User-side light storage optimization configuration and break-even analysis method thereof

Abstract

The application discloses a kind of user side light storage optimization configuration and its break-even analysis method, comprising the following steps: S1, establish user side demand response model, and according to the model established, with the maximum user economic benefit as target, the response amount of each period of user is solved;S2, establish user side light storage double-layer optimization configuration model, and in double-layer optimization configuration model, the upper model with the lowest annual comprehensive cost of user as objective function, optimize the capacity of additional photovoltaic, energy storage capacity and power, the lower model with the minimum daily operation scheduling cost as objective function, optimize energy storage charge-discharge control strategy;S3, the double-layer optimization configuration model optimization calculation established in step S2 is carried out, and optimization process is established, and the optimization result is compared and analyzed, to find the profit and loss balance point beneficial to user side.The application can optimize energy storage charge-discharge control strategy, improve the potential of user side resource participation demand response, and realize the profit and loss balance of user side.

Description

Technical Field

[0001] This invention relates to the field of distributed energy system technology, and in particular to a user-side photovoltaic-storage optimization configuration and its break-even analysis method. Background Technology

[0002] Microgrids maximize the integration of distributed power sources, improve power supply reliability, and enhance the grid's emergency power supply capacity. Compared to traditional grids, microgrids more easily achieve power supply and load balance within the system, ensuring economical system operation, reducing the impact of the inherent instability of distributed power sources on the grid, and meeting users' requirements for power quality and power supply security. As a supplement to the main grid, microgrids not only have the advantages of proximity to users, flexibility, and strong disaster resistance, but also improve the utilization rate of distributed energy resources. Because the output power of photovoltaics is fluctuating, photovoltaic microgrids need to be equipped with energy storage devices of appropriate capacity to ensure power supply reliability.

[0003] As the electricity market matures, users are increasingly participating in demand-side response, and the impact of user response behavior on photovoltaic-storage microgrids has become a research hotspot for energy storage. Introducing energy storage can reduce photovoltaic curtailment rates, but its high cost and excessive capacity can negatively impact the economics of the microgrid. Therefore, configuring appropriate energy storage capacity is crucial. Summary of the Invention

[0004] To address the problems mentioned in the background art, the present invention aims to provide a user-side optical storage optimization configuration and its break-even analysis method.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for optimizing the configuration of user-side optical storage and its break-even analysis includes the following steps:

[0007] S1. Establish a user-side demand response model, and based on the established model, with the goal of maximizing user-side economic benefits, solve for the user's response volume at each time period.

[0008] S2. Establish a user-side photovoltaic and energy storage dual-layer optimization configuration model. In the dual-layer optimization configuration model, the upper-layer model takes the minimum annual comprehensive cost of the user as the objective function to optimize the installed photovoltaic capacity, energy storage capacity and power, while the lower-layer model takes the minimum daily operation and scheduling cost as the objective function to optimize the energy storage charging and discharging control strategy.

[0009] S3. Optimize the two-layer optimization configuration model established in step S2, establish the optimization process, compare and analyze the optimization results, and find the break-even point that is beneficial to the user side.

[0010] In some embodiments, step S1 specifically includes:

[0011] S1a. Establish a user response cost model, dividing the user load response amount into two parts: load reduction amount and load transfer amount;

[0012] S1b. Establish a user response revenue model and combine it with the user response cost model in step S1a to obtain a net revenue model for user-side demand response.

[0013] S1c. During the demand response process, users determine the optimal response volume for each time period based on the published demand response price and their own demand response costs in order to maximize their own benefits. Therefore, based on the established net benefit model of user-side demand response, with the goal of maximizing user-side economic benefits, the response volume of users in each time period is solved.

[0014] In some embodiments, in step S1a, the response cost expression is:

[0015] (1)

[0016] In equation (1), , Let be the response costs for load reduction and load transfer for the i-th user, respectively. and The constant coefficient of the user load reduction cost function. and The constant coefficients of the user load transfer cost function. This represents the load response of the i-th user in the t-th time period. The ratio of load reduction to load response is a constant between 0 and 1, and the ratio of load transfer to load response is... ;

[0017] Combining and simplifying equation (1), we obtain the expression for the total user response cost as follows:

[0018] (2)

[0019] In equation (2), For the total user response cost, , All are constant coefficients;

[0020] In step S1b, the user's response benefit is calculated using the following formula:

[0021] (3)

[0022] In equation (3), For the user's response benefits, Let t be the time-of-use electricity price for the t-th time period. Time-of-use pricing for the period during which load transfer is received. The subsidized electricity price for the response volume of the first user unit;

[0023] Based on equations (2) and (3), the net benefit brought to the user by the user-side demand response is obtained. for:

[0024] (4)

[0025] In step S1c, the user's response time for each time period is calculated according to the following formula:

[0026] (5)

[0027] In the formula, , These are the upper and lower limits of the response volume for the i-th user during time period t, respectively.

