Energy storage power station capacity measuring and calculating method considering power grid bearing capacity and multiple application scenes

Abstract

The invention discloses an energy storage power station capacity measuring and calculating method considering the power grid bearing capacity and multiple application scenes. The method comprises the following steps: firstly, performing fitting correction on a scene by adopting a scene aggregation correction technology based on least square fitting; and constructing an energy storage power station capacity optimal measurement and calculation model considering the power grid bearing capacity and multiple application scenes, finally carrying out linear transformation on the energy storage power station capacity optimal measurement and calculation model, and solving by adopting commercial optimization software. According to the calculation model, multiple application scenes are introduced, and peak regulation, frequency modulation and standby multiple dimensions are considered, so that the operation and income calculation of the energy storage power station are more comprehensive, the behavior of investing the energy storage power station on the user side can be better simulated, and the influence of energy storage access on the power grid can be better evaluated. According to the scene aggregation correction technology, fitting correction is carried out on the scene by introducing the least square method, more efficient and accurate scene expression is achieved, and the problem solving scale and solving difficulty are reduced.

Description

Technical Field

[0001] This invention belongs to the field of power systems, and specifically relates to a method for calculating the capacity of energy storage power stations that takes into account the grid carrying capacity and multiple application scenarios. Background Technology

[0002] With the increasing penetration of renewable energy and the growing complexity of power systems, energy storage power stations are playing an increasingly important role in improving grid stability, promoting the integration of new energy sources, and participating in various ancillary services due to their flexible regulation capabilities. Currently, the power grid will no longer participate in the investment and construction of main energy storage systems; however, large-scale investment in energy storage plays a crucial supporting role in the safe and stable operation of the power grid. Nevertheless, disorderly and excessive investment in energy storage construction may impact the carrying capacity of local power grids.

[0003] Existing methods for optimizing energy storage capacity largely focus on user-side investment and construction, aiming to maximize investment benefits. They lack strict constraints on the capacity of upstream transformers and fail to fully consider the impact of energy storage charging and discharging behavior on the grid load curve. This may exacerbate existing peak-valley differences or even cause peak-valley inversion, thus increasing the burden on the grid. Therefore, it is necessary to focus on the grid side and conduct capacity calculations for energy storage power stations that take into account the grid's carrying capacity.

[0004] Furthermore, existing calculation models for energy storage after deployment often only consider peak-shaving scenarios, neglecting the fact that energy storage also serves multiple other scenarios such as frequency regulation and backup. This leads to operational strategies deviating from reality and insufficient basis for capacity calculation decisions. In energy storage capacity calculation, the full-scenario calculation model involves multiple scenarios, constraints, and high-dimensional variables, making the model difficult to solve. While existing scenario reduction methods (such as K-means clustering and forward selection) can reduce the number of scenarios, they often lack the ability to preserve the original probability distribution characteristics, causing the optimized energy storage capacity calculation results to deviate from the actual system requirements. Therefore, there is an urgent need for an optimal energy storage power station capacity calculation method that can simultaneously consider grid carrying capacity constraints and the synergistic benefits of multiple scenarios while possessing high computational efficiency. This will support the system's scientific decision-making regarding energy storage capacity and the effective assessment of the safe and stable operation of the power grid. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for calculating the capacity of energy storage power stations that considers grid carrying capacity and multiple application scenarios.

[0006] A method for calculating the capacity of energy storage power stations, considering grid carrying capacity and multiple application scenarios, includes the following steps:

[0007] First, a scene aggregation and correction technique based on least squares fitting is used to fit and correct the scenes. The predicted scenes are clustered to obtain a set of typical scenes. Then, the least squares method is used to obtain the natural power fluctuation curve of each typical scene, forming a set of typical scenes with smoother fluctuation characteristics and closer to the real scene.

