressure constraints, the optimal solution is no longer simply increasing flywheel capacity, but rather appropriately reducing the flywheel's share in the mid-frequency band while simultaneously increasing the rated energy of compressed air, thereby achieving the optimal balance between overall cost, performance, and lifetime. After the solution is completed, the system outputs the updated capacity configuration parameters and multi-timescale task allocation parameters for the next operating cycle, such as fine-tuning the flywheel's rated power, increasing the rated energy of compressed air, and updating the mid-frequency task allocation ratio, thus completing a full closed-loop optimization iteration and allowing the system configuration to continuously evolve based on actual operating experience.

[0052] The multi-timescale division of labor parameters and capacity configuration parameters are treated as related variables and can be implemented in one of the following ways: Method 1: The capacity configuration parameters and multi-timescale division of labor parameters are used together as decision variables and simultaneously optimized and determined during the re-solution process; Method 2: First, the multi-timescale division of labor parameters are corrected based on performance feedback indicators to obtain the corrected division of labor parameters. Then, the corrected multi-timescale division of labor parameters are used as fixed conditions to solve the simplified model in the capacity optimization model that only contains capacity configuration variables, so as to determine the updated capacity configuration parameters.

[0053] Specifically, this implementation method aims to explain in detail how to couple the multi-timescale division of labor parameters and energy storage capacity configuration parameters during the capacity reconfiguration process, so as to solve the technical problem that the two types of key variables are interdependent and their independent adjustment during the optimization process can easily lead to suboptimal solutions or configuration mismatches, thereby affecting the overall effect of closed-loop optimization.

[0054] In one specific embodiment, after the performance feedback index is generated and the adjustable parameters of the model are corrected, the key step of resolving the problem is to handle the correlation between the multi-timescale division of labor parameters and the capacity configuration parameters.

[0055] This embodiment provides two optional implementation methods: Method 1 is integrated collaborative optimization. This method incorporates capacity configuration parameters, such as rated power and rated energy, and multi-timescale division of labor parameters, such as the power allocation ratio of each frequency band, into an extended joint optimization model, which is solved simultaneously as a set of decision variables. In this joint optimization model, the objective function integrates investment cost, frequency performance penalty, and equipment lifetime loss cost. Its computational depth depends on the values ​​of the decision variables: the magnitude of the frequency performance penalty depends on whether each energy storage unit can meet the power demand within its corresponding capacity parameter limit under the given division of labor parameters; the lifetime loss cost is related to the expected working cycle depth of each energy storage unit under the division of labor strategy. The optimization solver explores all possible combinations of capacity and division of labor in a single calculation to find the solution that optimizes the overall objective. Mathematically, this method guarantees that the updated parameters of the output are globally optimal and self-consistent, that is, the division of labor strategy and hardware capabilities achieve the best match, but the computational complexity is relatively high.

[0056] Method two is a two-step sequential optimization. This method decomposes the complex joint optimization problem into two logically consecutive stages to reduce the difficulty of solving it. In the first stage, the system uses a rule-based or fast evaluation algorithm based on performance feedback indicators to specifically correct the division of labor parameters. For example, if the analysis finds that the flywheel energy storage is taking on too much mid-frequency energy tasks, resulting in frequent deep circulation, the algorithm can adjust the reference value of the proportion of mid-frequency flywheels to be borne downward according to the preset adjustment logic. This yields a set of corrected division of labor parameters. In the second stage, the corrected division of labor parameters determined in the first stage are treated as fixed conditions to construct a simplified capacity optimization model. The decision variables of this simplified model only include the rated power and rated energy of various types of energy storage. Since the division of labor strategy is determined, the calculation of cost terms related to task allocation in the objective function is simplified. The optimization problem is transformed into finding the optimal equipment capacity configuration that satisfies all technical constraints and minimizes total investment and operating costs under a given task allocation scheme. This approach, by first clarifying "how tasks are allocated" and then determining "how much capacity needs to be configured," achieves decoupling and sequential optimization of the two types of parameters, resulting in higher computational efficiency and better aligning with the engineering intuition of "strategy-guided configuration." Regardless of whether method one or method two is used, the core objective is to ensure that the newly generated capacity configuration parameters and the multi-timescale task allocation parameters have inherent logical consistency and physical feasibility, avoiding mismatches such as "high task allocation requirements but insufficient capacity support" or "large capacity redundancy but inefficient task allocation strategy," thereby ensuring the technical rationality and engineering value of the closed-loop optimization results.

