Hybrid energy storage system capacity configuration method and device, power equipment and storage medium

Abstract

The invention discloses a hybrid energy storage system capacity configuration method and device, power equipment and a storage medium, and relates to the technical field of power energy storage, and the method comprises the steps: obtaining an initial capacity configuration parameter set of a hybrid energy storage system and the occurrence probability of a target energy storage scene in a typical scene set; calculating the energy storage cost of the hybrid energy storage system based on the initial capacity configuration parameters in the initial capacity configuration parameter set to obtain an energy storage cost set, and obtaining an operation income set according to the operation income of the hybrid energy storage system in the target energy storage scene; solving an operation output curve set of the hybrid energy storage system according to the occurrence probability and the operation income set of the target energy storage scene; solving a capacity optimization configuration parameter set of the hybrid energy storage system according to the operation output curve set and the energy storage cost set; and carrying out capacity configuration on each type of energy storage equipment in the hybrid energy storage system by utilizing the target capacity optimization configuration parameters in the capacity optimization configuration parameter set. The capacity configuration reliability of the hybrid energy storage system is improved.

Description

Technical Field

[0001] This invention relates to the field of power energy storage technology, specifically to a method, apparatus, power equipment, and storage medium for configuring the capacity of a hybrid energy storage system. Background Technology

[0002] Optimizing the allocation of various energy storage resources and ensuring their stable operation in conjunction with multiple power sources are crucial for achieving efficient external transmission and consumption of large-scale new energy bases and reliable power supply support. However, the capacity configuration of hybrid energy storage systems in related technologies suffers from low reliability. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, apparatus, power equipment and storage medium for configuring the capacity of a hybrid energy storage system, in order to solve the problem of low reliability in the capacity configuration of hybrid energy storage systems in related technologies.

[0004] In a first aspect, embodiments of the present invention provide a method for configuring the capacity of a hybrid energy storage system, the method comprising: Obtain the initial capacity configuration parameter set of the hybrid energy storage system, as well as the probability of occurrence of the target energy storage scenario in the typical scenario set; Based on the initial capacity configuration parameters in the initial capacity configuration parameter set, the energy storage cost of the hybrid energy storage system is calculated to obtain the energy storage cost set, and the operating revenue of the hybrid energy storage system under the target energy storage scenario is calculated to obtain the operating revenue set. Based on the probability of occurrence of the target energy storage scenario and the set of operating benefits, the operating output curves of various types of energy storage devices in the hybrid energy storage system are solved to obtain the set of operating output curves of the hybrid energy storage system. Based on the set of operating output curves and the set of energy storage costs, the capacity optimization configuration parameters of each type of energy storage device in the hybrid energy storage system are solved to obtain the set of capacity optimization configuration parameters. The target capacity optimization configuration parameters in the capacity optimization configuration parameter set are used to configure the capacity of various types of energy storage devices in the hybrid energy storage system.

[0005] The hybrid energy storage system capacity configuration method provided in this invention calculates the energy storage cost set of the hybrid energy storage system based on the initial capacity configuration parameters in the initial capacity configuration parameter set, and calculates the operating benefits set of the hybrid energy storage system under the target energy storage scenario. Based on the probability of occurrence of the target energy storage scenario and the operating benefits set, the method solves for the operating output curves of various types of energy storage devices in the hybrid energy storage system, obtaining a set of operating output curves for the hybrid energy storage system. Then, based on the set of operating output curves and the energy storage cost set, the method solves for the capacity optimization configuration parameters of various types of energy storage devices in the hybrid energy storage system, obtaining a set of capacity optimization configuration parameters. This set of capacity optimization configuration parameters is then used to select target capacity optimization configuration parameters to optimize the overall cost-effectiveness of the hybrid energy storage system. The solution process employs a multi-objective optimization algorithm, comprehensively considering the balance between energy storage costs and operating benefits to ensure high robustness of the configuration scheme under different target energy storage scenarios. Finally, by iteratively updating the initial capacity configuration parameter set, the convergence of the capacity optimization configuration parameters is achieved, thereby improving the reliability of the hybrid energy storage system capacity configuration.

[0006] In one possible implementation, the initial capacity configuration parameters include a first annual value factor and a first energy storage parameter for the first type of energy storage device in the hybrid energy storage system, and a second annual value factor and a second energy storage parameter for the second type of energy storage device. The annual value factor is determined based on the lifespan and discount rate of the energy storage device. The first energy storage parameter includes a first power factor and a first rated power, and the second energy storage parameter includes a second power factor, a second rated power, rated energy, and an energy factor. The energy storage cost of the hybrid energy storage system is calculated based on the initial capacity configuration parameters, including: Calculate the first energy storage cost of the first type of energy storage equipment based on the first-class annual value coefficient and the first energy storage parameters; The second energy storage cost of the second type of energy storage equipment is calculated based on the second annual value coefficient and the second energy storage parameters. Calculate the energy storage cost of the hybrid energy storage system based on the first energy storage cost and the second energy storage cost.

[0007] In one possible implementation, the initial capacity configuration parameters also include the energy storage output of each type of energy storage device. Based on the initial capacity configuration parameters, the operational benefits of the hybrid energy storage system under the target energy storage scenario are calculated, including: Obtain the grid-connected electricity price of the hybrid energy storage system and the operating costs of various types of energy storage equipment in the target energy storage scenario; Based on energy storage output, grid connection price, and operating costs, calculate the operating revenue of the hybrid energy storage system in the target energy storage scenario.

[0008] In one possible implementation, based on the probability of occurrence of the target energy storage scenario and the set of operational benefits, the operational output curves of various types of energy storage devices in the hybrid energy storage system are solved to obtain the set of operational output curves of the hybrid energy storage system, including: Based on the occurrence probability and operational benefit set of the target energy storage scenario, an operational output function is constructed; Establish the first constraint on the operating output function, which includes the maximum output constraint, thermal power output constraint, and energy storage output constraint. Based on the first constraint, the probability of occurrence of the target energy storage scenario, and the set of operating benefits, the time series dataset of the operating output of various types of energy storage devices in the hybrid energy storage system is solved. Based on the operating output curves corresponding to each operating output time series data in the operating output time series dataset, the set of operating output curves for the hybrid energy storage system is determined.