[0008] Furthermore, an optimal capacity calculation model for energy storage power stations is constructed, taking into account the grid carrying capacity and multiple application scenarios. With the goal of minimizing the investment and construction capacity of energy storage, the model comprehensively considers the multi-dimensional constraints of system power balance, multi-scenario application needs, user investment side return needs, and grid carrying capacity and operation requirements. This enables the capacity optimization calculation of energy storage power stations, avoiding redundant investment and negative impacts on the grid.

[0009] Finally, the optimal capacity calculation model for energy storage power stations was linearized and combined with the redesigned scenario set to form a linearized model for optimal capacity calculation of energy storage power stations that takes into account the needs of multiple application scenarios. Commercial optimization software was then used to solve the model.

[0010] The beneficial effects of this invention are as follows:

[0011] This invention considers two dimensions of grid carrying capacity when constructing the optimal capacity calculation model for energy storage power stations: first, ensuring that the actual operation after energy storage integration still meets the carrying capacity of the upstream transformer and that the peak-valley difference in the grid is not aggravated or reversed. Second, the model incorporates multiple application scenarios and considers peak shaving, frequency regulation, and reserve dimensions, making the operation and revenue calculation of energy storage power stations more comprehensive. This helps to better simulate user-side investment in energy storage power stations and better assess the impact of energy storage integration on the grid.

[0012] This invention employs a scenario aggregation correction technique, applying it to the efficient solution of the optimal capacity calculation problem for energy storage power stations. This technique introduces the least squares method to correct the fitting of scenarios, achieving a more efficient and accurate scenario representation and reducing the problem's solution scale and difficulty. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other embodiments can be obtained based on these drawings.

[0014] Figure 1 This is a demonstration image of the effect of scene aggregation correction technology based on least squares fitting.

[0015] Figure 2 This diagram illustrates the system operation of energy storage in the context of peak shaving, frequency regulation, and standby markets, as described in this application embodiment. Detailed Implementation

[0016] 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 skilled in the art based on this application are within the scope of protection of this application.

[0017] To better achieve capacity calculation of energy storage power stations that takes into account the application needs of multiple power grid scenarios, this invention adopts a scenario aggregation and linear reshaping technology based on the least squares method. With the goal of minimizing the optimal calculation scale of energy storage power stations, it takes into account both the power grid carrying capacity and the application needs of multiple scenarios. It comprehensively considers the operational constraints of energy storage in multiple application scenarios under the aggregated and reshaped scenarios, thereby realizing the capacity optimization calculation of energy storage power stations.

[0018] A method for calculating the capacity of energy storage power stations, considering grid carrying capacity and multiple application scenarios, includes the following steps:

[0019] Step (1) The scene is fitted and corrected using a scene aggregation correction technique based on least squares fitting.

[0020] By combining data such as renewable energy output, historical load, and power load growth trends predicted by the power grid, a complete set of renewable energy output scenarios and a complete set of load scenarios for the energy storage planning year are obtained. However, directly using the complete set of scenarios in the optimization model would create an NP-hard problem, making the model difficult to solve. Therefore, clustering methods are needed to compress and aggregate the complete set of scenarios. Using common clustering methods to aggregate the complete set of scenarios can effectively compress the number of scenarios and obtain a certain number of scenarios that retain the characteristics of the original scenario set. However, the typical scenarios obtained by this method cannot accurately represent all possible situations. In view of this, a scenario aggregation and correction technique based on least squares fitting is proposed. It is worth noting that the core of energy storage power station capacity optimization is to deal with the imbalance in the power grid under different application scenarios. Therefore, the linear reshaping of scenarios also mainly revolves around the imbalance.

[0021] The least squares method is used to linearly fit and approximate the unbalance fluctuation amount in typical scenarios, thereby obtaining the unbalance fluctuation curve with similar characteristics and natural scenario fluctuation characteristics, namely the natural power fluctuation curve.