[0057] The execution of step S3 is controlled by any of the following conditions: the time control parameter reaches the preset fixed period duration; any performance feedback indicator exceeds the corresponding warning threshold; or an update instruction is received from an external scheduling system.

[0058] Specifically, this embodiment aims to explain in detail how to control the start time of the closed-loop optimization process in order to solve the technical problem that unclear start conditions of the closed-loop update process may lead to waste of optimization computing resources, delayed response to sudden performance degradation, or inability to coordinate with the overall power grid dispatch.

[0059] In one specific embodiment, step S3 is not unconditionally periodic, but controlled by a multi-mode intelligent triggering mechanism. This mechanism is executed by the system's built-in closed-loop triggering management module. The triggering mechanism integrates planning, responsiveness, and coordination. Its core lies in determining when to initiate a complete "data evaluation-model correction-resolution" process. The first triggering condition is based on time control parameters, i.e., a preset fixed cycle length. This is a planned triggering mode. For example, the system can be configured to automatically start closed-loop optimization at midnight every day or midnight every Monday. This mode ensures that the system can systematically review and update the accumulated operating data of the past cycle, such as the previous day or week, at a stable rhythm. It is suitable for scenarios with relatively stable operating environments that need to align with the daily or weekly plans of the power grid. In this mode, regardless of whether there are any unexpected events, the optimization process will start on time, ensuring the basic frequency and regularity of configuration updates.

[0060] The second triggering condition is event-driven, meaning it is triggered immediately when any performance feedback metric exceeds its corresponding warning threshold. The warning threshold is a pre-set critical value based on technical standards, equipment tolerance, or operational experience. The closed-loop trigger management module calculates and monitors these metrics in real-time or near real-time. Once any metric is detected to exceed its threshold, the module immediately generates a high-priority interrupt signal, suspends any other background tasks that may be in progress, and forcibly starts the closed-loop optimization process. This triggering method enables the system to respond quickly to performance degradation or abnormal equipment pressure, allowing for timely configuration adjustments before problems accumulate to a more serious level, demonstrating the agility and self-protection characteristics of the closed-loop system.

[0061] The third trigger condition is receiving an update command from an external dispatch system. This provides an interface for the hybrid energy storage system to coordinate with the upper-level power grid dispatch center for local self-optimization. External commands are usually sent in the form of digital messages through standard power communication protocols. The message content can include command codes and parameters. For example, when the weather forecast indicates that there will be strong winds in the next 24 hours, which may cause drastic fluctuations in wind power, the dispatch center can send a command with the code "OPT_REQUEST" to the hybrid energy storage system. After receiving and parsing the command, the closed-loop trigger management module will immediately start a closed-loop optimization, regardless of whether the fixed cycle has been reached or the indicator has exceeded the limit. This allows the optimization rhythm of the system to conform to the overall operation strategy and risk prevention and control arrangements of the power grid, realizing an effective combination of local adaptation and global centralized dispatch. The above three trigger conditions work in parallel through a logical OR relationship, and usually the event trigger is set with the highest priority, followed by the external command, and the fixed cycle is used as a backup. Together, they constitute a flexible, reliable, and tightly coupled closed-loop start-up control logic with the power grid operation.

[0062] The process, following step S3 and preceding step S4, includes an update verification step: Based on the updated capacity configuration parameters and updated multi-timescale division of labor parameters, a short-term trial run of the hybrid energy storage system is conducted. During this trial run, the system's operational performance indicators and equipment status indicators are monitored and collected. If both the operational performance indicators and equipment status indicators meet the preset access criteria, the updated capacity configuration parameters and updated multi-timescale division of labor parameters are confirmed to be applied to subsequent frequency regulation operations. Otherwise, a rollback to the capacity configuration parameters and multi-timescale division of labor parameters of the previous operating cycle is triggered, or the updated capacity configuration parameters and updated multi-timescale division of labor parameters are limited and corrected before re-verification. The access criteria are a pre-set set of quantitative or qualitative standards used to determine whether the capacity configuration parameters and multi-timescale division of labor parameters of the next operating cycle can be formally put into operation.

