User side shared energy storage optimal configuration method based on dynamic capacity increase

Abstract

According to the user-side shared energy storage optimal configuration method based on dynamic capacity increase, a shared energy storage operator SESO serves as a third party to construct a modular shared energy storage unit, energy storage leasing service is provided for users with seasonal dynamic capacity increase requirements, and user-side shared energy storage optimal configuration is achieved. According to the method, the situation of monthly overlapping or idle peak demand of each special transformer user is considered, a user dynamic capacity increasing-oriented shared energy storage multi-time scale optimization strategy is provided, under the background of two-part electricity price, SESO serves as third-party investment to construct a modular shared energy storage unit, energy storage leasing services are provided for users with seasonal dynamic capacity increasing demands, and the energy storage leasing efficiency is improved. While the economic benefit of an operator is improved, the demand cost of a special transformer user is saved.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, relates to an optimized configuration of energy storage in an electric power system, and is a user-side shared energy storage optimized configuration method based on dynamic capacity increase. Background Art

[0002] With the development of the economy and society, the demand for electricity has increased rapidly, and the contradiction between electricity supply and demand has become increasingly acute. Users with dedicated transformers will intermittently experience transformer overload, and the shortage period generally lasts 1-3 months. However, the economic appeal of modifying transformers or investing in energy storage is relatively low. The application of shared energy storage on the user side to improve the flexibility and economic benefits of dedicated transformer users has received widespread attention in recent years.

[0003] At present, most shared energy storage adopts a third-party investment business model. However, most existing studies only optimize the configuration based on the electricity consumption patterns of typical users, without considering the differences in peak demand of dedicated transformers and how to identify them. In addition, most existing studies are based on centralized shared energy storage, which has the following shortcomings:

[0004] 1) It can only be fixed in a single location and cannot be flexibly adjusted according to needs;

[0005] 2) Investment and expansion flexibility are poor, while shared energy storage systems with modular units can gradually deploy units according to demand, with small initial investment and flexible subsequent expansion;

[0006] 3) Centralized shared energy storage can easily lead to the paralysis of the entire energy storage system due to a single point failure.

[0007] In order to flexibly meet the needs of different users, it is particularly important to build a shared energy storage system that is highly mobile, easy to install and disassemble, and provide differentiated and diversified shared energy storage service solutions. Optimizing the capacity and operation scheduling strategy of the shared energy storage system has become an issue that urgently needs to be studied. Summary of the invention

[0008] The problem to be solved by the present invention is that the existing application research of shared energy storage on the user side cannot meet the differentiated electricity demand of different users. It is necessary to study how to share the capacity and operation scheduling strategy of the energy storage system to meet the differentiated and diversified needs of users.

[0009] The technical solution of the present invention is: a user-side shared energy storage optimization configuration method based on dynamic capacity expansion, which is characterized in that a shared energy storage operator SESO is used as a third party to build a modular shared energy storage unit, provide energy storage leasing services to users with seasonal dynamic capacity expansion needs, and realize user-side shared energy storage optimization configuration, including the following steps:

[0010] 1) Cluster identification is performed for users with dynamic capacity expansion needs. Based on the historical load curve data set of dedicated transformer users, the maximum load rate T of each user's transformer for 365 days a year is analyzed. factor ,After data dimension reduction and data clustering, seasonal user loads with dynamic capacity expansion requirements are screened out in combination with common load characteristic indicators;

[0011] 2) Analyze the overlap and idleness of the peak demand months for dedicated transformer users, and construct a shared energy storage multi-time scale optimization model. The model objective function is to maximize the average annual net benefit of the shared energy storage during the operation phase. The constraints include the number of modular shared energy storage units, energy storage power, state of charge, energy storage charge and discharge state, and transformer load rate. Under the shared energy storage multi-time scale optimization model, the monthly time scale is used to consider the user's demand management and optimize the dynamic configuration of the modular shared energy storage units among users in the peak demand month. The daily time scale is used to optimize the daily charge and discharge power of the shared energy storage in a peak-valley arbitrage manner in combination with the transformer load rate restrictions of different users.