[0028] In some embodiments, step S2 specifically includes the following steps:

[0029] S21. Establish the objective function of the upper-level optimization model, with the goal of minimizing the user's annual comprehensive cost. Optimize the installed photovoltaic capacity, energy storage capacity, and power. The user's comprehensive cost includes the electricity cost charged according to the electricity price, the basic electricity cost charged according to the maximum demand, and the annual investment cost and operation and maintenance cost of photovoltaic and energy storage.

[0030] S22. Establish constraints for the upper-level optimization model, including energy storage state of charge constraints and energy storage charge and discharge state constraints.

[0031] S23. Establish the objective function of the lower-level optimization model. The objective function of the lower-level optimization model is to minimize the daily operation scheduling cost.

[0032] S24. Establish constraints for the lower-level optimization model, including constraints on the continuity of energy storage state of charge and energy storage power.

[0033] In some embodiments, in step S21, the objective function of the upper-level optimization model is expressed as:

[0034] (6)

[0035] In equation (6), For the entire year's expenses, For electricity consumption and electricity costs, For basic electricity costs, The annual investment cost and operation and maintenance expenses for energy storage The annual investment cost and operation and maintenance expenses for photovoltaic power generation;

[0036] Electricity cost The formula is:

[0037] (7)

[0038] In equation (7), Representing the Heavenly User load power during the time period Representing the Heavenly Energy storage charging power during the time period, Representing the Heavenly Energy storage discharge power during the time period For the first Electricity prices during specific time periods;

[0039] Basic electricity cost The formula is:

[0040] (8)

[0041] In equation (8), The unit demand electricity cost represents the user's cost. Represents the actual maximum demand value. This represents 1.05 times the approved maximum demand value;

[0042] Annual investment cost and operation and maintenance expenses for energy storage Annual investment cost of energy storage system and annual operation and maintenance costs of energy storage systems sum;

[0043] Annual investment cost of energy storage system The formula is:

[0044] (9)

[0045] In equation (9), It is the cost per unit capacity of energy storage. For the rated capacity of energy storage, and These are the annual discount rate for energy storage and the service life of energy storage;

[0046] Annual maintenance cost of energy storage operation The formula is:

[0047] (10)

[0048] In equation (10), The annual operation and maintenance cost per unit power of the energy storage system. This represents the total power of the energy storage system.

[0049] Annual investment cost and operation and maintenance expenses of photovoltaic power Annual investment cost of photovoltaic system and annual operation and maintenance costs of photovoltaic systems sum;

[0050] Annual investment cost of photovoltaic systems The formula is:

[0051] (11)

[0052] In equation (11), It refers to the unit installed capacity of photovoltaic power. For photovoltaic installed capacity, This is the coefficient for total investment. , , This refers to the investment coefficient for the controller and inverter in the photovoltaic module array. This refers to the investment coefficient for auxiliary facilities such as brackets and cables in a photovoltaic module array. This is the investment coefficient for other costs in the photovoltaic module array. and These are the annual photovoltaic discount rate and the photovoltaic service life;

[0053] Annual operation and maintenance costs of photovoltaic systems The formula is:

[0054] (12)

[0055] In equation (12), The operating and maintenance cost per unit of electricity for a photovoltaic system. This represents the total annual output electricity of the photovoltaic system.

[0056] In some embodiments, the energy storage state of charge constraint in step S22 is expressed as:

[0057] (13)

[0058] In equation (13), Indicates the state of charge of the energy storage. and These represent the upper and lower limits of the allowable state of charge of the energy storage, respectively;

[0059] The energy storage charge / discharge state constraints are expressed as follows: at any given moment, the following must be satisfied:

[0060] (14)

[0061] In equation (14), and Let be a variable that takes the value 0 or 1, representing the th , respectively. Heavenly The state of constant charging and discharging, when A value of 1 indicates that the device is in a charging state. A value of 1 indicates that the device is in a discharge state.

