Hybrid energy storage two-layer model capacity optimization configuration method, system, equipment and medium

Abstract

This invention discloses a method, system, equipment, and medium for capacity optimization configuration of a hybrid energy storage two-layer model. It includes a power smoothing stage and a primary frequency regulation stage. In the power smoothing stage, the historical output power of the wind farm is decomposed using wavelet packets, and power that meets the wind power grid connection fluctuation standards is selected for grid connection. The remaining power is allocated to lithium batteries and flywheels for smoothing. The optimal capacity of the lithium battery and flywheel in the power smoothing stage is determined based on the smoothed power. In the primary frequency regulation stage, the power demand of the wind farm for primary frequency regulation is determined based on the historical frequency of the grid. Then, combined with the capacity range determined by the optimal lithium battery and flywheel capacities in the power smoothing stage, the optimal capacity of the lithium battery and flywheel in the primary frequency regulation stage is obtained. This invention participates in primary frequency regulation while smoothing wind power grid connection power fluctuations, improving the wind farm's response rate to grid primary frequency regulation, smoothing wind power fluctuations, and maintaining power system frequency stability.

Description

Technical Field

[0001] This invention relates to the field of hybrid energy storage, and in particular to a method, system, device and medium for optimizing the capacity configuration of a two-layer hybrid energy storage model. Background Art

[0002] Wind power generation has been vigorously developed due to its clean, efficient, and pollution-free characteristics. In 2021, the newly installed capacity reached 47.57 million kilowatts, a year-on-year increase of 20.9%. However, wind power generation is highly random and volatile. After high-penetration wind power is connected to the grid, it poses a great challenge to the safe and stable operation of the power system.

[0003] In a power system of a certain scale, the system's regulation capability is mainly determined by the power source's regulation performance. Therefore, improving the regulation performance of the power source side has become a key issue that needs to be addressed for grid-connected power generation from new energy sources such as wind power. For wind farms, on the one hand, it is necessary to strengthen their dispatch and operation controllability to smooth the output power of grid-connected power generation; on the other hand, it is necessary to have a certain primary frequency regulation capability to reduce the risk of grid frequency exceeding limits and instability.

[0004] With the rapid development of energy storage technology, configuring energy storage systems on the generation side to maintain power system frequency stability has become an effective way to solve the problems of new energy consumption and grid stability. Energy storage systems have the characteristics of energy time shifting, fast response and flexible deployment. They have the dual functions of improving wind power volatility and assisting in primary frequency regulation of the grid, thereby improving the dispatchability of wind power and reducing wind curtailment caused by wind power load shedding.

[0005] Energy storage can be broadly categorized into two types based on its output characteristics: energy storage and power storage. Energy storage, represented by lithium-ion battery energy storage, has high energy density but low power density and long response time, making it suitable for smoothing high-energy, low-frequency power fluctuations. Power storage, represented by flywheels, has high power density and short response time but low energy density, making it suitable for smoothing low-energy, high-frequency power fluctuations. Since wind power fluctuations contain various components of different frequencies and amplitudes, hybrid energy storage systems can more effectively absorb wind power than standalone systems. However, introducing energy storage systems into the power system increases investment costs. Therefore, how to configure hybrid energy storage systems to achieve maximum benefits has become a current research hotspot. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, device, and medium for optimizing the capacity configuration of a hybrid energy storage two-layer model, which can participate in primary frequency regulation while smoothing out wind power fluctuations, improve the response rate of wind farms in participating in the primary frequency regulation of the power grid, smooth out wind power fluctuations, and maintain the stability of the power system frequency.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A method for optimizing the capacity configuration of a hybrid energy storage two-layer model includes:

[0009] Establish upper-level models for hybrid energy storage capacity optimization configuration during the power smoothing stage and lower-level models for hybrid energy storage capacity optimization configuration during the primary frequency regulation stage, respectively.

[0010] During the power smoothing phase, the historical output power of the wind farm is decomposed by wavelet packet, and the grid-connected power that meets the wind power grid connection fluctuation standard is selected from the decomposition results for grid connection. The power other than the grid-connected power in the decomposition results is used as the hybrid energy storage power.

[0011] The hybrid energy storage power is distributed to the lithium battery and flywheel for smoothing;

[0012] Based on the power smoothed by the lithium battery and flywheel, with the goal of maximizing power utilization, the upper-level model of the hybrid energy storage capacity optimization configuration is solved using the particle swarm optimization algorithm to obtain the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the power smoothing stage.

