A method for optimizing the configuration of shared energy storage capacity based on the concept of overselling

Abstract

The present invention discloses a method for optimizing the configuration of shared energy storage capacity based on the concept of overselling. The shared energy storage of this method is comprehensively optimized to improve the utilization rate of energy storage and indirectly reduce the configuration cost of energy storage by utilizing the spatiotemporal complementarity of electricity consumption behaviors of different users and by multiple users jointly bearing the energy storage costs. For multi-microgrid power distribution systems, the spatiotemporal complementarity of each microgrid's new energy production and consumption capabilities is utilized to improve the utilization rate of shared energy storage and optimize resource allocation. By analyzing the spatiotemporal characteristics of microgrid energy storage demand and utilizing the complementarity of energy storage demand, a shared energy storage capacity plan that maximizes economic benefits is determined on the premise of determining a reasonable overselling amount based on historical data and ensuring service reliability. The present invention is a data-driven method that does not require detailed physical parameters and simplifies the microgrid's demand for energy storage, which not only reduces the energy storage configuration cost but also improves the energy storage utilization rate.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering technology, and in particular to a method for optimizing configuration of shared energy storage capacity based on an overselling concept. Background Art

[0002] As renewable energy sources such as wind and solar power have become key technologies for integrating distributed power sources into the grid, microgrids have become a research focus in the current power industry. Microgrids prioritize renewable energy output to meet internal load demands and integrate a significant amount of surplus power into the distribution network. However, the power fluctuations of renewable energy can impact system stability and hinder the delivery of high-quality electricity to users, leading to serious wind and solar curtailment issues within microgrid clusters. This has led to a higher demand for flexible power generation, spurring the development of the energy storage industry. Shared energy storage, as a new energy storage application model, leverages the temporal and spatial complementarity of different users' electricity consumption behaviors and allows multiple users to share storage costs. This approach allows for coordinated optimization to improve energy storage utilization, indirectly reduce storage deployment costs, and create value. It is widely used across all aspects of the power system, from generation to transmission, distribution, and consumption. However, due to the volatility of wind and solar power output, actual energy storage usage in microgrids on trading days is often lower than the day-ahead leased capacity. In practical applications of shared energy storage, there are instances where reserved capacity is not actually utilized, resulting in idle energy storage resources. Furthermore, for a multi-microgrid cluster within a new energy distribution system, wind and solar resources exhibit spatial correlation and complementarity. Fully tapping into this complementary potential can further improve energy storage utilization and the profitability of energy storage operators. To address this issue, a capacity allocation method that can improve energy storage utilization is needed. Research on optimal allocation methods for shared energy storage capacity has important theoretical and engineering value. Summary of the Invention

[0003] In response to the problems existing in the prior art, the present invention is based on the uncertainty and correlation of wind and solar power output. 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 is targeted. An improved high-dimensional ellipsoid uncertainty set is established to quantify the energy storage demand deviation considering the uncertainty and correlation of wind and solar power output, clarify the oversale of shared energy storage, and then construct a robust optimization model with optimal economic benefits based on the concept of energy storage oversale. A method for optimizing the configuration of shared energy storage capacity is proposed to achieve the goal of maximizing energy storage resource utilization 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 overselling concept includes the following steps:

[0006] (1) Based on 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 scenario considering the uncertainty and correlation of wind and solar power output; based on the energy storage leasing demand reported by the microgrid, the shared energy storage operator utilizes the uncertainty and spatiotemporal complementarity of wind and solar power output in each microgrid, as well as the energy storage capacity that was leased before the trading day but not called in real time, to minimize the energy storage configuration cost while meeting the total charging and discharging instructions;

[0007] (2) Shared energy storage operators quantitatively evaluate the new energy forecast error on trading days by analyzing the historical energy storage demand deviation data of microgrids. Operators comprehensively consider the residual power deviation forecasts and energy storage demand of multiple microgrids in the region, and adopt an overselling strategy to optimize the energy storage capacity configuration using the forecast error mechanism to obtain maximum benefits.