[0012] The modular shared energy storage units constitute a shared energy storage system. The modular shared energy storage units have physical mobility and are easy to disassemble and expand. Based on the monthly allocation results, SESO moves the modular shared energy storage units to meet the dynamic capacity expansion needs of user-side energy storage.

[0013] The present invention provides a user-side shared energy storage optimization configuration strategy based on dynamic capacity expansion. Under the two-part electricity price background, SESO, as a third party, invests in the construction of modular shared energy storage units and provides energy storage leasing services to users with seasonal dynamic capacity expansion needs. While improving the economic benefits of operators, it also achieves demand-based cost savings for dedicated transformer users.

[0014] Compared with the centralized shared energy storage system, the present invention provides an energy storage system with modular units and its configuration strategy, which can be moved and installed to the user side with demand, and multiple units are deployed in a dispersed manner, which has higher fault tolerance. Compared with the prior art, the present invention has four advantages:

[0015] (1) The present invention proposes a user clustering identification method for dynamic capacity expansion demand to screen potential users of shared energy storage. Compared with previous user clustering methods, it is more targeted at users who install dedicated transformers.

[0016] (2) The multi-time scale optimization model of shared energy storage units described in the present invention takes into account the situation where the peak demand months of each dedicated transformer user overlap or are idle, and proposes a multi-time scale optimization strategy for shared energy storage with dynamic capacity expansion for users. With the month as the time scale, the demand management of users is considered, and the dynamic optimization configuration of shared energy storage units among users in the peak demand month is optimized. With the day as the time scale, combined with the transformer load rate restrictions of different users, the revenue is expanded by peak-valley arbitrage to reduce the economic loss of SESO idle months.

[0017] (3) The operation optimization strategy described in the present invention is that the shared energy storage system is composed of a series of highly modular shared energy storage units, which have physical mobility and are easy to disassemble and expand. Based on the monthly energy storage optimization configuration results of the dedicated transformer users and taking into account the mobile transportation costs, the corresponding shared energy storage units can be moved and installed to the low-voltage side of the user, and can also play a key role as emergency power supply equipment in emergency situations.

[0018] (4) The SESO secondary profit distribution mechanism described in the present invention is that SESO will return a portion of the profit based on the dynamic configuration and operation strategy of the shared energy storage unit, taking into account the electricity consumption pattern and contribution of each user, to encourage users to actively participate in shared energy storage and achieve a win-win economic benefit. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a flow chart of the user-side shared energy storage optimization configuration method based on dynamic capacity increase. DETAILED DESCRIPTION

[0020] The present invention discloses a user-side shared energy storage optimization configuration strategy based on dynamic capacity increase. Considering that under the background of two-part electricity price, a shared energy storage operator (Shared Energy Storage System, SESO) as a third party invests in the construction of modular shared energy storage units, and provides energy storage leasing services to users with seasonal dynamic capacity increase (Dynamic Transformer Capacity Increment, DTCI) needs, while improving the economic benefits of the operator, it also achieves demand-based cost savings for dedicated transformer users.

