Energy storage configuration method and device of new energy system, terminal equipment and storage medium

Abstract

The invention discloses an energy storage configuration method and device of a new energy system, terminal equipment and a storage medium, and belongs to the technical field of energy storage configuration. According to the operation data, constructing a shared energy storage double-layer optimization configuration model; wherein the shared energy storage double-layer optimization configuration model comprises a shared energy storage upper-layer optimization model and a shared energy storage lower-layer optimization model; solving the shared energy storage double-layer optimization configuration model, and generating corresponding configuration data when the operation cost of the new energy system in the life cycle is minimum and the daily operation cost is minimum; and performing energy storage configuration on the new energy system according to the configuration data. Therefore, by implementing the invention, the problem that the cost is too high after the new energy system is configured in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage configuration technology, and in particular to an energy storage configuration method, device, terminal equipment and storage medium for a new energy system. Background Art

[0002] Shared energy storage, as an emerging configuration model, achieves efficient resource utilization through the joint investment and use of energy storage equipment by multiple users, taking into account the load characteristics of each user. While alleviating the volatility of renewable energy power generation and improving equipment utilization, it significantly reduces the cost of users configuring energy storage individually and optimizes the operation of high-proportion renewable energy systems. However, existing shared energy storage configuration technologies mainly focus on initial investment costs and have not yet considered the costs throughout the entire life cycle of equipment operation, maintenance, and scrapping and recycling. As a result, energy storage systems have the problem of high costs and low benefits in actual applications. It is difficult to fully meet the higher requirements of high-proportion renewable energy systems for economic analysis and optimized configuration, resulting in excessively high costs for new energy systems after configuration.

[0003] Therefore, there is an urgent need for a new energy system energy storage configuration strategy to solve the problem of excessively high costs after configuration of the new energy system. Summary of the Invention

[0004] Embodiments of the present invention provide a method, apparatus, terminal device, and storage medium for configuring energy storage in a new energy system to solve the problem of excessively high costs in configuring the new energy system.

[0005] To solve the above problem, an embodiment of the present invention provides an energy storage configuration method for a new energy system, comprising:

[0006] Obtain operating data of new energy systems;

[0007] Based on the operating data, a shared energy storage two-layer optimization configuration model is constructed; wherein the shared energy storage two-layer optimization configuration model includes: a shared energy storage upper-layer optimization model and a shared energy storage lower-layer optimization model, the objective function of the shared energy storage upper-layer optimization model is to minimize the operating cost of the new energy system during its life cycle, and the objective function of the shared energy storage lower-layer optimization model is to minimize the daily operating cost of the new energy system;

[0008] Solving the shared energy storage two-layer optimization configuration model to generate configuration data corresponding to the minimum operating cost of the new energy system during its life cycle and the minimum daily operating cost;

[0009] Perform energy storage configuration on the new energy system according to the configuration data.

[0010] As an improvement to the above solution, solving the shared energy storage two-layer optimization configuration model includes:

[0011] Through KKT condition processing operations, the shared energy storage lower-level optimization model is converted into KKT constraint conditions;

[0012] Under the conditions of satisfying KKT constraints and the constraints of the shared energy storage upper-level optimization model, the shared energy storage upper-level optimization model is solved.

[0013] As an improvement to the above solution, the objective function of the shared energy storage upper layer optimization model is specifically:

[0014]

[0015]

[0016] Where C1 is the objective function value of the upper optimization model of shared energy storage, m represents the typical day type number; M represents the number of typical day types; T m represents the number of typical days, represents the average daily construction cost of a shared energy storage power station; and They represent the cost of electricity purchased from users and the revenue from electricity sales by the shared energy storage power station; Represents the energy storage service fee paid by users to the shared energy storage power station; represents the average daily residual value of the shared energy storage power station; k p 、k e represents the unit power cost and capacity cost of shared energy storage; Represent the rated power and shared energy storage capacity of the shared energy storage, respectively; D represents the expected number of days of operation of the shared energy storage power station; N represents the number of users using the shared energy storage power station; T represents the scheduling period; σ(t) and ζ(t) represent the electricity price purchased from users and the electricity price sold to users by the shared energy storage power station during time period t, respectively; τ(t) represents the unit price of the service fee paid by users to the energy storage power station during time period t; They represent the power purchased and sold by the energy storage power station to the i-th user during the t period of each typical day; k r Represents the residual value coefficient of the shared energy storage power station.

