Shared energy storage capacity optimal configuration method based on oversale concept

Abstract

The invention discloses a shared energy storage capacity optimal configuration method based on an oversale idea, and the method achieves the overall planning and optimization of shared energy storage through employing the space-time complementarity of the power consumption behaviors of different users, and through the modes that multiple users bear the energy storage cost together, so as to improve the utilization rate of energy storage, indirectly reduce the configuration cost of energy storage, and improve the utilization rate of energy storage. For a multi-microgrid power distribution system, the space-time complementation characteristic of the new energy production and elimination capability of each microgrid is utilized, the utilization rate of shared energy storage is improved, and resource configuration is optimized. By analyzing the space-time characteristics of the energy storage demand of the micro-grid and utilizing the complementarity of the energy storage demand, the shared energy storage capacity planning with the maximum economic benefit is determined on the premise of determining the reasonable oversale amount based on historical data and ensuring the service reliability. The method is a data driving method, does not need detailed physical parameters, simplifies the energy storage demand of the microgrid, reduces the energy storage configuration cost, and improves the energy storage utilization rate.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical engineering, and particularly to a method for optimizing the configuration of shared energy storage capacity based on the overbooking concept. Background Art

[0002] Since new energy sources such as wind and light have become the key technologies for decentralized power grid connection, microgrids have also become the focus of research in the current power industry. Microgrids preferentially absorb the output of new energy sources to meet the internal load demand and feed a large amount of surplus power into the distribution grid. However, due to the power fluctuations of new energy sources, which will affect the system stability and cannot supply high-quality electric energy to users, there is a serious problem of "abandoning wind and light" in the microgrid cluster. Therefore, there is a higher demand for flexible regulating power sources, which has stimulated the development of the energy storage industry. As a new energy storage application mode, shared energy storage optimizes overall to improve the utilization rate of energy storage and indirectly reduce the configuration cost of energy storage by utilizing the spatio-temporal complementarity of the electricity consumption behaviors of different users and sharing the energy storage cost by multiple users, so as to create value, and is widely applied to various links of power system generation, transmission, distribution, and power consumption. However, due to the volatility of wind and light output, the actual energy storage usage of the microgrid on the trading day is often lower than its pre-day lease volume. When shared energy storage is actually applied, there is a situation where capacity is reserved but not actually called, resulting in the idle of energy storage resources. In addition, for a multi-microgrid cluster of a new energy distribution network system, wind and light resources have spatial correlation and wind-light complementarity. Fully exploiting the output complementarity potential of wind and light can further improve the utilization rate of energy storage and the revenue of energy storage operators. To address this problem, a capacity configuration method that can improve the utilization rate of energy storage is needed. The corresponding research work on the method for optimizing the configuration of shared energy storage capacity has important theoretical and engineering value. Summary of the Invention

[0003] Aiming at the problems existing in the prior art, based on the uncertainty and correlation of wind and light output, for the deviation between the energy storage demand submitted by the microgrid before the trading day and the actual energy storage usage on the trading day, an improved high-dimensional ellipsoid uncertainty set is established to quantitatively consider the energy storage demand deviation considering the uncertainty and correlation of wind and light output, clarify the overbooking volume of shared energy storage, and then construct a robust optimization model with the optimal economic benefit according to the overbooking concept of energy storage, and propose a method for optimizing the configuration of shared energy storage capacity to achieve the purpose of maximizing the utilization rate of energy storage resources and investment returns.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for optimizing the configuration of shared energy storage capacity based on the overbooking concept, comprising the following steps:

[0006] (1)An improved high-dimensional ellipsoidal uncertainty set is established for the deviation between the energy storage demand submitted by the microgrid before the trading day and the actual energy storage usage on the trading day to quantify the energy storage demand deviation scenarios considering the uncertainty and correlation of the renewable energy output. The shared energy storage operator, based on the energy storage leasing demands reported by the microgrids, utilizes the uncertainty and spatio-temporal complementarity of the renewable energy output in each microgrid and the energy storage capacity leased before the day but not called in real time to minimize the energy storage configuration cost while meeting the total charge-discharge instructions.

[0007] (2)The shared energy storage operator quantitatively evaluates the new energy prediction error on the trading day by analyzing the historical energy storage demand deviation data of the microgrid. The operator comprehensively considers the residual power deviation prediction and energy storage demand of multiple microgrids in the region and adopts an overselling strategy to optimize the energy storage capacity configuration using the prediction error mechanism to obtain the maximum benefit.