[0009] In one possible implementation, based on the set of operating output curves and the set of energy storage costs, the capacity optimization configuration parameters for each type of energy storage device in the hybrid energy storage system are solved to obtain a set of capacity optimization configuration parameters, including: Based on the set of operating output curves and the set of energy storage costs, a capacity configuration function is constructed. Establish a second constraint for the capacity configuration function, which includes energy storage state of charge constraint and operating boundary constraint; Based on the second constraint, the set of operating output curves, and the set of energy storage costs, the capacity configuration function is solved to obtain the capacity optimization configuration parameters for each type of energy storage device in the hybrid energy storage system, thus obtaining the set of capacity optimization configuration parameters.

[0010] In one possible implementation, the capacity of various types of energy storage devices in a hybrid energy storage system is configured using target capacity optimization configuration parameters from a set of capacity optimization configuration parameters, including: Select target capacity optimization configuration parameters from the set of capacity optimization configuration parameters, and construct a candidate configuration parameter set; Based on the parameter scores of each candidate configuration parameter in the candidate configuration parameter set, the target capacity optimization configuration parameters are determined, and the capacity of each type of energy storage device in the hybrid energy storage system is configured using the target capacity optimization configuration parameters.

[0011] In one possible implementation, the target capacity optimization configuration parameters are determined based on the parameter scores of each candidate configuration parameter in the candidate configuration parameter set, including: Calculate the operational index data, power generation cost data, and environmental benefit data corresponding to each candidate configuration parameter. The operational index data includes energy storage output and energy storage utilization rate, and the environmental benefit data includes carbon dioxide emissions and renewable energy utilization rate. Based on operational indicator data, power generation cost data, and environmental benefit data, the parameter scores for each candidate configuration parameter are calculated. Select the candidate configuration parameter with the highest parameter score as the target capacity optimization configuration parameter.

[0012] Secondly, embodiments of the present invention provide a capacity configuration device for a hybrid energy storage system, comprising: The data acquisition module is used to acquire the initial capacity configuration parameter set of the hybrid energy storage system, as well as the probability of occurrence of the target energy storage scenario in the typical scenario set; The cost calculation module is used to calculate the energy storage cost of the hybrid energy storage system based on the initial capacity configuration parameters in the initial capacity configuration parameter set, and to calculate the operating revenue of the hybrid energy storage system under the target energy storage scenario, thus obtaining the operating revenue set. The operation output module is used to solve the operation output curves of various types of energy storage devices in the hybrid energy storage system based on the occurrence probability of the target energy storage scenario and the operation benefit set, so as to obtain the operation output curve set of the hybrid energy storage system. The parameter solving module is used to solve for the capacity optimization configuration parameters of various types of energy storage devices in the hybrid energy storage system based on the set of operating output curves and the set of energy storage costs, and obtain the set of capacity optimization configuration parameters. The capacity configuration module is used to configure the capacity of various types of energy storage devices in a hybrid energy storage system by utilizing the target capacity optimization configuration parameters in the capacity optimization configuration parameter set.

[0013] The hybrid energy storage system capacity configuration device provided in this invention calculates the energy storage cost set of the hybrid energy storage system based on the initial capacity configuration parameters in the initial capacity configuration parameter set, and calculates the operating benefits set of the hybrid energy storage system under the target energy storage scenario. Based on the probability of occurrence of the target energy storage scenario and the operating benefits set, the device solves for the operating output curves of various types of energy storage devices in the hybrid energy storage system, obtaining a set of operating output curves for the hybrid energy storage system. Then, based on the set of operating output curves and the energy storage cost set, the device solves for the capacity optimization configuration parameters of various types of energy storage devices in the hybrid energy storage system, obtaining a set of capacity optimization configuration parameters. This set of capacity optimization configuration parameters is then used to select target capacity optimization configuration parameters to optimize the overall cost-effectiveness of the hybrid energy storage system. The solution process employs a multi-objective optimization algorithm, comprehensively considering the balance between energy storage costs and operating benefits to ensure high robustness of the configuration scheme under different target energy storage scenarios. Finally, by iteratively updating the initial capacity configuration parameter set, the device achieves convergence of the capacity optimization configuration parameters, thereby improving the reliability of the hybrid energy storage system capacity configuration.

[0014] Thirdly, embodiments of the present invention provide a power device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or its corresponding implementation.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause a computer to perform the method described in the first aspect or its corresponding implementation. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the capacity configuration method for a hybrid energy storage system provided according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating another hybrid energy storage system capacity configuration method provided by an embodiment of the present invention; Figure 3 This is a structural block diagram of a hybrid energy storage system capacity configuration device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of the power equipment according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0019] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0020] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] This embodiment provides a method for configuring the capacity of a hybrid energy storage system. Figure 1 This is a flowchart of a hybrid energy storage system capacity configuration method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step 110: Obtain the initial capacity configuration parameter set of the hybrid energy storage system, as well as the probability of occurrence of the target energy storage scenario in the typical scenario set.

[0022] Among them, the initial capacity configuration parameters are centrally stored in multiple sets of initial capacity configuration parameters, and the typical scenarios are centrally stored in multiple typical scenarios.

