Demand analysis method for energy storage power station cluster peak shaving and frequency regulation

Abstract

The application provides a demand analysis method for peak shaving and frequency regulation of an energy storage power station cluster, comprising the following steps: obtaining net load data; decomposing the net load data by using a variational mode decomposition method to obtain a frequency regulation demand component and a peak regulation demand component; performing frequency regulation demand analysis according to the frequency regulation demand component to obtain frequency regulation power demand; performing peak regulation demand analysis according to the peak regulation demand component to obtain peak regulation power demand; obtaining frequency regulation compensation and peak regulation compensation according to the frequency regulation power demand and the peak regulation power demand; and performing power distribution of the energy storage power station cluster with the sum of the frequency regulation compensation and the peak regulation compensation as the target for minimization.

Description

Technical Field

[0001] This invention belongs to the field of energy storage and power system management, and in particular to a demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters. Background Technology

[0002] In the field of energy storage power station clusters participating in peak shaving and frequency regulation services, several relevant technical solutions already exist. One common approach is for a single energy storage power station to perform peak shaving or frequency regulation services. In this solution, a single energy storage power station meets grid demand by adjusting its charging and discharging power. Another approach involves coordinating the operation of multiple independent energy storage power stations to achieve more efficient energy storage and dispatch capabilities.

[0003] However, these existing technological solutions have some drawbacks. First, a single energy storage power station may not be able to meet the needs of the entire system when facing large-scale peak shaving and frequency regulation demands. The energy storage capacity and power output of a single power station are limited, and it cannot provide sufficient energy and power support. Second, the lack of a unified scheduling strategy when multiple independent energy storage power stations operate in coordination may lead to uneven energy and power distribution, reducing the overall efficiency and flexibility of the system.

[0004] These drawbacks are primarily due to structural limitations in existing technologies. Traditional energy storage power stations have relatively simple structural designs and lack integration and coordination capabilities. The energy storage capacity and power output of a single power station are limited by its own structure and component characteristics, making it unable to quickly adapt to changes in system demand. Furthermore, the lack of effective communication and coordination mechanisms between independent energy storage power stations leads to imbalances in scheduling and energy distribution. Summary of the Invention

[0005] In view of this, the present invention provides a demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters.

[0006] In a first aspect, the present invention provides a demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters, including:

[0007] Obtain net load data;

[0008] The net load data is decomposed using variational mode decomposition to obtain the frequency regulation demand component and the peak regulation demand component.

[0009] FM demand analysis is performed based on FM demand components to obtain FM power demand.

[0010] Peak-shaving demand is analyzed based on peak-shaving demand components to obtain peak-shaving power demand.

[0011] The frequency regulation compensation amount and peak regulation compensation amount are obtained based on the frequency regulation power demand and peak regulation power demand; the power allocation of the energy storage power station cluster is carried out with the goal of minimizing the sum of the frequency regulation compensation amount and peak regulation compensation amount.

[0012] Secondly, the present invention provides an electronic device, including a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to realize the above-mentioned demand analysis method for peak shaving and valley filling and frequency regulation for energy storage power station clusters.

[0013] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention obtains frequency regulation demand components and peak shaving demand components, performs frequency regulation and peak shaving demand analysis, and obtains frequency regulation power demand and peak shaving power demand; it calculates frequency regulation compensation and peak shaving compensation, aiming to minimize the sum of frequency regulation compensation and peak shaving compensation, and then allocates power to the energy storage power station cluster. This invention's method achieves frequency regulation and peak shaving through flexible energy storage allocation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of a demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters provided in an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of histogram density estimation of a frequency-adjusted signal provided in an embodiment of the present invention;

[0018] Figure 3 A schematic diagram of the probability density function of discrete data provided in an embodiment of the present invention;

[0019] Figure 4 A schematic diagram of a typical waveform of the frequency adjustment signal provided in an embodiment of the present invention;