[0008] (3)An optimal energy storage capacity configuration model is established. The overselling power of the energy storage is determined by the sum of the prediction errors of the residual power of each microgrid, and the actually configured energy storage capacity is the total demand submitted before the day minus the overselling quantity. The optimal configuration objective function of the shared energy storage is the lowest comprehensive annual average cost of the shared energy storage, and the shared energy storage capacity planning is carried out with the goal of maximizing the cost-benefit of the shared energy storage. Finally, the optimal energy storage capacity configuration plan is determined.

[0009] Furthermore, considering the uncertainty and correlation of the renewable energy output in step (1) is based on constructing a high-dimensional ellipsoidal uncertainty set from historical data, and the covariance matrix between parameters is calculated to show the correlation between parameters.

[0010] Furthermore, the high-dimensional ellipsoidal uncertainty set in step (1) is specifically as follows:

[0011] Collect the predicted residual power and actual residual power of all microgrids in the multi-microgrid distribution system within one year to obtain their actual energy storage demands. The demand for energy storage leased before the day is optimized by the microgrid to comprehensively improve the new energy consumption and reduce the leasing cost. At this time, the energy storage demand deviation of the microgrid is expressed as the difference between the energy storage demand before the day and the actual demand. In the j-th typical scenario, the set of historical data of the random variable is the following expression:

[0012] ;

[0013] In the formula, j represents the j-th type of typical day, and there are J typical scenarios in total. Each typical day corresponds to N j days; i represents the i-th microgrid, and there are I microgrids in total; T is one day, T = 24h;

[0014] The set of historical data of the i-th microgrid at time t under all scenarios is expressed as

[0015]

[0016] Among them, represents the historical data of station i at time t under scenario j;

[0017] Since the high-dimensional closed ellipsoid can enclose all historical data, the high-dimensional ellipsoid algorithm is used to solve a set of convex hulls of closed historical scenarios:

[0018]

[0019] Among them, c represents the center point of the ellipsoid, the positive definite matrix Q represents the deviation direction of the ellipsoid symmetry axis, and the ellipsoid uncertainty set should contain all historical scenarios of the prediction error. represents a polyhedron with dimensions J×T;

[0020] Using the minimum volume closed ellipsoid algorithm, by solving the following optimization problem, Q and c of the ellipsoid uncertainty set are determined:

[0021] ;

[0022] Among them, represents the unit sphere volume of the multi-dimensional ellipsoid, which is a constant;

[0023] Then, the closed convex hull formed by the J T vertices in the above high-dimensional ellipsoid is used as the initial uncertainty set, and the model is balanced by translating, rotating, and scaling the ellipsoid set.

[0024] Perform orthogonal decomposition on Q, and the expression is:

[0025] ;

[0026] Among them, D represents a diagonal matrix, and the diagonal elements are all positive numbers, expressed in the form of ; P represents a transformation matrix; in order to determine the vertex position of the high-dimensional ellipsoid, the ellipsoid needs to be rotated and translated until its symmetry axis is completely aligned with the coordinate axis; this rotation and translation transformation process is expressed as:

[0027] ;

[0028] Among them, is the coordinate value of the random variable under the rotated coordinates, and the mathematical expression of the rotated high-dimensional ellipsoid is:

[0029] ;

[0030] Adjusted high-dimensional ellipsoid The vertex coordinates are expressed as:

[0031] ;

[0032] Among them, represents the coordinates of the i-th vertex of the high-dimensional ellipsoid; the vertex equation of the original high-dimensional ellipsoid E can be deduced by using the inverse transformation of formula . The expression of the polyhedron equation bounded by these vertices is:

[0033] ;

[0034] Since the polyhedron cannot cover all historical scenarios belonging to the ellipsoid, a scaling factor k is introduced to expand the region. The vertex coordinates of the corrected polyhedron are:

[0035] ;

[0036] The corrected convex polyhedron is expressed as:

[0037] ;

[0038] Among them, is the limit scenario obtained after the convex hull is scaled, and the relationship with the vertices of the rotated high-dimensional ellipsoid is expressed by the inverse transformation formula of formula :

[0039] ;

[0040] The maximum value of the scaling factor k is solved by the following formula:

[0041] ;

[0042] By solving the scaling factor k, it is ensured that the adjusted convex set can cover all historical data points; when the historical scenario points are outside the original convex hull, the minimum magnification factor k i needs to be calculated so that the point is exactly on the boundary of the enlarged convex hull.