[0023] In one possible implementation, when obtaining the initial capacity configuration parameter set of the hybrid energy storage system, the range of the initial capacity configuration parameters can be selected first. ,in It is a constant. According to the step size Generate several sets of initial capacity configuration parameters , That is, the interval of the configuration scheme corresponding to the initial capacity configuration parameters is When acquiring typical scenario sets, the historical operation data of wind power and photovoltaic power with a time granularity of T=8760 hours (i.e., 1 year) can be aligned and organized into n=365 sample data points per day, denoted as . (each) c i (For daily time-series data); set the number of clusters. ( , (As a cluster set), randomly selected from daily time-series data. Sample As the initial cluster centroid (for each cluster) (The center). Calculate the time series data for each day. c i With all initial cluster centroids DTW distance between ,Will c i Assign them to the cluster with the smallest distance; for each cluster, update its centroid to the average of all samples within that cluster, i.e. (in For the first The number of samples in a cluster, summed for the cluster All samples Check whether the change in the centroid of all clusters before and after the update is less than a preset threshold. (Right now If the conditions are met, clustering stops; otherwise, the calculation of each day's time series data is repeated. c i With all initial cluster centroids DTW distance between ,Will c i Operations that assign to the cluster with the smallest distance.

[0024] In one possible implementation, when acquiring the initial capacity configuration parameter set of the hybrid energy storage system, the initial capacity configuration parameter set can be directly loaded based on a preset database or configuration file. The database stores multiple sets of parameters, each corresponding to different operating conditions of the hybrid energy storage system. Alternatively, a set of parameters can be received through a user input interface, ensuring that the parameter set covers the needs of each scenario in the typical scenario set, thereby providing a comprehensive initial data foundation for subsequent capacity optimization. Furthermore, during the acquisition process, the parameter selection strategy can be dynamically adjusted based on the probability of occurrence of the target energy storage scenario in the typical scenario set. For example, parameter sets corresponding to high-probability scenarios can be loaded first to improve configuration efficiency.

[0025] Step 120: Calculate the energy storage cost of the hybrid energy storage system based on the initial capacity configuration parameters in the initial capacity configuration parameter set to obtain the energy storage cost set, and calculate the operating revenue of the hybrid energy storage system under the target energy storage scenario to obtain the operating revenue set.

[0026] The initial capacity configuration parameters include the first annual value coefficient and first energy storage parameter for the first type of energy storage equipment in the hybrid energy storage system, and the second annual value coefficient and second energy storage parameter for the second type of energy storage equipment. The annual value coefficient is determined based on the lifespan and discount rate of the energy storage equipment. The first energy storage parameter includes the first power coefficient and the first rated power, and the second energy storage parameter includes the second power coefficient, the second rated power, the rated energy, and the energy coefficient. The first type of energy storage equipment includes renewable energy storage equipment (such as photovoltaic equipment and wind power equipment) and thermal power equipment. The second type of energy storage equipment includes long-term energy storage equipment (such as battery energy storage equipment) and short-term energy storage equipment (such as compressed air energy storage equipment).

[0027] In one possible implementation, when calculating the energy storage cost of a hybrid energy storage system based on initial capacity configuration parameters, the first energy storage cost of a first type of energy storage device can be calculated based on a first-class annual value coefficient and a first energy storage parameter; the second energy storage cost of a second type of energy storage device can be calculated based on a second-class annual value coefficient and a second energy storage parameter; and the energy storage cost of the hybrid energy storage system can be calculated based on the first energy storage cost and the second energy storage cost.

[0028] Specifically, the model for calculating the energy storage cost of a hybrid energy storage system is as follows:

[0029] in, Indicates the cost of energy storage. Indicates the initial capacity configuration parameters. This represents the coefficient for the first-class annual value. Indicates the first power coefficient. Indicates the first rated power. This represents the coefficient for the second-class annual value. Indicates the second power coefficient. Indicates the second rated power. Indicates rated energy. This represents the energy coefficient.

[0030] The calculation model for the isochronous coefficient is as follows:

[0031] In this context, PV represents photovoltaic equipment, W represents wind power equipment, G represents thermal power equipment, BES represents battery energy storage equipment, and CAES represents compressed air energy storage equipment. denoted by , where represents the lifespan of the energy storage device, and r represents the discount rate of the energy storage device.

[0032] Therefore, based on several sets of initial capacity configuration parameters in the initial capacity configuration parameter set The initial energy storage cost of the hybrid energy storage system can be calculated. .

[0033] In one possible implementation, the initial capacity configuration parameters also include the energy storage output of each type of energy storage device. When calculating the operating revenue of the hybrid energy storage system in the target energy storage scenario based on the initial capacity configuration parameters, the grid-connected electricity price of the hybrid energy storage system and the operating costs of each type of energy storage device in the target energy storage scenario can be obtained. Based on the energy storage output, grid-connected electricity price and operating costs, the operating revenue of the hybrid energy storage system in the target energy storage scenario is calculated.

[0034] Specifically, the model for calculating the operational benefits of a hybrid energy storage system under the target energy storage scenario is as follows:

[0035] in, Indicates operating revenue, This indicates the on-grid electricity price for new energy bases. This indicates that the energy storage capacity of the new energy base is supplied by thermal power equipment. Photovoltaic equipment output Wind power output Battery energy storage equipment output Compressed air energy storage equipment output Joint decision, Indicates the operating benefits of a hybrid energy storage system. This indicates the operating cost of thermal power equipment. This indicates the operating cost of thermal power equipment. This indicates the operating cost of photovoltaic equipment. This indicates the operating cost of wind power equipment. This indicates the operating cost of compressed air energy storage (related to operating output). In summary, } represents the decision variables for the output function (running layer).

[0036] Step 130: Based on the occurrence probability and operating benefit set of the target energy storage scenario, solve for the operating output curves of each type of energy storage device in the hybrid energy storage system to obtain the operating output curve set of the hybrid energy storage system.

[0037] The operating output curve is generated based on the corresponding operating output time series data.

[0038] In one possible implementation, when solving for the operating output curves of various types of energy storage devices in the hybrid energy storage system based on the occurrence probability and operating benefit set of the target energy storage scenario, an operating output function can be constructed based on the occurrence probability and operating benefit set of the target energy storage scenario; a first constraint is established for the operating output function, which includes a maximum output constraint, a thermal power output constraint, and an energy storage output constraint; based on the first constraint, the occurrence probability and operating benefit set of the target energy storage scenario, the operating output time series dataset of various types of energy storage devices in the hybrid energy storage system is solved; and the operating output curve set of the hybrid energy storage system is determined based on the operating output time series data corresponding to each operating output time series data in the operating output time series dataset.