[0020] Figure 5 A schematic diagram of the cumulative distribution function (CDF) of frequency regulation requirements provided in an embodiment of the present invention;

[0021] Figure 6 A schematic diagram illustrating a typical daily frequency regulation capacity requirement provided by an embodiment of the present invention;

[0022] Figure 7 A schematic diagram illustrating a coordinated peak-shaving strategy in a typical scenario provided by an embodiment of the present invention;

[0023] Figure 8 A schematic diagram illustrating a coordinated frequency adjustment strategy in a typical scenario provided by an embodiment of the present invention;

[0024] Figure 9 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be noted that, unless otherwise specified, the features in the following embodiments and implementation methods can be combined with each other.

[0027] This invention aims to address issues such as the limited capacity and power of individual energy storage power stations and the uneven coordinated operation of multiple independent power stations, thereby improving the overall performance and collaborative efficiency of energy storage power station clusters. Through the technical solution of this invention, energy storage power station clusters can more flexibly respond to large-scale peak shaving and valley filling and frequency regulation demands. This solution employs a unified scheduling strategy, enabling efficient collaborative operation among various energy storage power stations, achieving optimized energy and power allocation, and improving system response speed and energy utilization. Furthermore, this invention also considers the energy and power limitations of energy storage power stations and employs intelligent control strategies to achieve optimized energy allocation and scheduling management.

[0028] like Figure 1 As shown, the method specifically includes the following steps:

[0029] S1. Obtain net load data; use variational mode decomposition method to decompose net load data to obtain frequency regulation demand component and peak regulation demand component.

[0030] When processing only net load data, using Variational Mode Decomposition (VMD) to decompose the net load data into low-frequency peak-shaving demand and high-frequency frequency regulation demand is a reasonable approach. Net load data includes fluctuations across various time scales and reflects the overall load changes in the power system. By applying the VMD method, long-term load adjustment demand and rapid frequency regulation demand of energy storage power station systems can be effectively identified and analyzed. The decomposed low-frequency component captures the main patterns of long-term load fluctuations, representing peak-shaving demand, while the high-frequency component represents the rapid fluctuations and adjustments in power system frequency, representing frequency regulation demand.

[0031] The net load signal is represented as x(t), where t represents time. VMD decomposes it into K mode components, represented as u k (t), and a residual term r(t). Here, k represents the index of the mode component, k = 1, 2, ..., K. Each mode component u k (t) can be represented as the superposition of the frequency modulation demand (FRD) component and the peak shaving demand (PSD) component:

[0032] u k (t)=f k (t)+p k (t)

[0033] Among them, f k (t) represents the frequency modulation demand (FRD) component, p k (t) represents the peak demand (PSD) component.

[0034] The mathematical model of VMD can be expressed as the following optimization problem:

[0035]

[0036] Where ω k The frequency parameter λ represents the frequency component, and λ is a regularization parameter used to balance the sparsity of the mode components and the fitting error. The mode components u can be obtained by iteratively solving the optimization problem. k (t), frequency modulation demand component f k (t), and peak-shaving demand component p k (t).

[0037] S2, perform frequency modulation demand analysis based on the frequency modulation demand component to obtain the frequency modulation power demand; perform peak shaving demand analysis based on the peak shaving demand component to obtain the peak shaving power demand.

[0038] Frequency regulation demand analysis mainly provides the total frequency regulation power demand, total frequency regulation energy demand, and frequency regulation rate demand of the energy storage power station group.

[0039] Based on the frequency regulation demand component obtained from VMD, this example first simulates and predicts the frequency regulation signal. The frequency regulation signal exhibits strong randomness and multiple peaks, along with asymmetric distribution characteristics, making it challenging to estimate and fit parameters using existing probability distribution models. Some studies have employed machine learning methods for prediction. However, due to prediction errors and imbalances between power generation and load caused by equipment failures in frequency regulation, data-driven prediction models struggle to capture its probabilistic characteristics. Therefore, this example employs a nonparametric estimation method based on histogram density estimation to predict the frequency regulation signal. Figure 2 The histogram of the frequency-adjusted signal is shown, revealing its asymmetry and the presence of multiple peaks.