[0022] The mathematical model after linear correction of the scene aggregation is shown in equation (1):

[0023] (1)

[0024] In the formula: Let T represent the typical scenario power vector, which consists of the power imbalance at each moment in scenario s, and let T represent the number of moments in a typical scenario day. Capacity optimization problems are usually handled on an hourly timescale, so T is usually 24. This represents a typical scenario in clustering schemes. The typical scenario power vector is composed of the power imbalance at each time step. For linear coefficients, and The constant linear coefficients are the part. A unit vector consisting of 1s, used to introduce The constant part of the linear deviation. This represents the linear deviation value that cannot be covered by the first-order linear coefficients and constant linear coefficients. Then, the least squares method is used to obtain the natural power fluctuation curve for each typical scenario.

[0025] The key to accurately describing the linearization of a scene lies in the linearization coefficients. and The optimization aims to minimize the linear deviation value. The objective function is constructed to achieve the goal, resulting in the following optimization model:

[0026] (2)

[0027] (3)

[0028] The specific mathematical process for transforming the optimization model is as follows:

[0029] (4)

[0030] Therefore, the optimal solution for the linear coefficients is shown below.

[0031] (5)

[0032] In summary, by obtaining the optimal linear coefficients and using formula (1) to obtain the power vector of typical scenarios after linearization and aggregation correction, we can achieve scene clustering and dimensionality reduction while obtaining a set of typical scenarios with smoother fluctuation characteristics and closer to the real scene.

[0033] Step (2) Construct an optimal capacity calculation model for energy storage power stations that considers grid carrying capacity and multiple application scenarios.

[0034] The construction of energy storage power stations can provide sufficient support to the power grid through peak shaving, frequency regulation, and backup. However, excessive investment in energy storage construction will bring about capacity problems to the power grid, which may cause peak-valley inversion and further difficulties in wind and solar power integration. Therefore, when calculating the capacity of energy storage power stations from the perspective of the power grid, it is necessary to combine the needs of multiple scenarios with the capacity of the power grid to determine the minimum energy storage investment capacity. Therefore, the objective function for optimization calculation is shown in equation (6):

[0035] (6)

[0036] In the formula, Investment capacity for energy storage.

[0037] Then determine the constraints:

[0038] To better optimize the capacity of energy storage power stations, it is necessary to simulate the operation of energy storage power stations in the power grid. The constraints involved mainly include the following aspects:

[0039] i) System power balance constraints:

[0040] With the assistance of energy storage, power balance is achieved among the power grid, new energy sources, energy storage and imbalance, as shown in equation (7).

[0041] (7)

[0042] In the formula: This indicates the total power supplied by the upstream power grid. Indicates unbalanced power quantity. This indicates the total output of the new energy power plant. Indicates the load value. and These represent the discharge power and charging power of energy storage in peak-shaving scenarios, respectively. and These represent the discharge power and charging power of energy storage in a frequency regulation scenario, respectively.

[0043] ii) Physical constraints on the operation of energy storage systems:

[0044] The operational constraints of energy storage systems include charging power constraints, discharging power constraints, energy state update constraints, energy limitation constraints, energy periodicity constraints, and charge / discharge logic limitations. Their mathematical expressions are as follows:

[0045] Charging power constraints:

[0046] (8)

[0047] Discharge power constraint:

[0048] (9)

[0049] Energy state update constraints:

[0050] (10)

[0051] Energy constraints:

[0052] (11)

[0053] Energy periodicity constraint:

[0054] (12)

[0055] Charge / discharge logic constraints:

[0056] (13)

[0057] In the formula: and These represent the discharge power and charging power of the energy storage, respectively. and These are the charging and discharging state variables, both of which are 0-1 variables. Investment capacity for energy storage. and These represent the stored energy states at time t and time t-1, respectively. For static energy loss in energy storage, and These represent charging and discharging efficiencies, respectively. Duration per unit step, This refers to the capacity-to-power ratio of energy storage, i.e., the energy storage multiplier. and These represent the minimum and maximum states of charge for energy storage, respectively. and These are the energy storage state values ​​at the start and end times of a typical daily scenario, respectively.