[0013] Based on the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the power smoothing phase, the optimization range of the lithium battery capacity and the optimization range of the flywheel capacity are determined respectively.

[0014] When the power grid has a primary frequency regulation requirement, the power demand of the wind farm for primary frequency regulation is determined based on the historical frequency of the power grid.

[0015] The power demand is allocated to the lithium battery and flywheel;

[0016] Based on the power output of the lithium battery and flywheel, with the goal of maximizing power utilization and the optimization ranges of lithium battery capacity and flywheel capacity as constraints, the particle swarm optimization algorithm is used to solve the lower-level model of the hybrid energy storage capacity optimization configuration, thereby obtaining the optimal capacity of the lithium battery and the optimal capacity of the flywheel in the primary frequency regulation stage.

[0017] A hybrid energy storage two-layer model capacity optimization configuration system includes:

[0018] The model building module is used to build the upper-level model of hybrid energy storage capacity optimization configuration in the power smoothing stage and the lower-level model of hybrid energy storage capacity optimization configuration in the primary frequency regulation stage, respectively.

[0019] The decomposition module is used to perform wavelet packet decomposition on the historical output power of the wind farm during the power smoothing phase, and select grid-connected power that meets the wind power grid connection fluctuation standard from the decomposition results for grid connection. Power other than grid-connected power in the decomposition results is used as hybrid energy storage power.

[0020] A smoothing module is used to distribute the hybrid energy storage power to the lithium battery and flywheel for smoothing.

[0021] The power smoothing stage optimization module is used to solve the upper-level model of the hybrid energy storage capacity optimization configuration based on the smoothed power of the lithium battery and flywheel, with the goal of maximizing power utilization, and obtain the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the power smoothing stage.

[0022] The optimization range determination module is used to determine the optimization range of lithium battery capacity and the optimization range of flywheel capacity based on the optimal capacity of lithium battery and flywheel during the power smoothing stage, respectively.

[0023] The power demand calculation module is used to determine the power demand of the wind farm for primary frequency regulation based on the historical frequency of the power grid when the power grid has a primary frequency regulation demand.

[0024] A distribution module is used to allocate the power demand to the lithium battery and flywheel;

[0025] The primary frequency regulation stage optimization module is used to solve the lower-level model of the hybrid energy storage capacity optimization configuration based on the power output of the lithium battery and flywheel, with the goal of maximizing power utilization and the optimization range of lithium battery capacity and flywheel capacity as constraints. This results in the optimal capacity of the lithium battery and flywheel during the primary frequency regulation stage.

[0026] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned hybrid energy storage two-layer model capacity optimization configuration method.

[0027] A computer-readable storage medium having a computer program stored thereon, which, when executed, implements the aforementioned method for optimizing the capacity configuration of a hybrid energy storage two-layer model.

[0028] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] This invention discloses a method, system, equipment, and medium for capacity optimization configuration of a hybrid energy storage two-layer model, comprising two stages: a power smoothing stage and a primary frequency regulation stage. In the power smoothing stage, the historical output power of the wind farm is decomposed using wavelet packets, and grid-connected power meeting the wind power grid connection fluctuation standard is selected from the decomposition results for grid connection. Power other than the grid-connected power in the decomposition results is used as hybrid energy storage power, which is allocated to lithium batteries and flywheels for smoothing. The optimal capacity of the lithium battery and flywheel in the power smoothing stage is determined based on the smoothed power of the lithium battery and flywheel. In the primary frequency regulation stage, the power demand for primary frequency regulation of the wind farm is determined based on the historical frequency of the grid. Then, combined with the capacity range determined by the optimal capacity of the lithium battery and flywheel in the power smoothing stage, the optimal capacity of the lithium battery and flywheel in the primary frequency regulation stage is obtained. This invention participates in primary frequency regulation while smoothing wind power grid connection power fluctuations, improving the wind farm's response rate to grid primary frequency regulation, smoothing wind power fluctuations, and maintaining power system frequency stability. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments 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.