[0043] Furthermore, in step (2), the overbooking strategy is adopted to optimize the energy storage capacity configuration by using the prediction error mechanism to obtain the maximum benefit. Specifically: according to the closed convex hull uncertainty set, the complementary characteristics of the wind and light output in different microgrids and the prediction error of the energy storage demand are quantified, and the prediction errors of multiple microgrids are integrated to calculate the energy storage overbooking amount. Its calculation expression is:

[0044] ;

[0045] Among them, the overselling power is represented by the comprehensive prediction error of the energy storage demand, denotes the prediction error of microgrid i, that is, the difference between the energy storage rental volume submitted in advance and the actual energy storage usage during the day. The value range of this random variable is determined by the uncertain convex hull set;

[0046] The overselling power of the energy storage is determined by the sum of the prediction errors of the remaining powers of each microgrid, and the actually configured energy storage capacity is the total demand submitted in advance minus the overselling volume; in this way, the shared energy storage operator can determine the optimal energy storage capacity configuration plan.

[0047] Specifically, in step (3), the objective function of the shared energy storage optimal configuration is the lowest comprehensive annual average cost of the shared energy storage, including the equal annual value initial investment cost C inv , operation and maintenance cost C inv , power purchase and sale cost C G , service fee R for providing microgrid rental services lea and overselling fine C os , and the annual average cost of the shared energy storage is expressed as follows:

[0048] .

[0049] Specifically, the annual value initial investment cost C inv : The initial investment cost is calculated by the equal annual value method, converting a one-time investment into an annual equal payment, and the expression is:

[0050] ;

[0051] Among them, and are the unit power and unit capacity construction costs of the centralized energy storage respectively; r n represents the equal annual value conversion coefficient, and its expression is:

[0052] ;

[0053] Among them, l SES represents the service life of the energy storage power station, and r represents the discount rate;

[0054] The operation and maintenance cost C inv : The operation and maintenance cost includes the expenditure on daily equipment maintenance and regular overhaul, and its expression is:

[0055] ;

[0056] The power purchase and sale cost C G: The purchase and sale cost of electricity reflects the energy exchange cost between the energy storage system and the power grid, including the electricity purchase expenditure during charging and the electricity sale income during discharging. Its expression is as follows:

[0057] ;

[0058] Service fee R for providing leasing services lea : The service fee is the leasing income when the shared energy storage provides electrical energy services. The expression is as follows:

[0059] ;

[0060] Among them, represents the charging and discharging power of the i-th microgrid under scenario j;

[0061] Overselling fine C os : When the shared energy storage fails to complete the real-time energy storage capacity delivery as required by the day-ahead schedule, a fine needs to be paid to prevent the shared energy storage operator from overselling capacity without restraint for arbitrage. The fine expression is as follows:

[0062] ;

[0063] Among them, λ t os represents the fine coefficient; N j represents the number of days in the typical day j;

[0064] Since the comprehensive prediction error is determined by the uncertainty set, a robust optimization algorithm is introduced to find the optimal energy storage configuration scheme under a certain degree of conservatism. The objective function is shown as the following min-max problem:

[0065] ;

[0066] Among them, the input variables include the predicted remaining power and the actual remaining power of the microgrid; the decision variable x is the energy storage configuration scheme, including the power and capacity of the energy storage (P max , E max ); the uncertain variable y includes the comprehensive prediction error ; Its constraint conditions include the charging and discharging power constraint of the energy storage power station, the state of charge SOC constraint, and the power balance constraint.

[0067] Furthermore, the constraint conditions include the charging and discharging power constraint of the energy storage power station, the state of charge SOC constraint, and the power balance constraint, specifically:

[0068] (a) Charging and discharging power constraint:

[0069] ;

[0070] Among them, P maxIt represents the upper limit of the charge and discharge power of the energy storage, and the energy storage cannot charge and discharge simultaneously;

[0071] (b)State of Charge (SOC) constraint:

[0072] ;

[0073] Among them, SOC represents the state of charge of the energy storage; E j,t represents the energy state of the energy storage at time t; E max represents the upper limit of the energy of the energy storage;

[0074] The energy state of the energy storage at time t depends on the state at time t - 1:

[0075] ;

[0076] Among them and respectively represent the charging power and discharging power of the energy storage;

[0077] During the optimization process, the initial energy state and the final energy state are equal:

[0078] ;

[0079] (c)Power balance constraint:

[0080] .

