Self-adaptive energy storage control method for distributed energy

Abstract

The invention relates to the technical field of energy storage management, in particular to a self-adaptive energy storage control method for distributed energy, and the method comprises the steps: calculating a battery damage rate of single charge and discharge of an energy storage system through obtaining energy storage data, charge and discharge data and economic data, and constructing an energy storage battery damage control model based on the battery damage rate; constructing an energy storage economic benefit control model, and obtaining an energy storage comprehensive control model based on the energy storage battery breakage control model and the energy storage economic benefit control model; and iteratively solving to obtain an optimal solution of the energy storage comprehensive control model, and extracting parameters when the optimal solution is used as an optimal energy storage control strategy. Therefore, operability is good, accuracy is high, flexibility is high, efficient, flexible and safe operation of the health state of the energy storage system can be effectively supported, and the economic benefit requirement of energy storage development is met.

Description

Background Art

[0002] With the rapid development of renewable energy, the proportion of distributed energy systems (such as solar energy, wind energy, etc.) in electricity supply is increasing. However, due to the intermittent and unstable nature of these energy sources, energy storage systems have become an important part of ensuring the stability of the power grid. In order to better adapt to the volatility of distributed energy, adaptive energy storage control methods have emerged. Distributed energy systems use renewable energy to generate electricity, but these energy sources are intermittent and unstable, which poses a challenge to the balance and stability of the power grid. The adaptive energy storage control method can effectively balance power generation and electricity demand and improve the efficiency of the overall system by intelligently regulating the energy storage system. Summary of the invention

[0003] The main purpose of the present invention is to provide an adaptive energy storage control method for distributed energy, which effectively solves the above-mentioned problems mentioned in the background technology.

[0004] The technical solution of the present invention is as follows:

[0005] An adaptive energy storage control method for distributed energy is proposed, the method comprising the following steps:

[0006] S1. Obtain energy storage data, charge and discharge data, and economic data, calculate the battery depreciation rate of a single charge and discharge of the energy storage system, and build an energy storage battery depreciation control model based on the battery depreciation rate;

[0007] S2. Construct an energy storage economic benefit control model, and obtain an energy storage comprehensive control model based on the energy storage battery depreciation control model and the energy storage economic benefit control model;

[0008] S3. Iteratively solve the optimal solution of the energy storage comprehensive control model and extract the parameters of the optimal solution as the optimal energy storage control strategy.

[0009] A further improvement of the present invention is that the energy storage data in S1 includes energy storage system rated capacity data E and energy storage system installed capacity data Q; the charge and discharge data includes energy storage battery cycle life data G, daily charge and discharge times data N, and discharge depth data H; the economic data includes energy storage battery manufacturing cost data a, power generation revenue data W, and power generation price difference data V.

[0010] A further improvement of the present invention is that S1 comprises the following specific steps:

[0011] S11, extracting energy storage system rated capacity data E, energy storage battery cycle life data G, discharge depth data H, and single-day charge and discharge times data N;

[0012] S12, calculating the battery depreciation rate r of a single charge and discharge of the energy storage system, the calculation formula of the battery depreciation rate is:

[0013] S13, extracting the battery depreciation rate r, the energy storage battery manufacturing cost data a, and the energy storage system installed capacity data Q, and constructing an energy storage battery depreciation control model. The specific formula of the energy storage battery depreciation control model is: f1=min(r×a×Q).

[0014] A further improvement of the present invention is that S2 comprises the following specific steps:

[0015] S21, extracting power generation revenue data W and power generation price difference data V;

[0016] S22, constructing an energy storage economic benefit control model, wherein the specific formula of the energy storage economic benefit control model is: f2=max(W+V);

[0017] S23. Based on the energy storage battery depreciation control model and the energy storage economic benefit control model, an energy storage comprehensive control model is obtained. The specific formula of the energy storage comprehensive control model is: f=f1+f2.

[0018] A further improvement of the present invention is that S3 comprises the following specific steps:

[0019] S31, using the gray wolf optimization algorithm to iteratively track and solve the energy storage integrated control model, setting the size n of the gray wolf population, initializing the position of each gray wolf to generate an initial solution, the solution vector is Xi, i is the gray wolf number, and Xi contains all decision variables;

[0020] S32, setting the maximum number of iterations T of the gray wolf algorithm, for each gray wolf individual Xi, calculating its fitness value f(Xi), in each iteration, selecting three gray wolves with the best fitness values, and iteratively updating the positions of other gray wolves until the maximum number of iterations T is reached, stopping the iteration, and selecting the position of the gray wolf with the best fitness value as the optimal solution of the objective function f;

[0021] S33. Extracting the parameters of the optimal solution as the optimal energy storage control strategy.

