Independent hybrid frequency modulation energy storage power station capacity configuration method and device, computer equipment, readable storage medium and program product

Abstract

The application relates to an independent hybrid frequency modulation energy storage power station capacity configuration method and device, computer equipment, a computer readable storage medium and a computer program product, relates to the field of electric power energy storage, and can improve the resource utilization rate of a hybrid energy storage system. The method comprises the following steps: acquiring total power control instructions of a hybrid energy storage system in a target frequency modulation scene and operation characteristic parameters of each energy storage unit; based on the operation characteristic parameters and the total power control instructions, determining first charge and discharge instructions and second charge and discharge instructions of different energy storage units according to a real-time power distribution strategy; based on the first charge and discharge instructions and a life attenuation model, determining a device replacement cycle of an energy type energy storage unit; based on system frequency modulation response and periodic resource consumption, determining a comprehensive performance model in a full life cycle; under the premise of meeting preset operation constraint conditions, taking the comprehensive performance value of the comprehensive performance model as a target, and determining capacity configuration parameters through a Bayesian optimization algorithm.

Description

Technical Field

[0001] This application relates to the field of power energy storage technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for configuring the capacity of an independent hybrid frequency regulation energy storage power station. Background Technology

[0002] With the construction of new power systems, independent energy storage power stations have become a key resource for mitigating grid frequency fluctuations and improving system stability. To balance frequency regulation performance and construction costs, hybrid energy storage systems that combine the advantages of energy-type (such as lithium batteries) and power-type (such as flywheels) systems are widely used.

[0003] However, existing capacity configuration methods still face several technical bottlenecks: First, some existing energy storage battery life assessments typically use simplified linear models for estimation, which are difficult to accurately reflect the working patterns of energy storage batteries under complex frequency regulation conditions, resulting in significant deviations in the prediction of equipment replacement cycles and replacement costs; Second, existing real-time power allocation strategies usually rely on signal decomposition methods and lack the ability to respond in real time to operating states (such as state of charge and health status), and are essentially static allocation strategies, which are difficult to adapt to the randomness of frequency regulation commands and the dynamic characteristics of energy storage units; Third, existing optimization algorithms for hybrid energy storage configurations also have certain limitations in solving the full life cycle model based on real-time dynamic power allocation.

[0004] Therefore, there is an urgent need to develop a hybrid energy storage capacity optimization method that can perform refined assessment of battery life loss, support real-time adaptive power allocation, and have efficient global search capabilities, so as to ultimately achieve capacity configuration that maximizes the economic benefits of the hybrid energy storage system throughout its entire life cycle. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for configuring the capacity of an independent hybrid frequency regulation energy storage power station, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for configuring the capacity of an independent hybrid frequency regulation energy storage power station, including:

[0007] Obtain the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system;

[0008] Based on the operating characteristic parameters and the total power control command, a real-time power allocation strategy is determined, and according to the real-time power allocation strategy, a first charge / discharge command corresponding to the energy-type energy storage unit and a second charge / discharge command corresponding to the power-type energy storage unit are determined.

[0009] Based on the first charge / discharge command and the preset lifespan decay model, the equipment replacement cycle of the energy storage unit is determined.

[0010] Based on the system frequency regulation response and periodic resource consumption, a comprehensive efficiency model is determined over the entire life cycle; the system frequency regulation response is determined based on the sum of the effective execution of the first charge / discharge command and the second charge / discharge command; the periodic resource consumption is determined based on the capacity configuration parameters of the hybrid energy storage system and the equipment replacement cycle.

[0011] Under the premise of meeting the preset operating constraints, with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model, the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit are determined by Bayesian optimization algorithm.

[0012] In one embodiment, the operating characteristic parameters include a set of variables relating to the real-time state of charge and operating capacity of each energy storage unit;

[0013] Based on the operating characteristic parameters and the total power control command, as well as the preset operating constraints, a real-time power allocation strategy is determined, and according to the real-time power allocation strategy, the first charge / discharge command corresponding to the energy-type energy storage unit and the second charge / discharge command corresponding to the power-type energy storage unit are determined, including: performing fuzzy control processing on the total power control command, the real-time state of charge of the energy-type energy storage unit, and the real-time state of charge of the power-type energy storage unit to obtain the power allocation coefficient of the energy-type energy storage unit;

[0014] Based on the power allocation coefficient, determine the first charge / discharge command corresponding to the energy storage unit;

[0015] Under preset operating constraints, the second charge / discharge command is determined based on the total power control command and the first charge / discharge command.

[0016] In one embodiment, fuzzy control processing is performed on the total power control command, the real-time state of charge of the energy-type energy storage unit, and the real-time state of charge of the power-type energy storage unit to obtain the power allocation coefficient of the energy-type energy storage unit, including:

[0017] Establish fuzzy membership functions for the total power control command, the real-time state of charge of the energy storage unit, and the real-time state of charge of the power storage unit, respectively.

[0018] Using a preset fuzzy inference rule base, logical operations are performed on the semantic variables mapped by each of the fuzzy membership functions to obtain a fuzzy set; for example, the fuzzy inference rule base is configured to adjust the power allocation coefficient to compensate for the power deficit through the energy storage unit when the real-time state of charge of the power storage unit deviates from the preset health range.

[0019] The power allocation coefficient of the energy storage unit is obtained by defuzzifying the fuzzy set.

[0020] Under preset operating constraints, the second charge / discharge command is determined based on the total power control command and the first charge / discharge command.

[0021] The second charge / discharge command is constrained and dynamically corrected based on safe operation constraints.

[0022] Based on the verified and corrected second charge / discharge command and total power control command, the first charge / discharge command is iteratively optimized and adjusted through a closed-loop feedback mechanism.

[0023] In one embodiment, the lifetime degradation model is constructed as a nonlinear function that is negatively correlated with the depth of charge / discharge and the device replacement cycle;

[0024] The process of determining the equipment replacement cycle of the energy storage unit based on the first charge / discharge command and a preset lifespan decay model includes:

[0025] Extract the time-series features of the first charge / discharge command;

[0026] The rainflow counting algorithm is used to identify the cyclical processes in the time series features, and the charge / discharge depth corresponding to each cyclical process is extracted;

[0027] Based on the charge / discharge depth and the preset lifetime decay model, the equipment replacement cycle of the energy storage unit is determined.

[0028] In one embodiment, the periodic resource consumption includes initial resource consumption, periodic operating resource consumption, periodic financial expense consumption, and periodic replacement resource consumption.

[0029] The comprehensive performance model for the entire lifecycle, based on system frequency regulation response and periodic resource consumption, includes:

[0030] Based on the capacity configuration parameters of the hybrid energy storage system, the initial resource consumption is determined.

[0031] Based on the equipment replacement cycle and preset operating life of the energy storage unit, the periodic replacement resource consumption is determined.

[0032] Based on the initial resource consumption, the periodic replacement resource consumption, the periodic operating resource consumption, the periodic financial expense consumption, and the total effective execution, the comprehensive efficiency model for the entire life cycle is determined.

[0033] In one embodiment, under the premise of satisfying preset operating constraints, the step of determining the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit respectively through a Bayesian optimization algorithm with the objective of maximizing the comprehensive efficiency value of the comprehensive efficiency model includes:

[0034] The rated power and rated capacity of the energy-type energy storage unit and the power-type energy storage unit are used as variables to be optimized, and their range of variation is determined.

[0035] The objective function is constructed based on the comprehensive performance value and constraint penalty terms of the comprehensive performance model.

[0036] Determine the sampling strategy, collect initial sample points within the range of optimization variable variation, and evaluate its objective function to obtain the initial sample dataset;

[0037] Determine the form of the probabilistic proxy model and train an initial probabilistic proxy model based on the sample dataset;

[0038] Construct a data collection function, find the next evaluation point based on the data collection function, evaluate its objective function value, add the evaluation result to the sample dataset, and update the probabilistic proxy model.

[0039] The probabilistic proxy model is iteratively updated until the preset convergence condition is met, and the historical global optimal solution is output as the capacity configuration parameter.

[0040] Secondly, this application also provides a capacity configuration device for an independent hybrid frequency regulation energy storage power station, comprising:

[0041] The data acquisition module is used to acquire the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system.

[0042] The power allocation module is used to determine a real-time power allocation strategy based on the operating characteristic parameters and the total power control command, and to determine a first charge / discharge command corresponding to the energy-type energy storage unit and a second charge / discharge command corresponding to the power-type energy storage unit according to the real-time power allocation strategy.

[0043] The replacement cycle determination module is used to determine the equipment replacement cycle of the energy storage unit based on the first charge / discharge command and a preset lifespan decay model.

[0044] The comprehensive performance model establishment module is used to determine the comprehensive performance model throughout the entire life cycle based on the system frequency regulation response and the periodic resource consumption; the system frequency regulation response is determined based on the effective execution sum of the first charge / discharge command and the second charge / discharge command; the periodic resource consumption is determined based on the capacity configuration-related parameters of the hybrid energy storage system and the equipment replacement cycle;

[0045] The capacity configuration parameter determination module is used to determine the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit respectively, with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model, under the premise of meeting preset operating constraints.

[0046] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0047] Obtain the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system;

[0048] Based on the operating characteristic parameters and the total power control command, a real-time power allocation strategy is determined, and according to the real-time power allocation strategy, a first charge / discharge command corresponding to the energy-type energy storage unit and a second charge / discharge command corresponding to the power-type energy storage unit are determined.

[0049] Based on the first charge / discharge command and the preset lifespan decay model, the equipment replacement cycle of the energy storage unit is determined.

[0050] Based on the system frequency regulation response and periodic resource consumption, a comprehensive efficiency model is determined over the entire life cycle; the system frequency regulation response is determined based on the sum of the effective execution of the first charge / discharge command and the second charge / discharge command; the periodic resource consumption is determined based on the capacity configuration parameters of the hybrid energy storage system and the equipment replacement cycle.

[0051] Under the premise of meeting the preset operating constraints, with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model, the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit are determined by Bayesian optimization algorithm.