[0058] iii) Constraints for multiple application scenarios:

[0059] The energy storage system participates in three scenarios of grid peak shaving, frequency regulation and standby at the same time. Therefore, it needs to meet the peak shaving output limit (14), frequency regulation output limit (15) and standby output limit (16). The charging and discharging capacity update after the energy storage participates in peak shaving, frequency regulation and standby at the same time is shown in equations (17) and (18).

[0060] (14)

[0061] (15)

[0062] (16)

[0063] (17)

[0064] (18)

[0065] In the formula: and These represent the discharge power and charging power of energy storage in peak-shaving scenarios, respectively. and These represent the discharge power and charging power of energy storage in a frequency regulation scenario, respectively. and These represent the power values ​​that the energy storage provides for positive and negative backup to the system, respectively. and These represent the positive and negative power demands of the peak-shaving market, respectively, and are forecast values. and These represent the positive and negative power demands of the FM market, respectively, and are also forecast values. and These represent the positive and negative power demands of the standby market, respectively, and are also forecast values.

[0066] iv) Grid carrying capacity limitations:

[0067] The grid carrying capacity limit refers to the fact that after energy storage is connected, considering the charging and discharging behavior of energy storage, the power flowing through the upstream transformer of the entire grid does not exceed its carrying capacity. Its mathematical model is shown in Equation (19). On the other hand, after the energy storage power station is connected, its charging and discharging behavior in different application scenarios may lead to peak-valley inversion. In order to avoid peak-valley inversion, constraints to avoid peak inversion, namely Equation (20) and constraints to avoid valley inversion, namely Equation (21), are added.

[0068] (19)

[0069] (20)

[0070] (twenty one)

[0071] In the formula: This is the maximum power that the upstream transformer can handle. This means that it holds true for any scenario. This means that it holds true for any given moment.

[0072] v) Enterprise operational efficiency assessment:

[0073] From the perspective of corporate energy storage investment, its operational strategy needs to ensure the achievement of expected profits, i.e., the return on investment should exceed the investment interest rate. Therefore, it is necessary to evaluate its operational efficiency, including five parts: energy storage investment costs. Peak shaving revenue Frequency modulation revenue Reserve income And the operation and maintenance costs of energy storage. The mathematical expression of its objective function is shown in equations (22)-(27).

[0074] (twenty two)

[0075] (twenty three)

[0076] (twenty four)

[0077] (25)

[0078] (26)

[0079] (27)

[0080] In the formula: This represents the total number of scenarios 's' within the investment cycle. This represents the number of hours in each typical scenario day. and These represent the discharge and charging electricity prices for energy storage in peak-shaving scenarios, respectively. and These represent the discharge power and charging power of energy storage in peak-shaving scenarios, respectively. and These represent the discharge and charging electricity prices for energy storage in frequency regulation scenarios, respectively. and These represent the discharge power and charging power of energy storage in a frequency regulation scenario, respectively. and These respectively represent positive and negative standby prices for energy storage in the standby market. and These represent the power values ​​that the energy storage provides for positive and negative backup to the system, respectively. is the present value, and n is the year value.

[0081] Step (3) Linearize the optimal capacity calculation model of the energy storage power station.

[0082] The scenarios from the obtained set of typical scenarios are embedded into the optimal capacity calculation model of the energy storage power station. Among them, the content strongly related to the scenario presentation is mainly the model part related to the system power balance constraint and the multi-scenario application constraint. Therefore, the linearized coefficient result obtained by optimization is introduced into the system power balance constraint equation (7), and its mathematical model can be updated as follows:

[0083] (28)

[0084] Introduce several additional variables Transform formula (28) and add variables that satisfy Then formula (28) can be equivalently transformed into a scenario-embedded energy storage power station capacity optimization calculation model that takes into account the needs of multiple application scenarios:

[0085] (29)

[0086] The equivalent transformation formula (29) introduces additional variables to distribute the linearization coefficients across typical daily decision-making scenarios, while maintaining consistency with the original model formula (7) in form. By solving the equivalent transformation model, typical scenarios are used to characterize the annual operation, effectively reducing the size of the original model while providing more robust scenario characteristics. The model built in this invention can be solved by calling the Gurobi commercial solver through the Matlab platform.