[0031] Figure 1 A flowchart illustrating a capacity optimization configuration method for a hybrid energy storage two-layer model provided in an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of a hybrid energy storage two-layer model capacity optimization configuration method provided in an embodiment of the present invention. Detailed Implementation

[0033] 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] To address the challenges of integrating existing wind power and meeting primary frequency regulation requirements, this invention proposes a hybrid energy storage dual-layer model capacity optimization configuration method. This method comprises two stages: a power smoothing stage and a primary frequency regulation stage. The time scales of the two stages are different: power smoothing is optimized on a 1-minute time scale, while primary frequency regulation is optimized on a 1-second time scale. In the power smoothing stage, historical power output data from wind farms is decomposed using an improved complete set empirical mode decomposition method (ICEEMDAN). The ICEEMDAN decomposition method processes wind power to obtain grid-connected power and hybrid energy storage power commands that conform to wind power grid connection fluctuation standards. The hybrid energy storage power is further decomposed into high and low frequencies, allocated to flywheel and lithium battery energy storage forms, respectively. Furthermore, since the wind farm is equipped with hybrid energy storage, to maximize efficiency, the wind turbines always operate in maximum power point tracking (MPPT) mode, and the entire primary frequency regulation power demand P of the wind farm is borne by the hybrid energy storage. When the grid experiences primary frequency regulation demand, the method establishes a frequency regulation power quantity ΔP based on the frequency deviation Δf of the primary frequency regulation demand. Similarly, using ICEEMDAN for decomposition, the required output power of the hybrid energy storage system to participate in primary frequency regulation is obtained. The low-frequency and high-frequency data obtained from the decomposition are then input into the lithium battery and flywheel energy storage systems, respectively. A weighted smoothing index and a mathematical model for optimizing the hybrid energy storage capacity configuration with the highest energy utilization rate are established. Considering constraints such as the charging and discharging power of the hybrid energy storage capacity and the energy storage SOC, the model aims to maximize benefits and solves the problem under the configuration constraints of the upper layer. The configuration results are then passed back to the upper layer, and the optimal configuration of the hybrid energy storage system is achieved through continuous interactive iteration.

[0036] like Figure 1 and Figure 2 As shown in the figure, an embodiment of the present invention provides a method for optimizing the capacity configuration of a hybrid energy storage two-layer model, comprising:

[0037] Step 1: Establish the upper-level model of hybrid energy storage capacity optimization configuration for the power smoothing stage and the lower-level model of hybrid energy storage capacity optimization configuration for the primary frequency regulation stage.

[0038] 1) Establish an upper-level model for the optimal configuration of hybrid energy storage capacity during the power smoothing phase, specifically including:

[0039] The smoothing index for the power smoothing phase is: In the formula, φ1 is the smoothing index of the power smoothing stage, PH′(t) and PL′(t) are the high-frequency fluctuation component and low-frequency fluctuation component after the hybrid energy storage smoothing at time t, respectively, and T is the sampling period;

[0040] Establish an objective function for electrical energy, E, with the goal of maximizing energy utilization. min1 =E qf +E qe In the formula, E min1The objective function for electrical energy during the power smoothing phase; E qf E is the penalty electricity for wind curtailment during the power smoothing phase. qf =∑P qf ·Δt, P qf E represents the wind curtailment power at time t, where Δt is the time interval. qe E is the penalty for the power smoothing phase. qe =∑P qe ·Δt, P qe Let be the power deficit at time t;

[0041] By weighting the smoothing index and the power objective function during the power smoothing phase, the capacity objective function for the power smoothing phase is obtained as F. min1 =α1·E min1 +β1·Φ1; where, F min1 Let α1 and β1 be the capacity objective function for the power smoothing stage, and α1 and β1 be the first and second weights, respectively.

[0042] The optimal values ​​of the first and second weights are determined by using the entropy weight method. These optimal values ​​are then substituted into the capacity objective function of the power smoothing stage to obtain the upper-level model for the hybrid energy storage capacity optimization configuration in the power smoothing stage.

[0043] Entropy weight method: for the two solutions E in the objective function min1 Standardize with φ1, calculate entropy, decompose and obtain the weight of each solution. The formula for calculating the weight is:

[0044]

[0045] In the formula, S j S represents the weight. j The larger θ is, the greater the role of the j-th evaluation indicator in the entire evaluation indicator system, and therefore the more weight should be assigned to it; j f is the standard deviation of the j-th indicator; ij The correlation coefficient between indicators i and j is used to evaluate the correlation between them.

[0046] Suppose there are n samples to be evaluated and p evaluation indicators. Let i be the i-th sample and j be the value of the j-th evaluation indicator. Finally, by weighted summation and comparison, the solution with the smallest S is obtained as the optimal solution, and the weights of the two solutions are obtained.