[0052] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0053] Obtain the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system;

[0054] Based on the operating characteristic parameters and the total power control command, a real-time power allocation strategy is determined, and according to the real-time power allocation strategy, a first charge / discharge command corresponding to the energy-type energy storage unit and a second charge / discharge command corresponding to the power-type energy storage unit are determined.

[0055] Based on the first charge / discharge command and the preset lifespan decay model, the equipment replacement cycle of the energy storage unit is determined.

[0056] Based on the system frequency regulation response and periodic resource consumption, a comprehensive efficiency model is determined over the entire life cycle; the system frequency regulation response is determined based on the sum of the effective execution of the first charge / discharge command and the second charge / discharge command; the periodic resource consumption is determined based on the capacity configuration parameters of the hybrid energy storage system and the equipment replacement cycle.

[0057] Under the premise of meeting the preset operating constraints, with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model, the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit are determined by Bayesian optimization algorithm.

[0058] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0059] Obtain the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system;

[0060] Based on the operating characteristic parameters and the total power control command, a real-time power allocation strategy is determined, and according to the real-time power allocation strategy, a first charge / discharge command corresponding to the energy-type energy storage unit and a second charge / discharge command corresponding to the power-type energy storage unit are determined.

[0061] Based on the first charge / discharge command and the preset lifespan decay model, the equipment replacement cycle of the energy storage unit is determined.

[0062] Based on the system frequency regulation response and periodic resource consumption, a comprehensive efficiency model is determined over the entire life cycle; the system frequency regulation response is determined based on the sum of the effective execution of the first charge / discharge command and the second charge / discharge command; the periodic resource consumption is determined based on the capacity configuration parameters of the hybrid energy storage system and the equipment replacement cycle.

[0063] Under the premise of meeting the preset operating constraints, with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model, the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit are determined by Bayesian optimization algorithm.

[0064] The aforementioned independent hybrid frequency regulation energy storage power station capacity configuration method, device, computer equipment, computer-readable storage medium, and computer program product acquire the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system. Based on the operating characteristic parameters and the total power control command, a real-time power allocation strategy is determined, and according to the real-time power allocation strategy, the first charge / discharge command corresponding to the energy-type energy storage unit and the second charge / discharge command corresponding to the power-type energy storage unit are determined. Based on the first charge / discharge command and a preset lifetime decay model, the equipment replacement cycle of the energy-type energy storage unit is determined. Based on the system frequency regulation response and the periodic resource consumption, a comprehensive efficiency model is determined throughout the entire life cycle. The system frequency regulation response is determined based on the effective execution sum of the first charge / discharge command and the second charge / discharge command. The periodic resource consumption is determined based on the capacity configuration-related parameters of the hybrid energy storage system and the equipment replacement cycle. Under the premise of meeting preset operating constraints, with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model, the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit are determined through a Bayesian optimization algorithm. In this application, a dynamic real-time power allocation strategy based on actual operating conditions is constructed by acquiring real-time operating characteristic parameters and total power control commands, realizing the complementary advantages of energy-type and power-type energy storage units. Based on this, a lifetime decay model is used to map the allocated charge and discharge commands to a precise equipment replacement cycle, and this cycle is incorporated into the calculation of cyclical resource consumption, including initial investment, operating expenses, and replacement costs, thus constructing a comprehensive life-cycle efficiency model. This effectively solves the problem of economic evaluation deviation caused by the inability to accurately quantify equipment lifetime loss under complex frequency regulation conditions in existing technologies. The numerical evaluation of the comprehensive efficiency model requires sequentially calling the real-time power allocation simulation module and the lifetime evaluation simulation module for each candidate energy storage capacity configuration scheme to generate charge and discharge time sequences, calculate battery lifetime loss and frequency regulation benefits, and finally output the net benefit over the entire life-cycle. The gradient information in this evaluation process is unavailable, and it needs to consider both long-term operation and real-time dynamic response, resulting in a large computational load and long processing time. This is a typical black-box optimization problem, and the parameter space exhibits low-dimensional continuous characteristics. To address this, a Bayesian optimization algorithm is proposed for global optimization. This method can accurately locate the optimal solution with fewer objective function evaluations, significantly improving overall computational efficiency while ensuring optimization accuracy. This ensures that the determined capacity configuration scheme maximizes economic benefits throughout its entire lifecycle while guaranteeing the system's frequency regulation response capability. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is an application environment diagram of an independent hybrid frequency regulation energy storage power station capacity configuration method in one embodiment;

[0067] Figure 2 This is a flowchart illustrating the capacity configuration method for an independent hybrid frequency regulation energy storage power station in one embodiment;

[0068] Figure 3 This is a flowchart illustrating the capacity configuration method for an independent hybrid frequency regulation energy storage power station in another embodiment;

[0069] Figure 4 This is a structural block diagram of an independent hybrid frequency regulation energy storage power station capacity configuration device in one embodiment;

[0070] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0072] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0073] The capacity configuration method for independent hybrid frequency regulation energy storage power stations provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown can be either a simulation workstation deployed to meet specific planning needs (such as a standalone planning tool used by a power design institute) or an energy management system (EMS) integrated into the power system.

[0074] Servers can be standalone physical servers, server clusters, or virtualized computing nodes based on cloud computing architectures. Data storage systems are used to store historical operating data and power grid parameters required for server processing; these systems can be integrated within the server or deployed independently in the cloud or other network nodes.

[0075] In one specific embodiment, the server can be configured to perform capacity planning for independent hybrid energy storage power stations. An independent energy storage power station can refer to an energy storage entity that possesses direct dispatch control capabilities, can independently sign grid connection dispatch agreements with power dispatching agencies, and sign power purchase and sale contracts with grid companies according to their access locations. A hybrid energy storage power station can adopt a hybrid energy storage form in its physical architecture, that is, combining two or more different types of energy storage technologies (such as energy-type and power-type) to leverage the complementary strengths of each technology to compensate for the shortcomings of a single technology, thereby achieving efficient energy storage, conversion, and release.

[0076] In one specific embodiment, the application environment is designed to optimize the power plant's ability to participate in frequency regulation ancillary services. This service requires the grid-connected entity (generator or independent third-party ancillary service provider, etc.) to use automatic power control technologies, including automatic generation control (AGC) and automatic power control (APC), to track instructions issued by the power dispatching agency and adjust power generation and consumption in real time at a certain adjustment rate to meet the power system frequency and tie-line power control requirements.

[0077] The terminals can be, but are not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers providing cloud computing services.

[0078] In one exemplary embodiment, such as Figure 2 As shown, a method for configuring the capacity of an independent hybrid frequency regulation energy storage power station is provided, which can be applied to... Figure 1 Taking the server in the example, the explanation includes the following steps S201 to S205. Wherein:

[0079] Step S201: Obtain the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system.

[0080] Hybrid energy storage systems can refer to a collection of systems that physically couple at least two types of electrical energy storage media with different energy and power density characteristics. They are typically composed of energy-type energy storage units (such as lithium batteries and flow batteries with high energy density and suitable for medium- and long-term throughput) and power-type energy storage units (such as flywheel energy storage and supercapacitors with high power density and suitable for short-term high-frequency response), which are used to utilize the complementary characteristics of the two to respond to grid demand.

[0081] Total power control commands characterize the power system's overall power throughput demand for energy storage power stations. They can be represented as a sequence of automatic generation control (AGC) commands or frequency regulation demand signals issued by the power grid dispatch center within a specific time period.

[0082] Operating characteristic parameters can be a set of variables that reflect the real-time physical state and operating capabilities of each energy storage unit, including but not limited to real-time state of charge (SOC), state of health (SOH), charging and discharging power limits, and frequency regulation performance (regulation rate, response time, regulation accuracy). These parameters constitute the boundary conditions for subsequent internal power allocation and capacity planning of the system.

[0083] Specifically, the server establishes connections with the external power grid dispatch center and the local monitoring system of the energy storage power station via wired or wireless communication interfaces. On one hand, the server reads the total power control command sequence issued by the power grid, which includes a timestamp and the corresponding power demand value (including power magnitude and direction). On the other hand, the server synchronously collects the current operating characteristic parameters of energy-type energy storage units (such as lithium battery packs) and power-type energy storage units (such as flywheel arrays) in the hybrid energy storage system. As an optional implementation, the server can preprocess the collected raw data, such as removing abnormal pixels or normalizing the data, thereby constructing an initial state space for subsequent capacity optimization calculations.

[0084] Step S202: Based on the operating characteristic parameters and total power control commands, determine the real-time power allocation strategy, and according to the real-time power allocation strategy, determine the first charge / discharge command corresponding to the energy-type energy storage unit and the second charge / discharge command corresponding to the power-type energy storage unit.

[0085] The real-time power allocation strategy can be a decision model built based on preset rules, optimization algorithms, or intelligent logic (such as fuzzy control or neural networks). It aims to dynamically allocate the power output of each energy storage medium according to its physical characteristics (such as the capacity advantage of energy-type units and the frequency response advantage of power-type units). In an optional embodiment, this can be represented as a process of calculating the power component or proportional coefficient undertaken by each energy-type and power-type energy storage unit based on input variables.

[0086] The first and second charge / discharge commands can be low-level control signals generated after strategy allocation. They are usually presented in the form of power values ​​(kW / MW) or current values ​​(A), and serve as converter operation command signals for energy-type energy storage units and power-type energy storage units, respectively, to achieve coordinated response to grid commands.

[0087] Specifically, the current real-time power allocation strategy is determined by combining the current operating characteristic parameters of each energy storage unit (especially the real-time SOC and charge / discharge power limits) and the total power control command.