[0063] Specifically, this implementation method aims to explain in detail how to perform engineering verification and security access for the new configuration parameters generated by closed-loop optimization, so as to solve the technical problem that directly deploying optimization results may lead to system instability, performance degradation or even equipment security risks due to model errors or sudden changes in the scenario.

[0064] In one specific embodiment, after calculating the updated capacity configuration parameters and multi-timescale division of labor parameters through closed-loop feedback and reconfiguration steps, the system does not immediately apply them to long-term operation. Instead, it first executes an update verification step. The core of this step is to organize a short-term trial run. The system controller loads the new configuration parameters but marks it as a "trial run" state and sets a short observation period, such as the next 30 minutes to 2 hours. During this trial run, the hybrid energy storage system participates in real-time grid frequency regulation based on new parameters such as the new flywheel rated power and the new intermediate frequency division of labor ratio. However, all its operation and monitoring... The testing logic is exactly the same as that in the formal operation phase; the system synchronously monitors and collects two types of key data: one is the operation performance indicators, which mainly refer to the stability of the grid frequency during the trial operation, such as the absolute value of the average frequency deviation, the maximum value of the frequency change rate, and the real-time tracking error of the frequency regulation power during this period; the other is the equipment status indicators, which focus on the operating boundary pressure of the energy storage unit under the new configuration, such as whether the state of charge of flywheel energy storage fluctuates more frequently or more significantly, whether it has reached the new energy limit, and whether the tank pressure of compressed air energy storage approaches its safe pressure lower limit faster under the newly set rated power output.

[0065] After the trial run, the system will compare the collected indicator data with the access criteria stored in the database. The access criteria are a set of quantitative standards that define whether the new configuration can be safely and effectively put into use. The criteria are set from technical procedures, equipment safety specifications and historical operating experience. If all monitoring indicators meet these preset criteria, the verification is successful. The system will confirm that the updated configuration parameters are officially effective and will then proceed to the subsequent long-term frequency regulation operation.

[0066] If the verification fails, meaning any one or more indicators do not meet the admission criteria, the system triggers preset fault-tolerant processing logic. One approach is direct rollback, whereby the new parameters generated by this optimization are discarded, and the system controller automatically switches back to the configuration parameters that have been verified to be stable in the previous operating cycle, ensuring uninterrupted frequency regulation service. Another, more refined approach is to re-verify after limiting correction. For example, if the trial operation finds that the compressed air storage pressure drops too quickly under the new configuration, a temporary limiting coefficient can be automatically applied to the rated power parameter of the compressed air in the new configuration, such as temporarily adjusting the maximum allowable output power to 90% of the original new value, or fine-tuning the intermediate frequency division parameters, such as slightly reducing the proportion of compressed air in the intermediate frequency. Then, based on this corrected parameter set, a new round of trial operation verification, which may be shorter in duration, is initiated. This process can be iterated until a set of parameters is found that both meets the admission criteria and retains as much optimization benefit as possible. Alternatively, a rollback may be determined in the final decision. This update verification step serves as a safety buffer, significantly improving the reliability and robustness of the closed-loop optimization system in practical engineering applications, and avoiding operational risks caused by the mismatch between the theoretical results of the optimization algorithm and the complex field environment. The basis for the amplitude limit correction is derived from the deviation between short-term trial operation monitoring data and the preset "entry criteria". When the new configuration does not fully meet the standards during trial operation, the specific root causes of the deviation are first analyzed, such as the rapid rate of change of key equipment state quantities like pressure or the deterioration of performance indicators like frequency recovery time. Subsequently, the required theoretical correction amount is quickly estimated based on the trial operation data. To ensure that the update process is absolutely stable and safe, all corrections are strictly limited by the preset maximum single correction step size. If the theoretical value exceeds this step size, only the step size value is applied for amplitude limit correction. At the same time, following the fundamental principle of "safety constraints are only tightened and never loosened", a set of slightly adjusted new parameters is generated for re-verification, achieving safe and gradual parameter optimization.

