Energy storage multi-element power system regulation method and system considering weighted scheduling factor

Abstract

The application discloses a kind of energy storage multivariate power system regulation methods and systems considering weighted scheduling factor, method includes: the kind of power system service needing to participate is obtained, the self-risk and mutual risk of each power system service are calculated respectively for the incentive signal of different power system service in past period;According to the preset binary tree weighted model, the weighted scheduling factor under different power system service is calculated;The energy storage charging regulation strategy considering weighted scheduling factor is constructed, and the energy storage charging regulation strategy is solved under the preset constraint condition with 24 hours efficiency maximization and the lowest combination risk as objective function, and the optimal result of charge and discharge is obtained;According to the optimal result of charge and discharge, the proportion and charge and discharge capacity of energy storage system under each power system service are determined.Can simultaneously quantify downlink risk and uplink opportunity, provide fine performance-risk boundary for subsequent scheduling, significantly improve the foresight and robustness of energy storage regulation decision.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system service regulation, and particularly relates to a multi-element power system regulation method and system for energy storage considering a weighted scheduling factor. BACKGROUND

[0002] In recent years, with the continuous increase of renewable energy installed capacity and the opening of multi-level power services such as power spot, auxiliary services and capacity, the compensation incentive of services presents new characteristics of "high frequency fluctuation + strong coupling across services": the superposition of sudden photovoltaic light abandonment, wind power jump and demand side flexible load makes the supply and demand relationship change dramatically in a short time; and the heteroscedasticity of fluctuation of multi-power service incentive signals amplifies the difference and arbitrage window of different power service incentives. Energy storage is considered as an important means to smooth fluctuations and reduce peak and valley due to its ability to quickly charge and discharge and move power across time periods, but objective conditions such as the attenuation of the intrinsic life and the limitation of the power / energy ratio determine that it is difficult to fully release the asset value by relying on traditional strategies, and even the performance-life mismatch may occur due to frequent deep cycling. In view of the above challenges, domestic and foreign researches have begun to combine mathematical modeling engineering concepts with power scheduling to influence energy storage operation decisions: on the one hand, a dynamic volatility model is adopted to describe the heteroscedasticity characteristics of the incentive signal sequence in real time; on the other hand, by means of mean-variance or mean-downside risk framework, the multi-service power allocation is regarded as a portfolio optimization problem to reduce the variance of performance and improve robustness. However, most of the work still stays at the "risk suppression" level, ignoring the numerical quantitative evaluation of the upside potential of the incentive signal; at the same time, the quantification of the coordination and conflict relationship between different services is also relatively rough, which makes it difficult to fully capture more performance improvement opportunities while keeping the risk controllable. SUMMARY

[0003] The application provides a multi-element power system regulation method and system for energy storage considering a weighted scheduling factor, which is used to solve the technical problem that the quantification of the coordination and conflict relationship between different services is relatively rough, which makes it difficult to fully improve performance while keeping the risk controllable.

[0004] In a first aspect, the application provides a multi-element power system regulation method for energy storage considering a weighted scheduling factor, comprising:

[0005] acquiring the types of power system services that need to be involved, and calculating the self-risk and mutual risk of each power system service according to the incentive signals of the past period of different power system services;

[0006] calculating the weighted scheduling factor under different power system services according to a preset binary tree weighting model;

[0007] The energy storage charging regulation strategy considering the weighted scheduling factor is constructed, and the energy storage charging regulation strategy is solved under preset constraint conditions with 24-hour efficiency maximization and minimum combined risk as the objective function, so as to obtain the optimal charging and discharging result.

[0008] According to the optimal charging and discharging result, the proportion and charging and discharging capacity of the energy storage system under each power system service are determined.