[0017] As an improvement to the above solution, the constraints of the shared energy storage upper layer optimization model meet the following conditions:

[0018]

[0019] U charge (t)+U dis (t)≤1

[0020] Where SOC(t) represents the power consumption of the shared energy storage power station at time t; represents the upper limit of the storage capacity of the shared energy storage power station; η charge ,η dis They represent the charging efficiency and discharging efficiency of the shared energy storage power station respectively; Respectively represent the charging power and discharging power of the shared energy storage power station at time t; ω1 and ω2 represent the minimum and maximum state of charge of the energy storage, respectively; ω0 represents the state of charge at the initial moment of the energy storage; U charge (t), U dis (t) is a 0-1 variable, which represents the charging and discharging status of the energy storage power station in the tth period.

[0021] As an improvement to the above solution, the objective function of the shared energy storage lower layer optimization model is specifically:

[0022]

[0023] Where C2 is the objective function value of the shared energy storage lower layer optimization model, C grid,m represents the cost of purchasing electricity from the grid on a typical day; C g,m P represents the gas purchase cost for micro-turbine operation; grid,m,i (t) represents the power purchased by the i-th user from the grid at time t; ε(t) represents the electricity price during time period t; P g,m,i (t) represents the output power of the micro gas turbine of the i-th user at time t; η represents the power generation efficiency of the micro gas turbine; L represents the calorific value of natural gas; δ(t) represents the price per unit volume of natural gas during time period t.

[0024] As an improvement to the above solution, the constraints of the shared energy storage lower-layer optimization model are specifically as follows:

[0025]

[0026] P g,i,min ≤P g,m,i (t)≤P g,i,max

[0027] -r g,i,down ≤P g,m,i (t)-P g,m,i (t-1)≤r g,i,up

[0028]

[0029] 0≤P grid,m,i (t)≤P grid,max

[0030]

[0031] Where, P wt,m,i(t) represents the wind power used by the i-th user in period t; P pv,m,i (t) represents the photovoltaic power used by the i-th user in period t; P Load,m,i (t) represents the load demand of the i-th user in period t; P g,m,i (t) represents the output power of the micro gas turbine of the i-th user at time t; P g,i,min and P g,i,max Respectively represent the lower and upper limits of the gas turbine output; r g,i,up and r g,i,down They represent the ramp-up rate and ramp-down rate of gas turbine output respectively; P are the upper limit of power purchase and power sale for each user by the shared energy storage power station; grid,max is the upper limit of the power purchase of user i; is a 0-1 variable, representing the electricity purchasing and selling status of user i from the shared energy storage power station in period t; P grid,m,i (t) represents the power purchased by the i-th user from the grid at time t; P grid,max Indicates the upper limit of purchased power.

[0032] As an improvement to the above solution, the KKT conditional processing operation includes:

[0033] Convert the objective function of the shared energy storage lower-level optimization model into a Lagrangian function;

[0034] Calculating partial derivatives based on the power purchased from the user by the energy storage station, the power sold from the energy storage station to the user, and the power purchased from the power grid by the user in the Lagrangian function;

[0035] When the partial derivative is 0, the gradient condition is obtained;

[0036] According to several dual variables in the Lagrangian function, combined with the constraint conditions of the shared energy storage lower layer optimization model corresponding to each dual variable, a complementary relaxation condition is obtained;

[0037] A KKT constraint condition is obtained according to the gradient condition and the complementary relaxation condition.