[0067] Before dynamically correcting the adjustable parameters of the capacity optimization model based on performance feedback indicators, the process includes a performance root cause analysis step: This involves fusing performance data and equipment status data to generate a feature vector characterizing the overall state of the hybrid energy storage system. The feature vector is then matched against a pre-defined diagnostic rule base, which defines the mapping relationship between different abnormal state patterns and potential root causes. This rule base is constructed based on historical operating data and simulation data. Based on the matching analysis, a diagnostic conclusion indicating the potential root cause of the current frequency regulation performance deviation is output. The types of diagnostic conclusions include insufficient total frequency regulation capacity configuration of the hybrid energy storage system, unreasonable multi-timescale division of labor between the first and second types of energy storage units, or excessive operating load on any type of energy storage unit. Based on the diagnostic conclusions, a targeted correction strategy corresponding to the type of diagnostic conclusion is selected and executed to correct the adjustable parameters of the capacity optimization model.

[0068] Specifically, this implementation method aims to explain in detail how to identify the root cause of frequency modulation performance problems through intelligent diagnosis and drive targeted correction in closed-loop optimization, so as to solve the defects of blind and inefficient correction actions that may be caused by simply relying on index values ​​for simple feedback adjustment, which cannot accurately solve complex coupled technical problems.

[0069] In one specific embodiment, before directly adjusting the model parameters based on performance feedback indicators, the system adds a performance root cause analysis step. This step begins with a deep fusion analysis of the original operating data. The heterogeneous data collected in step S2, such as frequency deviation, ROCOF sequence, historical state of charge curve and deep cycle count of flywheel energy storage, real-time pressure curve and pressure low limit exceedance times of compressed air energy storage, and unmet frequency regulation power gap records, are time-aligned and normalized. These refined data are combined into a feature vector, which quantitatively characterizes the comprehensive state of the hybrid energy storage system in the past operating cycle from multiple dimensions such as frequency quality, equipment stress, and task completion.

[0070] Subsequently, the feature vector is matched and analyzed against a pre-built diagnostic rule base. This rule base is not a black-box model generated by machine learning, but a set of judgment rules with a clear logical structure constructed from domain expert knowledge. Each rule defines the mapping relationship between a specific abnormal state pattern and one or more potential root causes. For example, a typical rule can be expressed as follows: if the "average frequency deviation" in the feature vector is greater than 0.06 Hz, and the proportion of "unmet frequency modulation energy" to the total required energy exceeds 5%, but the "flywheel average SOC" is in the normal range of 40%-60% and the "minimum compressed air pressure" is higher than the safety lower limit, then the diagnostic conclusion is "insufficient total frequency modulation capacity configuration". Another rule might stipulate that if the "frequency recovery time, such as the time it takes to recover from a drop to 49.8 Hz to above 49.9 Hz" exceeds 10 seconds, and the "number of deep flywheel cycles" increases significantly, while the "proportion of compressed air output to the total demand for low and medium frequencies" is below a certain threshold, then the diagnosis points to "unreasonable division of labor across multiple time scales, with the flywheel excessively undertaking the task of medium-frequency energy." There are also rules for equipment overload, for example: if the "cumulative time when the flywheel SOC is below 20%" exceeds 15% of the total time, or the "number of times the compressed air pressure touches the lower limit" exceeds 2 times per hour, then the diagnosis is "the corresponding energy storage unit is operating under excessive load."

[0071] Based on matching analysis, the system outputs a clear diagnostic conclusion, which is not simply "poor performance," but rather identifies the most likely technical root cause of the problem. Following this conclusion, the system selects and executes a targeted correction strategy from a pre-set strategy library. If the diagnosis is "insufficient total capacity," the correction strategy prioritizes significantly increasing the weight coefficient of the frequency performance objective term in the capacity optimization model, and may simultaneously reduce the weight of the investment cost term, thereby guiding the algorithm to output a larger rated power and energy configuration in the next round of solving. If the diagnosis is "unreasonable division of labor," the correction strategy focuses on adjusting the initial reference values ​​of the multi-timescale division of labor parameters used in the next round of optimization. For example, it may directly reduce the initial value of the proportion of the mid-frequency flywheel's load by 5%-10%, and may trigger a reassessment of the timescale division threshold. If the diagnosis is "excessive load on specific equipment," the correction strategy will focus on tightening the operational safety constraints of that equipment in the optimization model. For example, it may increase the minimum permissible SOC constraint value of the flywheel or the minimum permissible pressure constraint value of compressed air, forcing more safety margins to be reserved for the equipment. Through this intelligent chain of "data fusion → pattern recognition → root cause diagnosis → targeted measures", the system has achieved a closed-loop decision-making upgrade from perceiving phenomena to understanding the essence and then to precise intervention, which greatly improves the effectiveness and engineering practicality of dynamic parameter correction.

