Direct control type virtual power plant aggregation regulation and control method and device

Abstract

The present application relates to a kind of direct control type virtual power plant aggregation control method and device, the method includes by equivalent coefficient determination discharge depth and the polynomial fitting equation of cycle number of times;Different discharge depth is converted into the equivalent cycle number of times under preset discharge depth;Different discharge depth and corresponding equivalent cycle number of times are segmented linearization processing;Then calculate the cost of single cycle of electric energy energy storage under preset discharge depth, based on single cycle cost and linear equation obtain actual discharge depth single cycle cost;Through the constraint condition and objective function of pre-set, life model is constructed and is solved to obtain control instruction.The present application proposes to obtain equivalent coefficient for different models of battery energy storage, construct polynomial fitting curve equation, then obtain equivalent full cycle number according to different charge and discharge depth, construct life model, and then extend the service life of battery energy storage, and finally calculate the optimal control instruction decomposition scheme of life, improve the overall benefit level of battery energy storage.

Description

Technical Field

[0001] This invention belongs to the field of power market technology, specifically relating to a direct-control type virtual power plant aggregation control method and device. Background Technology

[0002] Against the backdrop of dual carbon emissions, my country is vigorously developing green energy and actively exploring power system transformation. This includes combining power generation, grid, load, and storage with multi-energy complementarity, exploring power generation structure reform and diversifying market players, and striving to build a smart grid. Especially with the grid connection of distributed energy resources and distributed energy storage, the power system will face even greater challenges. This has led to the emergence of the concept of "virtual power plants," aimed at responsive allocation of distributed resources, in-depth exploration of demand-side potential, and coordinated scheduling of various distributed resources. Battery energy storage, as a crucial component of virtual power plants, has faced significant obstacles to its widespread adoption due to its relatively high cost. Optimizing battery energy storage operation modes and extending battery lifespan can help improve the overall economic efficiency of battery energy storage, promote the development of virtual power plants, and enhance demand-side response capabilities.

[0003] Domestic and international research has focused on minimizing the total cost of battery energy storage over its lifespan, yielding relevant research results and solutions. Examples include limiting the number of charge-discharge cycles or the charge-discharge power of battery energy storage. More complex approaches consider the degradation mechanism of battery energy storage, modeling based on charge-discharge power and depth, introducing lifespan cost constraints, and creating economical operating models to prevent excessively rapid lifespan degradation. However, existing lifespan models are often established by fitting parameters based on the depth of discharge and the battery's inherent curve in a power-law manner. While this method is relatively simple, its accuracy is low, leading to a decrease in the overall profitability of battery energy storage. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a direct-control type virtual power plant aggregation regulation method and device to solve the problem that the accuracy of the life model in the prior art is not high, which leads to a decrease in the overall benefit level of battery energy storage.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a direct-control type virtual power plant aggregation and control method, comprising:

[0006] Obtain the equivalent coefficient of battery energy storage, and determine the polynomial fitting equation of discharge depth and cycle number based on the equivalent coefficient;

[0007] The equivalent number of cycles at a preset discharge depth is calculated based on the polynomial fitting equation.

[0008] By linearizing the different discharge depths and their corresponding equivalent cycle counts piecewise, a linear equation is obtained.

[0009] The single-cycle cost of energy storage at the preset discharge depth is calculated using the equivalent number of cycles at the preset discharge depth. Based on the single-cycle cost and the linear equation, the single-cycle cost at the actual discharge depth is obtained.

[0010] A lifetime model is constructed with battery energy storage operation constraints and minimizing actual discharge depth cycle cost as the objective functions.

[0011] Solve the lifetime model to obtain the control command.

[0012] Furthermore, it also includes:

[0013] The rationality of the control commands is verified.

[0014] If the verification result is reasonable, an adjustment command is output; otherwise, the lifetime model parameters are updated, and the updated lifetime model is solved again.

[0015] Furthermore, the step of piecewise linearizing different discharge depths and corresponding equivalent cycle counts to obtain linear equations includes:

[0016] Piecewise linearization was performed on different discharge depths and corresponding equivalent cycle counts to determine the linearization parameters;

[0017] The linear equation is obtained based on the linearization parameters.