[0087] Taking a 30-node IEEE network as an example, this paper calculates the optimal configuration of energy storage capacity and verifies the effectiveness of the method. The system includes 6 generator nodes, 21 load nodes, and 41 branches, with a base capacity of 100 MVA. Photovoltaic power plants with capacities of 34 MW, 40 MW, 34 MW, and 40 MW are installed at nodes 4, 7, 21, and 30, respectively. The energy storage device used in this paper employs lithium-ion batteries, with a unit capacity cost of 1500 yuan / kWh, a unit power cost of 100 yuan / kW, a charge / discharge efficiency of 90%, a static energy loss of 1%, a discount rate of 7%, and a service life of 10 years. Electricity price information is referenced from the 2024 provincial development and reform commission's policy on adjusting peak-valley time-of-use electricity pricing for industrial and commercial users. All optimization work in this experiment was completed on the MALTAB platform using the CPLEX commercial solver.

[0088] (1) Scene aggregation correction technique based on least squares fitting

[0089] Taking photovoltaic resources as an example, this paper analyzes scenarios based on least squares fitting. A typical scenario set obtained using traditional clustering methods is selected as historical samples. Figure 1 As shown. The clustering results are as follows. Figure 1The clustering scene obtained using the proposed scene correction technique, as shown by the bold blue dashed line, is as follows: Figure 1 The red circle and horizontal line indicate the clustering results. The mean absolute error (MAE) and root mean square error (RMSE) of the blue clustering results are 0.2141 and 0.2342, respectively. After applying the scene correction technique, the MAE and RMSE are reduced to 0.1332 and 0.1528, respectively. It can be seen that the clustering results obtained using only the traditional clustering method deviate relatively significantly from the actual clusters, while the scene clustering technique proposed in the experiment can more accurately represent the output conditions of more scenes. Therefore, the scene correction technique based on the least squares method can improve the accuracy of the corrected scene description.

[0090] (2) Energy storage configuration results

[0091] After simulating the IEEE 30 system, the optimized energy storage configuration is 40.3MW, 80.6MWh. Therefore, the one-time investment cost for the energy storage power station user is 68.51 million yuan. Figure 2 shows the system operation under the scenario where energy storage simultaneously participates in peak shaving, frequency regulation, and reserve markets. Among them, Figure 2 The blue energy curve indicates the change in energy storage energy curve under peak shaving. Green represents the energy change generated by frequency regulation. Orange and yellow represent the power supply of positive and negative reserves, respectively. Peak shaving revenue is 176.514 million yuan, frequency regulation revenue is 23.5352 million yuan, reserve revenue is 7.72248 million yuan, and operation and maintenance costs are 5.67669 million yuan. Therefore, when maintaining the existing peak-valley price difference, the 10-year return on investment for energy storage power station investors is 120.4%. This indicates a good return on investment. From the grid side perspective, under the influence of energy storage charging and discharging behavior, the system's peak net load is 2686MW, which does not exceed the system's carrying capacity limit.

[0092] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0093] The various embodiments in this specification are described in a related manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other.

[0094] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application are included within the scope of protection of this application.