[0081] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0082] 1. Innovative application of overbooking strategy: For the first time, the overbooking strategy is introduced into the field of shared energy storage capacity configuration. By analyzing the spatio-temporal characteristics of the energy storage demand in the microgrid and utilizing the complementarity of the energy storage demand, the problem of low energy storage utilization rate is effectively solved, providing a new idea for the operation of shared energy storage.

[0083] 2. Analysis of energy storage demand considering the characteristics of new energy: The randomness and correlation of the new energy output in the microgrid are fully considered, the deviation of the actual energy storage demand in the microgrid is quantified, a closed convex hull uncertain set of the energy storage demand deviation is constructed, and the energy storage demand is accurately analyzed, providing a reliable basis for subsequent optimal configuration.

[0084] 3. Robust optimal configuration method: A robust optimal configuration method for reducing the idle rate of the energy storage by using the overbooking amount of the energy storage is proposed. By analyzing historical data to determine a reasonable overbooking amount, the shared energy storage capacity planning that maximizes economic benefits is realized under the premise of ensuring service reliability. Compared with the traditional energy storage planning strategy, this method significantly reduces the energy storage configuration cost and improves the energy storage utilization rate. Description of the Drawings

[0085] Figure 1The basic idea of shared energy storage providing energy storage services for a multi - microgrid distribution system;

[0086] Figure 2 It is the flow chart for optimizing the configuration of shared energy storage. Specific implementation manner

[0087] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation examples.

[0088] Such as Figure 1 The basic idea of shared energy storage providing energy storage services for a multi - microgrid distribution system in the method for optimizing the configuration of shared energy storage capacity based on the over - selling concept of the present invention: The microgrid aims to improve the consumption of new energy and reduce the operating cost, and optimizes its demand planning and operation strategy for leased energy storage. After ensuring that the local load demand is met by using new energy, multiple microgrids lease energy storage to suppress the fluctuation of the remaining net power, and grid - connect the flattened power to achieve the power balance state of the system. The shared energy storage operator is responsible for the configuration and operation management of energy storage facilities, provides energy storage charging and discharging services for the microgrid group, and charges energy storage lease service fees. The shared energy storage operator aims to meet the requirements of the leased energy storage capacity of multiple microgrids while reducing the investment and construction costs. By building and operating a centralized shared energy storage power station, it provides energy storage lease services for the multi - microgrid system. According to the reported energy storage lease demands of the microgrids, it makes full use of the uncertainty and spatio - temporal complementarity of the wind and light output in each microgrid and the energy storage capacity leased but not called in real - time on the day - ahead, and minimizes the energy storage configuration cost while meeting the total charge - discharge instructions.

[0089] Such as Figure 2 It is the implementation process of the method for optimizing the configuration of shared energy storage capacity based on the over - selling concept of the present invention. The calculation of the energy storage over - selling volume and the robust optimization model for energy storage capacity configuration include the following steps:

[0090] (1) Establish a high - dimensional ellipsoidal uncertainty set. Collect the predicted remaining power and actual remaining power of all microgrids in the multi - microgrid distribution system within one year, and calculate their actual energy storage demands. The demand for leased energy storage on the day - ahead is optimized by considering factors such as the microgrid comprehensively improving the consumption of new energy and reducing the lease cost. At this time, the energy storage demand deviation of the microgrid is expressed as the difference between the day - ahead energy storage demand and the actual demand. In the j - th typical scenario, the set of historical data of the random variable is written in the following form:

[0091] ;

[0092] In the formula, j represents the j - th type of typical day, and there are J typical scenarios in total. Each typical day corresponds to N j days. i represents the i - th microgrid, and there are I microgrids in total. T is one day, and T = 24h.

[0093] The historical data set of the i-th microgrid at time t under all scenarios It is expressed as:

[0094] ;

[0095] Among them, represents the historical data of the station i at time t under scenario j.