[0072] Step S4: Apply the updated capacity configuration parameters and the updated multi-timescale division of labor parameters to the hybrid energy storage system, and return to step S2 to form a closed-loop optimization process.

[0073] Specifically, this implementation method aims to explain in detail how to safely and smoothly deploy the optimized new configuration to the actual system and start a new round of operation, so as to solve the "last mile" problem of the closed-loop optimization process, ensure that the theoretical optimization results can be transformed into sustainable and iterative actual operation, thereby achieving continuous evolution of system performance.

[0074] In one specific embodiment, after the update verification step confirms that the new set of configuration parameters, including the updated capacity configuration parameters and the updated multi-timescale division of labor parameters, meet all admission criteria, the application and iteration step begins. The primary action in this step is parameter deployment. The system controller's configuration management module writes the new parameters into the runtime database and sets them to an active state. Specifically, the deployment includes: issuing the new rated power and new rated energy of the flywheel energy storage to its local controller; issuing the new rated power and new effective volume of the compressed air storage tank, corresponding to the rated energy, to its control system. At the same time, new multi-timescale division parameters, especially the mid-frequency power allocation ratio, such as updating from flywheel:compressed air = 65%:35% to 60%:40%, and the possible adjustment of timescale frequency division thresholds, are configured into the algorithm core of the upper-level coordinator. To ensure a smooth transition, the deployment process usually adopts a gradual loading strategy. For example, for changes in division parameters, the switch is not instantaneous, but rather a smooth transition from the old value to the new value of the control command allocation weight over several minutes to ten minutes through linear interpolation, so as to avoid the impact of sudden power command changes on the power grid and the energy storage equipment itself.

[0075] After the parameters are deployed and take effect, the system returns to the frequency regulation operation and data acquisition steps, i.e., step S2 above. This means that the hybrid energy storage system will be put into a new round of real-time frequency regulation services for the power grid based on a brand-new, optimized "hardware capability definition" and "task allocation strategy". The control behavior at this time is logically consistent with the initial operation phase, but the configuration basis it is based on has been improved through a round of closed-loop optimization. The system will continue its core work: the upper-level coordinator allocates frequency regulation power commands in real time according to the new division of labor ratio, and the lower-level actuator drives the equipment to respond quickly within the newly defined power and energy boundaries. At the same time, the system continuously collects operating performance data and equipment status data under the new configuration, and its monitoring content and frequency are exactly the same as before.

[0076] This process of "deploying new configuration → operating based on new configuration → collecting new data" marks the end of a complete closed loop and the beginning of the next new closed loop. The newly collected operational data will serve as the input for the next round of closed-loop feedback and reconfiguration steps, i.e., step S3, to evaluate the actual effect of the new configuration and may drive further optimization adjustments. For example, the system may find that under the new division of labor, the pressure fluctuation of compressed air energy storage becomes smoother, but the frequency rapid recovery capability is slightly reduced. This new data performance will be recorded and fed back to provide a basis for the next optimization decision. Through such a recurring iterative cycle of "configuration optimization - deployment and operation - effect evaluation - re-optimization", the capacity configuration and coordination strategy of the entire hybrid energy storage system can continuously approach and dynamically adapt to changes in the actual operating environment of the power grid and frequency regulation requirements, thereby enabling the system to have the ability to continuously improve and evolve. Ultimately, it forms a stable, reliable, and continuously improving long-term operating state, fundamentally realizing the transformation of the energy storage system from a static asset to a dynamic intelligent resource.