Claims

1. A method for calculating the capacity of energy storage power stations considering grid carrying capacity and multiple application scenarios, characterized in that, Includes the following steps: First, a scene aggregation correction technique based on least squares fitting is used to fit and correct the scene; the predicted scene is clustered to obtain a set of typical scenes, and then the least squares method is used to obtain the natural power fluctuation curve of each typical scene, forming a set of typical scenes with smoother fluctuation characteristics and closer to the real scene. Furthermore, an optimal capacity calculation model for energy storage power stations is constructed, taking into account the grid carrying capacity and multiple application scenarios. With the goal of minimizing the investment and construction capacity of energy storage, the model comprehensively considers the multi-dimensional constraints of system power balance, multi-scenario application requirements, user investment side revenue requirements, and grid carrying capacity and operation requirements. This enables the capacity optimization calculation of energy storage power stations, avoiding redundant investment and negative impacts on the grid. Finally, the optimal capacity calculation model for energy storage power stations was linearized and combined with the redesigned scenario set to form a linearized model for optimal capacity calculation of energy storage power stations that takes into account the needs of multiple application scenarios. Commercial optimization software was then used to solve the model.

2. The energy storage power station capacity calculation method considering grid carrying capacity and multiple application scenarios according to claim 1, characterized in that, The mathematical model after linear correction of scene aggregation is shown in equation (1): （1） In the formula: The typical scenario power vector is composed of the power imbalance at each moment in scenario s, and T represents the number of moments in each typical scenario day. This represents a typical scenario in clustering schemes. A typical scenario power vector composed of the power imbalance at each time step; For linear coefficients, and The constant linear coefficients are the part; A unit vector consisting of 1s, used to introduce The constant part of the linear deviation; This refers to the linear deviation value that cannot be covered by the linear coefficients and constant linear coefficients. The least squares method is used to obtain the natural power fluctuation curve for each typical scenario; in order to minimize the linear deviation value. The objective function is constructed to achieve the goal, resulting in the following optimization model: （2） （3） The specific mathematical process for transforming the optimization model is as follows: （4） Therefore, the optimal solution for the linear coefficients is shown below; （5） In summary, by obtaining the optimal linear coefficients and using formula (1) to obtain the power vector of typical scenarios after linearization and aggregation correction, we can achieve scene clustering and dimensionality reduction while obtaining a set of typical scenarios with smoother fluctuation characteristics and closer to the real scene.

3. The energy storage power station capacity calculation method considering grid carrying capacity and multiple application scenarios as described in claim 1, characterized in that, The objective function for optimization is shown in equation (6): （6） In the formula, Investment capacity for energy storage.

4. The energy storage power station capacity calculation method considering grid carrying capacity and multiple application scenarios according to claim 3, characterized in that, Multidimensional constraints include: system power balance constraints, physical constraints on the operation of energy storage systems, constraints on multiple application scenarios, grid carrying capacity limitations, and enterprise operation benefit assessment.

5. The energy storage power station capacity calculation method considering grid carrying capacity and multiple application scenarios according to claim 4, characterized in that, The system power balance constraints are as follows: With the assistance of energy storage, power balance is achieved among the power grid, new energy sources, energy storage and imbalance, as shown in equation (7); （7） In the formula: This indicates the total power supplied by the upstream power grid. Indicates the amount of unbalanced power. This indicates the total output of the new energy power plant. Indicates the load value. and These represent the discharge power and charging power of energy storage in peak-shaving scenarios, respectively. and These represent the discharge power and charging power of energy storage in a frequency regulation scenario, respectively.

6. The energy storage power station capacity calculation method considering grid carrying capacity and multiple application scenarios according to claim 5, characterized in that, The physical constraints on the operation of energy storage systems include charging power constraints, discharging power constraints, energy state update constraints, energy limitation constraints, energy periodicity constraints, and charging / discharging logic limitations; their mathematical expressions are as follows: Charging power constraints: （8） Discharge power constraint: （9） Energy state update constraints: （10） Energy constraints: （11） Energy periodicity constraint: （12） Charge / discharge logic constraints: （13） In the formula: and These represent the discharge power and charging power of the energy storage, respectively. and These are the charging and discharging state variables, both of which are 0-1 variables. Investment capacity for energy storage; and These represent the stored energy states at time t and time t-1, respectively. For static energy loss in energy storage, and These represent charging and discharging efficiencies, respectively. Duration per unit step, This refers to the capacity-to-power ratio of energy storage, i.e., the energy storage multiplier. and These represent the minimum and maximum states of charge for energy storage, respectively. and These are the energy storage state values ​​at the start and end times of a typical daily scenario, respectively.