[0039] Specifically, the expression for the output function is:

[0040] in, This indicates the execution of the output function. Let w represent a set of typical scenarios, and w represent the target energy storage scenario within that set of typical scenarios. This represents the probability of scenario w occurring. This represents the output of renewable energy storage devices (such as wind power equipment and photovoltaic equipment) in scenario w. Indicates the operating benefits under scenario w. The decision variables represent the capacity allocation function (planning level or higher-level problem). This involves determining the power, rated power, rated energy, and energy coefficient of each type of energy storage device. The decision variable representing the output function (running layer or lower-level problem) is the output curve.

[0041] Furthermore, the maximum output constraints include:

[0042]

[0043] in, Indicates the output of photovoltaic equipment. This represents the maximum output curve of the photovoltaic equipment. Indicates the output of wind power equipment. This represents the maximum output curve of the wind power equipment. This indicates the utilization rate of renewable resources.

[0044] Thermal power output constraints include:

[0045]

[0046]

[0047] in, This indicates the minimum output coefficient of thermal power equipment. Indicates the climbing speed. This indicates the minimum boot time. Indicates the minimum downtime. This indicates the maximum power of the thermal power equipment. Indicates the start / stop status (0-1 variable). , Used as an auxiliary subscript.

[0048] Energy storage output constraints include:

[0049]

[0050] in, Indicates load demand. Indicates a power supply shortage. Indicates the output of photovoltaic equipment. Indicates the output of wind power equipment. Indicates the output of thermal power equipment. This indicates the energy storage output of the new energy base.

[0051] Therefore, for each set of initial capacity configuration parameters The operating output curve in the operating output function can be solved. .

[0052] Step 140: Based on the set of operating output curves and the set of energy storage costs, solve for the capacity optimization configuration parameters of each type of energy storage device in the hybrid energy storage system to obtain the set of capacity optimization configuration parameters.

[0053] Among them, the capacity optimization configuration parameters centrally store multiple capacity optimization configuration parameters corresponding to various types of energy storage devices.

[0054] In one possible implementation, based on the set of operating output curves and the set of energy storage costs, the capacity optimization configuration parameters for each type of energy storage device in the hybrid energy storage system are solved to obtain the set of capacity optimization configuration parameters. Then, a capacity configuration function can be constructed based on the set of operating output curves and the set of energy storage costs. A second constraint is established for the capacity configuration function, which includes the energy storage state of charge constraint and the operating boundary constraint for each type of energy storage device. Based on the second constraint, the set of operating output curves, and the set of energy storage costs, the capacity optimization configuration parameters for each type of energy storage device in the hybrid energy storage system are solved to obtain the set of capacity optimization configuration parameters.

[0055] Specifically, the expression for the capacity allocation function is:

[0056] in, This represents the capacity configuration function. The decision variables for the capacity configuration function include the configuration capacity of various types of power sources and energy storage. The decision variables represent the operating output function, including the operating output of various types of power sources and energy storage. The cost of energy storage is determined by the configuration capacity of the power source and the energy storage. Operating revenue (expected revenue from energy storage) is determined by the configured capacity and operating output, and is also influenced by the output of renewable energy storage devices (such as wind power equipment and photovoltaic equipment). The impact.

[0057] Therefore, taking into account the initial investment cost and operating output curve The objective function value of the capacity allocation function can be solved. This means obtaining the corresponding set of capacity optimization configuration parameters, and selecting the capacity optimization configuration parameter with the largest objective function value in the set as the target capacity optimization configuration parameter.

[0058] Furthermore, when constructing the second constraint, assuming that the hybrid energy storage system includes both long-term and short-term energy storage methods, the constraints can be based on the differences in response time, operating boundaries, power-to-energy ratio, and the general characteristics of the State of Charge (SOC) between the two energy storage methods (long-term and short-term). For ease of understanding, taking a hybrid energy storage system composed of a lithium-ion battery energy storage device (short-term energy storage) and a compressed air energy storage device (long-term energy storage) as an example, the established second constraint (operating characteristic constraint) includes the following parts (subscript b represents the battery energy storage device, and subscript c represents the compressed air energy storage device).

[0059] First, the response time of battery energy storage devices is much shorter than that of compressed air energy storage devices. Therefore, dispatch commands should have different durations to adapt to the device characteristics. The dispatch step sizes for battery energy storage devices and compressed air energy storage devices are respectively... ( Therefore, the coupling constraints of energy storage operation time have two changes, as follows: The SOC constraints for energy storage are different:

[0060]

[0061] This formula represents the two states of charge of energy storage ( , The new state after one scheduling cycle , The change between them is determined by the charging power during that period ( , ), discharge power ( , ), charge and discharge efficiency ( , (To be decided jointly)

[0062] For compressed air energy storage, the charging and discharging power should be kept stable within a short-term scheduling cycle.

[0063]

[0064]

[0065] in, This formula represents the charging power of compressed air energy storage. Discharge power In the cycle It remains constant, while battery energy storage can vary.

[0066] Secondly, regarding operating boundaries and adjustment ranges, batteries offer a wide and precise power output range, while compressed air energy storage devices have minimum safe operating boundaries due to the flow characteristics of compressors and expanders. The operation scheduling models for the two differ, therefore, the operating boundary constraints include:

[0067]

[0068] in, These represent the rated power (unit: MW) of battery energy storage devices and compressed air energy storage devices, respectively. This indicates the charge / discharge state of the battery energy storage device (0-1 variable). This indicates the charging and discharging status of the compressed air energy storage device. These represent the minimum output coefficients.