[0040] The frequency regulation power demand at time t is extracted from the frequency regulation demand component. Assuming the frequency regulation power demand at time t is represented as s(t), this example divides |s(t)| into N equally spaced intervals of length L. The number of times s(t) appears in the nth interval is denoted as k. n Let the frequency regulation power demand at all times be denoted as the frequency regulation signal S. If this example assumes that the frequency regulation signal S follows a uniform distribution in each interval, then the probability density function of the frequency regulation signal S is denoted as f. pdf , can be represented as:

[0041]

[0042] By integrating the frequency-adjusted signal S, the cumulative distribution function of the frequency-adjusted signal S can be obtained, denoted as f. cdf. :

[0043]

[0044] By setting a desired probability to represent the frequency regulation power demand of energy stored at time t, the frequency regulation signal (e.g.) can be satisfied. Figure 3 (As shown).

[0045]

[0046] Among them, f cdf -1 (·) is the inverse function of the cumulative probability distribution, and α is the expected probability.

[0047] Within each scheduling interval Δt, the expected probabilistic total demand for frequency regulation energy is:

[0048]

[0049] For frequency regulation, the response speed of energy storage is crucial. Therefore, this example requires calculating the maximum frequency regulation range per unit time to determine the required frequency regulation rate. Only energy storage power stations that meet the frequency regulation rate requirement can participate in the system's frequency regulation. This example obtains the frequency regulation capacity at each moment through nonparametric estimation. Based on this, the frequency regulation range can be calculated as the difference between the upward and downward adjustment capacity at the current moment. It is important to note that the absolute values ​​of the upward and downward adjustment capacity are equal at each moment.

[0050]

[0051] Among them, V reg This indicates the required frequency modulation rate. This indicates the current increase in capacity. This indicates the current reduction in capacity.

[0052] It should be emphasized that the calculated frequency adjustment range is relatively conservative, reflecting the frequency adjustment rate requirements under the most severe conditions.

[0053] The peak-shaving demand analysis primarily provides the total peak-shaving power demand, total peak-shaving energy demand, and continuous charge / discharge time for the energy storage power station cluster. Under typical operating scenarios, the rated peak-shaving power can be determined based on the maximum peak-shaving power observed within a typical operating cycle. A typical operating scenario is represented using an average peak-shaving curve:

[0054]

[0055] Similarly, the rated peak-shaving capacity can be determined based on the maximum cumulative charge or discharge observed throughout the entire operating cycle. This can be achieved by dividing the operating cycle into time intervals based on continuous charge and discharge, forming a new set of time intervals T. s ={H g |g=1,2,…,G s To achieve this, G is used. s H is the number of consecutive charge / discharge times in the scenario. g This is the continuous charge / discharge time of the g-th energy storage device. Calculate the maximum peak-shaving power demand. and maximum peak-shaving capacity requirements The formula is as follows:

[0056]

[0057]

[0058] in, This represents the cumulative peak-shaving energy demand during the continuous charging and discharging time of the g-th energy storage group.

[0059] S3 obtains the frequency regulation compensation amount and peak regulation compensation amount based on the frequency regulation power demand and peak regulation power demand; with the goal of minimizing the sum of the frequency regulation compensation amount and peak regulation compensation amount, it performs power allocation for the energy storage power station cluster.

[0060] An energy storage power station cluster is a system composed of multiple energy storage power stations. Through unified scheduling and mutual coordination, they achieve collaborative operation, providing efficient energy storage and dispatch capabilities. From a functional perspective, energy storage power station clusters can be divided into three different types: frequency regulation energy storage power stations, peak shaving energy storage power stations, and hybrid energy storage power stations with both frequency regulation and peak shaving functions. Dedicated frequency regulation energy storage power stations are specifically designed for frequency regulation, while dedicated peak shaving energy storage power stations are specifically designed for peak shaving. Hybrid energy storage power stations possess multifunctionality and adaptability, enabling them to perform frequency regulation in conjunction with frequency regulation energy storage power stations and peak shaving in conjunction with peak shaving energy storage power stations.