[0077] Through the coordination of the above steps, this application improves the matching accuracy of capacity configuration and multi-timescale operation strategies of hybrid energy storage systems, the overall economic efficiency of operation, and the reliability and adaptability of long-term service.

[0078] In summary, this application provides an innovative method for capacity optimization of multi-timescale hybrid energy storage systems. Its core lies in constructing a dynamic adaptive architecture integrating the entire process of "initial configuration - real-time operation - performance evaluation - closed-loop optimization." This method first performs initial modeling and optimization based on the frequency domain decomposition results of new energy power to determine the capacity foundation and task allocation framework of the energy storage system. Then, detailed performance and status data are collected during real-time frequency regulation operation, and these data are used to generate quantitative feedback indicators. Furthermore, through intelligent diagnosis and root cause analysis, key parameters of the optimization model are dynamically corrected. During this process, energy storage capacity configuration and multi-timescale task allocation strategies are treated as closely related variables for collaborative re-optimization, thereby generating an updated scheme more suitable for current and expected operating conditions. Finally, after rigorous update verification, the new parameters are safely deployed, and the next round of optimization iterations is initiated, forming a sustainable, self-improving intelligent closed loop.

[0079] Through the organic combination of these technical steps, this application effectively solves the key technical bottlenecks of traditional static planning methods, such as difficulty in adapting to dynamic operating environments, disconnect between capacity and strategy, and lack of long-term self-optimization capabilities. The final technical effect is systematic: it not only significantly improves the dynamic response accuracy and resource utilization efficiency of hybrid energy storage systems to the frequency regulation needs of the power grid at multiple time scales, but also effectively delays equipment wear and tear and improves the economic efficiency throughout the entire life cycle by optimizing operating conditions. Furthermore, it enables the entire system to have robustness and intelligence in dealing with complex operating conditions and continuous evolution, providing an advanced energy storage system solution to support the safe and stable operation of a high proportion of new energy power grids.

[0080] Example 2:

[0081] The capacity optimization method for the multi-timescale hybrid energy storage system in the embodiments of this application has been described above. The capacity optimization system for the multi-timescale hybrid energy storage system in the embodiments of this application is described below. Please refer to [link / reference]. Figure 5 The capacity optimization system for the multi-timescale hybrid energy storage system in this application embodiment includes:

[0082] The initial configuration module is used to establish and solve the initial capacity optimization model based on the multi-timescale decomposition results of new energy power fluctuations, and obtain the initial capacity configuration parameters and initial multi-timescale division of labor parameters of the hybrid energy storage system. The hybrid energy storage system includes a first type of energy storage unit and a second type of energy storage unit with complementary dynamic response characteristics.

[0083] The frequency regulation operation and data acquisition module is used to control the hybrid energy storage system to participate in grid frequency regulation based on the initial capacity configuration parameters and the initial multi-time scale division of labor parameters, and to collect performance data and equipment status data during operation.

[0084] The closed-loop feedback and reconfiguration module is used to generate performance feedback indicators based on performance data and equipment status data, dynamically correct the adjustable parameters of the capacity optimization model according to the performance feedback indicators, and re-solve the corrected capacity optimization model. In the re-solving process, the multi-timescale division of labor parameters and capacity configuration parameters are treated as correlation variables to obtain the updated capacity configuration parameters and updated multi-timescale division of labor parameters for the next running cycle.

[0085] The application and iteration module is used to apply the updated capacity configuration parameters and updated multi-timescale division of labor parameters to the hybrid energy storage system, and return to the execution frequency regulation operation and data acquisition module to form a closed-loop optimization process.

[0086] Through the synergistic cooperation of the above-mentioned components, this application further improves the matching accuracy of capacity configuration and multi-timescale operation strategies of hybrid energy storage systems, the overall economic efficiency of operation, and the reliability and adaptability of long-term service.