[0096] The high-dimensional closed ellipsoid can enclose all the historical data. Therefore, the high-dimensional ellipsoid algorithm can be used to solve a closed convex hull set of historical scenarios:

[0097] ;

[0098] Among them, c represents the center point of the ellipsoid, and the positive definite matrix Q represents the deviation direction of the ellipsoid symmetry axis. The above ellipsoidal uncertainty set should contain all historical scenarios of the prediction error; represents a polyhedron with dimensions J×T.

[0099] Using the minimum volume closed ellipsoid algorithm, by solving the following optimization problem, determine Q and c of the ellipsoidal uncertainty set:

[0100] ;

[0101] Among them, represents the unit sphere volume of the multi-dimensional ellipsoid, which is a constant.

[0102] Then, use the closed convex hull formed by the J T vertices in the above high-dimensional ellipsoid as the initial uncertainty set, and balance the accuracy and computational complexity of the model by translating, rotating and scaling the ellipsoid set to ensure the feasibility and effectiveness of the entire robust optimization process.

[0103] Perform orthogonal decomposition on Q, and the expression is:

[0104] ;

[0105] Among them, D represents a diagonal matrix, and the diagonal elements are all positive numbers, expressed as in the form of; among them and are the upper left and lower right elements in the diagonal matrix; P represents the transformation matrix. In order to determine the vertex position of the high-dimensional ellipsoid, it is necessary to perform rotation and translation operations on the ellipsoid until its symmetry axis is completely aligned with the coordinate axis. This rotation and translation transformation process can be expressed as:

[0106] ;

[0107] Among them, is a random variable The coordinate values in the rotated coordinates, the rotated high-dimensional ellipsoid The mathematical expression is:

[0108] ;

[0109] The adjusted high-dimensional ellipsoid The vertex coordinates are expressed as:

[0110] ;

[0111] Among them, represents the coordinates of the i-th vertex of the high-dimensional ellipsoid; the vertex equation of the original high-dimensional ellipsoid E can be deduced by using the inverse transformation of formula . The expression of the polyhedron equation defined by these vertices is:

[0112] ;

[0113] Since the polyhedron cannot cover all historical scenarios belonging to the ellipsoid, a scaling factor k is introduced to expand the region. The vertex coordinates of the corrected polyhedron are:

[0114]

[0115] The corrected convex polyhedron can be expressed as:

[0116]

[0117] Among them, is the limit scenario obtained after the convex hull scaling, and the relationship with the vertex of the rotated high-dimensional ellipsoid is represented by the inverse transformation formula of formula :

[0118] ;

[0119] The maximum value of the scaling factor k can be solved by the following formula:

[0120] ;

[0121] By solving the scaling factor k, it is ensured that the adjusted convex set can cover all historical data points. When the historical scenario point is outside the original convex hull, the minimum magnification factor k i needs to be calculated so that the point is exactly on the boundary of the enlarged convex hull.

[0122] (2) Energy storage overselling determination strategy considering prediction error application

[0123] Shared energy storage operators quantify the prediction error of new energy on trading days by analyzing the historical energy storage demand deviation data of microgrids. The operator comprehensively considers the remaining power deviation predictions and energy storage demands of multiple microgrids within the region, and adopts an overselling strategy to make full use of the prediction error mechanism, thereby optimizing the energy storage capacity configuration to obtain the maximum benefit.

[0124] Considering the uncertainty and correlation of the output of wind farms and photovoltaic power plants, the complementary characteristics of the wind and light output and the prediction error of the energy storage demand in different microgrids are quantified according to the closed convex hull uncertainty set. By synthesizing the prediction errors of multiple microgrids, the expression for the overselling amount of energy storage is obtained as follows:

[0125] ;

[0126] Among them, the overselling power is represented by the comprehensive prediction error of the energy storage demand, represents the prediction error of microgrid i, that is, the difference between the energy storage lease volume submitted in advance and the actual energy storage usage during the day. The value range of this random variable is determined by the uncertain convex hull set.

[0127] The overselling power of energy storage is determined by the sum of the prediction errors of the remaining power of each microgrid, and the actually configured energy storage capacity is the total demand submitted in advance minus the overselling amount. In this way, shared energy storage operators can determine the optimal energy storage capacity configuration plan on the basis of balancing investment returns and operation risks, and improve the overall efficiency of the system.

[0128] (3)Establish an optimal energy storage capacity configuration model based on the overselling concept

[0129] The optimization configuration objective function of shared energy storage is the lowest comprehensive annual average cost of shared energy storage, including the equal annual value of the initial investment cost, operation and maintenance cost, power purchase and sale cost from the power grid, overselling fine and service fee for providing microgrid lease services. The specific expression is as follows.