[0021] like Figure 1As shown, the process of the present invention includes: first, based on the historical load curve data set of special transformer users, the maximum daily transformer load rate value of each user is analyzed, and the principal component analysis (PCA) method is used for data dimension reduction and improved K-means algorithm clustering is used, and the seasonal user load with dynamic capacity expansion demand is screened out in combination with common load characteristic indicators; secondly, the overlapping or idle peak demand months of each special transformer user are analyzed to obtain a shared energy storage multi-time scale optimization strategy for user dynamic capacity expansion, with the month as the time scale, considering the user's demand management, optimizing the configuration of the peak demand monthly modular shared energy storage unit among each user, with the day as the time scale, combined with the transformer load rate limit of the differential users, and expanding the income in the form of peak-valley arbitrage to reduce the economic loss of the SESO idle month. The shared energy storage system constructed by the present invention is composed of a series of highly modular shared energy storage units, which have physical mobility, are easy to disassemble and expand, and are based on the monthly energy storage optimization configuration results of the dedicated transformer users. Considering the mobile transportation cost, the corresponding shared energy storage units can be moved and installed to the low-voltage side of the user, and can also play a key role in emergency power supply equipment in emergency situations. Finally, SESO distributes the benefits secondary according to the contribution of each user, and encourages the willingness and enthusiasm of users to participate in shared energy storage.

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0023] The implementation of the present invention includes the following parts.

[0024] (1) Based on the historical load curve data set of dedicated transformer users, the maximum load rate T of each user's transformer for 365 days a year is obtained. factor ,After PCA data dimension reduction and improved K-means algorithm clustering, the ,seasonal user load with dynamic capacity increase demand is screened out in combination of ,common load characteristic indicators.

[0025] (2) Analyze the situation of overlapping or idle peak demand months for each dedicated transformer user, and propose a shared energy storage multi-time scale optimization strategy for dynamic capacity expansion for users. Construct a shared energy storage multi-time scale optimization model. The model objective function is to maximize the annual average net benefit of the shared energy storage during the operation stage. The constraints include the number of modular shared energy storage units, energy storage power, state of charge, energy storage charge and discharge state, and transformer load rate. Under the shared energy storage multi-time scale optimization model, the monthly time scale is used to consider the user's demand management, and optimize the dynamic configuration of the modular shared energy storage units among users in the peak demand month. The daily time scale is used to optimize the daily charge and discharge power of the shared energy storage in combination with the transformer load rate restrictions of different users. The peak-valley arbitrage method is used to expand the income through peak-valley arbitrage, and reduce the economic losses of SESO idle months.

[0026] (3) The shared energy storage system is composed of a series of highly modular shared energy storage units, which have physical mobility and are easy to disassemble and expand. Based on the monthly energy storage optimization configuration results of dedicated transformer users and taking into account the mobile transportation costs, the corresponding shared energy storage units can be moved and installed to the user's low-voltage side, and can also play a key role as emergency power supply equipment in emergency situations.

[0027] (4) SESO redistributes revenue based on the contribution of each user, thus motivating users to participate in shared energy storage.

[0028] Furthermore, seasonal user load screening with dynamic capacity expansion requirements includes four steps:

[0029] First, the maximum load rate parameter of the dedicated transformer is introduced, and the historical load data of the dedicated transformer users is calculated to obtain the daily dedicated transformer load rate data T of the users throughout the year. factor .

[0030] Secondly, PCA is used to reduce the dimension of the data set, and the principal components with a cumulative contribution rate of 95% are selected for subsequent analysis. Here, the contribution rate refers to the contribution of each dimensional data to distinguishing the entire data.

[0031] Next, the selected special transformer users' daily maximum load rate data are clustered to classify users with similar electricity consumption behaviors and similar load rate data characteristics. The present invention adopts an improved K-means method combined with hierarchical clustering, and combines three clustering effectiveness indicators, namely the silhouette coefficient, the sum of squared errors, and the Davidson-Botting index, to determine the optimal number of clusters and the corresponding cluster centers. In existing clustering algorithms such as hierarchical clustering or K-means clustering, the initial cluster centers generated by the K-means clustering method are random, and the hierarchical clustering is irreversible in the process of forming a cluster tree. The present invention combines the two and proposes an improved clustering method, which first uses hierarchical clustering to preliminarily group the data, and then optimizes the clustering effect through the K-means algorithm.