[0061] For the dispatch center of an energy storage power station cluster, the goal is to minimize dispatch costs while meeting peak shaving and frequency regulation requirements. This study focuses primarily on power allocation within the energy storage power station cluster, with particular emphasis on power distribution within the cluster. The study temporarily disregards charging and discharging costs, prioritizing the analysis of power management and optimization strategies within the cluster. Therefore, the objective is to formulate an objective function to achieve this optimal balance.

[0062]

[0063] Among them, C fre Represents the cost of frequency modulation, while C pea This represents the cost of peak shaving.

[0064] For energy storage power stations, regardless of whether they are used for frequency regulation, peak shaving, or flexibility services, they must comply with power constraints, energy storage state constraints, energy balance constraints, and efficiency constraints. Power constraints specify the maximum permissible values ​​for charging and discharging power, and their upper and lower limits are defined by the following formula:

[0065] P t,n charge ≤P chargemax

[0066] P t,n discharge ≤P dischargemax

[0067] In the above equation, t represents the scheduling time, and n represents the energy storage type. The variable "n" can take the values ​​"fre", "flex", and "pea", representing frequency regulation energy storage, flexible energy storage, and peak shaving energy storage, respectively; flexible energy storage refers to a group of energy storage systems in an energy storage cluster that can participate in both peak shaving and frequency regulation. P t,n charge P represents the charging power. chargemax P represents the maximum allowable charging power. t,ndischarge P represents the discharge power. dischargemax This indicates the maximum permissible discharge power.

[0068] The State of Storage (SOC) constraint requires the SOC of the energy storage system to meet specified upper and lower thresholds.

[0069] SOC min ≤SOC t,n ≤SOC max

[0070] Among them, SOC min and SOC max These are the upper and lower limits of SOC.

[0071] The relationship between SOC and charge / discharge power in an energy storage power station is shown below:

[0072] SOC t,n =SOC t-1,n +P t,n charge -P t,n discharge

[0073] Flexible energy storage can be combined with peak-shaving energy storage to meet peak-shaving demands. Furthermore, flexible energy storage can also be combined with frequency regulation energy storage to meet frequency regulation requirements.

[0074]

[0075] P t,pea charge +P t,flex charge -P t,pea discharge -P t,flex discharge =P t,peadem

[0076] Among them, P t,peadem This represents the peak-shaving demand at time t.

[0077] Example 1

[0078] Forecast and actual power data for wind and solar loads are obtained from the Belgian power grid. In this example, peak power regulation and frequency power regulation use time scales of 15 minutes and 5 minutes, respectively. Given the 15-minute time granularity of the Belgian power grid data, cubic spline interpolation is used to extend the frequency control signal to 288 points per day. It is assumed that the energy storage cluster consists of three energy storage power stations, and their parameters are shown in Table 1.

[0079] Table 1. Parameters of each energy storage power station in the energy storage cluster

[0080]

[0081]

[0082] 1. Frequency Modulation Demand Analysis

[0083] Obtain the typical waveform of the frequency modulation signal, such as Figure 4 As shown. Through nonparametric estimation of the frequency-modulated signal, this example obtains the cumulative distribution function (CDF) of power and energy, as follows. Figure 5 As shown. Figure 5 This demonstrates the cumulative probability of frequency regulation demand obtained through nonparametric estimation at different time points. By setting the expected value to 0.80, this example obtains the frequency regulation energy demand at each time point, such as... Figure 4 As shown by the stars in the image. It should be noted that... Figure 4 Only data for five time points is displayed. In addition, this example also obtains the capacity increases and decreases for all 288 time points throughout the day, such as... Figure 6 As shown in the table, frequency regulation only needs to compensate for supply and demand imbalances in the power grid, therefore its power demand is smaller than that of peak load. Furthermore, due to the short timescale of frequency regulation, its energy storage capacity demand is also smaller. Table 2 lists the maximum values ​​for frequency regulation capacity demand, frequency regulation rate, and power demand. It should be noted that the frequency regulation capacity demand is calculated based on a 1C discharge rate.