7. The energy storage power station capacity calculation method considering grid carrying capacity and multiple application scenarios according to claim 6, characterized in that, Constraints across multiple application scenarios are as follows: The energy storage system participates in the three scenarios of peak shaving, frequency regulation and standby of the power grid at the same time. Therefore, it needs to meet the peak shaving output limit (14), frequency regulation output limit (15) and standby output limit (16). The charging and discharging capacity update after the energy storage participates in peak shaving, frequency regulation and standby at the same time is shown in equations (17) and (18). （14） （15） （16） （17） （18） In the formula: and These represent the discharge power and charging power of energy storage in peak-shaving scenarios, respectively. and These represent the discharge power and charging power of energy storage in a frequency regulation scenario, respectively. and These represent the power values ​​that the energy storage provides for positive and negative backup to the system, respectively. and These represent the positive and negative power demands of the peak-shaving market, respectively, and are forecast values. and These represent the positive and negative power demands of the FM market, respectively, and are also forecast values. and These represent the positive and negative power demands of the standby market, respectively, and are also forecast values.

8. The energy storage power station capacity calculation method considering grid carrying capacity and multiple application scenarios according to claim 7, characterized in that, The specific limitations on power grid carrying capacity are as follows: The carrying capacity limit of the power grid refers to the fact that after the energy storage is connected, considering the charging and discharging behavior of the energy storage, the power flowing through the upstream transformer of the whole network does not exceed its carrying capacity. Its mathematical model is shown in Equation (19). On the other hand, in order to avoid peak-valley inversion, constraints to avoid peak inversion, namely Equation (20) and constraints to avoid valley inversion, namely Equation (21), are added. （19） （20） （21） In the formula: This is the maximum power that the upstream transformer can handle. This means that it holds true for any scenario. This means that it holds true for any given moment.

9. The method for calculating the capacity of an energy storage power station considering grid carrying capacity and multiple application scenarios as described in claim 8, characterized in that, Enterprise operational efficiency assessment includes five parts: energy storage investment costs Peak shaving revenue Frequency modulation revenue Reserve income And the operation and maintenance costs of energy storage. The mathematical expression of its objective function is shown in equations (22)-(27); （22） （23） （24） （25） （26） （27） In the formula: This represents the total number of scenarios 's' within the investment cycle. This represents the number of hours in each typical scenario day. and These represent the discharge and charging electricity prices for energy storage in peak-shaving scenarios, respectively. and These represent the discharge power and charging power of energy storage in peak-shaving scenarios, respectively. and These represent the discharge and charging electricity prices for energy storage in frequency regulation scenarios, respectively. and These represent the discharge power and charging power of energy storage in a frequency regulation scenario, respectively. and These respectively represent positive and negative standby prices for energy storage in the standby market. and These represent the power values ​​that the energy storage provides for positive and negative backup to the system, respectively. is the present value, and n is the year value.

10. The method for calculating the capacity of an energy storage power station considering grid carrying capacity and multiple application scenarios according to claim 9, characterized in that, The optimal capacity calculation model for energy storage power stations is linearized as follows: The scenarios from the obtained set of typical scenarios are embedded into the optimal capacity calculation model of the energy storage power station. Specifically, the linearization coefficient results obtained from the optimization are introduced into the system power balance constraint equation (7), and its mathematical model can be updated as follows: （28） Introduce several additional variables Transform formula (28) and add variables that satisfy Then formula (28) can be equivalently transformed into a linearized model for optimal capacity calculation of energy storage power stations that takes into account the needs of multiple application scenarios: （29） Finally, the solution was obtained by calling the Gurobi commercial solver through the Matlab platform.

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