[0062] In some embodiments, in step S23, the objective function of the lower-level optimization model is expressed as:

[0063] (15)

[0064] In equation (15), This represents the user's daily operation and scheduling costs. Real-time electricity price for users, For user number Load during a specific time period. and Representing the first Discharge power and charging power of time-limited energy storage;

[0065] In step S24, the continuity constraint of the energy storage state of charge is expressed as:

[0066] (16)

[0067] in, for The remaining energy storage capacity at any given time. For charge / discharge time, for The remaining energy storage capacity at any given time. For energy storage charging and discharging efficiency, This refers to the rated capacity of the energy storage.

[0068] Energy storage power constraints mean that the charging and discharging power of the energy storage system cannot exceed the upper and lower limits of the system's allowable power, expressed as:

[0069] (17)

[0070] In equation (17), and These represent the maximum charging power and maximum discharging power of the energy storage system, respectively.

[0071] In some embodiments, step S3 specifically includes the following steps:

[0072] S31. First, we will analyze different scenarios for different users in different regions.

[0073] S32. For different user types, by inputting typical daily load curves and combining the two-layer optimization configuration model established in step S2, the photovoltaic storage capacity, energy storage power, and energy storage charging and discharging strategies are optimized.

[0074] S33. Use MATLAB for programming, call the yalmip toolbox, and use the built-in solver in the yalmip toolbox for optimization calculation. Use the BMINB solver on the outer layer to optimize capacity and power, and use the LINPROG solver on the inner layer to optimize charging and discharging strategies. Then compare and analyze the optimization results to find the break-even point that is beneficial to the user side.

[0075] Compared with the prior art, the beneficial effects of the present invention are:

[0076] The user-side photovoltaic-storage optimization configuration and its break-even analysis method provided by this invention can optimize the energy storage charging and discharging control strategy, enhance the potential of user-side resources to participate in demand response, and achieve break-even on the user side. Attached Figure Description

[0077] Figure 1 This is a flowchart illustrating the user-side optical storage optimization configuration and its break-even analysis method provided by the present invention. Detailed Implementation

[0078] To make the technical means, creative features, objectives and effects of this invention easier to understand, the following description, in conjunction with the accompanying drawings and specific embodiments, further explains how this invention is implemented.

[0079] Reference Figure 1 As shown, this invention provides a user-side optical storage optimization configuration and its break-even analysis method, including the following steps:

[0080] S1. Establish a user-side demand response model, and based on the established model, with the goal of maximizing user-side economic benefits, solve for the user's response volume at each time period.

[0081] S2. Establish a user-side photovoltaic and energy storage dual-layer optimization configuration model. In the dual-layer optimization configuration model, the upper-layer model takes the minimum annual comprehensive cost of the user as the objective function to optimize the installed photovoltaic capacity, energy storage capacity and power, while the lower-layer model takes the minimum daily operation and scheduling cost as the objective function to optimize the energy storage charging and discharging control strategy.

[0082] S3. Optimize the two-layer optimization configuration model established in step S2, establish the optimization process, compare and analyze the optimization results, and find the break-even point that is beneficial to the user side.

[0083] In one specific embodiment, step S1 specifically includes:

[0084] S1a. Establish a user response cost model, dividing the user load response amount into two parts: load reduction and load transfer. User response cost refers to the loss caused by users adjusting their own electricity consumption behavior. Users can participate in demand response by reducing or transferring load, or they may use both methods simultaneously.

[0085] In step S1a, the response cost expression is:

[0086] (1)

[0087] In equation (1), , Let be the response costs for load reduction and load transfer for the i-th user, respectively. and The constant coefficient of the user load reduction cost function. and The constant coefficients of the user load transfer cost function. This represents the load response of the i-th user in the t-th time period. The ratio of load reduction to load response is a constant between 0 and 1, and the ratio of load transfer to load response is... ;

[0088] Combining and simplifying equation (1), we obtain the expression for the total user response cost as follows:

[0089] (2)

[0090] In equation (2), For the total user response cost, , All are constant coefficients.

[0091] Because different users have different unit power consumption output or comfort sensitivity, the cost of responding to the same amount of electricity also varies. Therefore, the coefficients for different users are generally different. In practice, constant coefficients can be determined by using historical data of the i-th user's participation in demand response. and .