[0069] Furthermore, the two types of energy storage have different power / energy ratio constraints in terms of technical characteristics, as detailed below:

[0070]

[0071] in, These represent the rated energy (unit: MWh) of battery energy storage devices and compressed air energy storage devices, respectively. , This indicates the duration of the two energy storage configurations: battery energy storage devices typically last 2-4 hours, while compressed air energy storage devices typically last 8-10 hours.

[0072] Finally, the general state of charge constraints for both types of energy storage are as follows:

[0073]

[0074]

[0075]

[0076] The above four expressions represent finite energy storage capacity and have a maximum value. The initial state of charge (SOC) of the energy storage system during energy storage dispatch is 50%; the SOC of the energy storage system at the end of dispatch remains consistent with the initial state; the external power of the energy storage system... , A value greater than 0 indicates discharging, and a value less than 0 indicates charging.

[0077] Step 150: Utilize the target capacity optimization configuration parameters in the capacity optimization configuration parameter set to configure the capacity of various types of energy storage devices in the hybrid energy storage system.

[0078] Among them, the target capacity optimization configuration parameter is the core parameter in the capacity optimization configuration parameter set. This parameter is obtained from the capacity configuration function and the operating output function through the optimization algorithm. It is used to specifically determine the capacity allocation of each type of energy storage device in the hybrid energy storage system, so as to minimize the energy storage cost and maximize the operating benefits.

[0079] In one possible implementation, when configuring the capacity of various types of energy storage devices in a hybrid energy storage system using target capacity optimization configuration parameters from a set of capacity optimization configuration parameters, the baseline configuration capacity of each type of energy storage device in the hybrid energy storage system can be determined based on the target capacity optimization configuration parameters. Simultaneously, considering the characteristics of different types of energy storage devices (such as power density, energy density, response speed, cycle life, etc.) and their roles in the hybrid energy storage system (such as meeting short-term power fluctuation demands or providing long-term energy support), the baseline configuration capacity is adaptively adjusted in conjunction with the target capacity optimization configuration parameters. Furthermore, based on the target capacity optimization configuration parameters, the capacity allocation relationship between different types of energy storage devices can also be determined, such as the capacity allocation weight of energy storage units corresponding to wind power equipment and photovoltaic equipment, ensuring that the overall hybrid energy storage system minimizes energy storage costs and maximizes operational benefits while meeting technical constraints (such as charge / discharge rate limits and safe state of charge ranges).

[0080] The hybrid energy storage system capacity configuration method provided in this invention calculates the energy storage cost set of the hybrid energy storage system based on the initial capacity configuration parameters in the initial capacity configuration parameter set, and calculates the operating benefits set of the hybrid energy storage system under the target energy storage scenario. Based on the probability of occurrence of the target energy storage scenario and the operating benefits set, the method solves for the operating output curves of various types of energy storage devices in the hybrid energy storage system, obtaining a set of operating output curves for the hybrid energy storage system. Then, based on the set of operating output curves and the energy storage cost set, the method solves for the capacity optimization configuration parameters of various types of energy storage devices in the hybrid energy storage system, obtaining a set of capacity optimization configuration parameters. This set of capacity optimization configuration parameters is then used to select target capacity optimization configuration parameters to optimize the overall cost-effectiveness of the hybrid energy storage system. The solution process employs a multi-objective optimization algorithm, comprehensively considering the balance between energy storage costs and operating benefits to ensure high robustness of the configuration scheme under different target energy storage scenarios. Finally, by iteratively updating the initial capacity configuration parameter set, the convergence of the capacity optimization configuration parameters is achieved, thereby improving the reliability of the hybrid energy storage system capacity configuration.

[0081] Figure 2 A flowchart illustrating another embodiment of the capacity configuration method for a hybrid energy storage system according to the present invention is shown. Figure 2 As shown, the method includes the following steps: Step 210: Obtain the initial capacity configuration parameter set of the hybrid energy storage system, as well as the probability of occurrence of the target energy storage scenario in the typical scenario set.

[0082] Please see details Figure 1 Step 110 of the illustrated embodiment will not be described again here.

[0083] Step 220: Calculate the energy storage cost of the hybrid energy storage system based on the initial capacity configuration parameters in the initial capacity configuration parameter set to obtain the energy storage cost set, and calculate the operating revenue of the hybrid energy storage system under the target energy storage scenario to obtain the operating revenue set.

[0084] Please see details Figure 1 Step 120 of the illustrated embodiment will not be described again here.

[0085] Step 230: Based on the occurrence probability and operating benefit set of the target energy storage scenario, solve for the operating output curves of each type of energy storage device in the hybrid energy storage system to obtain the operating output curve set of the hybrid energy storage system.

[0086] Please see details Figure 1 Step 130 of the illustrated embodiment will not be described again here.

[0087] Step 240: Based on the set of operating output curves and the set of energy storage costs, solve for the capacity optimization configuration parameters of each type of energy storage device in the hybrid energy storage system to obtain the set of capacity optimization configuration parameters.

[0088] Step 250: Utilize the target capacity optimization configuration parameters in the capacity optimization configuration parameter set to configure the capacity of various types of energy storage devices in the hybrid energy storage system.

[0089] Specifically, step 250 includes: Step 2501: Select target capacity optimization configuration parameters from the capacity optimization configuration parameter set to construct a candidate configuration parameter set.

[0090] Specifically, based on the function values ​​of the capacity configuration functions corresponding to each capacity optimization configuration parameter in the capacity optimization configuration parameter set, the target capacity optimization configuration parameters with the largest function values ​​can be selected as candidate capacity configuration parameters. In the optional capacity configuration parameters In China, with Regenerate configuration parameters, for example, for the candidate capacity configuration parameters. Generate new capacity configuration parameters The candidate capacity configuration parameters and the newly generated capacity configuration parameters are input into the capacity configuration function for iterative calculation until the preset convergence condition is met or the maximum number of iterations is reached. Multiple candidate configuration parameters are then output, resulting in a candidate configuration parameter set. For example, after the nth iteration, d candidate capacity configuration parameters no longer change (i.e., the new scheme has a smaller objective function value), or the step size... (The accuracy of the capacity configuration parameter search fully meets the requirements of the planning scheme, for example) Stop iteration, return and select configuration parameters. .