[0032] Finally, combined with the commonly used load characteristic indicators, users with significant seasonal capacity expansion needs are screened. Common load characteristic indicators can be obtained based on the commonly used daily load characteristic indicators listed in the State Grid Corporation's documents. This indicator is used to describe the user's electricity consumption behavior characteristics. Furthermore, the screened users are given priority for shared energy storage optimization configuration. Users who are not screened can also trade energy with the shared energy storage system and conduct peak-valley arbitrage, which can avoid the idleness of shared energy storage and bring economic benefits to users.

[0033] Furthermore, the objective function and constraints included in the shared energy storage optimization configuration model are as follows:

[0034] (I) The objective function is to maximize the average annual net benefit F1 during the operation phase of shared energy storage:

[0035] maxF1=F xf +F base +F rent +F re -(C ini +C om +C it )

[0036] Among them, F1 is the annual net income of SESO; F xf The revenue obtained by seasonal DTCI demand users from participating in peak load shifting; F base F is the basic electricity cost income of the transformer reduced according to the maximum load demand; rent C is the rental service fee collected by SESO from users; ini is the one-time investment cost of the energy storage system during its entire life cycle converted to an annual amount; Com is the annual operation and maintenance cost of the energy storage system; C it Installation and transportation costs incurred for moving shared energy storage units between different users each month.

[0037] (1) Benefits from Peak Shaving and Valley Filling

[0038]

[0039] Among them, U DTCI A collection of seasonal electricity users who have DTCI needs and have signed contracts with operators; is the seasonal load u in the DTCI peak demand month and are the discharge and charging power of the shared energy storage system rented by user u in month m at time t; V e (t) is the time-of-use electricity price of the power grid at time t; Δt is the time interval, and T is 24 hours a day.

[0040] (2) Basic electricity fee benefits reduced by dedicated transformers through demand management

[0041]

[0042] in, and are the maximum load power of user u in month m before and after using shared energy storage; V c The monthly basic electricity price for transformers is charged according to the maximum load demand.

[0043] (3) Income from leasing service fees

[0044]

[0045] Where, ζ is the monthly rental fee of the unit power shared energy storage unit; N m,u is the number of shared energy storage units rented by user u in month m.

[0046] (4) Residual value income generated by secondary recycling

[0047] F re =C(d r ,Y)δC f

[0048]

[0049] Where δ is the residual value recovery coefficient of the battery energy storage system; C f is the initial one-time investment cost of the energy storage system; C(d r , Y) is the discount rate d considered in the whole life cycle Y years r The equal annual value coefficient.

[0050] (5) Initial one-time investment cost

[0051] C ini =C(d r ,Y)C f =C(d r,Y)N(C E E ESS +C P P ESS )

[0052] Where N is the total number of shared energy storage units invested by the operator; E ESS and P ESS are the capacity and power of a single shared energy storage unit respectively; C E and C P is the unit capacity and power investment cost of the energy storage system.

[0053] (6) Operation and maintenance costs

[0054] C om =Nc om P ESS

[0055] Among them, c om is the annual operation and maintenance cost per unit power of the energy storage system.

[0056] (7) Installation and transportation costs caused by dynamic configuration switching

[0057]

[0058] Among them, c it is the installation and transportation cost per unit capacity and per unit distance; l u is the distance from user u to the management base; N m,u is the number of shared energy storage units used by user u in month m. m,u >N m+1,u When , it indicates that the number of energy storage units of user u in month m+1 is reduced and needs to be transported back to the management base for transmission to other load nodes.

[0059] Furthermore, considering the electricity consumption pattern and contribution of each user, a part of the income is returned to encourage users to actively participate in the operation of the energy storage system. The SESO secondary income distribution mechanism is adopted to encourage dedicated transformer users to adopt energy storage leasing services and participate in the shared energy storage optimization configuration. SESO considers the electricity consumption pattern and contribution of each user, and shares the obtained income with the user to encourage users to participate in the operation of the shared energy storage system. The share is added to the shared energy storage multi-time scale optimization model. The model objective function is:

[0060] maxF1=F xf +F base +F rent +F re -(C ini +C om +C it +C discount )

[0061] C discount SESO divides the revenue with users according to the contribution of user load to its own revenue, and the expenditure incurred for secondary distribution is:

[0062]

[0063] in, and are the peak-shaving and valley-filling benefits obtained by user u after using energy storage in month m and the basic electricity fee benefits of the transformer obtained due to the reduction of maximum demand; α is the proportional coefficient, ranging from [0,1].