[0092] S1b. Establish a user response revenue model and combine it with the user response cost model from step S1a to obtain a net revenue model for user-side demand response. During user participation in demand response, users adjust their electricity consumption behavior to achieve demand response goals, reducing electricity costs while also receiving corresponding subsidies.

[0093] In step S1b, the user's response benefit is calculated using the following formula:

[0094] (3)

[0095] In equation (3), For the user's response benefits, Let t be the time-of-use electricity price for the t-th time period. Time-of-use pricing for the period during which load transfer is received. The subsidized electricity price for the response volume of the first user unit;

[0096] Based on equations (2) and (3), the net benefit brought to the user by the user-side demand response is obtained. for:

[0097] (4)

[0098] S1c. During the demand response process, users determine the optimal response volume for each time period based on the published demand response price and their own demand response costs in order to maximize their own benefits. Therefore, based on the established net benefit model of user-side demand response, with the goal of maximizing user-side economic benefits, the response volume of users in each time period is solved.

[0099] In step S1c, the user's response time for each time period is calculated according to the following formula:

[0100] (5)

[0101] In the formula, , These are the upper and lower limits of the response volume for the i-th user during time period t, respectively.

[0102] Further, in step S2, a user-side optical-storage two-layer optimization configuration model is established. The purpose of the two-layer optimization configuration model is to optimize the two-layer hierarchical structure of the system, specifically including the following steps:

[0103] S21. Establish the objective function of the upper-level optimization model, with the goal of minimizing the user's annual comprehensive cost. Optimize the installed photovoltaic capacity, energy storage capacity, and power. The user's comprehensive cost includes the electricity cost charged according to the electricity price, the basic electricity cost charged according to the maximum demand, and the annual investment cost and operation and maintenance cost of photovoltaic and energy storage.

[0104] S22. Establish constraints for the upper-level optimization model, including energy storage state of charge constraints and energy storage charge and discharge state constraints.

[0105] S23. Establish the objective function of the lower-level optimization model. The objective function of the lower-level optimization model is to minimize the daily operation scheduling cost.

[0106] S24. Establish constraints for the lower-level optimization model, including constraints on the continuity of energy storage state of charge and energy storage power.

[0107] In step S21, the objective function of the upper-level optimization model is expressed as:

[0108] (6)

[0109] In equation (6), For the entire year's expenses, For electricity consumption and electricity costs, For basic electricity costs, The annual investment cost and operation and maintenance expenses for energy storage This refers to the annual investment cost and operation and maintenance expenses of photovoltaic systems.

[0110] Electricity cost is calculated based on daily time-of-use pricing and actual electricity consumption. The formula is:

[0111] (7)

[0112] In equation (7), Representing the Heavenly User load power during the time period Representing the Heavenly Energy storage charging power during the time period, Representing the Heavenly Energy storage discharge power during the time period For the first Electricity prices during specific time periods.

[0113] For industrial and commercial users, basic electricity charges can be calculated based on transformer capacity or maximum demand. After applying for a dedicated distribution transformer, industrial and commercial users are required to pay a basic electricity charge monthly based on their maximum demand, regardless of whether they actually use electricity. Adding an energy storage system reduces the required distribution transformer capacity, thus reducing the basic electricity charge. The basic electricity charge is calculated based on the maximum demand. The formula is:

[0114] (8)

[0115] In equation (8), The unit demand electricity cost represents the user's cost. Represents the actual maximum demand value. This represents 1.05 times the approved maximum demand value.

[0116] Annual investment cost and operation and maintenance expenses for energy storage Annual investment cost of energy storage system and annual operation and maintenance costs of energy storage systems sum.

[0117] Energy storage systems mainly consist of battery packs, power control systems (PCS), control devices, and transformers. The costs incurred during the installation and deployment of these devices constitute the investment cost of energy storage, i.e., the funds for purchasing and installing equipment such as energy storage batteries, energy storage elements, and the PCS and monitoring systems required for grid connection. The PCS and monitoring systems determine the output and input power of the energy storage system; therefore, a portion of the cost is related to its input and output power, which can be called the power investment cost. The capacity of the energy storage batteries determines another portion of the overall cost, which is called the energy investment cost.

[0118] Therefore, the annual investment cost of energy storage systems The formula is:

[0119] (9)

[0120] In equation (9), It is the cost per unit capacity of energy storage. For the rated capacity of energy storage, and These are the annual discount rate for energy storage and the lifespan of energy storage.