[0130] ;

[0131] 1) The equal annual value of the initial investment cost C inv

[0132] The initial investment cost is calculated by the equal annual value method, which converts a one-time investment into an annual equal payment. This method takes into account the time value of funds and more accurately reflects the actual investment burden of the energy storage system. The expression is:

[0133] ;

[0134] Among them, and are the unit power and unit capacity construction costs of centralized energy storage respectively; rn Denote the equal annual value conversion coefficient, and the expression is:

[0135] ;

[0136] where l SES denotes the service life of the energy storage power station, and r denotes the discount rate.

[0137] 2) Operation and maintenance cost C inv

[0138] The operation and maintenance cost includes expenditures such as daily equipment maintenance and regular overhaul, and the expression is:

[0139] ;

[0140] 3) Cost of power purchase and sale C G

[0141] The cost of power purchase and sale reflects the energy exchange cost between the energy storage system and the power grid, including the power purchase expenditure during charging and the power sale income during discharging. The expression is:

[0142] ;

[0143] 4) Service fee R for providing microgrid leasing services lea

[0144] The service fee is the leasing income when the shared energy storage provides electrical energy services, and the expression is:

[0145] ;

[0146] where denotes the charging and discharging power of the i-th microgrid under scenario j.

[0147] 5) Over-selling fine C os

[0148] When the shared energy storage fails to complete the real-time energy storage capacity delivery as required by the day-ahead schedule, a certain fine needs to be paid to prevent the shared energy storage operator from over-selling capacity without restraint for arbitrage. The specific fine expression is:

[0149] ;

[0150] where λ t os denotes the fine coefficient; N j denotes the number of days in the typical day j.

[0151] Considering that the comprehensive prediction error is determined by the uncertainty set. Therefore, a robust optimization algorithm is introduced to find the optimal energy storage configuration scheme under a certain degree of conservatism. The objective function is shown as the following min-max problem.

[0152] ;

[0153] Among them, the input variables include the predicted remaining power and the actual remaining power of the microgrid; the decision variable x is the energy storage configuration scheme, including the power and capacity of the energy storage (P max , E max ); the uncertain variable y includes the comprehensive prediction error .

[0154] The constraint conditions mainly consider the charge-discharge power constraint, the state of charge SOC constraint, and the power balance constraint of the energy storage power station.

[0155] 1) Charge-discharge power constraint

[0156] ;

[0157] Among them, P max represents the upper limit of the charge-discharge power of the energy storage, and the energy storage cannot charge and discharge simultaneously.

[0158] 2) SOC constraint

[0159] ;

[0160] Among them, SOC represents the state of charge of the energy storage; E j,t represents the energy state of the energy storage at time t; E max represents the upper limit of the energy of the energy storage.

[0161] The energy state of the energy storage at time t depends on the state at time t-1:

[0162] ;

[0163] Among them, and respectively represent the charging power and discharging power of the energy storage;

[0164] In the optimization process, the initial energy state and the final energy state are equal:

[0165] ;

[0166] 3) Power balance constraint

[0167] .

[0168] In summary, in this configuration mode, the present invention can not only make full use of the shared energy storage resources, but also effectively reduce the energy storage configuration cost, while improving the energy storage utilization rate, providing a reference for the practical application of the energy storage.

[0169] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of shared energy storage capacity based on the overbooking concept, characterized in that It includes the following steps: (1) For the deviation between the energy storage demand submitted by the microgrid before the trading day and the actual energy storage usage on the trading day, an improved high-dimensional ellipsoid uncertainty set is established to quantify the energy storage demand deviation scenarios considering the uncertainty and correlation of wind and photovoltaic power generation. The shared energy storage operator, based on the energy storage leasing demands reported by the microgrids, utilizes the uncertainty and spatio-temporal complementarity of wind and photovoltaic power generation in each microgrid and the energy storage capacity that was leased before the day but not called in real time, and minimizes the energy storage configuration cost while meeting the total charge and discharge instructions; (2) The shared energy storage operator quantitatively evaluates the new energy prediction error on the trading day by analyzing the historical energy storage demand deviation data of the microgrids. The operator comprehensively considers the residual power deviation predictions and energy storage demands of multiple microgrids in the region, and adopts an over-selling strategy to optimize the energy storage capacity configuration using the prediction error mechanism to obtain the maximum benefit; (3) An optimal energy storage capacity configuration model is established. The over-selling power of the energy storage is determined by the sum of the prediction errors of the residual powers of each microgrid, and the actually configured energy storage capacity is the total demand submitted before the day minus the over-sold amount. The optimal configuration objective function of the shared energy storage is the lowest comprehensive annual average cost of the shared energy storage, and the shared energy storage capacity planning is carried out with the goal of maximizing the cost-benefit of the shared energy storage. Finally, the optimal energy storage capacity configuration plan is determined.