[0008] (3) Establish an optimal energy storage capacity configuration model. The oversold power of energy storage is determined by the sum of the prediction errors of the remaining power of each microgrid, and the actual configured energy storage capacity is the total demand submitted on the previous day minus the oversold amount. The objective function of the shared energy storage optimization configuration is the lowest comprehensive annual cost of shared energy storage, and the shared energy storage capacity is planned with the goal of maximizing the cost-effectiveness of shared energy storage. Finally, the optimal energy storage capacity configuration plan is determined.

[0009] Furthermore, the uncertainty and correlation of wind and solar power output are considered in step (1), and a high-dimensional ellipsoid uncertainty set is constructed based on historical data, and the correlation between parameters is displayed by calculating the covariance matrix between the parameters.

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

[0011] Collect the predicted surplus power and actual surplus power of all microgrids in the multi-microgrid distribution system within a year to calculate their actual energy storage demand; the demand for day-ahead rental energy storage is optimized by the microgrid to comprehensively improve the consumption of new energy and reduce the rental 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 random variable The collection of historical data is expressed as follows:

[0012] ;

[0013] In the formula, j represents the jth typical day, there are J typical scenes in total, and each typical day corresponds to N j day; i represents the i-th microgrid, and there are I microgrids in total; T represents one day, T=24h;

[0014] The historical data set of the i-th microgrid in all scenarios at time t Expressed as

[0015]

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

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

[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 of dimension J × T;

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

[0021] ;

[0022] in, Represents the unit sphere volume of the multidimensional ellipsoid, which is a constant;

[0023] Then take the J in the high-dimensional ellipsoid The closed convex hull surrounded by T vertices is used as the initial uncertain set, and the model is balanced by translating, rotating, and scaling the ellipsoid set;

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

[0025] ;

[0026] Where D represents a diagonal matrix, and all diagonal elements are positive numbers, which can be expressed as In the form of; P represents the 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] in, is a random variable Coordinate values ​​under the rotated coordinates, the rotated high-dimensional ellipsoid The mathematical expression is:

[0029] ;

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

[0031] ;

[0032] in, Represents the coordinates of the i-th vertex of the high-dimensional ellipsoid; using the formula The inverse transformation of can derive the vertex equations of the original high-dimensional ellipsoid E. The polyhedron equations defined by these vertices are expressed as follows:

[0033] ;

[0034] Because of the polyhedron It is impossible to cover all historical scenes belonging to the ellipsoid, so the scaling factor k is introduced to expand The area of ​​the modified polyhedron The vertex coordinates are:

[0035] ;

[0036] Corrected convex polyhedron Expressed as:

[0037] ;

[0038] in, The extreme scene obtained after scaling the convex hull and the vertices of the rotated high-dimensional ellipsoid The relationship is given by The inverse transformation expression is:

[0039] ;

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

[0041] ;

[0042] By solving the scaling factor k, we can ensure that the adjusted convex set can cover all historical data points. When the historical scene point is outside the original convex hull, we need to calculate the minimum magnification k. i , so that the point is exactly on the boundary of the enlarged convex hull.

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

[0044] ;

[0045] Among them, oversold power Represented by the comprehensive forecast error of energy storage demand, represents the prediction error of microgrid i, that is, the difference between the energy storage rental amount submitted on the day before 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 oversold power of energy storage is determined by the sum of the forecast errors of the remaining power of each microgrid, while the actual configured energy storage capacity is the total demand submitted on the previous day minus the oversold amount; 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 optimization configuration is the lowest comprehensive annual cost of shared energy storage, including the initial investment cost C of equal annual value. inv , operation and maintenance costs C inv , electricity purchase and sales cost C G , service fee for providing microgrid leasing services R lea Overbooking penalty C os , the average annual cost of shared energy storage The expression is as follows:

[0048] .