[0121] The operation and maintenance cost of an energy storage system includes the operating costs of operating the system and the maintenance costs of ensuring the energy storage power station is in good standby condition. It is primarily determined by the size of the energy storage battery. Therefore, the annual maintenance cost of energy storage operation... The formula is:

[0122] (10)

[0123] In equation (10), The annual operation and maintenance cost per unit power of the energy storage system. This represents the total power of the energy storage system.

[0124] Annual investment cost and operation and maintenance expenses of photovoltaic power Annual investment cost of photovoltaic system and annual operation and maintenance costs of photovoltaic systems sum.

[0125] The investment costs of a photovoltaic (PV) system mainly include installation foundations and supports, solar panels, PV inverters, AC / DC cables, and distribution cabinets. The annual investment cost of a PV system... The formula is:

[0126] (11)

[0127] In equation (11), It refers to the unit installed capacity of photovoltaic power. For photovoltaic installed capacity, This is the coefficient for total investment. , , This refers to the investment coefficient for the controller and inverter in the photovoltaic module array. This refers to the investment coefficient for auxiliary facilities such as brackets and cables in a photovoltaic module array. This is the investment coefficient for other costs in the photovoltaic module array. and These are the annual photovoltaic discount rate and the photovoltaic service life;

[0128] Photovoltaic power generation requires safety monitoring and maintenance during daily operation, and maintenance costs are directly proportional to the system's output power. Annual operation and maintenance costs for photovoltaic systems. The formula is:

[0129] (12)

[0130] In equation (12), The operating and maintenance cost per unit of electricity for a photovoltaic system. This represents the total annual output electricity of the photovoltaic system.

[0131] In step S22, the energy storage state of charge (SOC) constraint is expressed as follows:

[0132] (13)

[0133] In equation (13), Indicates the state of charge of the energy storage. and These represent the upper and lower limits of the allowable state of charge for energy storage, respectively. To avoid overcharging and over-discharging the battery and affecting its lifespan, the state of charge is subject to certain limitations. The battery cannot be completely discharged or fully charged.

[0134] The energy storage charge / discharge state constraints are expressed as follows: at any given moment, the following must be satisfied:

[0135] (14)

[0136] In equation (14), and Let be a variable that takes the value 0 or 1, representing the th , respectively. Heavenly The state of constant charging and discharging, when A value of 1 indicates that the device is in a charging state. A value of 1 indicates that the device is in a discharge state.

[0137] In step S23, the objective function of the lower-level optimization model is expressed as:

[0138] (15)

[0139] In equation (15), This represents the user's daily operation and scheduling costs. Real-time electricity price for users, For user number Load during a specific time period. and Representing the first Discharge power and charging power of time-limited energy storage.

[0140] In step S24, the continuity constraint of the energy storage state of charge is expressed as:

[0141] (16)

[0142] in, for The remaining energy storage capacity at any given time. For charge / discharge time, for The remaining energy storage capacity at any given time. For energy storage charging and discharging efficiency, This refers to the rated capacity of the energy storage.

[0143] Energy storage power constraints mean that the charging and discharging power of the energy storage system cannot exceed the upper and lower limits of the system's allowable power, expressed as:

[0144] (17)

[0145] In equation (17), and These represent the maximum charging power and maximum discharging power of the energy storage system, respectively.

[0146] Furthermore, step S3 specifically includes the following steps:

[0147] S31. First, we will analyze different scenarios for different users in different regions.

[0148] S32. For different user types, by inputting typical daily load curves and combining the two-layer optimization configuration model established in step S2, the photovoltaic storage capacity, energy storage power, and energy storage charging and discharging strategies are optimized.

[0149] S33. Use MATLAB for programming, call the yalmip toolbox, and use the built-in solver in the yalmip toolbox for optimization calculation. Use the BMINB solver on the outer layer to optimize capacity and power, and use the LINPROG solver on the inner layer to optimize charging and discharging strategies. Then compare and analyze the optimization results to find the break-even point that is beneficial to the user side.

[0150] Yalmip is a free optimization solver developed by Lofbeg. Its most unique feature is its integration of many external optimization solvers, forming a unified modeling and solving language, thus reducing the learning curve for users. Its built-in solvers include BMINB, LINPROG, CUTSDP, and MOMENT. BMINB can be used to solve global nonlinear nonconvex integer programming problems, while CUTSDP can be used to solve mixed integer second-order cone problems. It can also call external solvers such as CPLEX and FMINCON to solve linear, nonlinear, quadratic programming, and mixed-integer quadratic programming problems.