[0038] Accordingly, an embodiment of the present invention further provides an energy storage configuration device for a new energy system, comprising: a data acquisition module, a model building module, a data solving module, and an energy storage configuration module;

[0039] The data acquisition module is used to acquire the operating data of the new energy system;

[0040] The model construction module is configured to construct a shared energy storage dual-layer optimization configuration model based on the operating data; wherein the shared energy storage dual-layer optimization configuration model includes: a shared energy storage upper-layer optimization model and a shared energy storage lower-layer optimization model, wherein the objective function of the shared energy storage upper-layer optimization model is to minimize the operating cost of the new energy system over its life cycle, and the objective function of the shared energy storage lower-layer optimization model is to minimize the daily operating cost of the new energy system;

[0041] The data solving module is used to solve the shared energy storage two-layer optimization configuration model to generate configuration data corresponding to the minimum operating cost of the new energy system during its life cycle and the minimum daily operating cost;

[0042] The energy storage configuration module is used to perform energy storage configuration on the new energy system according to the configuration data.

[0043] Correspondingly, an embodiment of the present invention also provides a computer terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a new energy system energy storage configuration method as described in the present invention.

[0044] Correspondingly, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a new energy system energy storage configuration method as described in the present invention.

[0045] As can be seen from the above, the present invention has the following beneficial effects:

[0046] The present invention provides a method for configuring energy storage for a new energy system. This method constructs a two-layer shared energy storage optimization configuration model based on acquired operating data. The method solves an upper-layer shared energy storage optimization model whose objective function is to minimize the operating cost of the new energy system over its life cycle, and a lower-layer shared energy storage optimization model whose objective function is to minimize the daily operating cost of the new energy system. Finally, the method generates configuration data corresponding to the minimum operating cost of the new energy system over its life cycle and the minimum daily operating cost, thereby configuring energy storage for the new energy system based on the configuration data. The present invention automates the configuration of the new energy system and reduces the operating cost of the new energy system after configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 1 is a flow chart of a method for configuring energy storage in a new energy system according to an embodiment of the present invention;

[0048] Figure 2This is a structural diagram of an energy storage configuration device for a new energy system provided by an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the structure of a terminal device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0051] Example 1

[0052] See also Figure 1 In order to solve the problem of high cost of new energy system after configuration, Figure 1 FIG. 1 is a flow chart of a method for configuring energy storage in a new energy system according to an embodiment of the present invention. Figure 1 As shown, this embodiment includes steps 101 to 104, and each step is specifically as follows:

[0053] Step 101: Acquire operating data of the new energy system.

[0054] In this embodiment, the operating data includes: load data of each microgrid in the new energy system, new energy output data, gas turbine parameters, the power purchase price of the microgrid from the distribution network, the unit power cost and capacity cost of the shared energy storage power station, the expected number of operating days, the unit power maintenance cost, as well as the number of users of the shared energy storage power station, the scheduling cycle, the purchase and sale electricity price of the shared energy storage power station, the unit price of the service fee paid by users to the energy storage power station, and the recovery residual value coefficient of the shared energy storage power station.

[0055] Step 102: Construct a shared energy storage two-layer optimization configuration model based on the operating data; wherein the shared energy storage two-layer optimization configuration model includes: a shared energy storage upper-layer optimization model and a shared energy storage lower-layer optimization model, the objective function of the shared energy storage upper-layer optimization model is to minimize the operating cost of the new energy system during its life cycle, and the objective function of the shared energy storage lower-layer optimization model is to minimize the daily operating cost of the new energy system.

[0056] In this embodiment, the objective function of the shared energy storage upper layer optimization model is specifically:

[0057]

[0058]

[0059] Where C1 is the objective function value of the upper optimization model of shared energy storage, m represents the typical day type number; M represents the number of typical day types; T m represents the number of typical days, represents the average daily construction cost of a shared energy storage power station; and They represent the cost of electricity purchased from users and the revenue from electricity sales by the shared energy storage power station; Represents the energy storage service fee paid by users to the shared energy storage power station; represents the average daily residual value of the shared energy storage power station; k p 、k e represents the unit power cost and capacity cost of shared energy storage; Represent the rated power and shared energy storage capacity of the shared energy storage, respectively; D represents the expected number of days of operation of the shared energy storage power station; N represents the number of users using the shared energy storage power station; T represents the scheduling period; σ(t) and ζ(t) represent the electricity price purchased from users and the electricity price sold to users by the shared energy storage power station during time period t, respectively; τ(t) represents the unit price of the service fee paid by users to the energy storage power station during time period t; They represent the power purchased and sold by the energy storage power station to the i-th user during the t period of each typical day; k r Represents the residual value coefficient of the shared energy storage power station.