[0088] In one exemplary implementation, the server may employ calculation logic based on allocation coefficients. Specifically, the server first calculates the output coefficient of the energy storage unit (denoted as...). ), which is defined as the ratio of the power that an energy storage unit should currently output to its rated power, and its value range is usually [ [1] (positive indicates discharging, negative indicates charging). The magnitude of this coefficient depends on the current operating conditions and equipment status (e.g., when the flywheel SOC is at a high level and the system power demand is low, reduce...). The system prioritizes using flywheel power to reduce lithium battery output; conversely, when the flywheel SOC is low or the system power demand is high, the power consumption should be increased. (This value is used to mobilize energy storage units such as lithium batteries to provide support). Subsequently, the server uses the power allocation coefficient... Determine the first charge / discharge command for the energy-type energy storage unit, and calculate the second charge / discharge command for the power-type energy storage unit, provided that the following operating constraints are met, and correct the first charge / discharge command:

[0089] (1) Safety operation constraints: including the SOC limit and State of Health (SOH) limit of each energy storage unit and power storage unit, as well as the available charge and discharge power capabilities under different SOC ranges;

[0090] (2) Total power balance constraint: The total power control command received by the system is equal to the algebraic sum of the power corresponding to the first charge / discharge command and the power corresponding to the second charge / discharge command;

[0091] (3) Consistency constraint of output direction: The charging and discharging directions of the energy-type energy storage unit and the power-type energy storage unit are consistent with the power flow direction indicated by the total power control command.

[0092] In this way, the server achieves coordinated power allocation for the hybrid energy storage system, optimizing the usage strategies of different types of energy storage units while ensuring the safe operation of the system.

[0093] Step S203: Based on the first charge / discharge command and the preset lifespan decay model, determine the equipment replacement cycle of the energy storage unit.

[0094] The life decay model is a mapping relationship that can accurately describe the physical aging law of energy storage equipment. It is used to convert the operating pressure of the equipment under varying operating conditions (such as charge / discharge depth, rate, temperature, etc.) into a quantified life loss value. The model can be presented as a semi-empirical model in which cycle life is negatively correlated with charge / discharge depth. In addition, it can also be an equivalent circuit model, a data-driven model, or other types of models.

[0095] Equipment replacement cycle refers to the average time span from when an energy storage unit is put into operation until its state of health (SOH) degrades to the failure threshold (e.g., 80% of the initial capacity). It can also indicate the number of times energy storage devices in an energy storage unit can be replaced. This cycle serves as a time benchmark for economic evaluation and is used to determine the frequency of fixed asset replacement and reinvestment costs throughout the entire life cycle of an energy storage unit.

[0096] Specifically, based on the first charge / discharge command (i.e., the power / current time series of the energy storage unit), the server can convert the power sequence into a real-time state of charge (SOC) change curve. To extract effective cyclic features from this non-stationary stochastic process, the server can employ a rainflow counting algorithm. This algorithm identifies peaks and troughs in the SOC curve, decomposing the complex continuous charge / discharge process into several complete charge / discharge cycles and half-cycles, and extracts the charge / discharge depth corresponding to each cycle.

[0097] Subsequently, the server substitutes the extracted depth-of-charge and depth-of-discharge values ​​into a preset lifetime degradation model. Based on linear cumulative damage theory (such as Miner's rule), the server calculates the sum of the lifetime loss ratios caused by each of the above cycles, thereby obtaining the total lifetime degradation rate per unit time. Finally, based on this degradation rate, the server calculates the total operating time required for the energy storage unit to reach the retirement standard, thus establishing the equipment replacement cycle.

[0098] Step S204: Based on the system frequency regulation response and periodic resource consumption, determine the comprehensive efficiency model over the entire life cycle; the system frequency regulation response is determined based on the effective execution sum of the first charge / discharge command and the second charge / discharge command; the periodic resource consumption is determined based on the capacity configuration parameters of the hybrid energy storage system and the equipment replacement cycle.

[0099] Among them, the comprehensive performance model is a mathematical evaluation system that quantifies the economic value or technical cost-effectiveness of an engineering project over its entire life cycle. It can be constructed as a net present value function or total investment return rate model based on the time value of money, and used to discount cash flows generated at different points in time to the same benchmark for comparison.

[0100] System frequency regulation response is a measure of the actual execution effect of a hybrid energy storage system on grid AGC commands. From an economic settlement perspective, it can be represented by the product of frequency regulation mileage (i.e., the cumulative execution amount of charge and discharge commands) and the comprehensive frequency regulation performance coefficient.

[0101] Cycle resource consumption covers all material and financial costs incurred by a project from the construction phase to its decommissioning phase. Specifically, it includes the initial investment cost depending on the scale of the configuration, operating costs incurred during operation, financial expenses, and replacement costs incurred based on the depletion of equipment lifespan.

[0102] For example, firstly, the server calculates the cash flow from the revenue side. Specifically, the server calculates the total effective execution of the first and second charge / discharge commands, calculates the total frequency modulation mileage during the simulated operation period, and estimates the total frequency modulation revenue over the entire life cycle by combining preset frequency modulation market price parameters (such as mileage compensation price and capacity compensation price) and comprehensive performance coefficients.

[0103] Secondly, the server calculates the cash flow on the expenditure side, i.e., the cyclical resource consumption. Finally, the server substitutes the above-mentioned revenue cash flow and expenditure cash flow into the net present value formula, introducing a discount rate to reflect the time value of money, thereby determining the comprehensive efficiency model. This model ultimately outputs a numerical value (i.e., the comprehensive efficiency value) to characterize the economic merits of the current capacity allocation plan.

[0104] Step S205: Under the premise of meeting the preset operating constraints, with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model, the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit are determined by Bayesian optimization algorithm.

[0105] The preset operating constraints refer to the set of physical and logical boundaries that must be strictly adhered to during the simulation optimization process. These may include safe operating thresholds for the device (e.g., maintaining the battery's state of charge (SOC) between 10% and 90% to prevent overcharging and over-discharging), power balance constraints (the total system power command equals the algebraic sum of the actual charging and discharging power of each energy storage unit), and operating logic limitations (e.g., power direction constraints to prevent meaningless energy feed-in between different energy storage units). In an exemplary embodiment, the preset operating constraints may include the following constraints:

[0106]

[0107]

[0108]

[0109] δ

[0110]

[0111]

[0112]

[0113] in, Rated power for energy storage type. This refers to the rated power of a power-type energy storage system. This represents the total power of the hybrid energy storage project. This is a medium-to-long-term energy storage frequency regulation sub-command. This is a short-time power-type energy storage frequency modulation sub-command. This is the total power control command, where δ is the upper limit of the average absolute deviation. This refers to the real-time state of charge of medium- to long-term energy storage. For short-time power-type energy storage, the real-time state of charge is... This represents the state of charge limit for medium- and long-term energy storage. This represents the upper limit of the state of charge for medium- and long-term energy storage. This represents the state of charge limit for short-time power-type energy storage. This represents the upper limit of the state of charge for short-term power-type energy storage.

[0114] Capacity configuration parameters, including but not limited to the rated power and rated capacity of energy-type energy storage units and the rated power and rated capacity of power-type energy storage units.

[0115] For example, the server uses the rated power of the energy storage unit as the core variable to be optimized, and automatically determines the matching power of the power storage unit based on the total power demand of the system; the product of the rated power of each unit and its rated energy storage duration is defined as the corresponding rated capacity, thereby forming an optimization variable space and setting its range of variation.

[0116] Subsequently, the server uses the net present value of the entire life cycle calculated by the aforementioned comprehensive performance model as the core revenue basis, and introduces a constraint penalty term to jointly construct the final objective function. For example, the power point tracking accuracy constraint penalty term is activated when the average absolute deviation between the actual output of the energy storage system and the command signal exceeds a preset threshold, and the penalty value is proportional to the amount of deviation exceeding the threshold.

[0117] During the optimization process, the server first employs a uniform sampling strategy in the variable space to collect a certain number of initial sample points. For each sample point, the server calls the power allocation simulation model containing the fuzzy control strategy and the full life cycle comprehensive performance evaluation model to calculate its objective function value, forming the initial sample dataset.

[0118] Based on this dataset, the server uses Gaussian process regression to establish an initial probabilistic proxy model. This model not only predicts the mean of the objective function at unknown points, but also quantifies its uncertainty.

[0119] A collection function is constructed to guide efficient search: The server randomly generates a large number of candidate points within a certain range around the current best sample point. Using a surrogate model, the collection function value for each candidate point is calculated based on the expected value, and the point with the largest collection function value is selected as the next sample point to be evaluated. After evaluating the new point, it is added to the sample dataset and the surrogate model is updated, completing one iteration.

[0120] The server repeatedly executes the iterative process of "selecting a new evaluation point for the data collection function - evaluating the objective function - updating the proxy model" until the preset maximum number of iterations is reached. Finally, the capacity configuration parameter corresponding to the sample point with the highest objective function value in the entire optimization history is output as the global optimal solution.

[0121] Optionally, the implementation of this application is not limited to the specific parameters and optimization algorithms described above. In other embodiments, the acquisition function may also adopt an upper confidence bound or a probability improvement function; the kernel function of the surrogate model may adopt different forms such as a composite function; the convergence condition may also be set to the objective function value not significantly improving in several consecutive iterations; the implementation of this application is also compatible with various heuristic optimization strategies, including but not limited to particle swarm optimization, simulated annealing, etc.

[0122] In this embodiment, this application constructs a dynamic real-time power allocation strategy based on actual operating conditions by acquiring real-time operating characteristic parameters and total power control commands, realizing the complementary advantages of energy-type and power-type energy storage units. On this basis, the allocated charging and discharging commands are mapped to a precise equipment replacement cycle using a lifetime decay model, and this cycle is introduced into the calculation of cyclical resource consumption, which includes initial investment, operating expenses, and replacement costs, thus constructing a comprehensive life-cycle efficiency model. Finally, global optimization is performed with the goal of maximizing efficiency, thereby effectively solving the problem of economic evaluation deviation caused by the failure to accurately quantify equipment lifetime loss under complex frequency regulation conditions in the prior art, ensuring that the determined capacity configuration scheme maximizes economic benefits throughout the entire life cycle while guaranteeing the system's frequency regulation response capability.

[0123] In an exemplary embodiment, the operating characteristic parameters include the real-time state of charge of the energy-type energy storage unit, the real-time state of charge of the power-type energy storage unit, and a set of variables for operating capability; the set of variables for operating capability may be charge and discharge power limits.