[0009] In a second aspect, the present application provides a multi-element energy storage power system regulation system considering a weighted scheduling factor, comprising:

[0010] An acquisition module is configured to acquire the types of power system services to be involved, and calculate the self-risk and mutual risk of each power system service respectively according to the incentive signals of different power system services in the past period;

[0011] A calculation module is configured to calculate the weighted scheduling factor under different power system services according to a preset binary tree weighted model;

[0012] A solving module is configured to construct an energy storage charging regulation strategy considering the weighted scheduling factor, and solve the energy storage charging regulation strategy under preset constraint conditions with 24-hour efficiency maximization and minimum combined risk as the objective function, so as to obtain the optimal charging and discharging result;

[0013] A determination module is configured to determine the proportion and charging and discharging capacity of the energy storage system under each power system service according to the optimal charging and discharging result.

[0014] In a third aspect, an electronic device is provided, which comprises at least one processor and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the energy storage multi-element power system regulation method considering the weighted scheduling factor according to any one of the embodiments of the present application.

[0015] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, and the program instructions are executed by a processor to enable the processor to perform the steps of the energy storage multi-element power system regulation method considering the weighted scheduling factor according to any one of the embodiments of the present application.

[0016] The energy storage multi-element power system regulation method and system considering the weighted scheduling factor introduced in the application introduce a "self-risk-inter-risk + weighted scheduling factor" two-dimensional incentive signal evaluation mechanism in the energy storage multi-element power service scene, track the fluctuation correlation of each service incentive signal through the standard deviation-covariance matrix, and simultaneously calculate the uplink weighted scheduling factor based on the binary tree weighted model. The combination can simultaneously quantify the downlink risk and uplink opportunity, provide a refined performance-risk boundary for subsequent scheduling, and significantly improve the forward-looking and robustness of energy storage transaction decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0018] Figure 1 A flowchart of an energy storage multi-element power system regulation method considering a weighted scheduling factor provided by an embodiment of the present application;

[0019] Figure 2 A structural block diagram of an energy storage multi-element power system regulation system considering a weighted scheduling factor provided by an embodiment of the present application;

[0020] Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0022] Please refer to Figure 1 which shows a flowchart of an energy storage multi-element power system regulation method considering a weighted scheduling factor provided by the present application.

[0023] As shown in Figure 1 , the energy storage multi-element power system regulation method considering a weighted scheduling factor specifically includes the following steps:

[0024] Step S101, the types of power system services that need to be involved are obtained, and the self-risk and inter-risk of each power system service are calculated respectively for the incentive signals of different power system services in the past period.

[0025] In this step, first, the operator selects the participating power system services according to the technical characteristics and access threshold of the energy storage system. Generally, it can be divided into day-ahead, intra-day, real-time energy services and auxiliary services such as frequency modulation and voltage regulation. Then, extract the hourly pricing sequence of the last N days for each service and complete the preprocessing such as time zone alignment, missing data elimination and outlier repair. After cleaning, calculate the self-risk within a single service and the mutual risk between services. Here, the standard deviation and covariance of the service historical incentive data are used to represent:

[0026] ,

[0027] ,

[0028] In the formula, is the self-risk of the i-th participating power system service, is the mutual risk of the i-th participating power system service and the j-th participating power system service, is the total number of historical data days, is the value of the i-th power system service on the t-th day of historical incentive, is the average value of the i-th power service historical incentive, is the value of the j-th power system service on the t-th day of historical incentive, is the average value of the j-th power service historical incentive;

[0029] The generated and constitute the risk-covariance matrix, which measures the fluctuation amplitude of a single service and reveals the synchronicity of different service incentive signals, laying the foundation for weighted scheduling factor evaluation and performance-risk collaborative optimization.

[0030] Step S102, according to the preset binary tree weighted model, calculate the weighted scheduling factor under different power system services.

[0031] In this step, a binary tree weighted model based on binary tree decision is constructed, and the discharge opportunity of energy storage on service i is regarded as a bullish weighted factor. With the help of binary tree pricing, the scheduling weighted factor can be obtained at each time period. This factor quantifies the expected rise of future incentive signals in power services, enabling energy storage to capture additional regulation opportunities under the risk of incentive signal fluctuations. By incorporating this value into the performance function model, energy storage can strategically prioritize power services with higher upward potential.