[0064] The economic benefits achieved by each user using the modular shared energy storage unit provided by SESO reflects the user's contribution to the shared energy storage system, corresponding to C in the objective function. discount， The higher the contribution, the discount The higher the power consumption pattern of the user, the higher the return amount. The user's load curve can be distinguished by load indicators to obtain their respective power consumption patterns. For example, if the peak-to-valley difference of some users is large, the economic benefits they bring will be relatively large. In the incentive, the return amount is C discount , expressed as [benefit from peak shaving and valley filling + benefit from maximum demand reduction] × proportional coefficient. These two types of benefits can reflect the electricity consumption characteristics among users and their contribution to shared energy storage.

[0065] (II) Constraints:

[0066] (1) Constraints on the number of shared energy storage units

[0067]

[0068]

[0069]

[0070] Among them, S m,u =1 means that the mth month is the peak demand month for user u; S m,u =0 means that the mth month is the off-peak demand month for user u; N max It is the maximum empirical value obtained by considering the actual maintenance of the energy storage system.

[0071] (2) Energy storage power constraints

[0072]

[0073] in, and They represent the charging and discharging power of user u at time t in the mth month, and γ is the charging and discharging rate.

[0074] (3) State of charge constraints

[0075]

[0076] SoC min ≤SoC m,u (t)≤SoC max

[0077] SoC m,u (t start )=SoC m,u (t end )

[0078] Among them, SoC m,u (t) is the state of charge of the energy storage unit of user u at time t in the mth month; τ is the energy storage self-discharge rate; η ch and η dis They are charge and discharge efficiency; SoC min and SoC max are the minimum and maximum state of charge, respectively.

[0079] (4) Charge and discharge state constraints

[0080]

[0081] in, and Indicates that the energy storage system is charging; and Indicates that the energy storage system is discharging; and It means that the energy storage system is neither charging nor discharging.

[0082] (5) Transformer load factor constraint

[0083]

[0084] In the formula, and They are the maximum load power of user u in month m before and after using shared energy storage; is the rated capacity of the transformer of user u; 0.9 is the power factor; and L m,u (t) are the load power of user u before and after using energy storage in month m.

[0085] The shared energy storage system of the present invention is composed of a series of highly modular shared energy storage units, which have physical mobility and are easy to disassemble and expand. Based on the monthly energy storage optimization configuration results of dedicated transformer users and taking into account the mobile transportation costs, the corresponding shared energy storage units can be moved and installed to the low-voltage side of the user, and can also play a key role as emergency power supply equipment in emergency situations.