[0047] 2) Establish a lower-level model for the optimal configuration of hybrid energy storage capacity during the primary frequency regulation stage, specifically including:

[0048] Constructing the smoothing index for the primary frequency regulation phase is as follows In the formula, φ2 is the smoothing index of the primary frequency modulation stage, and P n H′(t) and P nL′(t) represents the high-frequency fluctuation component and the low-frequency fluctuation component of the hybrid energy storage output at time t, respectively.

[0049] Establish an objective function for electrical energy, E, with the goal of maximizing energy utilization. min2 =E pun -E stp In the formula, E min2 The objective function for electrical energy during the primary frequency regulation stage; E pun E is the penalty electricity for wind curtailment during the first frequency regulation phase. pun =∑Plack·Δt, where Plack is the differential power of the primary frequency modulation at time t; E stp E is the penalty for the shortfall during a frequency modulation phase. stp =∑(P na (t)+P nf (t))Δt,P na (t) and P nf (t) represents the power of the lithium battery and flywheel participating in the first frequency modulation at time t;

[0050] By weighting the smoothing index and the power objective function for the primary frequency regulation stage, the capacity objective function for the primary frequency regulation stage is obtained as F. min2 =α2·E min2 +β2·Φ2; where, F min2 Let α2 and β2 be the capacity objective function for the first frequency regulation stage, with α2 and β2 being the third and fourth weights, respectively.

[0051] The optimal values ​​of the third and fourth weights are determined by the entropy weight method, and then the optimal values ​​of the third and fourth weights are substituted into the capacity objective function of the primary frequency regulation stage to obtain the lower-level model for the optimal configuration of hybrid energy storage capacity in the primary frequency regulation stage.

[0052] Among them, the upper-level model for hybrid energy storage capacity optimization configuration in the power smoothing stage and the lower-level model for hybrid energy storage capacity optimization configuration in the primary frequency regulation stage both include SOC constraints, energy storage charging and discharging power constraints at time t, and volatility constraints.

[0053] The SOC constraint is: In the formula, SOC a (t) represents the state of charge (SOC) of the lithium battery at time t. oclow and S ocup These represent the minimum and maximum SOC values ​​for lithium batteries; SOC f (t) represents the state of charge (SOC) of the flywheel, S OClow and S OCup These are the minimum and maximum SOC values ​​of the flywheel, respectively.

[0054] The energy storage charging and discharging power constraint at time t is: and In the formula, P a(t) Let P be the power of the lithium battery at time t. ar For the stable range of lithium batteries, η a The charge / discharge efficiency of lithium batteries; P f(t) Let P be the power of the flywheel at time t. fr At the stable kilometer point of the flywheel, η f The charging and discharging efficiency of the flywheel;

[0055] The volatility constraint is: In the formula, α1 and α 10 The volatility α represents the 1-minute and 10-minute fluctuations required for grid connection, respectively. one and α ten These represent the maximum power changes over 1 minute and 10 minutes, respectively, in accordance with the standards.

[0056] Step 2: In the power smoothing stage, perform wavelet packet decomposition on the historical output power of the wind farm, and select the grid-connected power that meets the wind power grid connection fluctuation standard from the decomposition results for grid connection. The power other than the grid-connected power in the decomposition results is used as the hybrid energy storage power.

[0057] Step 3: Distribute the hybrid energy storage power to the lithium battery and flywheel for smoothing.

[0058] The improved complete set empirical mode decomposition method (ICEEMDAN) is used to decompose the power P and Pn (hereinafter referred to as P). The decomposition steps are as follows:

[0059] Definition: P is the signal to be decomposed, E k () represents the k-th mode component generated by EMD decomposition, N() represents the local mean of the generated signal, and W (i) This represents Gaussian white noise.

[0060] 1) Add I sets of white noise W to the data to be decomposed (i) Construct P (i) =P+ε1E(W (i) ), thus obtaining the first set of residuals R1 = N(P (i) ).

[0061] In the formula, ε1 represents the first IMF component E1(W) with added Gaussian white noise. (i) The coefficient multiplied when multiplying is the ratio of the signal-to-noise ratio to the standard deviation of the Gaussian white noise.

[0062] 2) Calculate the first modal component imf1 = P - R1

[0063] 3) Continue adding white noise, and use local mean decomposition to calculate the second set of residuals R1+ε1E(W). (i) Define the second modal component imf2.