[0049] Specifically, the annual value of the initial investment cost C inv :The initial investment cost is calculated using the equal annual value method, which converts the one-time investment into equal annual payments. The expression is:

[0050] ;

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

[0052] ;

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

[0054] Operation and maintenance cost C inv :Operation and maintenance costs include the expenses for daily maintenance and regular inspection of equipment, and its expression is:

[0055] ;

[0056] Electricity purchase and sales cost C GThe electricity purchase and sales cost reflects the energy exchange cost between the energy storage system and the grid, including the electricity purchase expenditure during charging and the electricity sales revenue during discharging. Its expression is:

[0057] ;

[0058] Service fee for providing rental services R lea :The service fee is the rental income when shared energy storage provides electric energy services, and the expression is:

[0059] ;

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

[0061] Overbooking Penalty C os : When shared energy storage fails to deliver real-time energy storage capacity as required by the day-ahead requirement, a penalty must be paid to prevent the shared energy storage operator from uncontrolled overselling of capacity for arbitrage. The penalty expression is:

[0062] ;

[0063] Where λ t os Indicates the penalty coefficient; N j represents the number of days of a 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 solution under a certain degree of conservatism. The objective function is shown as the following min-max problem:

[0065] ;

[0066] The input variables include the predicted residual power and 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 ; Its constraints include the charging and discharging power constraints of the energy storage power station, the state of charge SOC constraints and the power balance constraints.

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

[0068] (a) Charge and discharge power constraints:

[0069] ;

[0070] Among them, P maxIndicates the upper limit of the charging and discharging power of the energy storage. The energy storage cannot be charged and discharged simultaneously.

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

[0072] ;

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

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

[0075] ;

[0076] in and Respectively represent the charging power and discharging power of energy storage;

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

[0078] ;

[0079] (c) Power balance constraints:

[0080] .

[0081] Compared with the prior art, the present invention has the following beneficial effects:

[0082] 1. Innovative application of overselling strategy: For the first time, the overselling strategy is introduced into the field of shared energy storage capacity configuration. By analyzing the spatiotemporal characteristics of microgrid energy storage demand and leveraging the complementarity of energy storage demand, it effectively solves the problem of low energy storage utilization and provides a new approach to shared energy storage operations.

[0083] 2. Energy storage demand analysis considering the characteristics of new energy sources: Fully consider the randomness and correlation of new energy output in the microgrid, quantify the deviation of the actual energy storage demand of the microgrid, construct a closed convex hull uncertainty set of energy storage demand deviations, accurately analyze energy storage demand, and provide a reliable basis for subsequent optimization configuration.

[0084] 3. Robust Optimization Configuration Method: This paper proposes a robust optimization configuration method that uses oversold energy storage to reduce the energy storage idle rate. By analyzing historical data to determine the appropriate oversold amount, this method achieves shared energy storage capacity planning that maximizes economic benefits while ensuring service reliability. Compared with traditional energy storage planning strategies, this method significantly reduces energy storage configuration costs and improves energy storage utilization. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0086] Figure 2 Flowchart for optimizing configuration of shared energy storage. DETAILED DESCRIPTION

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

[0088] like Figure 1 The basic concept behind the shared energy storage capacity optimization configuration method based on the overbooking concept of this invention is that shared energy storage provides energy storage services for a multi-microgrid distribution system. Microgrids optimize their energy storage rental demand planning and operation strategies with the goal of increasing renewable energy consumption and reducing operating costs. After ensuring that renewable energy meets local load demand, multiple microgrids lease energy storage to smooth fluctuations in excess net power, connecting the smoothed power to the grid to achieve system power balance. The shared energy storage operator is responsible for the configuration and operational management of energy storage facilities and provides energy storage charging and discharging services to the microgrid cluster, charging a storage rental service fee. Aiming to meet the energy storage capacity requirements of multiple microgrids while reducing investment and construction costs, the shared energy storage operator invests in, builds, and operates centralized shared energy storage power stations to provide energy storage rental services for the multi-microgrid system. Based on the energy storage rental demand reported by the microgrids, the operator leverages the uncertainty and temporal and spatial complementarity of wind and solar power output within each microgrid, as well as the energy storage capacity leased the day before but not yet utilized in real time, to minimize energy storage configuration costs while meeting overall charging and discharging requirements.