[0087] This application also provides a capacity optimization device for a multi-timescale hybrid energy storage system. The capacity optimization device for the multi-timescale hybrid energy storage system includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the capacity optimization method for the multi-timescale hybrid energy storage system in the above embodiments.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A capacity optimization method for a multi-timescale hybrid energy storage system, characterized in that, The method includes: Step S1: Based on the multi-timescale decomposition results of new energy power fluctuations, establish and solve the initial capacity optimization model to obtain the initial capacity configuration parameters and initial multi-timescale division of labor parameters of the hybrid energy storage system. The hybrid energy storage system includes a first type of energy storage unit and a second type of energy storage unit with complementary dynamic response characteristics. Step S1 further includes: the first type of energy storage unit is flywheel energy storage or supercapacitor energy storage; the second type of energy storage unit is compressed air energy storage, flow battery or hydrogen energy storage; the multi-timescale decomposition result includes high-frequency components, mid-frequency components and low-frequency components; the multi-timescale division of labor parameters are used to define the power allocation ratio of the first type of energy storage unit and the second type of energy storage unit for power components of different time scales; the solution result of the capacity optimization model is configured as follows: the high-frequency component is mainly undertaken by the first type of energy storage unit, the low-frequency component is mainly undertaken by the second type of energy storage unit, and the mid-frequency component is jointly undertaken by the first type of energy storage unit and the second type of energy storage unit according to the multi-timescale division of labor parameters; Step S2: Based on the initial capacity configuration parameters and the initial multi-timescale division of labor parameters, control the hybrid energy storage system to participate in grid frequency regulation, and collect performance data and equipment status data during operation; Step S3: Based on the performance data and equipment status data, generate performance feedback indicators, and dynamically correct the adjustable parameters of the capacity optimization model according to the performance feedback indicators, and re-solve the corrected capacity optimization model. In the re-solve process, the multi-timescale division of labor parameters and the capacity configuration parameters are processed as correlation variables to obtain the updated capacity configuration parameters and updated multi-timescale division of labor parameters for the next running cycle. The performance data includes at least the grid frequency deviation, frequency change rate, and frequency regulation power tracking error; the equipment status data includes at least the real-time SOC and speed of flywheel energy storage, and the real-time tank pressure and temperature of compressed air energy storage; the performance feedback indicators include at least the frequency deviation integral value, the unmet frequency regulation energy value, and the equipment cycle loss index; the adjustable parameters include at least the weight coefficients of each sub-objective function in the capacity optimization model, the boundary conditions of the energy storage unit's operational safety constraints, and the initial or reference values ​​of the multi-timescale division parameters. The multi-timescale division of labor parameters and the capacity configuration parameters are treated as related variables and can be implemented in any of the following ways: Method 1: The capacity configuration parameters and the multi-timescale division of labor parameters are used together as decision variables and simultaneously optimized and determined during the re-solution process; Method 2: First, the multi-timescale division of labor parameters are corrected based on the performance feedback index to obtain the corrected division of labor parameters, and then the simplified model containing only capacity configuration variables in the capacity optimization model is solved using the corrected multi-timescale division of labor parameters as fixed conditions to determine the updated capacity configuration parameters; Step S4: Apply the updated capacity configuration parameters and the updated multi-timescale division of labor parameters to the hybrid energy storage system, and return to execute step S2 to form a closed-loop optimization process.

2. The capacity optimization method for a multi-timescale hybrid energy storage system according to claim 1, characterized in that, In step S3, dynamically correcting the adjustable parameters of the capacity optimization model includes adjusting at least one of the following parameters: The weighting coefficients of each sub-objective function related to frequency performance, investment cost, and equipment life loss in the capacity optimization model; Boundary conditions in the capacity optimization model regarding the operational safety constraints of the first type of energy storage unit and / or the second type of energy storage unit; The initial or reference values ​​of the multi-timescale division of labor parameters used in the re-solution process.

3. The capacity optimization method for a multi-timescale hybrid energy storage system according to claim 1, characterized in that, The execution of step S3 is controlled by any of the following conditions: The time control parameters reach the preset fixed cycle duration; If any of the performance feedback metrics exceeds the corresponding warning threshold; An update instruction was received from an external scheduling system.