[0064] imf2=R1-R2=R1-N(R1+ε1E(W (i) )).

[0065] 4) Calculate the Kth residual R k =(N(R) k-1 +ε k-1 E(w (i) ))) and modal components imfk=R k-1 -R k

[0066] 5) Continue until the calculation decomposition is completed, and obtain all modes and residuals.

[0067] The decomposition results are reconstructed to allocate power for hybrid energy storage.

[0068] The reconstructed high-frequency and low-frequency components are as follows:

[0069]

[0070]

[0071] In the formula, P H For the reconstructed high-frequency components, P L For the reconstructed low-frequency components, imfi is the i-th mode component, imfj is the j-th mode component, r is the residual, k is the decomposition level, and n is the boundary point.

[0072] Based on the characteristics of sodium-ion batteries and flywheels, they exhibit excellent complementary properties. In the hybrid energy storage system, the low-frequency portion of the total power is smoothed by the sodium-ion battery, while the high-frequency portion is smoothed by the flywheel, thus completing the power distribution of the hybrid energy storage system. This can improve the load adaptability of the energy storage device and the reliability of the power supply. For the power P that needs to be smoothed: P = P H +P L .

[0073] Step 4: Based on the power smoothed by the lithium battery and flywheel, with the goal of maximizing power utilization, the upper-level model of the hybrid energy storage capacity optimization configuration is solved using the particle swarm optimization algorithm to obtain the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the power smoothing stage.

[0074] Particle Swarm Optimization (PSO) is a swarm-based evolutionary algorithm that employs a velocity-position search model. In this algorithm, a swarm consists of m particles. The performance of each particle depends on its fitness value determined by the objective function of the problem. Each particle's direction and speed are determined by a velocity, and the particles search the solution space by following the current best particle. PSO is initialized with a swarm of random particles and iteratively searches for the optimal solution. Assuming a particle swarm of m particles searches in a D-dimensional objective space, the position of the i-th particle in the d-dimensional space is represented by the vector X. i =(x i1 , x i2 , x i3 …x id (i = 1, 2, 3, ..., m), the flight speed is represented by the vector V. i =(v i1 v i2 v i3 …v id (i = 1, 2, 3, ..., m). Each particle has a fitness value determined by the function being optimized. For the i-th particle, its best position is called its individual historical best position, P. i =(p i1 p i2 , ..., p il ), let p best (f), the corresponding fitness value is the individual's historical best fitness value F. fitness (i). The best position experienced by all particles is called the global best position, denoted by P. g =(p g1 p g2 , ..., p gl ), denoted as g best The corresponding fitness value is the global historical best fitness value F. g For the (n+1)th iteration, each particle changes as follows:

[0075] v ij (n+1)=v ij (n)w i +rand1c1[(p bestij -x ij (n)]+rand2c2[(g bestj -x ij (n)x ij (n+1)=x ij (n)+v ij (n+1)

[0076] In the formula, n is the number of iterations, n = 1, 2, 3, ... N; rand1 and rand2 are random numbers between [0, 1]; c1 and c2 are learning factors; w i This is the inertial weight.

[0077] Step 5: Based on the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the power smoothing phase, determine the optimization range of the lithium battery capacity and the optimization range of the flywheel capacity, respectively.

[0078] Step 6: When the power grid has a primary frequency regulation requirement, determine the power demand of the wind farm for primary frequency regulation based on the historical frequency of the power grid.

[0079] The formula for calculating the power demand of primary frequency regulation in a wind farm is as follows:

[0080]

[0081] In the formula, P n P represents the power demand for primary frequency regulation of the wind farm, δ% represents the frequency regulation droop rate of the wind farm (generally taken as 2%-5%), and P N f is the rated power of the wind farm, f is the historical frequency of the power grid, f0 is the rated frequency of the power grid, and f d This is a frequency modulation dead zone.

[0082] Step 7: Allocate the power demand to the lithium battery and flywheel.

[0083] The specific process is as follows: The power demand is decomposed using an improved complete set empirical mode decomposition method to obtain all modal components and residuals; then, based on all modal components and residuals, the formula is used... and The reconstruction yields high-frequency and low-frequency components. The reconstructed low-frequency components are allocated to the lithium battery, and the reconstructed high-frequency components are allocated to the flywheel.