[0091] Step 2502: Based on the parameter scores of each candidate configuration parameter in the candidate configuration parameter set, determine the target capacity optimization configuration parameters, and use the target capacity optimization configuration parameters to configure the capacity of each type of energy storage device in the hybrid energy storage system.

[0092] Among them, the target capacity optimization configuration parameter is the capacity optimization configuration parameter with the highest parameter score among the candidate configuration parameters.

[0093] In one possible implementation, when determining the target capacity optimization configuration parameter based on the parameter scores of each candidate configuration parameter in the candidate configuration parameter set, the operating index data, power generation cost data, and environmental benefit data corresponding to each candidate configuration parameter can be calculated first. The operating index data includes energy storage output and energy storage utilization rate, and the environmental benefit data includes carbon dioxide emissions and renewable energy utilization rate. Based on the operating index data, power generation cost data, and environmental benefit data, the parameter score of each candidate configuration parameter is calculated. The candidate configuration parameter with the highest parameter score is selected as the target capacity optimization configuration parameter.

[0094] Specifically, operational metrics include energy storage output and energy storage utilization rate; energy storage output and energy storage utilization rate (including battery energy storage utilization rate) Compressed air energy storage utilization rate The formula for calculating (unit: cycles / day) is:

[0095]

[0096]

[0097] The formula for calculating power generation cost data is:

[0098] Environmental benefit data includes carbon dioxide emissions and renewable energy utilization rate; carbon dioxide emissions and renewable energy utilization rate The calculation formula is:

[0099]

[0100]

[0101] in, The carbon emission factor for coal-fired power generation (unit: g) / kWh), which is composed of the standard coal consumption coefficient and standard coal of coal-fired power units. Emissions are determined by factors, express quantity; Indicates the utilization rate of photovoltaic energy. This indicates the utilization rate of wind power energy.

[0102] Furthermore, when calculating the parameter scores for each candidate configuration parameter based on operational indicator data, power generation cost data, and environmental benefit data, a weighted summation method can be used. This involves assigning corresponding weight coefficients to the operational indicator data, power generation cost data, and environmental benefit data. Specifically, this includes setting weights for operational indicators, power generation costs, and environmental benefits. Then, after normalizing each data point, it is multiplied by the corresponding weight coefficient and summed to obtain the comprehensive parameter score. The normalization process can be based on the min-max scaling method or the standard score method to ensure the comparability of data with different dimensions. The allocation of weight coefficients is based on the system optimization objective; for example, when prioritizing reducing power generation costs, the proportion of the power generation cost weight is increased, or when focusing on environmental benefits, the proportion of the environmental benefit weight is enhanced.

[0103] In addition, due to energy storage cost parameters Compared to renewable energy installations, the cost is higher and the technology changes rapidly. To estimate the changing trends in configuration schemes under future cost reduction trends, we can take... , ,in , The linear fit was obtained using cost coefficients from the past 5 years. Therefore, it can be... , The data are input into the operating output function and capacity configuration function for solving, and the operating index data, power generation cost data and environmental benefit data corresponding to each candidate configuration parameter are re-evaluated. This yields the configuration scheme changes under the cost reduction trend, and thus sets a reasonable cost reduction target for the development of energy storage technology.

[0104] The hybrid energy storage system capacity configuration method provided in this invention provides a more accurate description of the operating characteristics of various types of energy storage by establishing first constraints based on differences in response time, operating boundaries, adjustment range, energy conversion form, and technical characteristics. By constructing a capacity configuration function and an operating output function, and applying the DTW clustering method to transform the operating output problem into a deterministic optimization problem, the complexity of the hybrid energy storage system capacity optimization problem is significantly reduced, and computational efficiency is improved. A heuristic search optimization method based on configuration-operation simulation-configuration update iteration is established. Initial capacity configuration parameters are re-generated iteratively based on suboptimal solutions, and the initial solution interval is adaptively reduced based on the discrete characteristics of the capacity configuration parameters to balance the solution speed and result accuracy of the capacity configuration problem. By calculating the operating index data, power generation cost data, and environmental benefit data corresponding to each candidate configuration parameter, and by comparing and analyzing these data, the optimal configuration scheme that is economical, efficient, and environmentally friendly is selected. Furthermore, by combining the cost reduction trend to predict future optimization paths, a comprehensive evaluation and reliable configuration of the hybrid energy storage system performance is achieved.

[0105] Figure 3 A schematic diagram of an embodiment of a hybrid energy storage system capacity configuration device according to the present invention is shown. Figure 3 As shown, the device includes: The data acquisition module 310 is used to acquire the initial capacity configuration parameter set of the hybrid energy storage system, as well as the probability of occurrence of the target energy storage scenario in the typical scenario set; The cost calculation module 320 is used to calculate the energy storage cost of the hybrid energy storage system based on the initial capacity configuration parameters in the initial capacity configuration parameter set to obtain the energy storage cost set, and to calculate the operating revenue of the hybrid energy storage system under the target energy storage scenario to obtain the operating revenue set. The operation output module 330 is used to solve the operation output curves of various types of energy storage devices in the hybrid energy storage system based on the occurrence probability of the target energy storage scenario and the operation benefit set, so as to obtain the operation output curve set of the hybrid energy storage system. The parameter solving module 340 is used to solve the capacity optimization configuration parameters of various types of energy storage devices in the hybrid energy storage system based on the set of operating output curves and the set of energy storage costs, and to obtain the set of capacity optimization configuration parameters. The capacity configuration module 350 is used to configure the capacity of various types of energy storage devices in a hybrid energy storage system by utilizing the target capacity optimization configuration parameters in the capacity optimization configuration parameter set.