[0084] Table 2. Overall Frequency Modulation Demand Analysis

[0085]

[0086] 2. Peak Shaving Demand Analysis

[0087] For peak shaving demand in energy storage, and based on historical data and peak shaving curves derived from VMD decomposition, this example obtains the total peak shaving capacity demand, peak shaving power demand, and maximum continuous charge-discharge duration. These results are presented in Table 3 below:

[0088] Table 3. Analysis of Total Peak Shaving Demand

[0089]

[0090] 3. Energy storage cluster allocation strategy

[0091] By analyzing the six requirements for peak shaving and frequency regulation, this case study reveals that relying solely on ESS1 for frequency regulation and ESS2 for peak shaving is insufficient to meet the overall peak shaving and frequency regulation needs of the system. In some cases, flexible energy storage needs to be introduced.

[0092] Figure 7This demonstrates the coordination strategy employed by the Peak Storage System (ESS1) and the Flexible Storage System (ESS3). Using a typical scenario as an example, this example shows that during peak periods, the Flexible Storage System only contributes to the power output of the energy storage cluster. This is because during these specific time intervals, the maximum power, available capacity, or continuous discharge level of ESS1 cannot meet the peak storage demand.

[0093] Figure 8 The coordinated energy storage scheduling strategy is demonstrated under a specific typical frequency regulation scenario, covering a duration of one hour and a total of 12 scheduling intervals. The power output profile of the Flexible Energy Storage System (ESS3) appears in time intervals with higher frequency regulation rates, and the overall number of power outputs is relatively small.

[0094] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters as described above. Figure 9 The diagram shown is a hardware structure diagram of any device with data processing capabilities used in the demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters provided in this embodiment of the invention, except for... Figure 9 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0095] Accordingly, this application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the demand analysis method described above for peak shaving and frequency regulation of energy storage power station clusters. The computer-readable storage medium can be an internal storage unit of any data processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0096] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters, characterized in that, include: Obtain net load data; The net load data is decomposed using variational mode decomposition to obtain the frequency regulation demand component and the peak regulation demand component. FM demand analysis is performed based on FM demand components to obtain FM power demand. Peak-shaving demand is analyzed based on peak-shaving demand components to obtain peak-shaving power demand. The frequency regulation compensation amount and peak regulation compensation amount are obtained based on the frequency regulation power demand and peak regulation power demand; the power allocation of the energy storage power station cluster is carried out with the goal of minimizing the sum of the frequency regulation compensation amount and peak regulation compensation amount. The objective function is expressed as follows: ； In the formula, t represents the t-th scheduling time, n represents the energy storage type, and C fre This represents the frequency modulation compensation amount, while C pea Represents peak-shaving compensation amount; Indicates charging power. Indicates discharge power; The combination of flexible energy storage and peak-shaving energy storage can be used to meet peak-shaving demands; or, the combination of flexible energy storage and frequency regulation energy storage can be used to meet frequency regulation requirements, as expressed below: ； ； In the formula, The charging power for frequency modulation energy storage, Charging power for flexible energy storage, For frequency-modulated energy storage discharge power, For flexible energy storage discharge power, To meet the power requirements for frequency regulation, The charging power for peak-shaving energy storage For peak-shaving energy storage discharge power, Let t be the peak shaving demand at time t.