[0018] Furthermore, the objective function is

[0019]

[0020] in, Let be the equivalent number of cycles reached by system i in time period t. This represents the cost per cycle for the actual depth of discharge.

[0021] Furthermore, the constraints include:

[0022] Charge / discharge state constraints

[0023] Cumulative power consumption constraint

[0024] Depth of charge / discharge constraints

[0025] Lifetime cost constraints

[0026] in, g represents the accumulated electricity of energy storage system i during the t-th time period before the state of charge / discharge transition; i,t This indicates the switching of the charging and discharging states of energy storage system i during time period t, gi,t =1 indicates that a charge / discharge switch exists; The upper limit of electrical energy of energy storage system i; This represents the 0 and 1 variables representing the discharge state of energy storage system i during time period t. Energy storage system i is in a discharging state. The energy storage system is in either a charging or non-charging / non-discharging state; similarly, The variables 0 and 1 represent the discharge state of energy storage system i during time period t-1; This represents the depth of charge and discharge of energy storage system i during time period t; This represents the cost per cycle at a preset discharge depth.

[0027] Furthermore, the lifetime model is solved using the gurobi solver to obtain the control instructions.

[0028] Furthermore, the equivalent coefficient of battery energy storage is obtained through the data interface;

[0029] The rainflow counting method is used to determine the functional relationship between the discharge depth and the number of cycles based on the equivalent coefficient, and a polynomial fitting equation is determined.

[0030] This application provides a direct-control type virtual power plant aggregation and control device, including:

[0031] The acquisition module is used to acquire the equivalent coefficient of battery energy storage and determine the polynomial fitting equation of discharge depth and cycle number based on the equivalent coefficient.

[0032] The first calculation module is used to calculate the equivalent number of cycles at a preset discharge depth for different discharge depths based on the polynomial fitting equation.

[0033] The processing module is used to linearize the different discharge depths and corresponding equivalent cycle counts piecewise to obtain linear equations;

[0034] The second calculation module is used to calculate the single-cycle cost of energy storage at the preset discharge depth using the equivalent number of cycles at the preset discharge depth, and to obtain the single-cycle cost at the actual discharge depth based on the single-cycle cost and the linear equation.

[0035] The module is used to build a lifetime model with the objective function of minimizing battery energy storage operation constraints and actual discharge depth cycle cost.

[0036] The solver module is used to solve the lifetime model to obtain control commands.

[0037] Furthermore, it also includes:

[0038] A verification module is used to verify the rationality of the control command;

[0039] If the verification result is reasonable, an adjustment command is output; otherwise, the lifetime model parameters are updated, and the updated lifetime model is solved again.

[0040] The beneficial effects that can be achieved by adopting the above technical solution in this invention include:

[0041] This invention provides a direct-control virtual power plant aggregation regulation method and device. This application proposes a method to obtain equivalent coefficients through a data interface for different battery storage models, construct a polynomial fitting curve equation, and then obtain the equivalent full cycle number based on different charge / discharge depths to construct a more accurate fitting curve equation, thereby obtaining a more accurate lifespan model and extending the battery storage lifespan. Furthermore, based on the battery storage operation constraints, the optimal regulation command decomposition scheme for the virtual power plant is calculated, improving the overall profitability of battery storage. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram illustrating the steps of the direct-control virtual power plant aggregation control method of the present invention;

[0044] Figure 2 This is a schematic flowchart of the direct-control virtual power plant aggregation control method of the present invention;

[0045] Figure 3 This is a schematic diagram of the direct-control virtual power plant aggregation and control device of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0047] The following describes a specific direct-control virtual power plant aggregation control method and apparatus provided in the embodiments of this application, with reference to the accompanying drawings.

[0048] like Figure 1 As shown in the embodiments of this application, the direct-control type virtual power plant aggregation control method includes:

[0049] S101, Obtain the equivalent coefficient of battery energy storage, and determine the polynomial fitting equation of discharge depth and cycle number based on the equivalent coefficient.