[0151] In summary, the user-side photovoltaic-storage optimization configuration and its break-even analysis method provided by this invention can optimize the energy storage charging and discharging control strategy, enhance the potential of user-side resources to participate in demand response, and achieve break-even on the user side.

[0152] This invention establishes a demand response optimization model. In the upper-level planning stage, the objective function is to minimize the user's annual comprehensive cost, optimizing the planned capacity of the equipment to be installed. In the lower-level operation stage, the objective function is to minimize the daily operation scheduling cost, optimizing the user-side photovoltaic-storage dual-layer optimization configuration model for energy storage charging and discharging control strategies. This method is more conducive to finding the break-even point on the user side, reducing the configured energy storage capacity, and lowering the cost of the energy storage system.

[0153] This invention analyzes the theoretical basis and configuration principles of photovoltaic and energy storage configuration, and has certain reference value for microgrid investors in the context of electricity market to make decisions on the balance between the economics of energy storage configuration and the promotion of photovoltaic consumption.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A user-side optical storage optimization configuration and its break-even analysis method, characterized in that, Includes the following steps: S1. Establish a user-side demand response model, and based on the established model, with the goal of maximizing user-side economic benefits, solve for the user's response volume at each time period. S2. Establish a user-side photovoltaic and energy storage dual-layer optimization configuration model. In the dual-layer optimization configuration model, the upper-layer model takes the minimum annual comprehensive cost of the user as the objective function to optimize the installed photovoltaic capacity, energy storage capacity and power, while the lower-layer model takes the minimum daily operation and scheduling cost as the objective function to optimize the energy storage charging and discharging control strategy. S3. Optimize the two-layer optimization configuration model established in step S2, establish the optimization process, compare and analyze the optimization results, and find the break-even point that is beneficial to the user side. Step S1 specifically includes: S1a. Establish a user response cost model, dividing the user load response amount into two parts: load reduction amount and load transfer amount; S1b. Establish a user response revenue model and combine it with the user response cost model in step S1a to obtain a net revenue model for user-side demand response. S1c. During the demand response process, users determine the optimal response volume for each time period based on the published demand response price and their own demand response costs in order to maximize their own benefits. Therefore, based on the established net benefit model of user-side demand response, with the goal of maximizing user-side economic benefits, the response volume of users in each time period is solved. In step S1a, the response cost expression is: （1） In equation (1), , Let be the response costs for load reduction and load transfer for the i-th user, respectively. and The constant coefficient of the user load reduction cost function. and The constant coefficients of the user load transfer cost function. This represents the load response of the i-th user in the t-th time period. The ratio of load reduction to load response is a constant between 0 and 1, and the ratio of load transfer to load response is... ; Combining and simplifying equation (1), we obtain the expression for the total user response cost as follows: （2） In equation (2), For the total user response cost, , All are constant coefficients; In step S1b, the user's response benefit is calculated using the following formula: （3） In equation (3), For user response benefits, Let be the time-of-use electricity price for the t-th time period. Time-of-use pricing for the period during which load transfer is received. The subsidized electricity price for the response volume of the first user unit; Based on equations (2) and (3), the net benefit brought to the user by the user-side demand response is obtained. for: （4） In step S1c, the user's response time for each time period is calculated according to the following formula: （5） In the formula, , These are the upper and lower limits of the response volume for the i-th user during time period t, respectively.

2. The user-side optical storage optimization configuration and its break-even analysis method according to claim 1, characterized in that, Step S2 specifically includes the following steps: S21. Establish the objective function of the upper-level optimization model, with the goal of minimizing the user's annual comprehensive cost. Optimize the installed photovoltaic capacity, energy storage capacity, and power. The user's comprehensive cost includes the electricity cost charged according to the electricity price, the basic electricity cost charged according to the maximum demand, and the annual investment cost and operation and maintenance cost of photovoltaic and energy storage. S22. Establish constraints for the upper-level optimization model, including energy storage state of charge constraints and energy storage charge and discharge state constraints. S23. Establish the objective function of the lower-level optimization model. The objective function of the lower-level optimization model is to minimize the daily operation scheduling cost. S24. Establish constraints for the lower-level optimization model, including constraints on the continuity of energy storage state of charge and energy storage power.