[0060] In this embodiment, the constraints of the shared energy storage upper layer optimization model meet the following conditions:

[0061]

[0062] U charge (t)+U dis (t)≤1

[0063] Where SOC(t) represents the power consumption of the shared energy storage power station at time t; represents the upper limit of the storage capacity of the shared energy storage power station; η charge ,η dis They represent the charging efficiency and discharging efficiency of the shared energy storage power station respectively; Respectively represent the charging power and discharging power of the shared energy storage power station at time t; ω1 and ω2 represent the minimum and maximum state of charge of the energy storage, respectively; ω0 represents the state of charge at the initial moment of the energy storage; U charge (t), U dis (t) is a 0-1 variable, which represents the charging and discharging status of the energy storage power station in the tth period.

[0064] In this embodiment, the objective function of the shared energy storage lower layer optimization model is specifically:

[0065]

[0066] Where C2 is the objective function value of the shared energy storage lower layer optimization model, C grid,m represents the cost of purchasing electricity from the grid on a typical day; C g,m P represents the gas purchase cost for micro-turbine operation; grid,m,i (t) represents the power purchased by the i-th user from the grid at time t; ε(t) represents the electricity price during time period t; P g,m,i (t) represents the output power of the micro gas turbine of the i-th user at time t; η represents the power generation efficiency of the micro gas turbine; L represents the calorific value of natural gas; δ(t) represents the price per unit volume of natural gas during time period t.

[0067] In this embodiment, the constraints of the shared energy storage lower layer optimization model are specifically:

[0068]

[0069] P g,i,min ≤P g,m,i (t)≤P g,i,max

[0070] -r g,i,down ≤P g,m,i (t)-P g,m,i (t-1)≤r g,i,up

[0071]

[0072] 0≤P grid,m,i (t)≤P grid,max

[0073]

[0074] Where, P wt,m,i (t) represents the wind power used by the i-th user in period t; P pv,m,i (t) represents the photovoltaic power used by the i-th user in period t; P Load,m,i (t) represents the load demand of the i-th user in period t; P g,m,i (t) represents the output power of the micro gas turbine of the i-th user at time t; P g,i,min and P g,i,max Respectively represent the lower and upper limits of the gas turbine output; r g,i,up and r g,i,down They represent the ramp-up rate and ramp-down rate of gas turbine output respectively; P are the upper limit of power purchase and power sale for each user by the shared energy storage power station; grid,max is the upper limit of the power purchase of user i; is a 0-1 variable, representing the electricity purchasing and selling status of user i from the shared energy storage power station in period t; P grid,m,i (t) represents the power purchased by the i-th user from the grid at time t; P grid,max Indicates the upper limit of purchased power.

[0075] Step 103: Solve the shared energy storage two-layer optimization configuration model to generate configuration data corresponding to the minimum operating cost of the new energy system during its life cycle and the minimum daily operating cost.

[0076] In this embodiment, solving the shared energy storage two-layer optimization configuration model includes:

[0077] Through KKT condition processing operations, the shared energy storage lower-level optimization model is converted into KKT constraint conditions;

[0078] Under the conditions of satisfying KKT constraints and the constraints of the shared energy storage upper-level optimization model, the shared energy storage upper-level optimization model is solved.

[0079] In this embodiment, the KKT conditional processing operation includes:

[0080] Convert the objective function of the shared energy storage lower-level optimization model into a Lagrangian function;

[0081] Calculating partial derivatives based on the power purchased from the user by the energy storage station, the power sold from the energy storage station to the user, and the power purchased from the power grid by the user in the Lagrangian function;

[0082] When the partial derivative is 0, the gradient condition is obtained;

[0083] According to several dual variables in the Lagrangian function, combined with the constraint conditions of the shared energy storage lower layer optimization model corresponding to each dual variable, a complementary relaxation condition is obtained;

[0084] A KKT constraint condition is obtained according to the gradient condition and the complementary relaxation condition.