[0050] In some embodiments, the equivalent coefficient of battery energy storage is obtained through a data interface;

[0051] The rainflow counting method is used to determine the functional relationship between the discharge depth and the number of cycles based on the equivalent coefficient, and a polynomial fitting equation is determined.

[0052] In this application, the equivalent coefficient of battery energy storage is obtained from the data interface. Based on data measured using the rainflow counting method, a polynomial fitting is performed to derive the functional relationship between the depth of discharge and the number of cycles, i.e., the polynomial fitting equation. For example, the cycle count data for a certain type of lithium iron phosphate battery at different depths of discharge are as follows:

[0053] N s (D i,t )=-2083D i,t 3 +8750D i,t 2 -13170D i,t +11200 (1)

[0054] Among them, D i,t Let N be the depth of discharge of system i during time period t. s The values ​​represent the number of cycles at different depths of discharge, with -2083, 8750, -13170, and 11200 being equivalent coefficients.

[0055] S102, calculate the equivalent number of cycles at a preset discharge depth based on the polynomial fitting equation for different discharge depths;

[0056] The preset discharge depth is the equivalent number of full cycles at 100% discharge depth.

[0057] Specifically, in this application, the number of cycles at different discharge depths is converted into the equivalent total number of cycles at 100% discharge depth using a polynomial fitting equation based on the discharge depth and the number of cycles.

[0058]

[0059] in, Let be the equivalent number of cycles reached by system i in time period t; The number of cycles required to reach the end of the energy storage life when charging and discharging at 100% depth of discharge; D i,t Let be the actual depth of cyclic discharge of system i during time period t.

[0060] S103, the different discharge depths and the corresponding equivalent cycle number are piecewise linearized to obtain a linear equation;

[0061] In some embodiments, the step of piecewise linearizing different discharge depths and corresponding equivalent cycle counts to obtain linear equations includes:

[0062] Piecewise linearization was performed on different discharge depths and corresponding equivalent cycle counts to determine the linearization parameters;

[0063] The linear equation is obtained based on the linearization parameters.

[0064] Specifically, according to equation (2), piecewise linearization is performed for different discharge depths to obtain...

[0065]

[0066] in, The parameters are obtained by linearization, and equation (3) is the linear equation.

[0067] S104, calculate the single-cycle cost of energy storage at the preset discharge depth using the equivalent number of cycles at the preset discharge depth, and obtain the single-cycle cost at the actual discharge depth based on the single-cycle cost and the linear equation;

[0068] Specifically, we first calculate the cost of a single cycle of battery energy storage at 100% depth of discharge:

[0069]

[0070] in, These are the investment and maintenance costs per unit power, respectively. P represents the investment cost per unit capacity. i C i These are the rated power and rated capacity of energy storage system i, respectively.

[0071] Then, based on the single-cycle cost and the linear equation, the actual discharge depth single-cycle cost is calculated as follows:

[0072]

[0073] in, For D i,t The lifetime cost of a complete charge-discharge cycle based on the depth of discharge.

[0074] S105, with the battery energy storage operation constraints and the minimum actual discharge depth cycle cost as the objective function, constructs a lifetime model;

[0075] The objective function is:

[0076]

[0077] in, Let be the equivalent number of cycles reached by system i in time period t. This represents the cost per cycle for the actual depth of discharge.

[0078] The constraints include:

[0079] Charge / discharge state constraints

[0080] Cumulative power consumption constraint

[0081] Depth of charge / discharge constraints

[0082] Lifetime cost constraints

[0083] in, g represents the accumulated electricity of energy storage system i during the t-th time period before the state of charge / discharge transition; i,t This indicates the switching of the charging and discharging states of energy storage system i during time period t, g i,t =1 indicates that a charge / discharge switch exists; The upper limit of electrical energy of energy storage system i; This represents the 0 and 1 variables representing the discharge state of energy storage system i during time period t. Energy storage system i is in a discharging state. The energy storage system is in either a charging or non-charging / non-discharging state; similarly, The variables 0 and 1 represent the discharge state of energy storage system i during time period t-1; This represents the depth of charge and discharge of energy storage system i during time period t; This represents the cost per cycle at a preset discharge depth.