[0124] Based on operating characteristic parameters and total power control commands, a real-time power allocation strategy is determined. Based on this strategy, the first charge / discharge command corresponding to the energy-type energy storage unit and the second charge / discharge command corresponding to the power-type energy storage unit are determined, including:

[0125] Fuzzy control processing is performed on the total power control command, the real-time state of charge of the energy-type energy storage unit, and the real-time state of charge of the power-type energy storage unit to obtain the power allocation coefficient of the energy-type energy storage unit.

[0126] Based on the power allocation coefficient, determine the first charge / discharge command corresponding to the energy storage unit;

[0127] Under the premise of meeting the following preset operating constraints, the second charge / discharge command corresponding to the power-type energy storage unit is determined according to the total power control command and the first charge / discharge command.

[0128] Among them, fuzzy control processing is an intelligent control method based on fuzzy set theory and fuzzy logic reasoning. By imitating the decision-making thinking of human experts, it maps continuously changing precise quantities (such as power, SOC) into fuzzy linguistic variables (such as "high", "low", "moderate") to solve the complex nonlinear coupling and multi-objective trade-off problems between energy-type and power-type units in hybrid energy storage systems.

[0129] Output coefficient ( The output variable of the fuzzy controller is designed to characterize the ratio of the energy storage unit's charging and discharging power to its rated power at the current moment (usually limited to...). (Between 1 and 1). It should be noted that the power allocation coefficient mentioned in the embodiments of this application aims to establish a macro-level load allocation strategy from the system function level. It has a wider applicability and protection range, and is used to indicate the proportion of the output of each energy-type energy storage unit and power-type energy storage unit to the target command value. In the system implementation mechanism, after the server obtains the output coefficient of the energy storage unit, it maps it to the proportion of the output of the energy-type and power-type energy storage units to the target output (i.e., the power allocation coefficient) according to the power demand command. By directly clamping and adjusting this output coefficient, the system can accurately execute the power allocation task and realize equipment protection by utilizing its physical boundary attributes in the low-level control.

[0130] The operational constraints include: a safe operation constraint, meaning that the real-time state of charge (SOC) of each energy storage unit is within its permissible range and its charging / discharging power does not exceed the available power limit corresponding to the current SOC; a power balance constraint, meaning that the algebraic sum of the power corresponding to the first charging / discharging command and the power corresponding to the second charging / discharging command is equal to the total power control command; and a power output direction consistency constraint, meaning that the power flow direction indicated by the first charging / discharging command and the second charging / discharging command is consistent with the direction of the total power control command. In some embodiments, if the first charging / discharging command or the second charging / discharging command violates any operational constraint, the corresponding command is limited, its direction is corrected, or it is reset to zero, and another command is coordinated and adjusted to maintain power balance and satisfy all operational constraints.

[0131] Specifically, power balance constraints can be the concrete manifestation of the real-time power balance requirements of the power system in the frequency regulation scenario of a hybrid energy storage system; requiring that at any given time, the algebraic sum of the charge and discharge of energy-type energy storage units and power-type energy storage units equals the total power control command issued by the grid, so as to ensure the response accuracy and performance compliance rate of the frequency regulation service.

[0132] For example, the server first constructs or invokes a multi-input single-output fuzzy controller structure. The server then receives the total power control commands. Real-time state of charge of energy storage units and the real-time state of charge of power-type energy storage units These three input variables serve as the basis for the fuzzy controller. The selection of these input variables characterizes the dual constraint between external power demand (the magnitude and direction of the grid command) and the remaining capacity level of the internal energy storage units.

[0133] At the same time, the server defines the output variable, the power output coefficient of the energy storage unit. Its domain is the interval of normalized coefficients. , represents the per-unit ratio of the expected output (or charging) power of an energy storage unit (lithium battery) at time t to its rated power.

[0134] Fuzzy rule base with total power control instructions State of charge of energy storage unit (lithium battery) State of charge of power-type energy storage unit (flywheel) The output coefficient of the energy storage unit (lithium battery) is the input and output power. (Per-unit value). The rule base logic prioritizes energy management strategies. For example, in a charging scenario, if the power demand is low and the state of charge (SOC) of the power-type energy storage unit is low, the fuzzy controller outputs a lower output coefficient, prioritizing the power-type energy storage unit to handle the charging power. In a discharging scenario, if the power demand is moderate, the SOC of the power-type energy storage unit is low, and the SOC of the energy-type energy storage unit is in the medium to high range, the fuzzy controller increases the output coefficient, prioritizing the energy-type energy storage unit to handle the discharging power.

[0135] The output coefficient of the energy storage unit output by fuzzy inference The data is then refined and commands are generated by a subsequent constraint verification module. This module first implements hard protection and tiered power limits based on the real-time state of charge (SOC) of the energy storage unit (lithium battery) and the power storage unit (flywheel): for example, discharging is prohibited when SOC ≤ 10%, and the maximum charging and discharging power is limited when SOC is within a specific range; subsequently, based on power balance constraints... Dynamically calculate and constrain the output coefficient of power-type energy storage units (flywheels) Throughout the process, always ensure and symbols and The directions are consistent; and the first and second charge / discharge commands are checked to ensure that they are limited or reset to zero in case of violations of safety constraints, ultimately outputting a coordinated power allocation result that meets the equipment safety boundary and power balance requirements. and This mechanism ensures that control commands both satisfy the optimization intent of the fuzzy strategy and operate strictly within the safe operating range of each energy storage device. In this embodiment, by introducing a multivariate fuzzy control strategy based on the state of charge of each energy storage unit and the total power control command, compared to traditional fixed-ratio allocation or simple filtering allocation, the power allocation coefficient generated by this method can be adaptively and dynamically adjusted according to the real-time state of energy-type and power-type energy storage units. This not only ensures the strict satisfaction of power balance constraints (i.e., high responsiveness to grid commands), but also significantly reduces the impact risk and overcharge / over-discharge risk faced by energy-type energy storage units through this peak-shaving and valley-filling allocation, thereby extending the service life of the equipment from the source and providing technical support for maximizing the comprehensive efficiency throughout the entire life cycle.

[0136] In an exemplary embodiment, fuzzy control processing is performed on the total power control command, the real-time state of charge of the energy-type energy storage unit, and the real-time state of charge of the power-type energy storage unit to obtain the power allocation coefficient of the energy-type energy storage unit, including:

[0137] Fuzzy membership functions are established for the total power control command, the real-time state of charge of the energy storage unit, and the real-time state of charge of the power storage unit, respectively.

[0138] Using a pre-defined fuzzy inference rule base, logical operations are performed on the semantic variables of each fuzzy membership function mapping to obtain a fuzzy set; wherein, the fuzzy inference rule base is configured to adjust the power allocation coefficient to compensate for the power deficit through the energy storage unit when the real-time state of charge of the power-type energy storage unit deviates from the pre-defined health range.

[0139] The power allocation coefficient of the energy storage unit is obtained by defuzzifying the fuzzy set.

[0140] Among them, the fuzzy membership function is used to describe the degree to which a certain input element belongs to a certain fuzzy set (i.e., membership degree, usually in the interval [0, 1]). For example, it maps the precise state of SOC of 80% to the membership degree of the fuzzy concept of "high state of charge" (e.g., 0.8). In terms of construction form, it can be designed as a triangular, trapezoidal, Gaussian distribution curve or other continuous / piecewise function. The specific shape can be configured according to the comprehensive requirements of the control system for response smoothness, computational efficiency and rule coverage characteristics.

[0141] To improve the versatility of the fuzzification process, the total power control command is standardized: firstly, in the normalized domain... Five standard triangular membership functions are defined above, corresponding to the fuzzy linguistic variables "negative large (NB)", "negative small (NS)", "zero (ZO)", "positive small (PS)" and "positive large (PB)" respectively; each triangular membership function is determined by three feature points [a, b, c] (for example, the feature point corresponding to "negative large (NB)" is...). In practical applications, multiplying the per-unit characteristic points mentioned above by the system's base power (such as the rated total power of a hybrid energy storage system) maps the domain of the membership function to the actual physical power range. The fuzzy controller calculates the membership degree of each fuzzy set within this physical power domain based on the input actual power value and then performs subsequent fuzzy inference. This method eliminates the need to redesign the fuzzy rule base for energy storage systems of different capacities, significantly improving the portability and adaptability of the control strategy. For the state of charge of energy storage units (lithium batteries)... Its domain is the actual state of charge percentage range [0, 100]. Three trapezoidal membership functions are used to correspond to the three fuzzy linguistic values ​​of "low (L)", "medium (M)" and "high (H)" respectively. Their parameters are directly set based on the physical percentage value (e.g., L is [0, 0, 20, 30]), so as to directly and intuitively reflect the actual energy state of the battery.

[0142] State of charge of power-type energy storage units (flywheels) Its domain is the actual speed / energy state percentage range [0, 100]. To accurately reflect the operating characteristics of the flywheel in different speed (energy) ranges, five trapezoidal membership functions are specially adopted, corresponding to five fuzzy linguistic values: "Very Low (VL)", "Low (L)", "(M)", "(H)", and "Very High (VH)". The parameters are directly set based on the physical percentage values.

[0143] For the output force coefficient of fuzzy control Similarly, five triangular membership functions (negative large NL, negative small NS, zero Z, positive small PS, positive large PL) can be used to fuzzify it, clearly characterizing the process from discharging at rated power (+1) to charging at rated power ( The system provides continuous and smooth charge and discharge commands, thereby enabling precise dynamic allocation of energy storage power.

[0144] For example, the input variables can be determined using the fuzzy inference rule base defined in Table 1. With output variables ( The mapping relationship between ( ) should be noted. It should be pointed out that the total power control command... The "ZO" fuzzy set and output coefficient The “Z” fuzzy set represents a small fuzzy interval around zero, rather than a strict mathematical zero. The “Z” output represents suppressing the output of the energy storage unit (lithium battery) to a very low level to achieve a smooth transition in power distribution. The “*” in Table 1 indicates that the corresponding input variable can take any fuzzy state. This table lists 18 exemplary rules from the fuzzy rule base.