[0089] like Figure 2 The present invention is an implementation process of the shared energy storage capacity optimization configuration method based on the overselling concept. The energy storage overselling amount calculation and energy storage capacity configuration robust optimization model includes the following steps:

[0090] (1) Establish a high-dimensional ellipsoid uncertainty set. Collect the predicted surplus power and actual surplus power of all microgrids in the multi-microgrid distribution system within one year, and calculate their actual energy storage demand. The demand for day-ahead rental energy storage is optimized by the microgrid to comprehensively improve the consumption of new energy and reduce the rental 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 random variable The collection of historical data is written as follows:

[0091] ;

[0092] Where j represents the jth typical day, there are J typical scenes in total, and each typical day corresponds to N j day. i represents the i-th microgrid, there are I microgrids in total, T is one day, T = 24h.

[0093] The historical data set of the i-th microgrid in all scenarios at time t Expressed as:

[0094] ;

[0095] in, Represents the historical data of station i in scenario j at time t.

[0096] The high-dimensional closed ellipsoid can surround all historical data, so the high-dimensional ellipsoid algorithm can be used to solve a closed convex hull set of historical scenes:

[0097] ;

[0098] Where c represents the center point of the ellipsoid, and the positive definite matrix Q represents the deviation direction of the ellipsoid's symmetry axis. The above ellipsoid uncertainty set should include all historical scenarios of the prediction error; Represents a polyhedron of dimension J × T.

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

[0100] ;

[0101] in, Represents the volume of the unit sphere of the multidimensional ellipsoid, which is a constant.

[0102] Then take the J in the high-dimensional ellipsoid The closed convex hull surrounded by T vertices is used as the initial uncertain set. The ellipsoid set is translated, rotated and scaled to balance the accuracy and computational complexity of the model, ensuring the feasibility and effectiveness of the entire robust optimization process.

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

[0104] ;

[0105] Where D represents a diagonal matrix, and all diagonal elements are positive numbers, which can be expressed as in the form of and are the upper left and lower right elements in the diagonal matrix; P represents the transformation matrix. To determine the vertex positions 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 can be expressed as:

[0106] ;

[0107] in, is a random variable Coordinate values ​​under the rotated coordinates, the rotated high-dimensional ellipsoid The mathematical expression is:

[0108] ;

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

[0110] ;

[0111] in, Represents the coordinates of the i-th vertex of the high-dimensional ellipsoid; using the formula The inverse transformation can be used to derive the vertex equations of the original high-dimensional ellipsoid E. The polyhedron equation defined by these vertices is expressed as:

[0112] ;

[0113] Because of the polyhedron It is impossible to cover all historical scenes belonging to the ellipsoid, so the scaling factor k is introduced to expand The modified polyhedron The vertex coordinates are:

[0114]

[0115] Corrected convex polyhedron It can be expressed as:

[0116]

[0117] in, The extreme scene obtained after scaling the convex hull and the vertices of the rotated high-dimensional ellipsoid The relationship is given by The inverse transformation expression is:

[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, we can ensure that the adjusted convex set can cover all historical data points. When the historical scene point is outside the original convex hull, we need to calculate the minimum magnification k. i , so that the point is exactly on the boundary of the enlarged convex hull.

[0122] (2) Energy storage oversale determination strategy considering forecast error

[0123] Shared energy storage operators analyze historical storage demand deviation data from microgrids to quantify the error in renewable energy forecasts for each trading day. Taking into account the remaining power deviation forecasts and storage demand across multiple microgrids within the region, operators employ overselling strategies to fully exploit the forecast error mechanism, thereby optimizing storage capacity allocation for maximum benefit.