[0084] Step 8: Based on the power output of the lithium battery and flywheel, with the goal of maximizing power utilization and the optimization ranges of lithium battery capacity and flywheel capacity as constraints, the particle swarm optimization algorithm is used to solve the lower-level model of the hybrid energy storage capacity optimization configuration to obtain the optimal capacity of the lithium battery and the optimal capacity of the flywheel in the primary frequency regulation stage.

[0085] The present invention provides a hybrid energy storage two-layer model capacity optimization configuration method, which can participate in primary frequency regulation while smoothing the power fluctuation of wind power grid connection, improve the response rate of wind farms to participate in the primary frequency regulation of the power grid, smooth wind power fluctuation, and maintain the frequency stability of the power system.

[0086] This invention also provides a hybrid energy storage two-layer model capacity optimization configuration system, comprising:

[0087] The model building module is used to build the upper-level model of hybrid energy storage capacity optimization configuration in the power smoothing stage and the lower-level model of hybrid energy storage capacity optimization configuration in the primary frequency regulation stage, respectively.

[0088] The decomposition module is used to perform wavelet packet decomposition on the historical output power of the wind farm during the power smoothing phase, and select grid-connected power that meets the wind power grid connection fluctuation standard from the decomposition results for grid connection. Power other than grid-connected power in the decomposition results is used as hybrid energy storage power.

[0089] A smoothing module is used to distribute the hybrid energy storage power to the lithium battery and flywheel for smoothing.

[0090] The power smoothing stage optimization module is used to solve the upper-level model of the hybrid energy storage capacity optimization configuration based on the smoothed power of the lithium battery and flywheel, with the goal of maximizing power utilization, and obtain the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the power smoothing stage.

[0091] The optimization range determination module is used to determine the optimization range of lithium battery capacity and the optimization range of flywheel capacity based on the optimal capacity of lithium battery and flywheel during the power smoothing stage, respectively.

[0092] The power demand calculation module is used to determine the power demand of the wind farm for primary frequency regulation based on the historical frequency of the power grid when the power grid has a primary frequency regulation demand.

[0093] A distribution module is used to allocate the power demand to the lithium battery and flywheel;

[0094] The primary frequency regulation stage optimization module is used to solve the lower-level model of the hybrid energy storage capacity optimization configuration based on the power output of the lithium battery and flywheel, with the goal of maximizing power utilization and the optimization range of lithium battery capacity and flywheel capacity as constraints. This results in the optimal capacity of the lithium battery and flywheel during the primary frequency regulation stage.

[0095] The hybrid energy storage dual-layer model capacity optimization configuration system provided in this embodiment of the invention has a similar working principle and beneficial effects to the hybrid energy storage dual-layer model capacity optimization configuration method described in the above embodiments, so it will not be described in detail here. For details, please refer to the introduction of the above method embodiments.

[0096] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned hybrid energy storage two-layer model capacity optimization configuration method.

[0097] Furthermore, when the computer program in the aforementioned memory is implemented as 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 technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0098] Furthermore, the present invention also provides a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed, implements the aforementioned hybrid energy storage two-layer model capacity optimization configuration method.

[0099] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0100] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimizing the capacity configuration of a hybrid energy storage two-layer model, characterized in that, include: Establish upper-level models for hybrid energy storage capacity optimization configuration during the power smoothing stage and lower-level models for hybrid energy storage capacity optimization configuration during the primary frequency regulation stage, respectively. During the power smoothing phase, the historical output power of the wind farm is decomposed by wavelet packet, and the grid-connected power that meets the wind power grid connection fluctuation standard is selected from the decomposition results for grid connection. The power other than the grid-connected power in the decomposition results is used as the hybrid energy storage power. The hybrid energy storage power is distributed to the lithium battery and flywheel for smoothing; Based on the power smoothed by the lithium battery and flywheel, with the goal of maximizing power utilization, the upper-level model of the hybrid energy storage capacity optimization configuration is solved using the particle swarm optimization algorithm to obtain the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the power smoothing stage. Based on the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the power smoothing phase, the optimization range of the lithium battery capacity and the optimization range of the flywheel capacity are determined respectively. When the power grid has a primary frequency regulation requirement, the power demand of the wind farm for primary frequency regulation is determined based on the historical frequency of the power grid. The power demand is allocated to the lithium battery and flywheel; Based on the power output of the lithium battery and flywheel, with the goal of maximizing power utilization, and with the optimization range of lithium battery capacity and flywheel capacity as constraints, the particle swarm optimization algorithm is used to solve the lower-level model of the hybrid energy storage capacity optimization configuration, so as to obtain the optimal capacity of lithium battery and flywheel in the primary frequency regulation stage. Distributing the hybrid energy storage power to the lithium battery and flywheel for smoothing out the impact specifically includes: An improved complete set empirical mode decomposition method is used to decompose the hybrid energy storage power to obtain all modal components and residuals; Based on all modal components and residuals, using the formula and The reconstruction yields high-frequency and low-frequency components; where P H For the reconstructed high-frequency components, P L For the reconstructed low-frequency components, imfi is the i-th mode component, imfj is the j-th mode component, r is the residual, k is the decomposition level, and n is the boundary point; The reconstructed low-frequency components are distributed to the lithium battery for smoothing, and the reconstructed high-frequency components are distributed to the flywheel for smoothing.