[0145] Table 1 Fuzzy Inference Rule Base

[0146]

[0147] As shown in Table 1, the design of this fuzzy inference rule follows the collaborative control principle of "ensuring power balance, extending battery life, and ensuring energy storage safety," which is specifically reflected in the following three types of typical rules:

[0148] Conventional coordination rules based on power demand and energy storage state: These rules dynamically allocate responsibilities based on the magnitude and direction of the total power control command (power demand) and the real-time state of charge of the two energy storage systems. For example, when a hybrid energy storage system requires high-power discharge (… (PB) and the power-type energy storage unit (flywheel) has a low capacity ( When the value is L), in order to protect the power-type energy storage unit (flywheel) and meet the power demand, the rule will instruct the energy-type energy storage unit (lithium battery) to output power at a larger coefficient. (PL), see Rule 2. Conversely, if high-power charging is required and the energy storage unit (lithium battery) is already at a high charge level (PL), If it is H), then the rule will restrict the charging of energy storage units (lithium batteries). (Z), the charging task will be preferentially assigned to the power storage unit (flywheel) with lower power, see rule 13.

[0149] Active safety protection rules for power-type energy storage units (flywheels): To prevent power-type energy storage units (flywheels) from entering extreme charge / discharge states, the rule base includes protective rules for extremely high (VH) and extremely low (VL) charge states of the power-type energy storage unit (flywheel). These rules have high priority and, when triggered, will partially override the regular coordination logic. For example, regardless of the state of the energy storage unit (lithium battery), when an extremely low charge state of the power-type energy storage unit (flywheel) is detected... When the system has a discharge requirement (VL), the rule can limit the output of the energy storage unit (lithium battery) to increase (Rule 15), thereby actively reducing the discharge depth of the power storage unit (flywheel) and ensuring its safe operation.

[0150] System steady-state and boundary rules: To ensure the stable operation of the system, the rule base contains explicit boundary handling rules. For example, rule 7 states that when the power demand is close to zero (ZO), regardless of the SOC state (represented by "*"), "Z" (approximately zero) is output to keep the lithium battery at extremely low output and reduce unnecessary cycle losses.

[0151] Specifically, after obtaining the total power control command, the real-time state of charge of the energy-type energy storage unit, and the fuzzy membership function of the real-time state of charge of the power-type energy storage unit, a preset fuzzy inference rule base can be invoked for parallel inference. This rule base can be configured to adjust the power allocation coefficient to compensate for the power deficit through the energy-type energy storage unit when the real-time state of charge of the power-type energy storage unit deviates from the preset healthy range. For example, input variables... Indicators (PB, H, H) can be used to infer fuzzy sets, i.e., output variables, based on fuzzy inference rules. (PL) is the fuzzy set. After obtaining the fuzzy set, the output coefficient of the energy storage unit is obtained by defuzzifying it, and the corresponding distribution value is obtained.

[0152] In this embodiment, by combining the above-mentioned multiple types of rules and fuzzy reasoning, under complex and ever-changing operating scenarios, the conclusions of multiple related rules can be automatically activated and integrated based on the real-time matching degree of the input state, and finally a precise optimized control command can be generated that meets the real-time power requirements while taking into account the characteristics and lifespan management of different energy storage media.

[0153] In one exemplary embodiment, the lifetime degradation model is constructed as a nonlinear function that is negatively correlated with the depth of charge / discharge and the device replacement cycle.

[0154] Based on the first charge / discharge command and a preset lifetime degradation model, the equipment replacement cycle of the energy storage unit is determined, including:

[0155] Extract the time series features of the first charge and discharge command; use the rainflow counting algorithm to identify the cyclic process in the time series features and extract the charge and discharge depth corresponding to each cyclic process; based on the charge and discharge depth and the preset lifetime decay model, determine the equipment replacement cycle of the energy storage unit.

[0156] Specifically, the first charge / discharge command (power / current sequence) can be mapped to a real-time state of charge (SOC) curve that varies over time. This curve objectively records all charging and discharging behaviors of the energy storage unit during frequency regulation, including long cycles of deep charging and discharging, as well as minor oscillations with frequent direction switching. These constitute the time series characteristics to be analyzed. The server identifies all local extreme points (peaks and valleys) in the curve and performs paired analysis on adjacent extreme points according to the rainflow rule. Through this processing, the continuous waveform is decoupled into a series of independent full cycles and unclosed half cycles.

[0157] Since the rated capacity of energy storage units (lithium batteries) degrades with increasing usage, a semi-empirical model is established to correlate lithium battery lifespan with its depth of discharge, enabling accurate quantitative assessment of device replacement cycles and frequency. In one embodiment, this semi-empirical, pre-defined lifespan degradation model is constructed as a negatively correlated nonlinear function of charge / discharge depth and maximum cycle life of the device, as shown in the following function:

[0158]

[0159] in, The depth of charge / discharge during the i-th charge / discharge cycle; The rated depth of charge and discharge can be taken as 100%. This represents the theoretical number of cycles before battery failure.

[0160] Next, based on the linear cumulative damage theory (Miner's rule), the total lifetime loss is calculated, and the sum of the damage ratios caused by all identified cycles within a unit of time (e.g., one year) is statistically analyzed. :

[0161]

[0162] Therefore, the number of times the battery needs to be replaced is: Indicates the power plant's operating period (in years).

[0163] In this embodiment, instead of simple calendar lifespan calculation, a semi-empirical lifespan model based on rainflow counting is proposed. Addressing the frequent and irregular charging and discharging characteristics of energy storage in frequency regulation scenarios, this model can statistically analyze the cyclic distribution of different charge / discharge depths, accurately reflecting the nonlinear cumulative damage of depth of charge / discharge (DOD) to the lifespan of lithium-ion battery energy storage. This makes the prediction of the replacement lifespan of energy storage devices more consistent with physical reality, providing a scientific basis for calculating replacement costs.

[0164] In an exemplary embodiment, the periodic resource consumption includes initial resource consumption, periodic operating resource consumption, periodic financial expense consumption, and periodic replacement resource consumption.

[0165] Based on the system frequency regulation response and periodic resource consumption, a comprehensive performance model is determined over the entire lifecycle, including:

[0166] Based on the capacity configuration parameters of the hybrid energy storage system, the initial resource consumption is determined; based on the equipment replacement cycle and preset operating years of the energy storage unit, the periodic replacement resource consumption is determined; based on the initial resource consumption, periodic replacement resource consumption, periodic operating resource consumption, periodic financial cost consumption, and the total effective execution, a comprehensive efficiency model for the entire life cycle is determined.

[0167] The initial resource consumption refers to the total fixed asset investment required for the construction and start-up phase of a hybrid energy storage power station project, which serves as the initial negative outflow item for the entire life cycle cash flow.

[0168] The periodic replacement resource consumption represents the future capital investment required for the replacement of energy storage units during long-term operation due to the exhaustion of their physical lifespan (i.e., meeting the conditions for lifespan decay model determination).

[0169] The periodic operating resource consumption covers all annual operating expenses and end-of-pipe treatment costs required for the power plant to maintain normal commercial operation.

[0170] Periodic financial expenses refer to the direct costs incurred by energy storage power stations during financing, investment, and capital turnover, reflecting the cost of using external funds by energy storage power stations.

[0171] Effective execution total is a quantitative measure of the actual economic value generated by a power plant's participation in power ancillary services, typically expressed as frequency regulation mileage revenue. Frequency regulation mileage refers to the output adjustment value that a frequency regulation unit contributes to frequency regulation each time it responds to an AGC frequency regulation control command (the absolute value of the difference between the output value at the end of the response and the output value at the time of the response). The total frequency regulation mileage within a certain time period is the sum of the frequency regulation mileage of the frequency regulation units responding to AGC control commands during that period. The comprehensive frequency regulation performance coefficient measures the overall performance of the frequency regulation unit in responding to AGC commands and serves as the basis for calculating frequency regulation costs.

[0172] For example, before constructing the comprehensive performance model, the capacity configuration parameters of the hybrid energy storage system are first obtained based on the construction and operation data of similar energy storage power stations in the project location, including initial investment cost parameters, operating cost parameters, frequency regulation revenue parameters, and other parameters.

[0173] Specifically, as shown in Table 2, the initial investment cost parameters cover the unit cost and investment cost coefficient of each energy-type energy storage unit (e.g., lithium battery energy storage) and power-type energy storage unit (e.g., flywheel energy storage); the frequency regulation revenue parameters and capacity electricity price parameters are used to calculate expected returns, specifically including the average frequency regulation mileage clearing price and the average comprehensive frequency regulation performance coefficient; the operating cost parameters are used to refine operating expenses throughout the entire life cycle, specifically including: charging efficiency, discharging efficiency, average charging electricity price, average discharging electricity price, and material cost per unit discharge for lithium battery energy storage and flywheel energy storage; as well as the number of operating personnel, average annual salary, welfare expense coefficient, repair and inventory rate, unit capacity scrapping and recycling disposal fee, and insurance rate for the overall operation of the power station; other parameters include the equipment residual value, discount rate, and power station operating period (preset operating years) used for capital value conversion and period definition.

[0174] Table 2 Capacity Configuration Related Parameters

[0175]

[0176]

[0177] For example, after obtaining the capacity configuration-related parameters, the initial resource consumption, periodic replacement resource consumption, periodic operating resource consumption, periodic financial expense consumption, and total effective execution are further determined to obtain the comprehensive performance model throughout the entire lifecycle, which can be expressed by the following formula:

[0178]

[0179] in, The overall performance value is... This indicates the project's capital (in yuan). This represents the revenue from FM mileage (in yuan). This represents the capacity compensation revenue (in yuan). This represents the annual charge / discharge loss cost (in yuan). This indicates the annual material cost (in yuan). This represents annual salary and benefits (in yuan). This indicates the annual repair cost (in yuan). This indicates the insurance premium (in yuan). This indicates the annual transmission and distribution electricity price. This indicates the annual line loss cost. This indicates the annual system operating cost. This indicates the principal repaid each year. This represents the annual interest payment (in yuan). This represents the cost (in yuan) for recycling and processing lithium battery energy storage. This indicates the residual value (in yuan) of lithium battery energy storage equipment. This indicates the replacement cost of lithium battery energy storage. This represents the cost (in yuan) for recycling and processing flywheel energy storage. This indicates the residual value (in yuan) of the flywheel energy storage equipment. Indicates the discount rate. This indicates the number of times the lithium battery energy storage can be replaced, m represents the loan term (in years), and i represents the year of operation.