[0124] Considering the uncertainty and correlation of wind farm and photovoltaic power station output, the closed convex hull uncertainty set is used to quantify the complementary characteristics of wind and solar power output in different microgrids and the prediction error of energy storage demand. Combining the prediction errors of multiple microgrids, the expression of energy storage oversale is obtained as:

[0125] ;

[0126] Among them, oversold power Represented by the comprehensive forecast error of energy storage demand, represents the prediction error of microgrid i, that is, the difference between the energy storage rental amount submitted on the day before 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 oversold power of energy storage is determined by the sum of the forecast errors for each microgrid's surplus power, while the actual allocated storage capacity is the total demand submitted on the previous day minus the oversold amount. In this way, shared energy storage operators can determine the optimal energy storage capacity allocation based on a balance between investment returns and operational risks, thereby improving overall system efficiency.

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

[0129] The objective function for optimizing shared energy storage configuration is to minimize the comprehensive annual cost of shared energy storage, including the initial investment cost (equal annual value), operation and maintenance costs, the cost of purchasing and selling electricity from the grid, oversale penalties, and service fees for providing microgrid leasing services. The specific expression is shown below.

[0130] ;

[0131] 1) Initial investment cost C of equal annual value inv

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

[0133] ;

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

[0135] ;

[0136] Among them, l SES represents the life of the energy storage power station, and r represents the discount rate.

[0137] 2) Operation and maintenance cost C inv

[0138] Operation and maintenance costs include daily equipment maintenance, regular inspections, and other expenses, and can be expressed as:

[0139] ;

[0140] 3) Electricity purchase and sales cost C G

[0141] The electricity purchase and sales cost reflects the energy exchange cost between the energy storage system and the grid, including the electricity purchase expenditure during charging and the electricity sales revenue during discharging. The expression is:

[0142] ;

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

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

[0145] ;

[0146] in, represents the charging and discharging power of the i-th microgrid under scenario j.

[0147] 5) Overbooking Penalty C os

[0148] When shared energy storage fails to deliver real-time energy storage capacity as required by the day-ahead requirement, a penalty must be paid to prevent the shared energy storage operator from uncontrolled overselling of capacity for arbitrage. The specific penalty expression is:

[0149] ;

[0150] Where λ t os Indicates the penalty coefficient; N j Indicates the day number of a typical day j.

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

[0152] ;

[0153] The input variables include the predicted residual power and 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 .

[0154] The constraints mainly consider the charging and discharging power constraints, state of charge (SOC) constraints, and power balance constraints of the energy storage power station.

[0155] 1) Charge and discharge power constraints

[0156] ;

[0157] Among them, P max Indicates the upper limit of the charging and discharging power of the energy storage. The energy storage cannot be charged and discharged simultaneously.

[0158] 2) SOC Constraint

[0159] ;

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

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

[0162] ;

[0163] in, and Respectively represent the charging power and discharging power of energy storage;

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

[0165] ;

[0166] 3) Power balance constraints

[0167] .

[0168] In summary, the present invention, under this configuration mode, can not only fully utilize shared energy storage resources, but also effectively reduce energy storage configuration costs, while improving energy storage utilization, providing a reference for the practical application of energy storage.

[0169] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within 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 scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of shared energy storage capacity based on the concept of overselling, characterized in that: The steps include: (1) Based on 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 scenario considering the uncertainty and correlation of wind and solar power output; based on the energy storage rental demand reported by the microgrid, the shared energy storage operator utilizes the uncertainty and spatiotemporal complementarity of wind and solar power output in each microgrid, as well as the energy storage capacity that was leased before the trading day but not called in real time, to minimize the energy storage configuration cost while meeting the total charging and discharging instructions; (2) Shared energy storage operators quantitatively evaluate the new energy forecast error on trading days by analyzing the historical energy storage demand deviation data of microgrids. Operators comprehensively consider the residual power deviation forecasts and energy storage demand of multiple microgrids in the region, and adopt an overselling strategy to optimize the energy storage capacity configuration using the forecast error mechanism to obtain maximum benefits. (3) Establish an optimal energy storage capacity configuration model. The oversold power of energy storage is determined by the sum of the prediction errors of the remaining power of each microgrid, and the actual configured energy storage capacity is the total demand submitted on the previous day minus the oversold amount; use the shared energy storage optimization configuration objective function as the lowest comprehensive annual cost of shared energy storage, and plan the shared energy storage capacity with the goal of maximizing the cost-effectiveness of shared energy storage, and finally determine the optimal energy storage capacity configuration plan.