[0084] S106, Solve the lifetime model to obtain the control command.

[0085] In some embodiments, the gurobi solver is invoked to solve the lifetime model to obtain control commands.

[0086] In this application, the results obtained from the calculation of the model are verified for reasonableness. If the verification result is reasonable, the optimal lifespan mode control command result is output. Otherwise, the model parameters are updated through adaptive correction, and the result is calculated again.

[0087] This application proposes a method to obtain equivalent coefficients for different battery storage models via a data interface, and then construct a more accurate fitting curve equation based on the depth of charge and discharge using a polynomial fitting method, thereby obtaining a more accurate lifespan model. The results of the polynomial fitting method are closer to the actual relationship between the depth of discharge and its corresponding cycle number, effectively improving the accuracy of the battery storage lifespan model. The optimal lifespan control command scheme also provides a new model for the decomposition of dispatch commands for directly controlled energy storage virtual power plants.

[0088] As shown in the figure, this application provides a direct-control type virtual power plant aggregation and control device, comprising:

[0089] The acquisition module 201 is used to acquire the equivalent coefficient of battery energy storage and determine the polynomial fitting equation of discharge depth and cycle number based on the equivalent coefficient.

[0090] The first calculation module 202 is used to calculate the equivalent number of cycles at a preset discharge depth for different discharge depths based on the polynomial fitting equation.

[0091] Processing module 203 is used to linearize the different discharge depths and corresponding equivalent cycle numbers piecewise to obtain linear equations;

[0092] The second calculation module 204 is used to calculate the single-cycle cost of energy storage at the preset discharge depth using the equivalent number of cycles at the preset discharge depth, and to obtain the single-cycle cost at the actual discharge depth based on the single-cycle cost and the linear equation.

[0093] Module 205 is used to construct a lifetime model with the objective function of minimizing battery energy storage operation constraints and actual discharge depth cycle cost;

[0094] The solver module 206 is used to solve the lifetime model to obtain control commands.

[0095] In some embodiments, it also includes:

[0096] Verification module 207 is used to verify the rationality of the control command;

[0097] If the verification result is reasonable, an adjustment command is output; otherwise, the lifetime model parameters are updated, and the updated lifetime model is solved again.

[0098] The working principle of the direct-control virtual power plant aggregation and regulation device provided in this application is as follows: The acquisition module 201 acquires the equivalent coefficient of battery energy storage, and determines the polynomial fitting equation of discharge depth and cycle count based on the equivalent coefficient; the first calculation module 202 calculates the equivalent cycle count at a preset discharge depth for different discharge depths based on the polynomial fitting equation; the processing module 203 linearizes the different discharge depths and corresponding equivalent cycle counts piecewise to obtain a linear equation; the second calculation module 204 calculates the single-cycle cost of energy storage at the preset discharge depth using the equivalent cycle count at the preset discharge depth, and obtains the single-cycle cost at the actual discharge depth based on the single-cycle cost and the linear equation; the construction module 205 constructs a lifetime model with the battery energy storage operation constraints and the minimum actual discharge depth cycle cost as the objective function; the solution module 206 solves the lifetime model to obtain the regulation command; the verification module 207 verifies the rationality of the regulation command; if the verification result is reasonable, the regulation command is output; otherwise, the lifetime model parameters are updated, and the updated lifetime model is solved again.

[0099] In summary, this invention provides a direct-control virtual power plant aggregation regulation method and device. By proposing equivalent coefficients for different battery energy storage models, constructing polynomial fitting curve equations, obtaining equivalent full cycle counts based on different charge and discharge depths, constructing a lifespan model, and thus extending the battery energy storage lifespan, the optimal regulation command decomposition scheme for lifespan is calculated, thereby improving the overall benefit level of battery energy storage.