[0085] In a specific embodiment, the Lagrangian function is specifically:

[0086]

[0087] Where λ(i,t) represents the dual variable of the power balance constraint of the i-th user in the t-th period; μ b (i,t) represents the dual variable of the energy storage charging power constraint of the i-th user in the t-th period; μ s(i,t) represents the dual variable of the energy storage discharge power constraint of the i-th user in the t-th period; ν(i,t) represents the dual variable of the power grid purchase power constraint of the i-th user in the t-th period.

[0088] The gradient condition requires that the Lagrangian function (i.e. the power purchased by the energy storage station of the present invention from the user), (i.e., the power sold by the energy storage station to users according to the present invention), P grid,m,i The partial derivative of (t) (i.e., the power purchased by the user from the grid as described in the present invention) is equal to zero. The gradient condition is specifically:

[0089]

[0090] The complementary slack condition requires that the product of the dual variable of the constraint of the underlying optimization problem and the corresponding constraint is zero. The complementary slack condition is specifically:

[0091]

[0092] By converting the KKT conditions into upper-level constraints, the constraints of the shared energy storage lower-level optimization model can be embedded into the constraints of the shared energy storage lower-level optimization model. This allows the constraints of the shared energy storage lower-level optimization model to be explicitly considered when solving the objective function of the shared energy storage lower-level optimization model, resulting in more accurate optimization results.

[0093] For better explanation, the KKT constraints are as follows:

[0094]

[0095] In a specific embodiment, simulation is performed on a MATLAB platform, and a mature commercial solver or branch-and-bound algorithm is used to obtain an optimal configuration scheme for a shared energy storage power station.

[0096] Step 104: Perform energy storage configuration on the new energy system according to the configuration data.

[0097] In this embodiment, the configuration data includes the daily operating power, capacity data and rated power of the shared energy storage device, as well as the daily operating power of other devices. The energy storage configuration of the new energy system is performed based on the values of the capacity data and rated power at each moment.

[0098] In a specific embodiment, a new energy system consisting of three microgrids is adopted, each microgrid includes a new energy unit and a gas unit, and the microgrid group shares a shared energy storage power station. This embodiment is simulated on the MATLAB platform and solved using the commercial solver GUROBI to obtain the optimal configuration scheme for the shared energy storage power station. After model solving, the shared energy storage capacity of the system configuration is 2106.242kWh, the rated power is 424.288kW, the investment and construction operation and maintenance costs are 29.5178 million yuan, and the average daily income is 1799.011 yuan. Under the traditional solution of each microgrid configuring energy storage separately, the total capacity of the energy storage device is 2654.487kWh, the maximum total charge and discharge power is 572.288kW, the investment and construction operation and maintenance costs are 37.5767 million yuan, and the average daily income is 1474.207 yuan. By comparing the two methods, it can be seen that the method proposed in this patent reduces the total configuration capacity of the energy storage power station by 20.654%, the total configuration power by 25.861%, the investment, construction and operation and maintenance costs by 21.447%, and the average daily income by 22.032%, which illustrates the effectiveness of the method involved in the present invention.

[0099] See also Figure 2 , Figure 2 2 is a schematic structural diagram of an energy storage configuration device for a new energy system provided by an embodiment of the present invention, comprising: a data acquisition module 201, a model building module 202, a data solution module 203, and an energy storage configuration module 204;

[0100] The data acquisition module is used to acquire the operating data of the new energy system;

[0101] The model construction module is configured to construct a shared energy storage dual-layer optimization configuration model based on the operating data; wherein the shared energy storage dual-layer optimization configuration model includes: a shared energy storage upper-layer optimization model and a shared energy storage lower-layer optimization model, wherein the objective function of the shared energy storage upper-layer optimization model is to minimize the operating cost of the new energy system over its life cycle, and the objective function of the shared energy storage lower-layer optimization model is to minimize the daily operating cost of the new energy system;

[0102] The data solving module is used to solve the shared energy storage two-layer optimization configuration model to generate configuration data corresponding to the minimum operating cost of the new energy system during its life cycle and the minimum daily operating cost;

[0103] The energy storage configuration module is used to perform energy storage configuration on the new energy system according to the configuration data.