2. The method for optimizing shared energy storage capacity based on the overselling concept according to claim 1 is characterized in that: In step (1), the uncertainty and correlation of wind and solar power output are considered, and a high-dimensional ellipsoid uncertainty set is constructed based on historical data, and the correlation between parameters is displayed by calculating the covariance matrix between the parameters.

3. The method for optimizing shared energy storage capacity based on the overselling concept according to claim 2 is characterized in that: The high-dimensional ellipsoid uncertainty set in step (1) is specifically: The predicted and actual surplus power of all microgrids in the multi-microgrid distribution system within a year are collected to determine their actual energy storage demand. The day-ahead demand for energy storage rental is optimized by comprehensively improving the microgrid's renewable energy consumption and reducing rental costs. 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 ω is expressed as follows: In the formula, j represents the jth typical day, there are J typical scenes in total, and each typical day corresponds to N j day; i represents the i-th microgrid, and there are I microgrids in total; T represents one day, T = 24h; The historical data set ω of the i-th microgrid in all scenarios at time t i,t Expressed as oh i,t ={ω 1,i,t …oh j,i,t …oh J,i,t } Among them, ω j,i,t represents the historical data of station i under scenario j at time t; Since the high-dimensional closed ellipsoid can surround all historical data, the high-dimensional ellipsoid algorithm is used to solve a closed convex hull set of historical scenes: E(Q,c)={ω∈R J·T |(ω-c) T Q(ω-c)≤1} 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, R J·T represents a polyhedron of dimension J × T; Using the minimum volume closed ellipsoid algorithm, the Q and c of the ellipsoid uncertainty set are determined by solving the following optimization problem: Among them, ρ vol Represents the unit sphere volume of the multidimensional ellipsoid, which is a constant; Then, the closed convex hull formed by J×T vertices in the high-dimensional ellipsoid is used as the initial uncertainty set, and the model is balanced by translating, rotating, and scaling the ellipsoid set. Perform orthogonal decomposition on Q, the expression is: Q=P T DP=P -1 DP; Where D represents a diagonal matrix, and all diagonal elements are positive numbers, which is expressed as D=diag(λ1…λ J·T ) form; P represents the 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: ω'=P×(ω-c); Among them, ω' is the coordinate value of the random variable ω in the rotated coordinates, and the mathematical expression of the rotated high-dimensional ellipsoid E' is: E'(D)={ω'∈R J·T |oh' T Dω'≤1}; The vertex coordinates of the adjusted high-dimensional ellipsoid E' are expressed as: Among them, ω' v,1 Represents the coordinates of the i-th vertex of the high-dimensional ellipsoid E'; using the inverse transformation of the formula ω'=P×(ω-c), the vertex equations of the original high-dimensional ellipsoid E can be derived. The polyhedron equation defined by these vertices is expressed as follows: Because of the polyhedron It is impossible to cover all historical scenes belonging to the ellipsoid, so the scaling factor k is introduced to expand The area of ​​the modified polyhedron The vertex coordinates are: Corrected convex polyhedron Expressed as: Among them, ω e,i is the extreme scene obtained after scaling the convex hull, and the vertex ω' of the rotated high-dimensional ellipsoid v,i The relationship is expressed by the inverse transformation of the formula ω'=P×(ω-c): oh e,i =c+kP -1 oh v,i ; The maximum value of the scaling factor k is solved by the following formula: By solving the scaling factor k, we can ensure that the adjusted convex set can cover all historical data points. When the historical scene point is outside the original convex hull, we need to calculate the minimum magnification k. i , so that the point is exactly on the boundary of the enlarged convex hull.