[0100] It is understood that the method embodiments provided above correspond to the device embodiments described above, and the specific details can be referred to each other, which will not be repeated here.

[0101] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0102] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A direct-control type virtual power plant aggregation control method, characterized in that, include: Obtain the equivalent coefficient of battery energy storage, and determine the polynomial fitting equation of discharge depth and cycle number based on the equivalent coefficient; The equivalent number of cycles at a preset discharge depth is calculated based on the polynomial fitting equation. By linearizing the different discharge depths and their corresponding equivalent cycle counts piecewise, a linear equation is obtained. The single-cycle cost of energy storage at the preset discharge depth is calculated using the equivalent number of cycles at the preset discharge depth. Based on the single-cycle cost and the linear equation, the single-cycle cost at the actual discharge depth is obtained. A lifetime model is constructed with battery energy storage operation constraints and minimizing actual discharge depth cycle cost as the objective functions. Solve the lifetime model to obtain the control command; The objective function is: in, For the system exist The equivalent number of cycles reached during the time period. This represents the cost per cycle for the actual depth of discharge.

2. The method according to claim 1, characterized in that, Also includes: The rationality of the control commands is verified. If the verification result is reasonable, an adjustment command is output; otherwise, the lifetime model parameters are updated, and the updated lifetime model is solved again.

3. The method according to claim 1 or 2, characterized in that, The step of piecewise linearizing different discharge depths and their corresponding equivalent cycle counts to obtain linear equations includes: Piecewise linearization was performed on different discharge depths and corresponding equivalent cycle counts to determine the linearization parameters; The linear equation is obtained based on the linearization parameters.

4. The method according to claim 1, characterized in that, The battery energy storage operation constraints include: Charge / discharge state constraints ; Cumulative power consumption constraint ; Depth of charge / discharge constraints ; Lifetime cost constraints ; in, Indicates energy storage system Before the state of charge / discharge transition The cumulative electricity consumption over a given period; Indicates energy storage system During the period The switching of charging and discharging states, This indicates that a charge / discharge switching has occurred; Energy storage system The upper limit of electrical energy; Indicates energy storage system The discharge state variables are 0 and 1 during time period t. Energy storage system In the discharge state, Energy storage system Charging or non-charging / non-discharging state; similarly, Indicates energy storage system The discharge state variables are 0 and 1 in time period t-1; Indicates energy storage system During the period Depth of charge and discharge; This represents the cost per cycle at a preset discharge depth.

5. The method according to claim 1, characterized in that, The lifetime model is solved using the gurobi solver to obtain the control commands.

6. The method according to claim 1, characterized in that, Obtain the equivalent coefficient of battery energy storage through the data interface; The rainflow counting method is used to determine the functional relationship between the discharge depth and the number of cycles based on the equivalent coefficient, and a polynomial fitting equation is determined.

7. A direct-control type virtual power plant aggregation and control device, characterized in that, include: The acquisition module is used to acquire the equivalent coefficient of battery energy storage and determine the polynomial fitting equation of discharge depth and cycle number based on the equivalent coefficient. The first calculation module is used to calculate the equivalent number of cycles at a preset discharge depth for different discharge depths based on the polynomial fitting equation. The processing module is used to linearize the different discharge depths and corresponding equivalent cycle counts piecewise to obtain linear equations; The second calculation module is used to calculate the single-cycle cost of energy storage at the preset discharge depth using the equivalent number of cycles at the preset discharge depth, and to obtain the single-cycle cost at the actual discharge depth based on the single-cycle cost and the linear equation. The module is used to build a lifetime model with the objective function of minimizing battery energy storage operation constraints and actual discharge depth cycle cost. The solver module is used to solve the lifetime model to obtain control commands; The objective function is: in, For the system exist The equivalent number of cycles reached during the time period. This represents the cost per cycle for the actual depth of discharge.

8. The apparatus according to claim 7, characterized in that, Also includes: A verification module is used to verify the rationality of the control command; If the verification result is reasonable, an adjustment command is output; otherwise, the lifetime model parameters are updated, and the updated lifetime model is solved again.

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