[0086] .The present invention provides energy storage optimization configuration for different needs of special transformer users. Among special transformer users, some users have greater capacity expansion requirements and more rental units, while some users have smaller requirements and fewer rental units. Moreover, different users have different leasing time spans. The present invention provides differentiated services for this purpose. According to the capacity expansion requirements of each user, the number and time of modular shared energy storage units are configured. It can also provide diversified services, rent shared energy storage units to users with capacity expansion requirements, conduct energy transactions for users without capacity expansion requirements, charge corresponding service fees, and improve the utilization rate of energy storage.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A user-side shared energy storage optimization configuration method based on dynamic capacity expansion, characterized by As a third party, the shared energy storage operator SESO builds modular shared energy storage units and provides energy storage leasing services to users with seasonal dynamic capacity expansion needs, realizing the optimized configuration of shared energy storage on the user side, including the following steps: 1) Cluster identification is performed for users with dynamic capacity expansion needs. Based on the historical load curve data set of dedicated transformer users, the maximum load rate T of each user's transformer for 365 days a year is analyzed. factor ,After data dimension reduction and data clustering, seasonal user loads with dynamic capacity expansion requirements are screened out in combination with common load characteristic indicators; 2) Analyze the overlap and idleness of the peak demand months for dedicated transformer users, and construct a shared energy storage multi-time scale optimization model. The model objective function is to maximize the average annual net benefit of the shared energy storage during the operation phase. The constraints include the number of modular shared energy storage units, energy storage power, state of charge, energy storage charge and discharge state, and transformer load rate. Under the shared energy storage multi-time scale optimization model, the monthly time scale is used to consider the user's demand management and optimize the dynamic configuration of the modular shared energy storage units among users in the peak demand month. The daily time scale is used to optimize the daily charge and discharge power of the shared energy storage in a peak-valley arbitrage manner in combination with the transformer load rate restrictions of different users. The modular shared energy storage units constitute a shared energy storage system. The modular shared energy storage units have physical mobility and are easy to disassemble and expand. Based on the monthly allocation results, SESO moves the modular shared energy storage units to meet the dynamic capacity expansion needs of user-side energy storage.

2. The user-side shared energy storage optimization configuration method based on dynamic capacity expansion according to claim 1 is characterized by: Cluster identification for users with dynamic capacity expansion needs is as follows: First, the maximum load rate parameter of dedicated transformers is introduced, and the historical load data of dedicated transformer users is calculated to obtain the daily dedicated transformer load rate data T of users throughout the year. factor ; Secondly, PCA is used to reduce the dimension of the data set, and the principal components with a cumulative contribution rate of 95% are selected for subsequent analysis; then, the daily maximum load rate data of the selected special transformer users are clustered, and users with similar electricity consumption behaviors and similar load rate data characteristics are classified. The improved K-means method combined with hierarchical clustering is adopted. Hierarchical clustering is first used to preliminarily group the data, and then the clustering results are optimized through the K-means algorithm. The three clustering effectiveness indicators, namely the silhouette coefficient, the sum of squared errors, and the Davidson-Botting index, are combined to determine the optimal number of clusters and the corresponding cluster centers; finally, combined with commonly used load characteristic indicators, users with significant seasonal capacity increase needs are screened.

3. The user-side shared energy storage optimization configuration method based on dynamic capacity expansion according to claim 1 is characterized by: The objective function of the shared energy storage multi-time scale optimization model is: maxF1=F xf +F base +F rent +F re -(C ini +C om +C it ) Among them, F1 is the annual net income of SESO; F xf The revenue obtained by seasonal DTCI demand users from participating in peak load shifting; F base F is the basic electricity cost income of the transformer reduced according to the maximum load demand; rent C is the rental service fee collected by SESO from users; ini C is the one-time investment cost of the energy storage system converted to annually over its entire life cycle; om is the annual operation and maintenance cost of the energy storage system; C it The installation and transportation costs for moving the shared energy storage unit between different users every month; C discount SESO shares its revenue with users based on the contribution of user load to its own revenue, and makes secondary distribution to cover the expenses incurred.