[0022] The technical effects of the present invention are as follows:

[0023] An adaptive energy storage control method for distributed energy is constructed. The present invention obtains energy storage data, charging and discharging data, and economic data, calculates the battery depreciation rate of a single charge and discharge of an energy storage system, and constructs an energy storage battery depreciation control model based on the battery depreciation rate; constructs an energy storage economic benefit control model, and obtains an energy storage comprehensive control model based on the energy storage battery depreciation control model and the energy storage economic benefit control model; it iteratively solves to obtain the optimal solution of the energy storage comprehensive control model, and extracts the parameters at the optimal solution as the optimal energy storage control strategy. Therefore, it has good operability, high accuracy, and high flexibility, and can effectively support the efficient, flexible, and safe operation of the energy storage system in a healthy state, and meet the economic benefit requirements of energy storage development. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0025] Figure 1 The figure is a flow chart of an adaptive energy storage control method for distributed energy according to the present invention. DETAILED DESCRIPTION

[0026] The present invention aims to propose an adaptive energy storage control method for distributed energy. By acquiring energy storage data, charging and discharging data, and economic data, the battery depreciation rate of a single charge and discharge of an energy storage system is calculated, and an energy storage battery depreciation control model is constructed based on the battery depreciation rate; an energy storage economic benefit control model is constructed, and an energy storage comprehensive control model is obtained based on the energy storage battery depreciation control model and the energy storage economic benefit control model; the optimal solution of the energy storage comprehensive control model is obtained by iterative solution, and the parameters at the optimal solution are extracted as the optimal energy storage control strategy. Therefore, the method has good operability, high accuracy, and high flexibility, and can effectively support the efficient, flexible, and safe operation of the energy storage system in a healthy state, and meet the economic benefit requirements of energy storage development.

[0027] Embodiment 1:

[0028] This embodiment proposes an adaptive energy storage control method for distributed energy. Figure 1 As shown, the following specific steps are included:

[0029] S1. Obtain energy storage data, charge and discharge data, and economic data, calculate the battery depreciation rate of a single charge and discharge of the energy storage system, and build an energy storage battery depreciation control model based on the battery depreciation rate;

[0030] S2. Construct an energy storage economic benefit control model, and obtain an energy storage comprehensive control model based on the energy storage battery depreciation control model and the energy storage economic benefit control model;

[0031] S3. Iteratively solve the optimal solution of the energy storage comprehensive control model and extract the parameters of the optimal solution as the optimal energy storage control strategy.

[0032] In this embodiment, the energy storage data in S1 includes energy storage system rated capacity data E and energy storage system installed capacity data Q, which reflect the scale and design capacity of the energy storage system; the charge and discharge data includes energy storage battery cycle life data G, daily charge and discharge times data N, and discharge depth data H, which are used to evaluate the battery usage and life; the economic data includes energy storage battery manufacturing cost data a, power generation revenue data W, and power generation price difference data V, which are used to evaluate the economic benefits of the energy storage system.

[0033] In this embodiment, S1 includes the following specific steps:

[0034] S11, extracting energy storage system rated capacity data E, energy storage battery cycle life data G, discharge depth data H, and single-day charge and discharge times data N;

[0035] S12, calculating the battery depreciation rate r of a single charge and discharge of the energy storage system, the calculation formula of the battery depreciation rate is: The formula calculates the damage caused to the battery with each charge and discharge, helping to assess the health of the battery.

[0036] S13, extract the battery depreciation rate r, the energy storage battery manufacturing cost data a, and the energy storage system installed capacity data Q, and construct an energy storage battery depreciation control model. The specific formula of the energy storage battery depreciation control model is: f1 = min (r × a × Q). This formula represents the product of the depreciation rate and the manufacturing cost and installed capacity, that is, the economic cost caused by battery depreciation. By minimizing this cost, the battery life can be extended and the operating cost can be reduced.

[0037] In this embodiment, S2 includes the following specific steps:

[0038] S21, extracting power generation revenue data W and power generation price difference data V;

[0039] S22. Construct an energy storage economic benefit control model. The specific formula of the energy storage economic benefit control model is: f2=max(W+V); this formula represents the sum of power generation income and power generation price difference, and the purpose is to maximize the income of the energy storage system.

[0040] S23. Based on the energy storage battery depreciation control model and the energy storage economic benefit control model, an energy storage comprehensive control model is obtained. The specific formula of the energy storage comprehensive control model is: f=f1+f2. The energy storage comprehensive control model comprehensively considers the battery depreciation cost and economic benefit, and the goal is to maximize the economic benefit of the system while reducing battery depreciation.