[0032] The model needs to calculate the actual weighted scheduling factor value in the next 24 hours. Since the weighted scheduling factor of the same power service is closely related to the historical incentive signal at the same period, the incentive signal of the same period of the previous day can be selected as the starting point of the binary tree model for modeling. The actual weighted scheduling factor obtained can reflect the fluctuation of the historical incentive signal at the same period.

[0033] The expression of the weighted scheduling factor is:

[0034] ,

[0035] In the formula, is the incentive of the weighted scheduling factor at time t, is the potential increase amplitude of the incentive signal, is the potential decrease amplitude of the incentive signal, is the performance improvement rate under no risk, is the original incentive, is the self-risk of the participated i-th power service at time t, is the incentive of the participated i-th power service at time t.

[0036] Step S103, a storage energy charging regulation strategy considering the weighted scheduling factor is constructed, and the storage energy charging regulation strategy is solved under a preset constraint condition with 24-hour performance maximization and combined risk minimization as an objective function, to obtain an optimal charging and discharging result.

[0037] In this step, the expression of the objective function is:

[0038] ,

[0039] ,

[0040] ,

[0041] ,

[0042] ,

[0043] In the formula, is the total performance of the storage energy system under multiple services, is the number of power service types participated by the storage energy system, is the incentive of the participated i-th power service at time t, is the discharging power of the storage energy system at time t under the i-th power service, is the charging power of the storage energy system at time t under the i-th power service, is the potential performance improvement brought by the weighted scheduling factor, Total cost of risk for energy storage participating in multiple services, Risk preference for energy storage operator, Proportion of the energy storage system participating in the i-th power service at time t, Proportion of the energy storage system participating in the j-th power service at time t, Mutual risk between the i-th power service and the j-th power service at time t, Self-risk of the i-th power service at time t, Self-risk of the j-th power service at time t, Incentive of the weighted scheduling factor at time t, Proportion of the energy storage system participating in the n-th power service at time t.

[0044] The preset constraint conditions include:

[0045] The SoC constraint is expressed as:

[0046] ,

[0047] ,

[0048] In the formula, is the minimum limit of SoC, is the maximum limit of SoC, is the remaining capacity of the battery at time t, is the SoC at the 24th hour, is the SoC at the 1st hour;

[0049] The energy storage charging and discharging constraint is expressed as:

[0050] ,

[0051] ,

[0052] ,

[0053] ,

[0054] In the formula, is the lower limit of the charging power of the energy storage system, is the charging power of the energy storage system at time t, is the upper limit of the charging power of the energy storage system, is the lower limit of the discharging power of the energy storage system, is the discharging power of the energy storage system at time t, is the upper limit of the discharging power of the energy storage system, is a binary variable, which takes a value of 0 or 1.

[0055] Step S104, according to the charging and discharging optimal result, determine the proportion and charging and discharging capacity of the energy storage system under each power system service.

[0056] In this step, the power of the energy storage under different services is obtained, and the charging and discharging operation is performed according to the result. For the energy storage participating in multiple power services with fixed capacity, the proportion under different time and different service types is different. According to the proportion weight, the relationship between the output under different services and the total output is determined. The expression for calculating the proportion and charging and discharging capacity of the energy storage system under each power system service is:

[0057]

[0058]

[0059]

[0060]

[0061] In the formula, is the charging power of the energy storage system at the t time, is the discharging power of the energy storage system at the t time, is the proportion of the energy storage system participating in the i-th power service at the t time, is the charging power of the energy storage system under the i-th power service, is the discharging power of the energy storage system under the i-th power service, is the self-loss rate of the battery, is the power of the energy storage system at the t time, is the power of the energy storage system at the t-1 time, is the charging power of the energy storage system, is the discharging power of the energy storage system, is the total capacity of the energy storage system, is the remaining capacity of the battery at the t time.

[0062] In this embodiment, first, the system accesses the basic information of the energy storage station, determines the day-ahead spot, reserve capacity, frequency regulation and other power services that can be participated in parallel according to the site power-energy configuration and grid access requirements, and automatically pulls the historical incentive signals of the above services. After all the original data are excluded from the outliers, the self-risk fluctuation level of each service and the mutual risk matrix across services are calculated. With the aid of these statistics, the platform can quantify the efficiency-risk characteristics of each business and their mutual correlation, providing rigorous data support for subsequent weighted scheduling factor evaluation and capacity allocation.