[0104] It can be understood that the above-mentioned system embodiment corresponds to the method embodiment of the present invention, which can implement the energy storage configuration method of the new energy system provided by any of the above-mentioned method embodiments of the present invention.

[0105] This embodiment constructs a two-tiered shared energy storage optimization configuration model based on acquired operational data. It then solves an upper-tier shared energy storage optimization model whose objective function is to minimize the operating cost of the new energy system over its lifecycle, and a lower-tier shared energy storage optimization model whose objective function is to minimize the daily operating cost of the new energy system. Finally, it generates configuration data corresponding to the minimum operating cost of the new energy system over its lifecycle and the minimum daily operating cost, thereby configuring energy storage for the new energy system based on the configuration data. This invention automates the configuration of the new energy system and reduces the operating cost of the new energy system after configuration.

[0106] Example 2

[0107] See also Figure 3 , Figure 3 It is a schematic diagram of the terminal device structure provided by one embodiment of the present invention.

[0108] A terminal device of this embodiment includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, the steps of the energy storage configuration method of each new energy system described above in the embodiment are implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are realized, for example: Figure 2 All modules of the energy storage configuration device of the new energy system shown.

[0109] In addition, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the energy storage configuration method of the new energy system as described in any of the above embodiments.

[0110] Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0111] The processor 301 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor 301 is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0112] The memory 302 can be used to store the computer programs and / or modules. The processor 301 implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0113] Wherein, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0114] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0115] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for configuring energy storage in a new energy system, characterized in that: include: Obtain operating data of new energy systems; Based on the operating data, a shared energy storage two-layer optimization configuration model is constructed; wherein the shared energy storage two-layer optimization configuration model includes: a shared energy storage upper-layer optimization model and a shared energy storage lower-layer optimization model, the objective function of the shared energy storage upper-layer optimization model is to minimize the operating cost of the new energy system during its life cycle, and the objective function of the shared energy storage lower-layer optimization model is to minimize the daily operating cost of the new energy system; Solving the shared energy storage two-layer optimization configuration model to generate configuration data corresponding to the minimum operating cost of the new energy system during its life cycle and the minimum daily operating cost; According to the configuration data, energy storage configuration is performed on the new energy system, including shared energy storage capacity and rated power.

2. The energy storage configuration method of the new energy system according to claim 1, characterized in that: Solving the shared energy storage two-layer optimization configuration model includes: Through KKT condition processing operations, the shared energy storage lower-level optimization model is converted into KKT constraint conditions; Under the conditions of satisfying KKT constraints and the constraints of the shared energy storage upper-level optimization model, the shared energy storage upper-level optimization model is solved.

3. The energy storage configuration method of the new energy system according to claim 2, characterized in that: The objective function of the shared energy storage upper layer optimization model is specifically: Where C1 is the objective function value of the shared energy storage upper optimization model, m represents the typical day type number; M represents the number of typical day types; T m represents the number of typical days, represents the average daily construction cost of a shared energy storage power station; and They represent the cost of electricity purchased from users and the revenue from electricity sales by the shared energy storage power station; Represents the energy storage service fee paid by users to the shared energy storage power station; represents the average daily residual value of the shared energy storage power station; k p 、k e represents the unit power cost and capacity cost of shared energy storage; Represent the rated power and shared energy storage capacity of the shared energy storage, respectively; D represents the expected number of days of operation of the shared energy storage power station; N represents the number of users using the shared energy storage power station; T represents the scheduling period; σ(t) and ζ(t) represent the electricity price purchased from users and the electricity price sold to users by the shared energy storage power station during time period t, respectively; τ(t) represents the unit price of the service fee paid by users to the energy storage power station during time period t; They represent the power purchased and sold by the energy storage power station to the i-th user during the t period of each typical day; k r Represents the residual value coefficient of the shared energy storage power station.