[0106] In one possible implementation, the initial capacity configuration parameters include a first annual value factor and a first energy storage parameter for the first type of energy storage device in the hybrid energy storage system, and a second annual value factor and a second energy storage parameter for the second type of energy storage device. The annual value factor is determined based on the lifespan and discount rate of the energy storage device. The first energy storage parameter includes a first power factor and a first rated power, and the second energy storage parameter includes a second power factor, a second rated power, rated energy, and an energy factor. The cost calculation module 320 includes: The first cost calculation unit is used to calculate the first energy storage cost of the first type of energy storage equipment based on the first annual value coefficient and the first energy storage parameters. The second cost calculation unit is used to calculate the second energy storage cost of the second type of energy storage equipment based on the second annual value coefficient and the second energy storage parameters. The energy storage cost calculation unit is used to calculate the energy storage cost of the hybrid energy storage system based on the first energy storage cost and the second energy storage cost.

[0107] In one possible implementation, the initial capacity configuration parameters also include the energy storage output of various types of energy storage devices, and the cost calculation module 320 further includes: The data acquisition unit is used to obtain the grid-connected electricity price of the hybrid energy storage system and the operating costs of various types of energy storage devices in the target energy storage scenario. The operating revenue calculation unit is used to calculate the operating revenue of the hybrid energy storage system under the target energy storage scenario based on energy storage output, grid-connected electricity price and operating costs.

[0108] In one possible implementation, the power output module 330 includes: The power output function construction unit is used to construct the power output function based on the occurrence probability of the target energy storage scenario and the set of operating benefits. The first constraint unit is used to establish the first constraint of the operating output function. The first constraint includes the maximum output constraint, the thermal power output constraint and the energy storage output constraint. The operation output time series solution unit is used to solve the operation output time series dataset of various types of energy storage devices in the hybrid energy storage system based on the first constraint, the occurrence probability of the target energy storage scenario and the operation benefit set; The operating output curve determination unit is used to determine the set of operating output curves of the hybrid energy storage system based on the operating output curves corresponding to each operating output time series data in the operating output time series dataset.

[0109] In one possible implementation, the parameter solving module 340 includes: The capacity configuration function construction unit is used to construct the capacity configuration function based on the set of operating output curves and the set of energy storage costs. The second constraint establishment unit is used to establish the second constraint of the capacity configuration function. The second constraint includes energy storage state of charge constraint and operating boundary constraint. The capacity configuration parameter solving unit is used to solve the capacity configuration function for the capacity optimization configuration parameters of various types of energy storage devices in the hybrid energy storage system based on the second constraint, the set of operating output curves and the energy storage cost set, and to obtain the capacity optimization configuration parameter set.

[0110] In one possible implementation, the capacity configuration module 350 includes: The candidate configuration parameter set construction unit is used to select target capacity optimization configuration parameters from the capacity optimization configuration parameter set and construct the candidate configuration parameter set. The energy storage device capacity configuration unit is used to determine the target capacity optimization configuration parameters based on the parameter scores of each candidate configuration parameter in the candidate configuration parameter set, and to configure the capacity of various types of energy storage devices in the hybrid energy storage system using the target capacity optimization configuration parameters.

[0111] In one possible implementation, the energy storage device capacity configuration unit includes: The configuration parameter calculation subunit is used to calculate the corresponding operational index data, power generation cost data, and environmental benefit data for each candidate configuration parameter. The operational index data includes energy storage output and energy storage utilization rate, and the environmental benefit data includes carbon dioxide emissions and renewable energy utilization rate. The parameter scoring calculation subunit is used to calculate the parameter scores of each candidate configuration parameter based on operational indicator data, power generation cost data, and environmental benefit data. The configuration parameter sub-selection unit is used to select the candidate configuration parameter with the highest parameter score as the target capacity optimization configuration parameter.

[0112] For a description of the features in the embodiment corresponding to the capacity configuration device of the hybrid energy storage system in this application, please refer to the relevant description of the embodiment corresponding to the capacity configuration method of the hybrid energy storage system, which will not be repeated here.

[0113] Embodiments of this application also provide a power device, such as... Figure 4As shown, there are one or more processors 410, memory 420, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other using different buses and can be mounted on a common motherboard or otherwise as needed. The processors can process instructions that execute within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interface). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 410 as an example.

[0114] Processor 410 may be a central processing unit, a network processor, or a combination thereof. Processor 410 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0115] The memory 420 stores instructions executable by at least one processor 410 to cause the at least one processor 410 to perform the method shown in the above embodiments.

[0116] The memory 420 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device as displayed on a mini-program landing page. Furthermore, the memory 420 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 420 may optionally include memory remotely located relative to the processor 410, and these remote memories may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0117] The memory 420 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 420 may also include a combination of the above types of memory.

[0118] The electronic device also includes an input device 430 and an output device 440. The processor 410, memory 420, input device 430, and output device 440 can be connected via a bus or other means.

[0119] Input device 430 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 440 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touch screen.

[0120] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0121] A portion of the embodiments of this application can be applied as a computer program product, such as computer program instructions. When executed by a computer, these instructions, through the operation of the computer, can invoke or provide the methods and / or technical solutions according to the present invention. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Accordingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0122] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for various specific applications, but such implementations should not be considered beyond the scope of this application.

[0123] The above provides a detailed description of a hybrid energy storage system capacity configuration method, apparatus, power equipment, and storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only intended to help understand the method and core ideas of this application. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for mixed energy storage system capacity configuration, characterized in that, The method includes: Obtain the initial capacity configuration parameter set of the hybrid energy storage system, as well as the probability of occurrence of the target energy storage scenario in the typical scenario set; Based on any initial capacity configuration parameter in the initial capacity configuration parameter set, the energy storage cost of the hybrid energy storage system is calculated to obtain an energy storage cost set, and the operating revenue of the hybrid energy storage system under the target energy storage scenario is calculated to obtain an operating revenue set. Based on the probability of occurrence of the target energy storage scenario and the set of operating benefits, the operating output curves of each type of energy storage device in the hybrid energy storage system are solved to obtain the set of operating output curves of the hybrid energy storage system. Based on the set of operating output curves and the set of energy storage costs, the capacity optimization configuration parameters of each type of energy storage device in the hybrid energy storage system are solved to obtain the set of capacity optimization configuration parameters. The capacity of each type of energy storage device in the hybrid energy storage system is configured using the target capacity optimization configuration parameters in the capacity optimization configuration parameter set.