4. The capacity optimization method for a multi-timescale hybrid energy storage system according to claim 1, characterized in that, The process includes an update verification step after step S3 and before step S4: Based on the updated capacity configuration parameters and the updated multi-timescale division of labor parameters, the hybrid energy storage system is subjected to a short-term trial operation. During the short-term trial operation, the operating performance indicators and equipment status indicators of the hybrid energy storage system are monitored and collected. If both the operating performance indicators and the equipment status indicators meet the preset admission criteria, then the updated capacity configuration parameters and the updated multi-timescale division of labor parameters will be confirmed to be applied to subsequent frequency regulation operations. Otherwise, the operation will be triggered to roll back to the capacity configuration parameters and multi-timescale division of labor parameters of the previous operating cycle, or the updated capacity configuration parameters and the updated multi-timescale division of labor parameters will be limited and then re-verified. The admission criteria are a set of pre-set quantitative or qualitative standards used to determine whether the capacity configuration parameters and multi-timescale division of labor parameters for the next operating cycle can be formally put into operation.

5. The capacity optimization method for a multi-timescale hybrid energy storage system according to claim 1, characterized in that, In step S3, before dynamically correcting the adjustable parameters of the capacity optimization model based on the performance feedback indicators, a performance root cause analysis step is also included: The performance data and equipment status data are fused and analyzed to generate a feature vector characterizing the overall status of the hybrid energy storage system. The feature vector is then matched and analyzed with a pre-set diagnostic rule base, which defines the mapping relationship between different abnormal state patterns and potential root causes. Based on the matching analysis, a diagnostic conclusion indicating the potential root cause of the current frequency regulation performance deviation is output; the types of the diagnostic conclusion include insufficient total frequency regulation capacity configuration of the hybrid energy storage system, unreasonable multi-timescale division of labor ratio between the first type of energy storage unit and the second type of energy storage unit, or excessive operating load of any type of energy storage unit; Based on the diagnostic conclusion, a targeted correction strategy corresponding to the type of the diagnostic conclusion is selected and executed to correct the adjustable parameters of the capacity optimization model.

6. The capacity optimization method for a multi-timescale hybrid energy storage system according to claim 1, characterized in that, In step S2, controlling the hybrid energy storage system to participate in grid frequency regulation adopts a hierarchical coordinated control strategy, including: The upper-level coordinator generates a total frequency regulation power command and power allocation commands for each energy storage unit based on the grid frequency deviation, frequency change rate, and real-time state margins of the first and second types of energy storage units. The lower-level actuator performs rapid tracking control on the power allocation commands. When the upper-level coordinator determines that the real-time state margin of any type of energy storage unit is lower than a safety threshold, it initiates a power command reallocation logic and reallocates the portion of the power commands that the energy storage unit with the real-time state margin lower than the safety threshold cannot safely handle to another type of energy storage unit or a backup resource.

7. A capacity optimization system for a multi-timescale hybrid energy storage system, used to implement the capacity optimization method for a multi-timescale hybrid energy storage system as described in any one of claims 1-6, characterized in that, The system includes: The initial configuration module is used to establish and solve the initial capacity optimization model based on the multi-timescale decomposition results of new energy power fluctuations, and obtain the initial capacity configuration parameters and initial multi-timescale division of labor parameters of the hybrid energy storage system. The hybrid energy storage system includes a first type of energy storage unit and a second type of energy storage unit with complementary dynamic response characteristics. The frequency regulation operation and data acquisition module is used to control the hybrid energy storage system to participate in grid frequency regulation based on the initial capacity configuration parameters and the initial multi-time scale division of labor parameters, and to collect performance data and equipment status data during operation. The closed-loop feedback and reconfiguration module is used to generate performance feedback indicators based on the performance data and equipment status data, dynamically correct the adjustable parameters of the capacity optimization model according to the performance feedback indicators, and re-solve the corrected capacity optimization model. In the re-solving process, the multi-timescale division of labor parameters and the capacity configuration parameters are processed as correlation variables to obtain the updated capacity configuration parameters and updated multi-timescale division of labor parameters for the next running cycle. The application and iteration module is used to apply the updated capacity configuration parameters and the updated multi-timescale division of labor parameters to the hybrid energy storage system, and return to execute the frequency regulation operation and data acquisition module to form a closed-loop optimization process.

8. A capacity optimization device for a multi-timescale hybrid energy storage system, characterized in that, The device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the device to execute the capacity optimization method for a multi-timescale hybrid energy storage system as described in any one of claims 1-6.

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