[0063] ​​​​Secondly, after obtaining the basic fluctuation information, the system introduces a binary tree weighted decision model to evaluate the uplink potential of the future incentive signal of each service. The platform combines the short-term incentive signal prediction results with the volatility rate, calculates the weighted scheduling factor in different time periods through the binary tree algorithm, and superimposes it with the benchmark quotation to generate a weighted incentive sequence. This sequence not only reflects the current service performance improvement level, but also explicitly depicts the excess performance improvement that may be brought about by the incentive signal breaking through the key threshold, thereby providing more comprehensive reference coordinates for performance-risk joint decision-making.

[0064] Finally, the scheduling optimization module will allocate the energy storage power among multiple power services as a distributable resource under the constraints of state of charge, safe power, cycle life, and power service access. The model aims to maximize 24-hour performance and minimize combined risk, and comprehensively utilizes weighted incentive signals, standard deviations, and covariance information to solve the optimal capacity weight and hourly charge-discharge plan for each service. The optimization results are transmitted to the battery management system and the converter through the system interface to achieve fine automatic scheduling. At the same time, the platform generates expected effects such as performance, risk exposure, and weighted scheduling factor capture rate to support the operation subject to continuously evaluate the strategy effect and iterate parameters, ultimately achieving performance-risk synergistic optimization of energy storage assets in multiple power services.

[0065] In summary, the method of the present application introduces a "self-risk-inter-risk + weighted scheduling factor" two-dimensional incentive signal evaluation mechanism in the energy storage multi-power service scenario, tracks the fluctuation correlation of each service incentive signal through the standard deviation-covariance matrix, and calculates the uplink weighted scheduling factor in real time based on the binary tree weighted model. This combination can quantify both downlink risk and uplink opportunity, providing fine performance-risk boundaries for subsequent scheduling and significantly improving the forward-looking and robustness of energy storage transaction decisions;

[0066] A multiplexing control model is constructed by embedding the weighted scheduling factor, which continuously and proportionally allocates the energy storage power among multiple power services without breaking the state of charge, safe power, and cycle life constraints. The model actively captures the uplink space of the incentive signal using the binary tree model, and suppresses the high correlation of power services through standard deviation and covariance constraints, thereby maintaining the safe operation of the equipment while improving the depth of cross-service arbitrage, significantly improving the performance improvement density of individual energy storage assets;

[0067] By combining the basic efficiency, the weighted scheduling factor and the risk cost in the objective function, a three-dimensional trade-off framework of adjustable efficiency-risk-weighted scheduling is formed. The operator can flexibly set the weight of the risk tolerance and the weighted scheduling factor according to the policy preference of the power service, to realize the continuous adjustment from 'aggressive high scheduling possibility' to 'cautious low fluctuation'. This mechanism improves the adaptability of the energy storage strategy, supports dynamic switching in different incentive signal periods and equipment life stages, and further guarantees the long-term profitability and sustainable operation of the energy storage project.

[0068] Please refer to Figure 2 , which shows a structure block diagram of a kind of energy storage multi-element power system regulation system considering weighted scheduling factor of the application.

[0069] As shown in Figure 2 , the energy storage multi-element power system regulation system 200 includes an acquisition module 210, a calculation module 220, a solving module 230 and a determination module 240.

[0070] The acquisition module 210 is configured to acquire the types of power system services that need to be involved, and calculate the self-risk and mutual risk of each power system service respectively according to the incentive signals of different power system services in the past period. The calculation module 220 is configured to calculate the weighted scheduling factor under different power system services according to a preset binary tree weighting model. The solving module 230 is configured to construct an energy storage charging regulation strategy considering the weighted scheduling factor, and solve the energy storage charging regulation strategy under the preset constraint condition with the objective function of maximizing the 24-hour efficiency and minimizing the combined risk to obtain the optimal charging and discharging result. The determination module 240 is configured to determine the proportion and charging and discharging capacity of the energy storage system under each power system service according to the optimal charging and discharging solving result.