[0180] In an optional embodiment, the initial resource consumption can be calculated based on the unit cost of the equipment and the investment cost coefficient according to the capacity configuration parameters, as shown in the following formula:

[0181]

[0182] Where TI represents the project's capital (RMB); TB represents the total investment in lithium battery energy storage (RMB). This indicates the loan ratio; TF represents the total investment in flywheel energy storage (RMB). Expressed as lithium battery energy storage capacity (MW); The unit cost of lithium battery energy storage equipment is expressed as RMB / MW. This is expressed as the investment cost coefficient for lithium battery energy storage; Expressed as flywheel energy storage capacity (MW); Expressed as the unit equipment cost of flywheel energy storage (RMB / MW); It is expressed as the flywheel energy storage investment cost coefficient.

[0183] In a specific embodiment, the annual effective total can also be calculated based on FM mileage revenue, as shown in the following formula:

[0184]

[0185] in, This represents the revenue from FM mileage (in yuan). This represents the revenue from frequency regulation mileage of lithium battery energy storage; This represents the revenue from frequency regulation mileage of flywheel energy storage; This represents the average value of the overall frequency modulation performance coefficient; Indicates the range of lithium battery frequency regulation (MW); Indicates flywheel cadence (MW); This represents the average frequency regulation mileage clearing price (RMB / MW).

[0186] In a specific embodiment, capacity compensation revenue can also be calculated based on the peak capacity of the energy storage power station, as shown in the following formula:

[0187]

[0188] in, This represents the capacity compensation revenue (in yuan). This refers to the capacity compensation revenue from lithium battery energy storage. This represents the capacity compensation revenue of flywheel energy storage; This indicates the continuous discharge time (h) during which a lithium battery can operate stably at its rated discharge power. This indicates the continuous discharge time (h) during which the flywheel energy storage can operate stably at its rated discharge power. Indicates the duration of the longest peak net load throughout the year (h); The capacity-based electricity price standard is expressed in yuan / MW·year; where the continuous discharge duration of the rated energy storage power is divided by the duration of the longest net load peak throughout the year, and the maximum value shall not exceed 1.

[0189] In an optional embodiment, the periodic operating resource consumption may include charging and discharging loss costs, as shown in the formula:

[0190]

[0191]

[0192]

[0193] in, Indicates the annual charge / discharge loss cost (in yuan). Indicates the annual charging loss cost (in yuan). Indicates the annual discharge loss cost (yuan); This indicates the annual charging capacity of lithium battery energy storage (MWh). This represents the annual charging capacity of flywheel energy storage (MWh). Electricity price for charging (RMB / MWh) This indicates the annual discharge capacity of lithium battery energy storage (MWh). This represents the annual discharge capacity of flywheel energy storage (MWh). Discharge electricity price (RMB / MWh); Indicates the energy storage and charging efficiency of lithium batteries; Indicates the flywheel energy storage charging efficiency; Indicates the energy storage and discharge efficiency of a lithium battery; This indicates the flywheel's energy storage and discharge efficiency.

[0194] In an optional embodiment, the cyclical operating resource consumption may include grid connection transmission and distribution tariffs, grid connection line loss costs, and system operating costs, as shown in the formula:

[0195]

[0196]

[0197]

[0198] This indicates the annual transmission and distribution electricity price. Indicates transmission and distribution price; This indicates the annual line loss cost. This indicates a discount on line loss fees during internet access. This indicates the annual system operating cost. This indicates a discount on system operation fees for internet access.

[0199] In an optional embodiment, the periodic operating resource consumption may include material costs, as shown in the following formula:

[0200]

[0201] in, This indicates the annual material cost (in yuan). This represents the material cost per unit discharge capacity of lithium battery energy storage (RMB / MWh). This represents the material cost per unit discharge of flywheel energy storage (RMB / MWh).

[0202] In an optional embodiment, the periodic operating resource consumption may include wages and welfare expenses, as shown in the following formula:

[0203]

[0204] in: This represents annual salary and benefits (in yuan). Indicates the staffing capacity (in people) of the energy storage power station; This represents the average annual salary per person (in yuan). This represents the welfare expense coefficient.

[0205] In an optional embodiment, the periodic operating resource consumption may include repair costs, as shown in the following formula:

[0206]

[0207] in, This indicates the annual repair cost (in yuan). Indicates the repair and retention rate of lithium battery energy storage; This indicates the flywheel energy storage repair and replenishment rate.

[0208] In an optional embodiment, the periodic operating resource consumption may include insurance premiums, as shown in the following formula:

[0209]

[0210] in, Indicates the insurance premium (in yuan); This indicates the insurance rate for lithium battery energy storage. This indicates the insurance rate for flywheel energy storage.

[0211] In an optional embodiment, the periodic operating resource consumption may include recycling and processing fees, as shown in the following formula:

[0212]

[0213] in, This refers to the cost of recycling and processing lithium battery energy storage. This represents the cost of recycling and processing flywheel energy storage. This indicates the cost of recycling and disposing of a unit capacity of lithium battery energy storage (RMB / MWh). This represents the cost of recycling and disposing of flywheel energy storage units at the end of their lifespan (RMB / MWh).

[0214] In an optional embodiment, the periodic operating resource consumption may include the equipment residual value, as shown in the following formula:

[0215]

[0216]

[0217] in, Indicates the residual value of the lithium battery (in yuan); This indicates the residual value (in yuan) of the flywheel energy storage device. This represents the residual value (in yuan) of the remaining fixed assets. Indicates the residual value rate of lithium battery energy storage; Indicates the residual value rate of flywheel energy storage; This represents the residual value rate of the remaining fixed assets. In a specific embodiment, the periodic replacement resource consumption can be determined by calculating the replacement cost of battery energy storage, as shown in the following formula:

[0218]

[0219] in, Indicates replacement cost (yuan); This represents the cost coefficient of lithium batteries, which, combined with the equipment replacement cycle and the preset operating life (operating cycle of the energy storage power station), determines the final resource consumption for cycle replacement.

[0220] In an optional embodiment, the long-term loan interest can be paid in equal principal installments. The periodic financial expense can then include the principal and interest repaid annually, as shown in the following formula:

[0221]

[0222]

[0223] in, This indicates the principal repaid each year; This indicates the interest paid annually; represents the loan-to-value ratio; m represents the loan term; f represents the loan interest rate.

[0224] In this embodiment, the proposed comprehensive performance model fully covers key dimensions such as initial investment cost, frequency regulation mileage and capacity compensation revenue, multi-dimensional operating costs (including losses, labor, maintenance, etc.), financial expenses, and equipment residual value. Combined with the global optimization capability of the Bayesian optimization algorithm, it can automatically balance the contradiction between "high frequency regulation mileage revenue brought by high performance" and "high investment cost brought by large capacity," thereby outputting the optimal capacity configuration scheme that truly maximizes the full lifecycle benefits of the independent hybrid energy storage power station.

[0225] In an exemplary embodiment, under the premise of satisfying preset operating constraints, and with the objective of maximizing the overall efficiency value of the comprehensive efficiency model, the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit are determined by a Bayesian optimization algorithm, including:

[0226] The rated power and rated capacity of the energy-type energy storage unit and the power-type energy storage unit are used as variables to be optimized, and their range of variation is determined.

[0227] The objective function is constructed based on the comprehensive performance value and constraint penalty terms of the comprehensive performance model.

[0228] Determine the sampling strategy, collect initial sample points within the range of optimization variable variation, and evaluate its objective function to obtain the initial sample dataset;

[0229] Determine the form of the probabilistic proxy model and train an initial probabilistic proxy model based on the sample dataset;

[0230] Construct a data collection function, find the next evaluation point based on the data collection function, evaluate its objective function value, add the evaluation result to the sample dataset, and update the probabilistic proxy model.

[0231] The probabilistic proxy model is iteratively updated until the preset convergence condition is met, and the historical global optimal solution is output as the capacity configuration parameter.

[0232] Specifically, optimization variables are defined, with the rated power of the energy-type energy storage unit as the core variable to be optimized, and the rated power of the power-type energy storage unit is determined based on the total system power constraint. Based on the preset rated energy storage duration, their respective rated capacities are further calculated.

[0233] Construct an objective function. This function is based on the net present value calculated by the comprehensive performance model and introduces a penalty term related to the constraints. This penalty term is activated when the constraints are not met.

[0234] Determine the sampling strategy. Within the domain of the optimization variables, a specified number of initial sample points are collected using a uniform sampling strategy. For each initial sample point, the power allocation simulation model and the comprehensive performance evaluation model are invoked to calculate its objective function value, thereby forming an initial sample dataset containing the correspondence between variables and objective values.

[0235] An initial probabilistic surrogate model is constructed. Based on the initial sample dataset, a Gaussian process regression method is used to train the initial probabilistic surrogate model. The kernel function of the Gaussian process surrogate model is Matern52 to improve the model's fitting accuracy to complex function distributions. The Gaussian process regression model takes the optimized variables as input and can output the predicted mean of the objective function value for unevaluated points, as well as an estimate of the uncertainty of that prediction.

[0236] A sampling function is constructed to guide sequential sampling. This sampling function is used to quantitatively evaluate the potential benefits of new sampling points, guiding the search process by balancing "utilization" (based on regions with high predicted mean) and "exploration" (based on regions with high uncertainty).