2. The demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters according to claim 1, characterized in that, The net load data is decomposed using variational mode decomposition to obtain the frequency regulation demand component and peak regulation demand component, including: The net load data x(t) is decomposed into K mode components u using the variational mode decomposition method. k (t), where k represents the index of the pattern component, k = 1, 2, ..., K; Each mode component u k (t) represents the frequency modulation demand component f. k (t) and peak-shaving demand component p k The superposition of (t); Variational mode decomposition is formulated as the following optimization problem: ； Where, ω k For frequency parameters, u k (t) represents the mode component, f k (t) represents the frequency modulation demand component, p k (t) represents the peak-shaving demand component, and λ is a regularization parameter used to balance the sparsity of the mode components and the fitting error.

3. The demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters according to claim 1, characterized in that, Frequency modulation demand analysis is performed based on the frequency modulation demand components to obtain the frequency modulation power demand, including: Extract the frequency regulation power demand at time t from the frequency regulation demand component; The frequency regulation power demand at all times is denoted as the frequency regulation signal S; Calculate the probability density function of the frequency-adjusted signal S, and integrate it to obtain the cumulative distribution function f of the frequency-adjusted signal S. cdf ; Calculate frequency regulation power requirements The expression is as follows: ； In the formula, It is the inverse function of the cumulative probability distribution. Let be the expected probability.

4. The demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters according to claim 3, characterized in that, Frequency modulation demand analysis based on frequency modulation demand components also includes: Calculate in each scheduling interval The expected probabilistic total demand for the required frequency regulation capacity is expressed as follows: 。 5. The demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters according to claim 3, characterized in that, Frequency modulation demand analysis based on frequency modulation demand components also includes: Calculate in each scheduling interval The required frequency modulation rate is expressed as follows: ； Among them, V reg Indicates the required frequency modulation rate. This indicates the current increase in capacity. This indicates the current reduction in capacity.

6. The demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters according to claim 1 or 2, characterized in that, Peak shaving demand analysis based on peak shaving demand components includes: Obtain peak shaving demand at time t The expression is as follows: ； In the formula, For peak-shaving demand components, k represents the index of the component, k = 1, 2, ..., K, where K represents the total number of pattern components; By dividing the operating cycle into time intervals based on continuous charging and discharging, a new set of time intervals is formed. Among them, G s H is the number of consecutive charge / discharge times in the scenario. g It is the continuous charging and discharging time of the g-th energy storage device; Calculate the maximum peak power demand and maximum peak-shaving capacity requirements The formula is as follows: ； ； Where T is the total number of scheduling times. This represents the cumulative peak-shaving energy demand during the continuous charging and discharging time of the g-th energy storage group. This represents each scheduling interval.

7. The demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters according to claim 1, characterized in that, Energy storage power station types include: frequency regulation energy storage, flexible energy storage, and peak shaving energy storage; flexible energy storage is a group of energy storage in an energy storage power station cluster that can participate in both peak shaving and frequency regulation; each type of energy storage power station must meet power constraints, energy storage state constraints, energy balance constraints, and efficiency constraints.

8. The demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters according to claim 1, characterized in that, Each type of energy storage power station must meet power constraints, energy storage state constraints, energy balance constraints, and efficiency constraints, including: The power constraint specifies the maximum allowable value of charging and discharging power, and its upper and lower limits are defined by the following formula: ； ； In the formula, t represents the scheduling time, and n represents the energy storage type; n takes the values ​​fre, flex, and pea, which represent frequency regulation energy storage, flexible energy storage, and peak shaving energy storage, respectively. Indicates charging power. Indicates the maximum allowable charging power. Indicates discharge power. Indicates the maximum permissible discharge power; The expression for the energy storage state constraint is as follows: ； in, and These are the upper and lower limits of SOC, respectively; The relationship between SOC and charge / discharge power in an energy storage power station is shown below: 。 9. An electronic device comprising a memory and a processor, characterized in that, The memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the demand analysis method for peak shaving and valley filling and frequency regulation of energy storage power station clusters as described in any one of claims 1-8.

10. 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 demand analysis method for peak shaving and frequency regulation of energy storage power station clusters as described in any one of claims 1-8.

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