3. The user-side optical storage optimization configuration and its break-even analysis method according to claim 2, characterized in that, In step S21, the objective function of the upper-level optimization model is expressed as: （6） In equation (6), For the entire year's expenses, For electricity consumption and electricity costs, For basic electricity costs, The annual investment cost and operation and maintenance expenses for energy storage The annual investment cost and operation and maintenance expenses for photovoltaic power generation; Electricity cost The formula is: （7） In equation (7), Representing the Heavenly User load power during the time period Representing the Heavenly Energy storage charging power during the time period, Representing the Heavenly Energy storage discharge power during the time period For the first Electricity prices during specific time periods; Basic electricity cost The formula is: （8） In equation (8), The unit demand electricity cost represents the user's electricity bill. Represents the actual maximum demand value. This represents 1.05 times the approved maximum demand value; Annual investment cost and operation and maintenance expenses for energy storage Annual investment cost of energy storage system and annual operation and maintenance costs of energy storage systems sum; Annual investment cost of energy storage system The formula is: （9） In equation (9), It is the cost per unit capacity of energy storage. For the rated capacity of energy storage, and These are the annual discount rate for energy storage and the service life of energy storage; Annual maintenance cost of energy storage operation The formula is: （10） In equation (10), The annual operation and maintenance cost per unit power of the energy storage system. This represents the total power of the energy storage system. Annual investment cost and operation and maintenance expenses of photovoltaic power Annual investment cost of photovoltaic system and annual operation and maintenance costs of photovoltaic systems sum; Annual investment cost of photovoltaic systems The formula is: （11） In equation (11), It refers to the unit installed capacity of photovoltaic power. For photovoltaic installed capacity, This is the coefficient for total investment. , , This refers to the investment coefficient for the controller and inverter in the photovoltaic module array. This refers to the investment coefficient for auxiliary facilities such as brackets and cables in a photovoltaic module array. This is the investment coefficient for other costs in the photovoltaic module array. and These are the annual photovoltaic discount rate and the photovoltaic service life; Annual operation and maintenance costs of photovoltaic systems The formula is: （12） In equation (12), The operating and maintenance cost per unit of electricity for a photovoltaic system. This represents the total annual output electricity of the photovoltaic system.

4. The user-side optical storage optimization configuration and its break-even analysis method according to claim 3, characterized in that, In step S22, the energy storage state of charge constraint is expressed as: （13） In equation (13), Indicates the state of charge of the energy storage. and These represent the upper and lower limits of the allowable state of charge of the energy storage, respectively; The energy storage charge / discharge state constraints are expressed as follows: at any given moment, the following must be satisfied: （14） In equation (14), and Let be a variable that takes the value 0 or 1, representing the th , respectively. Heavenly The state of constant charging and discharging, when A value of 1 indicates that the device is in a charging state. A value of 1 indicates that the device is in a discharge state.

5. The user-side optical storage optimization configuration and its break-even analysis method according to claim 4, characterized in that, In step S23, the objective function of the lower-level optimization model is expressed as: （15） In equation (15), This represents the user's daily operation and scheduling costs. Real-time electricity price for users, For user number Load during a specific time period and Representing the first Discharge power and charging power of time-limited energy storage; In step S24, the continuity constraint of the energy storage state of charge is expressed as: （16） in, for The remaining energy storage capacity at any given time. For charge / discharge time, for The remaining energy storage capacity at any given time. For energy storage charging and discharging efficiency, This refers to the rated capacity of the energy storage. Energy storage power constraints mean that the charging and discharging power of the energy storage system cannot exceed the upper and lower limits of the system's allowable power, expressed as: （17） In equation (17), and These represent the maximum charging power and maximum discharging power of the energy storage system, respectively.

6. The user-side optical storage optimization configuration and its break-even analysis method according to claim 1, characterized in that, Step S3 specifically includes the following steps: S31. First, we will analyze different scenarios for different users in different regions. S32. For different user types, by inputting typical daily load curves and combining the two-layer optimization configuration model established in step S2, the photovoltaic storage capacity, energy storage power, and energy storage charging and discharging strategies are optimized. S33. Use MATLAB for programming, call the yalmip toolbox, and use the built-in solver in the yalmip toolbox for optimization calculation. Use the BMINB solver on the outer layer to optimize capacity and power, and use the LINPROG solver on the inner layer to optimize charging and discharging strategies. Then compare and analyze the optimization results to find the break-even point that is beneficial to the user side.

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