2. The method according to claim 1, characterized in that, The initial capacity configuration parameters include the first annual value coefficient and first energy storage parameter of the first type of energy storage device in the hybrid energy storage system, and the second annual value coefficient and second energy storage parameter of the second type of energy storage device. The annual value coefficient is determined based on the lifespan and discount rate of the energy storage device. The first energy storage parameter includes a first power coefficient and a first rated power. The second energy storage parameter includes a second power coefficient, a second rated power, rated energy, and an energy coefficient. The energy storage cost of the hybrid energy storage system is calculated based on the initial capacity configuration parameters, including: The first energy storage cost of the first type of energy storage equipment is calculated based on the first annual value coefficient and the first energy storage parameters. The second energy storage cost of the second type of energy storage equipment is calculated based on the second equivalent annual value coefficient and the second energy storage parameters. The energy storage cost of the hybrid energy storage system is calculated based on the first energy storage cost and the second energy storage cost.

3. The method according to claim 1, characterized in that, The initial capacity configuration parameters also include the energy storage output of various types of energy storage devices. Based on the initial capacity configuration parameters, the operational benefits of the hybrid energy storage system under the target energy storage scenario are calculated, including: Obtain the grid-connected electricity price of the hybrid energy storage system and the operating costs of various types of energy storage devices in the target energy storage scenario; Based on the energy storage output, the grid-connected electricity price, and the operating cost, calculate the operating revenue of the hybrid energy storage system in the target energy storage scenario.

4. The method according to claim 1, characterized in that, The step involves calculating the operating output curves of various types of energy storage devices in the hybrid energy storage system based on the probability of occurrence of the target energy storage scenario and the operating benefit set, thereby obtaining the operating output curve set of the hybrid energy storage system, including: Based on the occurrence probability of the target energy storage scenario and the operational benefit set, an operational output function is constructed; Establish a first constraint on the operating output function, the first constraint including maximum output constraint, thermal power output constraint and energy storage output constraint; Based on the first constraint, the probability of occurrence of the target energy storage scenario, and the operating benefit set, the operating output time series dataset of each type of energy storage device in the hybrid energy storage system is solved. The set of operating output curves for the hybrid energy storage system is determined based on the operating output curves corresponding to each operating output time series data in the operating output time series dataset.

5. The method according to claim 1, characterized in that, The process involves solving for the capacity optimization configuration parameters of various types of energy storage devices in the hybrid energy storage system based on the set of operating output curves and the set of energy storage costs, resulting in a set of capacity optimization configuration parameters, including: Based on the set of operating output curves and the set of energy storage costs, a capacity configuration function is constructed. Establish a second constraint for the capacity configuration function, the second constraint including energy storage state of charge constraint and operating boundary constraint; Based on the second constraint, the set of operating output curves, and the set of energy storage costs, the capacity optimization configuration parameters of each type of energy storage device in the hybrid energy storage system are solved to obtain the set of capacity optimization configuration parameters.

6. The method according to claim 1, characterized in that, The process of configuring the capacity of various types of energy storage devices in the hybrid energy storage system using the target capacity optimization configuration parameters in the set of capacity optimization configuration parameters includes: Select a target number of capacity optimization configuration parameters from the set of capacity optimization configuration parameters to construct a candidate configuration parameter set; Based on the parameter scores of each candidate configuration parameter in the candidate configuration parameter set, the target capacity optimization configuration parameters are determined, and the capacity of each type of energy storage device in the hybrid energy storage system is configured using the target capacity optimization configuration parameters.

7. The method according to claim 6, characterized in that, The step of determining the target capacity optimization configuration parameters based on the parameter scores of each candidate configuration parameter in the candidate configuration parameter set includes: Calculate the operational index data, power generation cost data, and environmental benefit data corresponding to each candidate configuration parameter. The operational index data includes energy storage output and energy storage utilization rate, and the environmental benefit data includes carbon dioxide emissions and renewable energy utilization rate. Based on the aforementioned operational indicator data, power generation cost data, and environmental benefit data, the parameter scores for each candidate configuration parameter are calculated. The candidate configuration parameter with the highest parameter score is selected as the target capacity optimization configuration parameter.

8. A capacity configuration device for a hybrid energy storage system, characterized in that, The device includes: The data acquisition module is used to acquire the initial capacity configuration parameter set of the hybrid energy storage system, as well as the probability of occurrence of the target energy storage scenario in the typical scenario set; The cost calculation module is used to calculate the energy storage cost of the hybrid energy storage system based on any initial capacity configuration parameter in the initial capacity configuration parameter set to obtain an energy storage cost set, and to calculate the operating revenue of the hybrid energy storage system under the target energy storage scenario to obtain an operating revenue set. The operation output module is used to solve the operation output curves of each type of energy storage device in the hybrid energy storage system based on the probability of occurrence of the target energy storage scenario and the operation benefit set, so as to obtain the operation output curve set of the hybrid energy storage system. The parameter solving module is used to solve for the capacity optimization configuration parameters of each type of energy storage device in the hybrid energy storage system based on the set of operating output curves and the set of energy storage costs, so as to obtain the set of capacity optimization configuration parameters. The capacity configuration module is used to configure the capacity of various types of energy storage devices in the hybrid energy storage system using the target capacity optimization configuration parameters in the capacity optimization configuration parameter set.

9. An electrical device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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