2. The method for optimizing the allocation of shared energy storage capacity based on the overbooking concept according to claim 1, wherein In step (1), considering the uncertainty and correlation of wind and photovoltaic power generation is based on constructing a high-dimensional ellipsoid uncertainty set from historical data, and the covariance matrix between parameters is calculated to show the correlation existing between the parameters.

3. The method for optimizing the configuration of shared energy storage capacity based on the overbooking concept according to claim 2, wherein The high-dimensional ellipsoid uncertainty set in step (1) is specifically as follows: Collect the predicted remaining power and actual remaining power of all microgrids in the multi-microgrid distribution system within one year, and calculate its actual energy storage demand; the demand for day-ahead leased energy storage is optimized and solved by comprehensively improving the new energy consumption of the microgrid and reducing the leasing cost. At this time, the energy storage demand deviation of the microgrid is expressed as the difference between the day-ahead energy storage demand and the actual demand; in the jth typical scenario, the set of historical data of the random variable is the following expression: ; where j represents the jth type of typical day, there are J typical scenarios in total, and each typical day corresponds to N j days; i represents the ith microgrid, and there are I microgrids in total; T is one day, T = 24h; The historical data set of the i-th microgrid at time t under all scenarios is denoted as ; where represents the historical data of station i under scenario j at time t; Since the high-dimensional closed ellipsoid can enclose all historical data, the high-dimensional ellipsoid algorithm is used to solve a convex hull set of closed historical scenarios: ； Among them, \(c\) represents the center point of the ellipsoid, the positive definite matrix \(Q\) represents the deviation direction of the ellipsoid axis of symmetry, and the ellipsoidal uncertainty set should contain all historical scenarios of the prediction error. represents a polyhedron of dimension \(J\times T\); Using the minimum volume closed ellipsoid algorithm, by solving the following optimization problem, Q and c of the ellipsoid uncertainty set are determined: ； Among them, represents the unit sphere volume of the multi-dimensional ellipsoid, which is a constant; Then use the closed convex hull formed by the J vertices in the above high-dimensional ellipsoid among the T vertices as the initial uncertainty set, and balance the model by translating, rotating, and scaling the ellipsoid set; The orthogonal decomposition of Q is expressed as: ； where D represents a diagonal matrix with all diagonal elements being positive, expressed in the form of ; P represents a transformation matrix; to determine the vertex positions of the high-dimensional ellipsoid, rotation and translation operations need to be performed on the ellipsoid until its axis of symmetry is completely aligned with the coordinate axes; this transformation process of rotation and translation is expressed as: ； Among them, is a random variable The coordinate value under the rotated coordinates, and the rotated high-dimensional ellipsoid The mathematical expression is: ； Adjusted high-dimensional ellipsoid The vertex coordinates are expressed as: ； Among them, represents the coordinates of the \(i\)-th vertex of the high-dimensional ellipsoid; by using the inverse transformation of Equation , the vertex equation of the original high-dimensional ellipsoid \(E\) can be deduced, and the expression of the polyhedron equation defined by these vertices is: ； Due to the polyhedron not being able to cover all historical scenarios belonging to the ellipsoid, a scaling factor k is introduced to expand the region. The vertex coordinates of the corrected polyhedron are as follows: ； Modified convex polyhedron Expressed as: ； Among them, is the limit scenario obtained after convex hull scaling, and the relationship with the vertices of the rotated high-dimensional ellipsoid is represented by the inverse transformation formula of Equation as follows: ； The maximum value of the scaling factor k is solved by the following formula: ； By solving the scaling factor \(k\), ensure that the adjusted convex set can cover all historical data points; when the historical scenario points are outside the original convex hull, it is necessary to calculate the minimum magnification factor \(k\) i so that the point is exactly on the boundary of the magnified convex hull.