[0071] It should be understood that Figure 2 each step in the method described with reference to Figure 1 . Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in Figure 2 , and will not be repeated here.

[0072] In some other embodiments, the present application also provides a computer readable storage medium having a computer program stored thereon, wherein the program instructions are executed by a processor to make the processor execute the energy storage multi-element power system regulation method considering weighted scheduling factor in any of the above method embodiments.

[0073] As an implementation manner, the computer readable storage medium of the present application stores computer executable instructions, and the computer executable instructions are configured to:

[0074] Obtain the types of power system services that need to be participated in, and calculate the self-risk and mutual risk of each power system service based on the incentive signals of different power system services over a past period.

[0075] The weighted scheduling factor under different power system services is calculated based on the preset binary tree weighted model;

[0076] An energy storage charging control strategy considering the weighted scheduling factor is constructed, and the energy storage charging control strategy is solved under preset constraints with the objective function of maximizing 24-hour efficiency and minimizing combined risk to obtain the optimal charging and discharging result.

[0077] The proportion and charging / discharging capacity of the energy storage system under the service of each power system are determined based on the optimal charging and discharging results.

[0078] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the energy storage multi-electrode power system control system considering weighted dispatch factors, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely located relative to a processor, which can be connected via a network to the energy storage multi-electrode power system control system considering weighted dispatch factors. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0079] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the energy storage multi-electrode power system control method considering weighted scheduling factors as described in the above method embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the energy storage multi-electrode power system considering weighted scheduling factors. The output device 340 may include a display screen or other display device.

[0080] The electronic device can execute the method provided by the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method. Technical details not described in detail in the embodiments can be referred to the method provided by the embodiments of the present application.

[0081] As an implementation, the electronic device is applied to a multi-energy storage power system regulation system considering a weighted scheduling factor, and is used for a client, comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0082] obtain the types of power system services that need to be involved, and calculate the self-risk and mutual risk of each power system service respectively according to the incentive signals of different power system services in a past period;

[0083] calculate the weighted scheduling factor under different power system services according to a preset binary tree weighted model;

[0084] construct an energy storage charging regulation strategy considering the weighted scheduling factor, and solve the energy storage charging regulation strategy under a preset constraint condition to obtain an optimal charging and discharging result, with the objective function of maximizing 24-hour efficiency and minimizing combined risk;

[0085] determine the proportion and charging and discharging capacity of the energy storage system under each power system service according to the optimal charging and discharging result.

[0086] From the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and a necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions essentially or in other words the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the method of each embodiment or some part of the embodiment.

[0087] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for regulating a multi-element energy storage power system considering a weighted dispatch factor, characterized in that, include: The types of power system services that need to be participated in are identified, and the self-risk and mutual risk of each power system service are calculated based on the incentive signals of different power system services over a past period. The weighted scheduling factor under different power system services is calculated based on a preset binary tree weighted model, wherein the expression of the weighted scheduling factor is: ， In the formula, For the excitation of the weighted scheduling factor at time t, To increase the potential amplitude of the excitation signal, To determine the potential decrease in the excitation signal, The efficiency improvement rate under risk-free conditions. As the original incentive, The self-risk of the i-th electricity service participated in at time t. The incentive for the i-th power service participated in at time t; An energy storage charging control strategy considering the weighted scheduling factor is constructed, and the strategy is solved under preset constraints with the objective function of maximizing 24-hour efficiency and minimizing combined risk to obtain the optimal charging and discharging result. The expression for the objective function is: ， ， ， ， ， In the formula, The overall efficiency of an energy storage system under multiple services, The number of types of electricity services that energy storage participates in. The incentive for participating in the i-th power service at time t. Let be the discharge power of the energy storage system at time t during the i-th power service. Let be the charging power of the energy storage system at time t during the i-th power service. The potential performance improvement brought about by the weighted scheduling factor The total risk cost of energy storage participating in diversified services Based on the risk appetite of energy storage operators, To select the proportion of the energy storage system participating in the i-th power service at time t, To select the proportion of the energy storage system participating in the j-th electricity service at time t, To determine the mutual risk between the i-th and j-th power services at time t, The self-risk of the i-th electricity service participated in at time t. The self-risk of the j-th electricity service participated in at time t, For the excitation of the weighted scheduling factor at time t, Select the proportion of the energy storage system participating in the nth electricity service at time t; The proportion and charging / discharging capacity of the energy storage system under the service of each power system are determined based on the optimal charging and discharging results.