[0237] Iterative optimization is performed. In each iteration, based on the current probabilistic proxy model and the sampling function, a new sampling point with the most information is selected from the candidate point set for evaluation. The true objective function value of the new evaluation point is added to the sample dataset, and the probabilistic proxy model is updated accordingly. At the same time, the historical global optimal solution is updated.

[0238] Determine the convergence condition. Repeat the above iterative process until the preset maximum number of iterations is reached or the convergence accuracy requirement is met. Finally, output the capacity configuration parameters corresponding to the historical global optimal solutions.

[0239] For example, the rated power x of the energy storage unit is used as the core variable to be optimized, and its value range is set to [0, 100] MW. The rated power of the power storage unit is determined based on the constraint of a total power of 100 MW. MW. The rated capacities of the lithium battery and flywheel are respectively MWh and MWh.

[0240] The objective function Z(x) is the overall performance value after penalty term correction, and it is calculated as follows:

[0241] )

[0242] Where Z0(x) represents the initial net present value (NPV) return, and P(x) represents the penalty term. The upper limit of the average absolute deviation between the energy storage system output and the frequency regulation command is set at δ. For example, if the preset deviation threshold is 0.1MW and the penalty coefficient is 50 million yuan / MW, then:

[0243]

[0244] The initial sampling adopted a uniform strategy, selecting 15 sample points in the interval [0, 100]. The objective function value is obtained by evaluating each point. The dataset D = {( Based on this dataset, a Gaussian process regression model was trained using the Matern52 kernel function as the initial surrogate model.

[0245] The desired improvement function is selected as the acquisition function EI(x). In each iteration, 100 candidate points are randomly generated within a range of ±20MW, centered on the current optimal solution xbest. The EI value of each candidate point is calculated using the current surrogate model, and the point xnew with the largest EI value is selected as the evaluation point for this round.

[0246] Evaluate the true objective function value ynew of xnew, add the data pair (xnew, ynew) to dataset D, and retrain the surrogate model. If ynew is better than the historical best value, update the global optimal solution xbest = xnew. When the number of iterations reaches the preset maximum of 30 iterations or other convergence conditions are met, output the configuration scheme corresponding to xbest.

[0247] Optionally, the acquisition function can be replaced by an upper confidence bound function or a probability improvement function; the kernel function of the surrogate model can be a composite kernel or other forms; the bias penalty coefficient and threshold can be adjusted according to actual engineering requirements.

[0248] To enable those skilled in the art to better understand the above steps, the following example illustrates the embodiments of this application, but it should be understood that the embodiments of this application are not limited thereto.

[0249] In recent years, to achieve the "dual carbon" goal, building a new power system with a high proportion of renewable energy has become an important task for my country's energy development. However, the inherent volatility, uncertainty, and intermittency of wind and solar power generation pose challenges to the stable operation of the power system, necessitating the development of various energy storage technologies to fill the gap in flexibility regulation. Single energy storage technologies often struggle to simultaneously address multiple dimensions of requirements, including cost, cycle life, capacity, power response speed, and security, failing to meet increasingly refined market demands. In contrast, hybrid energy storage systems, by combining lower-cost but shorter-lifespan energy storage (such as lithium batteries) with higher-cost but longer-lifespan power storage (such as flywheels and supercapacitors), can achieve complementary advantages and generate a synergistic effect of "1+1>2". However, in practical applications, how to rationally configure the capacity of hybrid energy storage systems according to grid frequency regulation needs to extend system lifespan while reducing total lifespan costs and achieving optimal economic benefits remains a key issue that urgently needs to be addressed.

[0250] Existing technologies for hybrid energy storage capacity configuration have the following main shortcomings: First, some solutions (such as the CEEMDAN decomposition method) have high computational complexity and are time-consuming, failing to meet the low latency requirements in real-time scenarios, and are difficult to achieve high-precision real-time allocation of frequency components in engineering practice; Second, some solutions (such as the fixed interval division method) have too coarse optimization granularity (e.g., 2MW intervals), easily overlooking better subdivided capacity combinations, resulting in limited optimization accuracy; Finally, existing configuration methods based on maximizing returns often use overly simplified battery degradation models (only relating to the number of calls and mileage), failing to accurately reflect the actual physical losses under frequency regulation conditions, and do not fully consider the capacity conflict between the spot market and ancillary services when calculating returns, resulting in insufficient accuracy of economic assessment throughout the entire life cycle.

[0251] To address the aforementioned problems, the method of this application has emerged. In an exemplary embodiment, such as... Figure 3The diagram illustrates the entire process of capacity configuration optimization for a standalone hybrid energy storage power station project. In one optional application scenario, based on the local power system regulation requirements, the total power control target for the standalone hybrid energy storage power station project is set at 100 megawatts (MW). To meet high-frequency regulation requirements while considering energy throughput costs, the system pre-selects lithium-ion battery energy storage technology for medium-to-long-duration energy storage units and locks their rated continuous discharge time to 2 hours; simultaneously, it selects flywheel energy storage technology for power storage units and locks their rated continuous discharge time to 15 minutes.

[0252] Based on historical data of the project location, a multi-dimensional parameter set can be initialized and constructed, including initial investment, operating expenses and frequency regulation revenue, covering all elements of data from equipment unit cost, charging and discharging electricity price, personnel wages and benefits to frequency regulation mileage clearing price and comprehensive performance coefficient.

[0253] After initial configuration, the server initiates a global optimization program based on a Bayesian algorithm, aiming to find the optimal power ratio between lithium batteries and flywheel energy storage. During the optimization process, the server first defines several sets of capacity configuration schemes to be verified. Each scheme must meet the hard constraint that the sum of the rated power of lithium battery energy storage and flywheel energy storage is no less than 100 MW. For each candidate scheme, the server calls the fuzzy control strategy module for dynamic simulation. In this simulation, the server inputs a 24 / 7 automatic generation control command sequence and real-time state-of-charge feedback, using fuzzy inference rules to calculate the power allocation coefficient in real time. This strategy adaptively and dynamically adjusts based on the real-time state of the battery and flywheel. This not only ensures strict satisfaction of the power balance constraint (i.e., high responsiveness to grid commands) but also significantly reduces the impact risk and overcharge / over-discharge risk faced by energy storage units, thereby extending the equipment's lifespan from the source and providing technical support for maximizing overall efficiency throughout its lifecycle.

[0254] Subsequently, based on the simulated charge and discharge command sequence, the server uses a rainflow counting algorithm to extract features from the entire day's state-of-charge curve for the lithium battery side. This identifies the specific distribution of deep charge / discharge cycles and shallow oscillation cycles. Combined with a nonlinear lifetime decay model, the server calculates the actual physical lifespan of the lithium battery under this candidate scheme and the number of replacements required during the energy storage power station's operation. Next, the server performs a full lifecycle economic accounting. On the expenditure side, the server accumulates the initial construction cost and calculates future equipment replacement costs based on the number of replacements and the discount rate. It also details the annual operating expenses, including power loss, material consumption, labor costs, insurance premiums, repair costs, scrapping and recycling costs, and replacement costs. On the revenue side, the server calculates the full lifecycle frequency regulation revenue based on the simulated total frequency regulation mileage and the weighted comprehensive performance coefficient, and calculates capacity compensation revenue based on peak capacity.

[0255] Finally, the server subtracts the present value of all costs and end-of-period processing expenses from the above present value of revenue, and adds the residual value recovery amount to obtain the net present value of the candidate scheme. Through iterative optimization using a Bayesian optimization algorithm, the server ultimately locks in a set of optimal configuration parameters that maximizes the net present value from hundreds or thousands of possible power ratios. For example, the final output scheme might be configured as "80 MW / 160 MWh lithium battery energy storage" paired with "20 MW / 5 MWh flywheel energy storage". This result shows that, under the current techno-economic boundary, this ratio can both extend the life of lithium batteries by having the flywheel bear most of the high-frequency losses, and control the initial investment scale of the expensive flywheel, thereby maximizing the economic benefits of the independent hybrid energy storage power station throughout its entire life cycle.

[0256] In this embodiment, the rated power and real-time operating characteristic parameters of the hybrid energy storage power station are obtained. A two-layer architecture is adopted. In the inner layer, based on the input of configuration parameters, power commands, and real-time status, the charging and discharging commands of energy-type and power-type energy storage units are dynamically allocated through a real-time power allocation strategy. The rainflow counting method is used to extract the command sequence features and combined with a semi-empirical model to evaluate the lithium battery life and replacement cycle. In the outer layer, a full life cycle revenue present value model is constructed, which includes initial investment, frequency regulation revenue, operating costs, and replacement costs. The Bayesian optimization algorithm is used to achieve the accurate positioning of the optimal capacity configuration scheme with the goal of maximizing net present value through fewer objective function evaluations, and the lithium battery degradation and economic benefits under frequency regulation conditions are accurately evaluated, significantly improving the power station's revenue.

[0257] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0258] Based on the same inventive concept, this application also provides an independent hybrid frequency regulation energy storage power station capacity configuration device for implementing the above-mentioned independent hybrid frequency regulation energy storage power station capacity configuration method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more independent hybrid frequency regulation energy storage power station capacity configuration device embodiments provided below can be found in the limitations of the independent hybrid frequency regulation energy storage power station capacity configuration method above, and will not be repeated here.

[0259] In one exemplary embodiment, such as Figure 4 As shown, an independent hybrid frequency regulation energy storage power station capacity configuration device is provided, including: a data acquisition module 410, a power allocation module 420, a replacement cycle determination module 430, a comprehensive efficiency model establishment module 440, and a capacity configuration parameter determination module 450, wherein:

[0260] The data acquisition module 410 is used to acquire the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system.

[0261] The power allocation module 420 is used to determine a real-time power allocation strategy based on the operating characteristic parameters and the total power control command, and to determine a first charge / discharge command corresponding to the energy-type energy storage unit and a second charge / discharge command corresponding to the power-type energy storage unit according to the real-time power allocation strategy.

[0262] Replacement cycle determination module 430 is used to determine the equipment replacement cycle of the energy storage unit based on the first charge and discharge command and the preset life decay model.