4. The energy storage configuration method of the new energy system according to claim 3, characterized in that: The constraints of the shared energy storage upper-layer optimization model meet the following conditions: U charge (t)+U dis (t)≤1 Where SOC(t) represents the power consumption of the shared energy storage power station at time t; Indicates the upper limit of storage capacity of the shared energy storage power station; η charge ,η dis They represent the charging efficiency and discharging efficiency of the shared energy storage power station respectively; They represent the charging power and discharging power of the shared energy storage power station at time t respectively; ω1 and ω2 represent the minimum and maximum state of charge of the energy storage, respectively; ω0 represents the state of charge at the initial moment of energy storage; U charge (t), U dis (t) is a 0-1 variable, which represents the charging and discharging status of the energy storage power station in the tth period.

5. The energy storage configuration method of the new energy system according to claim 4, characterized in that: The objective function of the shared energy storage lower layer optimization model is specifically: Where C2 is the objective function value of the shared energy storage lower layer optimization model, C grid,m represents the cost of purchasing electricity from the grid on a typical day; C g,m P represents the gas purchase cost for micro-turbine operation; grid,m,i (t) represents the power purchased by the i-th user from the grid at time t; ε(t) represents the electricity price during time period t; P g,m,i (t) represents the output power of the micro gas turbine of the i-th user at time t; η represents the power generation efficiency of the micro gas turbine; L represents the calorific value of natural gas; δ(t) represents the price per unit volume of natural gas during time period t.

6. The energy storage configuration method of the new energy system according to claim 5, characterized in that: The constraints of the shared energy storage lower-level optimization model are specifically: P g,i,min ≤P g,m,i (t)≤P g,i,max -r g,i,down ≤P g,m,i (t)-P g,m,i (t-1)≤r g,i,up 0≤P grid,m,i (t)≤P grid,max Where, P wt,m,i (t) represents the wind power used by the i-th user in period t; P pv,m,i (t) represents the photovoltaic power used by the i-th user in period t; P Load,m,i (t) represents the load demand of the i-th user in period t; P g,m,i (t) represents the output power of the micro gas turbine of the i-th user at time t; P g,i,min and P g,i,max They represent the lower and upper limits of the gas turbine output respectively; r g,i,up and r g,i,down They represent the ramp-up rate and ramp-down rate of gas turbine output respectively; They are the upper limit of power purchase and power sale for each user by the shared energy storage power station; P grid,max is the upper limit of the power purchase of user i; is a 0-1 variable, representing the electricity purchasing and selling status of user i from the shared energy storage power station in period t; P grid,m,i (t) represents the power purchased by the i-th user from the grid at time t; P grid,max Indicates the upper limit of purchased power.

7. The energy storage configuration method of the new energy system according to claim 6, characterized in that: The KKT conditional processing operation includes: Convert the objective function of the shared energy storage lower-level optimization model into a Lagrangian function; Calculating partial derivatives based on the power purchased from the user by the energy storage station, the power sold from the energy storage station to the user, and the power purchased from the power grid by the user in the Lagrangian function; When the partial derivative is 0, the gradient condition is obtained; According to several dual variables in the Lagrangian function, combined with the constraint conditions of the shared energy storage lower layer optimization model corresponding to each dual variable, a complementary relaxation condition is obtained; A KKT constraint condition is obtained according to the gradient condition and the complementary relaxation condition.

8. An energy storage configuration device for a new energy system, characterized in that: include: Data acquisition module, model building module, data solution module and energy storage configuration module; The data acquisition module is used to acquire the operating data of the new energy system; The model construction module is configured to construct a shared energy storage dual-layer optimization configuration model based on the operating data; wherein the shared energy storage dual-layer optimization configuration model includes: a shared energy storage upper-layer optimization model and a shared energy storage lower-layer optimization model, wherein the objective function of the shared energy storage upper-layer optimization model is to minimize the operating cost of the new energy system over its life cycle, and the objective function of the shared energy storage lower-layer optimization model is to minimize the daily operating cost of the new energy system; The data solving module is used to solve the shared energy storage two-layer optimization configuration model to generate configuration data corresponding to the minimum operating cost of the new energy system during its life cycle and the minimum daily operating cost; The energy storage configuration module is used to perform energy storage configuration on the new energy system according to the configuration data.

9. A computer terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the energy storage configuration method of a new energy system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the energy storage configuration method for a new energy system according to any one of claims 1 to 7.

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