2. The energy storage multi-element power system regulation method considering weighted dispatch factors according to claim 1, characterized in that, The preset constraints include: SoC constraints, expressed as: ， ， In the formula, The minimum limit for SoC, The maximum limit of SoC, Let be the remaining battery charge at time t. For the SoC at time 24, For the SoC at time 1; The energy storage charge and discharge constraints are expressed as follows: ， ， ， ， In the formula, The lower limit of the charging power for energy storage systems. The charging power of the energy storage system at time t. The upper limit of the charging power of the energy storage system. This represents the lower limit of the discharge power of the energy storage system. Let be the discharge power of the energy storage system at time t. This represents the upper limit of the discharge power of the energy storage system. It is a binary variable, and its value can be 0 or 1.

3. The energy storage multi-element power system regulation method considering weighted dispatch factors according to claim 1, characterized in that, The expression for calculating the proportion of energy storage systems served by various power systems and their charging and discharging capacity is as follows: ， ， ， ， In the formula, The charging power of the energy storage system at time t. Let be the discharge power of the energy storage system at time t. To select the proportion of the energy storage system participating in the i-th power service at time t, The charging power of the energy storage system during the i-th electricity service. Let be the discharge power of the energy storage system during the i-th power service. This refers to the battery's self-discharge rate. Let be the power of the energy storage system at time t. Let be the power of the energy storage system at time t-1. The charging power for the energy storage system, To enable the system's discharge power, The total capacity of the energy storage system Let t be the remaining battery charge at time t.

4. A multi-element power system control system for energy storage considering weighted dispatch factors, characterized in that, include: The acquisition module is configured to acquire the types of power system services that need to be participated in, and calculate the self-risk and mutual risk of each power system service based on the excitation signals of different power system services over a past period. The calculation module is configured to calculate the weighted scheduling factor under different power system services based on a preset binary tree weighted model, wherein the expression for the weighted scheduling factor is: ， In the formula, For the excitation of the weighted scheduling factor at time t, To increase the potential amplitude of the excitation signal, To determine the potential decrease in the excitation signal, The efficiency improvement rate under risk-free conditions. As the original incentive, The self-risk of the i-th electricity service participated in at time t. The incentive for the i-th power service participated in at time t; The solution module is configured to construct an energy storage charging control strategy that considers the weighted scheduling factor, and solve the energy storage charging control strategy under preset constraints with the objective function of maximizing 24-hour efficiency and minimizing combined risk to obtain the optimal charging and discharging result. The expression for the objective function is: ， ， ， ， ， In the formula, The overall efficiency of an energy storage system under multiple services, The number of types of electricity services that energy storage participates in. The incentive for participating in the i-th power service at time t. Let be the discharge power of the energy storage system at time t during the i-th power service. Let be the charging power of the energy storage system at time t during the i-th power service. The potential performance improvement brought about by the weighted scheduling factor The total risk cost of energy storage participating in diversified services Based on the risk appetite of energy storage operators, To select the proportion of the energy storage system participating in the i-th power service at time t, To select the proportion of the energy storage system participating in the j-th electricity service at time t, To determine the mutual risk between the i-th and j-th power services at time t, The self-risk of the i-th electricity service participated in at time t. The self-risk of the j-th electricity service participated in at time t, For the excitation of the weighted scheduling factor at time t, Select the proportion of the energy storage system participating in the nth electricity service at time t; The module is configured to determine the proportion and charging / discharging capacity of the energy storage system under each power system service based on the optimal charging / discharging results.

5. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1 to 3.

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