4. The method for optimizing shared energy storage capacity based on the overselling concept according to claim 1 is characterized in that: In step (2), an overselling strategy is adopted to optimize the energy storage capacity configuration by utilizing the prediction error mechanism to obtain the maximum benefit. Specifically, the complementary characteristics of wind and solar power output in different microgrids and the prediction error of energy storage demand are quantified based on the closed convex hull uncertainty set. The prediction errors of multiple microgrids are integrated to calculate the energy storage overselling amount. The calculation expression is: Among them, oversold power Represented by the comprehensive forecast error of energy storage demand, represents the prediction error of microgrid i, that is, the difference between the energy storage rental amount submitted on the day before and the actual energy storage usage during the day. The value range of this random variable is determined by the uncertain convex hull set; The oversold power of energy storage is determined by the sum of the forecast errors of the remaining power of each microgrid, while the actual configured energy storage capacity is the total demand submitted on the previous day minus the oversold amount; in this way, the shared energy storage operator can determine the optimal energy storage capacity configuration plan.

5. The method for optimizing shared energy storage capacity based on the overselling concept according to claim 1 is characterized in that: In step (3), the objective function of the shared energy storage optimization configuration is the lowest comprehensive annual cost of shared energy storage, including the initial investment cost C inv , operation and maintenance costs C om , electricity purchase and sales cost C G , service fee for providing microgrid leasing services R lea Overbooking penalty C os , the average annual cost of shared energy storage minC SES The expression is as follows: minC SES =C inv +C om +C G +C os -R lea 。 6. The method for optimizing shared energy storage capacity based on the overselling concept according to claim 5 is characterized in that: The annual value of the initial investment cost C inv :The initial investment cost is calculated using the equal annual value method, which converts the one-time investment into equal annual payments. The expression is: C inv =r n (c P P max +c E E max ); Among them, c P and c E are the unit power and unit capacity construction costs of centralized energy storage respectively; r n It represents the conversion coefficient of equal annual value, and its expression is: Among them, l SES represents the life of the energy storage power station, and r represents the discount rate; Operation and maintenance cost C om :Operation and maintenance costs include the expenses for daily maintenance and regular inspection of equipment, and its expression is: C om =r n c om P SES ; Electricity purchase and sales cost C G The electricity purchase and sales cost reflects the energy exchange cost between the energy storage system and the grid, including the electricity purchase expenditure during charging and the electricity sales revenue during discharging. Its expression is: Service fee for providing rental services R lea :The service fee is the rental income when shared energy storage provides electric energy services, and the expression is: in, represents the charging and discharging power of the i-th microgrid under scenario j; Overbooking Penalty C os : When shared energy storage fails to deliver real-time energy storage capacity as required by the day-ahead requirement, a penalty must be paid to prevent the shared energy storage operator from uncontrolled overselling of capacity for arbitrage. The penalty expression is: in, Indicates the penalty coefficient; N j represents the number of days of a 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 solution under a certain degree of conservatism. The objective function is shown as the following min-max problem: The input variables include the predicted residual power and 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 Its constraints include the charging and discharging power constraints of the energy storage power station, the state of charge (SOC) constraints, and the power balance constraints.

7. The method for optimizing shared energy storage capacity configuration based on the overselling concept according to claim 6 is characterized in that: The constraints include the charging and discharging power constraints, the state of charge (SOC) constraints, and the power balance constraints of the energy storage station, specifically: (a) Charge and discharge power constraints: Among them, P max Indicates the upper limit of the charging and discharging power of the energy storage. The energy storage cannot be charged and discharged simultaneously. (b) State of charge (SOC) constraint: SOC min ·IN max ≤E j,t ≤SOC max ·IN max ; Among them, SOC represents the state of charge of energy storage; E j,t Indicates the energy state of energy storage at time t; E max Indicates the upper limit of energy storage; The energy state of the stored energy at time t depends on the state at time t-1: Among them, η ch and η dis Respectively represent the charging power and discharging power of energy storage; During the optimization process, the initial energy state and the final energy state are equal: AND j,0 =And j,T ; (c) Power balance constraints:

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