4. The user-side shared energy storage optimization configuration method based on dynamic capacity expansion according to claim 3 is characterized by: The specific objective function is: (1) Benefits from Peak Shaving and Valley Filling Among them, U DTCI A collection of seasonal electricity users who have DTCI needs and have signed contracts with operators; is the set of seasonal load u in the DTCI peak demand month; and are the discharge and charging power of the shared energy storage system rented by user u in month m at time t; V e (t) is the time-of-use electricity price of the power grid at time t; Δt is the time interval, and T is 24 hours a day; (2) Basic electricity fee benefits reduced by dedicated transformers through demand management in, and are the maximum load power of user u in month m before and after using shared energy storage; V c The monthly basic electricity price for transformers is charged according to the maximum load demand; (3) Income from leasing service fees Where, ζ is the monthly rental fee of the unit power shared energy storage unit; N m,u is the number of shared energy storage units rented by user u in month m; (4) Residual value income generated by secondary recycling F re =C(d r ,Y)δC f Where δ is the residual value recovery coefficient of the battery energy storage system; C f is the initial one-time investment cost of the energy storage system; C(d r , Y) is the discount rate d considered in the whole life cycle Y years r The equal annual value coefficient of (5) Initial one-time investment cost C ini =C(d r ,Y)C f =C(d r ,Y)N(C E E ESS +C P P ESS ) Where N is the total number of shared energy storage units invested by the operator; E ESS and P ESS are the capacity and power of a single shared energy storage unit respectively; C E and C P The unit capacity and power investment cost of the energy storage system; (6) Operation and maintenance costs C om =Nc om P ESS Among them, c om is the annual operation and maintenance cost per unit power of the energy storage system; (7) Installation and transportation costs caused by dynamic configuration switching Among them, c it is the installation and transportation cost per unit capacity and per unit distance; l u is the distance from user u to the management base; N m,u is the number of shared energy storage units used by user u in month m. m,u >N m+1,u When , it indicates that the number of energy storage units of user u in month m+1 is reduced and needs to be transported back to the management base for transmission to other load nodes.

5. The user-side shared energy storage optimization configuration method based on dynamic capacity expansion according to claim 3 is characterized by: The SESO secondary income distribution mechanism is used to encourage dedicated transformer users to adopt energy storage leasing services and participate in the shared energy storage optimization configuration. SESO considers the electricity consumption mode and contribution of each user, and shares the obtained income with the user to encourage users to participate in the operation of the shared energy storage system. The share is added to the shared energy storage multi-time scale optimization model. The model objective function is: maxF1=F xf +F base +F rent +F re -(C ini +C om +C it +C discount ) C discount SESO divides the revenue with users according to the contribution of user load to its own revenue, and the expenditure incurred by secondary distribution is: in, and are the peak-shaving and valley-filling benefits obtained by user u after using energy storage in month m and the basic electricity fee benefits of the transformer obtained by reducing the maximum demand; α is the proportional coefficient, ranging from [0,1], U DTCI A collection of seasonal electricity users who have DTCI needs and have signed contracts with operators; is the set of seasonal load u in the DTCI peak demand month.

6. The user-side shared energy storage optimization configuration method based on dynamic capacity expansion according to claim 1 is characterized by: The constraints of the shared energy storage multi-time scale optimization model are as follows: (1) Constraints on the number of shared energy storage units Among them, S m,u =1 means that the mth month is the peak demand month for user u; S m,u =0 means that the mth month is the off-peak demand month for user u; N max The maximum empirical value obtained to take into account the actual maintenance of the energy storage system; (2) Energy storage power constraints in, and They represent the charging and discharging power of user u at time t in the mth month, and γ is the charging and discharging rate; (3) State of charge constraints SoC min ≤SoC m,u (t)≤SoC max SoC m,u (t start )=SoC m,u (t end ) Among them, SoC m,u (t) is the state of charge of the energy storage unit of user u at time t in the mth month; τ is the energy storage self-discharge rate; η ch and η dis They are charge and discharge efficiency; SoC min and SoC max are the minimum and maximum values ​​of the state of charge, respectively; (4) Charge and discharge state constraints in, and Indicates that the energy storage system is charging; and Indicates that the energy storage system is discharging; and It means that the energy storage system is neither charging nor discharging; (5) Transformer load factor constraints In the formula, and They are the maximum load power of user u in month m before and after using shared energy storage; is the rated capacity of the transformer of user u; 0.9 is the power factor; and L m,u (t) are the load power of user u before and after using energy storage in month m.

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