4. The method for optimizing the configuration of shared energy storage capacity based on the overbooking concept according to claim 1, wherein In step (2), adopting an over-selling strategy to optimize the energy storage capacity configuration using the prediction error mechanism to obtain the maximum benefit is specifically as follows: According to the closed convex hull uncertainty set, the complementary characteristics of wind and photovoltaic power generation in different microgrids and the prediction errors of energy storage demands are quantified. By synthesizing the prediction errors of multiple microgrids, the energy storage over-sold amount is calculated, and its calculation expression is: ； Among them, the oversold power is represented by the comprehensive prediction error of the energy storage demand, indicating the prediction error of microgrid i, that is, the difference between the pre-day submitted energy storage rental volume and the actual energy storage usage volume during the day. The value range of this random variable is determined by the uncertain convex hull set; The over-selling power of the energy storage is determined by the sum of the prediction errors of the residual powers of each microgrid, and the actually configured energy storage capacity is the total demand submitted before the day minus the over-sold amount. In this way, the shared energy storage operator can determine the optimal energy storage capacity configuration plan.

5. The method for optimizing the allocation of shared energy storage capacity based on the overbooking concept according to claim 1, wherein In step (3), the objective function of the optimal configuration of the shared energy storage is the lowest comprehensive annual average cost of the shared energy storage, including the equal annual value of the initial investment cost C inv , operation and maintenance cost C inv , electricity purchase and sale cost C G , service fee R for providing microgrid leasing services lea and over-sale penalty C os . The annual average cost of the shared energy storage is expressed as follows: 。 6. The method for optimizing the configuration of shared energy storage capacity based on the overbooking concept according to claim 5, wherein The annualized initial investment cost C inv : The initial investment cost is calculated using the equal annual value method, which converts a one-time investment into an annual equal payment. The expression is: ； Among them, and are respectively the unit power and unit capacity construction costs of centralized energy storage; r n represents the equal annual value conversion factor, and its expression is: ； where, l SES represents the lifespan of the energy storage power station, and r represents the discount rate; Operation and maintenance cost C inv : The operation and maintenance cost includes the expenditure for daily equipment maintenance and regular inspections, and its expression is: ； The cost C of power purchase and sale G : The cost of power purchase and sale reflects the energy exchange cost between the energy storage system and the power grid, including the power purchase expenditure during charging and the power sale income during discharging. Its expression is as follows: ； Service fee R for providing rental services lea : The service fee is the rental income when providing the electrical energy service for shared energy storage, and the expression is: ； Among them, represents the charging and discharging power of the i-th microgrid under scenario j; Overselling Penalty C os : When the shared energy storage fails to deliver the real-time energy storage capacity as required by the day-ahead schedule, a penalty needs to be paid to prevent the shared energy storage operator from overselling capacity without restraint for arbitrage. The penalty expression is as follows: ； Among them, λ t os represents the fine coefficient; N j represents the number of days of the typical day j; Since the comprehensive prediction error is determined by the uncertainty set, a robust optimization algorithm is introduced to find the optimal energy storage configuration plan under a certain degree of conservatism, and the objective function is as shown in the following min-max problem: ； Among them, the input variables include the predicted residual power and the actual residual power of the microgrid; the decision variable x is the energy storage configuration scheme, including the power and capacity of the energy storage (P max , E max ); the uncertain variable y includes the comprehensive prediction error ; and its constraint conditions include the charge and discharge power constraint, the state of charge SOC constraint, and the power balance constraint of the energy storage power station.

7. The method for optimizing the configuration of shared energy storage capacity based on the overbooking concept according to claim 6, wherein The constraint conditions include the charge and discharge power constraints of the energy storage power station, the state of charge SOC constraints, and the power balance constraints, specifically as follows: (a) Charge and discharge power constraints: ； Among them, P max represents the upper limit of the charging and discharging power of energy storage, and energy storage cannot charge and discharge simultaneously; (b) State of charge SOC constraints: ； Among them, SOC represents the state of charge of energy storage; E j,t represents the energy state of energy storage at time t; E max represents the upper limit of the energy of energy storage; The energy state of the energy storage at time t depends on the state at time t - 1: ； Among them, and respectively represent the charging power and discharging power of energy storage; In the optimization process, the initial energy state and the final energy state are equal: ； (c) Power balance constraint: 。

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