[0041] In order to solve the optimal solution of the energy storage integrated control model, the gray wolf optimization algorithm (GWO) is used. GWO is an intelligent optimization algorithm that simulates the hunting behavior of gray wolf groups and is suitable for nonlinear and multi-peak optimization problems. In this embodiment, S3 includes the following specific steps:

[0042] S31, using the gray wolf optimization algorithm to iteratively track and solve the energy storage integrated control model, setting the size n of the gray wolf population, initializing the position of each gray wolf to generate an initial solution, the solution vector is Xi, i is the gray wolf number, and Xi contains all decision variables;

[0043] S32. Set the maximum number of iterations T of the gray wolf algorithm. For each gray wolf individual Xi, calculate its fitness value f(Xi). In each iteration, select three gray wolves with the best fitness values, and iteratively update the positions of other gray wolves until the maximum number of iterations T is reached. Stop the iteration and select the position of the gray wolf with the best fitness value as the optimal solution of the objective function f. These parameters will guide how the energy storage system regulates the charging and discharging operations in actual operation to achieve the best economic benefits and equipment health management.

[0044] S33. Extracting the parameters of the optimal solution as the optimal energy storage control strategy.

[0045] Through this method, the energy storage system can intelligently control the charging and discharging strategy while taking into account both battery life and economic benefits. The application of the Gray Wolf Optimization Algorithm enables the system to effectively handle complex nonlinear optimization problems, find the global optimal solution, and ultimately improve the operating efficiency and economic benefits of the energy storage system.

[0046] Embodiment 2:

[0047] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned adaptive energy storage control method for distributed energy by calling the computer program stored in the memory.

[0048] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement an adaptive energy storage control method for distributed energy provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0049] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), and it can also be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0050] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0051] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and boxes Figure 1 The steps for the functions specified in one or more boxes.

[0053] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. An adaptive energy storage control method for distributed energy, characterized in that: The specific steps include: S1. Obtain energy storage data, charge and discharge data, and economic data, calculate the battery depreciation rate of a single charge and discharge of the energy storage system, and build an energy storage battery depreciation control model based on the battery depreciation rate; S2. Construct an energy storage economic benefit control model, and obtain an energy storage comprehensive control model based on the energy storage battery depreciation control model and the energy storage economic benefit control model; S3. Iteratively solve the optimal solution of the energy storage comprehensive control model and extract the parameters of the optimal solution as the optimal energy storage control strategy.

2. The adaptive energy storage control method for distributed energy according to claim 1, characterized in that: The energy storage data in S1 includes the energy storage system rated capacity data E and the energy storage system installed capacity data Q; the charge and discharge data includes the energy storage battery cycle life data G, the daily charge and discharge times data N, and the discharge depth data H; the economic data includes the energy storage battery manufacturing cost data a, the power generation income data W, and the power generation price difference data V.

3. The adaptive energy storage control method for distributed energy according to claim 2, characterized in that: The S1 comprises the following specific steps: S11, extracting energy storage system rated capacity data E, energy storage battery cycle life data G, discharge depth data H, and single-day charge and discharge times data N; S12, calculating the battery depreciation rate r of a single charge and discharge of the energy storage system, the calculation formula of the battery depreciation rate is: S13, extracting the battery depreciation rate r, the energy storage battery manufacturing cost data a, and the energy storage system installed capacity data Q, and constructing an energy storage battery depreciation control model. The specific formula of the energy storage battery depreciation control model is: f1=min(r×a×Q).

4. The adaptive energy storage control method for distributed energy according to claim 3, characterized in that: The S2 comprises the following specific steps: S21, extracting power generation revenue data W and power generation price difference data V; S22, constructing an energy storage economic benefit control model, wherein the specific formula of the energy storage economic benefit control model is: f2=max(W+V); S23. Based on the energy storage battery depreciation control model and the energy storage economic benefit control model, an energy storage comprehensive control model is obtained. The specific formula of the energy storage comprehensive control model is: f=f1+f2.

5. The adaptive energy storage control method for distributed energy according to claim 4, characterized in that: The S3 comprises the following specific steps: S31, using the gray wolf optimization algorithm to iteratively track and solve the energy storage integrated control model, setting the size n of the gray wolf population, initializing the position of each gray wolf to generate an initial solution, the solution vector is Xi, i is the gray wolf number, and Xi contains all decision variables; S32, setting the maximum number of iterations T of the gray wolf algorithm, for each gray wolf individual Xi, calculating its fitness value f(Xi), in each iteration, selecting three gray wolves with the best fitness values, and iteratively updating the positions of other gray wolves until the maximum number of iterations T is reached, stopping the iteration, and selecting the position of the gray wolf with the best fitness value as the optimal solution of the objective function f; S33. Extracting the parameters of the optimal solution as the optimal energy storage control strategy.

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