[0263] The comprehensive performance model establishment module 440 is used to determine the comprehensive performance model throughout the entire life cycle based on the system frequency regulation response and the periodic resource consumption; the system frequency regulation response is determined based on the effective execution sum of the first charge-discharge command and the second charge-discharge command; the periodic resource consumption is determined based on the capacity configuration-related parameters of the hybrid energy storage system and the equipment replacement cycle;

[0264] The capacity configuration parameter determination module 450 is used to determine the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit respectively by Bayesian optimization algorithm, under the premise of meeting preset operating constraints and with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model.

[0265] In one embodiment, the operating characteristic parameters include a set of variables relating to the real-time state of charge and operating capacity of each energy storage unit; the power allocation module 420 is further configured to:

[0266] The total power control command, the real-time state of charge of the energy-type energy storage unit, and the real-time state of charge of the power-type energy storage unit are subjected to fuzzy control processing to obtain the power allocation coefficient of the energy-type energy storage unit.

[0267] Based on the power allocation coefficient, determine the first charge / discharge command corresponding to the energy storage unit;

[0268] Under preset operating constraints, the second charge / discharge command is determined based on the total power control command and the first charge / discharge command.

[0269] In one embodiment, the power distribution module 420 is further configured to:

[0270] Establish fuzzy membership functions for the total power control command, the real-time state of charge of the energy storage unit, and the real-time state of charge of the power storage unit, respectively.

[0271] Using a preset fuzzy inference rule base, logical operations are performed on the semantic variables mapped by each of the fuzzy membership functions to obtain a fuzzy set; wherein, the fuzzy inference rule base is configured to adjust the power allocation coefficient to compensate for the power deficit through the energy storage unit when the real-time state of charge of the power-type energy storage unit deviates from the preset health range.

[0272] The power allocation coefficient of the energy storage unit is obtained by defuzzifying the fuzzy set.

[0273] In one embodiment, the lifetime degradation model is constructed as a negatively correlated nonlinear function of charge / discharge depth and device replacement cycle; the replacement cycle determination module 430 is further configured to:

[0274] Extract the time-series features of the first charge / discharge command;

[0275] The rainflow counting algorithm is used to identify the cyclical processes in the time series features, and the charge / discharge depth corresponding to each cyclical process is extracted;

[0276] Based on the charge / discharge depth and the preset lifetime decay model, the equipment replacement cycle of the energy storage unit is determined.

[0277] In one embodiment, the periodic resource consumption includes initial resource consumption, periodic operating resource consumption, periodic financial expense consumption, and periodic replacement resource consumption; the comprehensive efficiency model establishment module 440 is further used for:

[0278] Based on the capacity configuration parameters of the hybrid energy storage system, the initial resource consumption is determined.

[0279] Based on the equipment replacement cycle and preset operating life of the energy storage unit, the periodic replacement resource consumption is determined.

[0280] Based on the initial resource consumption, the periodic replacement resource consumption, the periodic operation resource consumption, and the total effective execution, a comprehensive efficiency model for the entire life cycle is determined.

[0281] In one embodiment, the capacity configuration parameter determination module 450 is further configured to:

[0282] The rated power and rated capacity of the energy-type energy storage unit and the power-type energy storage unit are used as variables to be optimized, and their range of variation is determined.

[0283] The objective function is constructed based on the comprehensive performance value and constraint penalty terms of the comprehensive performance model.

[0284] Determine the sampling strategy, collect initial sample points within the range of optimization variable variation, and evaluate its objective function to obtain the initial sample dataset;

[0285] Determine the form of the probabilistic proxy model and train an initial probabilistic proxy model based on the sample dataset;

[0286] Construct a data collection function, find the next evaluation point based on the data collection function, evaluate its objective function value, add the evaluation result to the sample dataset, and update the probabilistic proxy model.

[0287] The probabilistic proxy model is iteratively updated until the preset convergence condition is met, and the historical global optimal solution is output as the capacity configuration parameter.

[0288] Each module in the aforementioned independent hybrid frequency regulation energy storage power station capacity configuration device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0289] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores operational characteristic parameter data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a capacity configuration method for an independent hybrid frequency regulation energy storage power station.

[0290] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0291] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0292] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0293] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0294] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0295] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0296] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0297] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for configuring the capacity of an independent hybrid frequency regulation energy storage power station, characterized in that, The method includes: The system acquires the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system; the operating characteristic parameters include the real-time state of charge of each energy storage unit. The total power control command, the real-time state of charge of the energy-type energy storage unit, and the real-time state of charge of the power-type energy storage unit are subjected to fuzzy control processing to obtain the power allocation coefficient of the energy-type energy storage unit. Based on the power allocation coefficient, determine the first charge / discharge command corresponding to the energy storage unit; Under preset operating constraints, a second charge / discharge command is determined based on the total power control command and the first charge / discharge command; Based on the first charge / discharge command and the preset lifespan decay model, the equipment replacement cycle of the energy storage unit is determined. Based on the system frequency regulation response and periodic resource consumption, a comprehensive efficiency model is determined over the entire life cycle; the system frequency regulation response is determined based on the sum of the effective execution of the first charge / discharge command and the second charge / discharge command; the periodic resource consumption is determined based on the capacity configuration parameters of the hybrid energy storage system and the equipment replacement cycle. Under the premise of meeting the preset operating constraints, with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model, the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit are determined by Bayesian optimization algorithm.

2. The method according to claim 1, characterized in that, The total power control command, the real-time state of charge of the energy-type energy storage unit, and the real-time state of charge of the power-type energy storage unit are subjected to fuzzy control processing to obtain the power allocation coefficient of the energy-type energy storage unit, including: Establish fuzzy membership functions for the total power control command, the real-time state of charge of the energy storage unit, and the real-time state of charge of the power storage unit, respectively. Using a preset fuzzy inference rule base, logical operations are performed on the semantic variables mapped by each of the fuzzy membership functions to obtain a fuzzy set; wherein, the fuzzy inference rule base is configured to adjust the power allocation coefficient to compensate for the power deficit through the energy storage unit when the real-time state of charge of the power-type energy storage unit deviates from the preset health range. The power allocation coefficient of the energy storage unit is obtained by defuzzifying the fuzzy set.

3. The method according to claim 1, characterized in that, The lifetime decay model is constructed as a nonlinear function that is negatively correlated with the depth of charge / discharge and the device replacement cycle; Based on the first charge / discharge command and a preset lifetime degradation model, the equipment replacement cycle of the energy storage unit is determined, including: Extract the time-series features of the first charge / discharge command; The rainflow counting algorithm is used to identify the cyclical processes in the time series features, and the charge / discharge depth corresponding to each cyclical process is extracted; Based on the charge / discharge depth and the preset lifetime decay model, the equipment replacement cycle of the energy storage unit is determined.

4. The method according to claim 1, characterized in that, The periodic resource consumption includes initial resource consumption, periodic operating resource consumption, periodic financial expense consumption, and periodic replacement resource consumption. The comprehensive performance model for the entire lifecycle, based on system frequency regulation response and periodic resource consumption, includes: Based on the capacity configuration parameters of the hybrid energy storage system, the initial resource consumption is determined. Based on the equipment replacement cycle and preset operating life of the energy storage unit, the periodic replacement resource consumption is determined. Based on the initial resource consumption, the periodic replacement resource consumption, the periodic operating resource consumption, the periodic financial expense consumption, and the total effective execution, the comprehensive efficiency model for the entire life cycle is determined.

5. The method according to any one of claims 1 to 4, characterized in that, Under the premise of meeting preset operating constraints, and with the objective of maximizing the overall efficiency value of the comprehensive efficiency model, the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit are determined by Bayesian optimization algorithm, including: The rated power and rated capacity of the energy-type energy storage unit and the power-type energy storage unit are used as variables to be optimized, and their range of variation is determined. The objective function is constructed based on the comprehensive performance value and constraint penalty terms of the comprehensive performance model. Determine the sampling strategy, collect initial sample points within the range of optimization variable variation, and evaluate its objective function to obtain the initial sample dataset; Determine the form of the probabilistic proxy model and train an initial probabilistic proxy model based on the sample dataset; Construct a data collection function, find the next evaluation point based on the data collection function, evaluate its objective function value, add the evaluation result to the sample dataset, and update the probabilistic proxy model. The probabilistic proxy model is iteratively updated until the preset convergence condition is met, and the historical global optimal solution is output as the capacity configuration parameter.

6. A capacity configuration device for an independent hybrid frequency regulation energy storage power station, characterized in that, The device includes: The data acquisition module is used to acquire the total power control command of the hybrid energy storage system under the target frequency regulation scenario, as well as the operating characteristic parameters of the energy-type energy storage unit and the power-type energy storage unit in the hybrid energy storage system; the operating characteristic parameters include the real-time state of charge of each energy storage unit. A power allocation module is used to perform fuzzy control processing on the total power control command, the real-time state of charge of the energy-type energy storage unit, and the real-time state of charge of the power-type energy storage unit to obtain the power allocation coefficient of the energy-type energy storage unit; determine the first charge / discharge command corresponding to the energy-type energy storage unit based on the power allocation coefficient; and determine the second charge / discharge command based on the total power control command and the first charge / discharge command under preset operating constraints. The replacement cycle determination module is used to determine the equipment replacement cycle of the energy storage unit based on the first charge / discharge command and a preset lifespan decay model. The comprehensive performance model establishment module is used to determine the comprehensive performance model throughout the entire life cycle based on the system frequency regulation response and the periodic resource consumption; the system frequency regulation response is determined based on the effective execution sum of the first charge / discharge command and the second charge / discharge command; the periodic resource consumption is determined based on the capacity configuration-related parameters of the hybrid energy storage system and the equipment replacement cycle; The capacity configuration parameter determination module is used to determine the capacity configuration parameters corresponding to the energy-type energy storage unit and the power-type energy storage unit respectively, with the goal of maximizing the comprehensive efficiency value of the comprehensive efficiency model, under the premise of meeting preset operating constraints.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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