2. The capacity optimization configuration method for a hybrid energy storage two-layer model according to claim 1, characterized in that, The establishment of the upper-level model for the hybrid energy storage capacity optimization configuration during the power smoothing phase specifically includes: The smoothing index for the power smoothing phase is: In the formula, φ1 is the smoothing index of the power smoothing stage, PH′(t) and PL′(t) are the high-frequency fluctuation component and low-frequency fluctuation component after the hybrid energy storage smoothing at time t, respectively, and T is the sampling period; Establish an objective function for electrical energy, E, with the goal of maximizing energy utilization. min1 =E qf +E qe In the formula, E min1 The objective function for electrical energy during the power smoothing phase; E qf E is the penalty electricity for wind curtailment during the power smoothing phase. qf =∑P qf ·Δt, P qf E represents the wind curtailment power at time t, where Δt is the time interval. qe E is the penalty for the power smoothing phase. qe =∑P qe ·Δt, P qe Let be the power deficit at time t; By weighting the smoothing index and the power objective function during the power smoothing phase, the capacity objective function for the power smoothing phase is obtained as F. min1 =α1·E min1 +β1·Φ1; where, F min1 Let α1 and β1 be the capacity objective function for the power smoothing stage, and α1 and β1 be the first and second weights, respectively. The optimal values ​​of the first and second weights are determined by using the entropy weight method. These optimal values ​​are then substituted into the capacity objective function of the power smoothing stage to obtain the upper-level model for the hybrid energy storage capacity optimization configuration in the power smoothing stage.

3. The capacity optimization configuration method for a hybrid energy storage two-layer model according to claim 2, characterized in that, Establish a lower-level model for optimal configuration of hybrid energy storage capacity during the primary frequency regulation stage, specifically including: Constructing the smoothing index for the primary frequency regulation phase is as follows In the formula, φ2 is the smoothing index of the primary frequency modulation stage, and P n H′(t) and P n L′(t) represents the high-frequency fluctuation component and the low-frequency fluctuation component of the hybrid energy storage output at time t, respectively. Establish an objective function for electrical energy, E, with the goal of maximizing energy utilization. min2 =E pun -E stp In the formula, E min2 The objective function for electrical energy during the primary frequency regulation phase; E pun E is the penalty electricity for wind curtailment during the first frequency regulation phase. pun =∑Plack·Δt, where Plack is the differential power of the primary frequency modulation at time t; E stp E is the penalty for the shortfall during a frequency modulation phase. stp =∑(P na (t)+P nf (t))Δt,P na (t) and P nf (t) represents the power of the lithium battery and flywheel participating in the first frequency modulation at time t, respectively; By weighting the smoothing index and the power objective function for the primary frequency regulation stage, the capacity objective function for the primary frequency regulation stage is obtained as F. min2 =α2·E min2 +β2·Φ2; where, F min2 Let α2 and β2 be the capacity objective function for the first frequency regulation stage, with α2 and β2 being the third and fourth weights, respectively. The optimal values ​​of the third and fourth weights are determined by the entropy weight method, and then the optimal values ​​of the third and fourth weights are substituted into the capacity objective function of the primary frequency regulation stage to obtain the lower-level model for the optimal configuration of hybrid energy storage capacity in the primary frequency regulation stage.

4. The capacity optimization configuration method for a hybrid energy storage two-layer model according to claim 3, characterized in that, The upper-level model for hybrid energy storage capacity optimization configuration in the power smoothing stage and the lower-level model for hybrid energy storage capacity optimization configuration in the primary frequency regulation stage both include SOC constraints, energy storage charging and discharging power constraints at time t, and volatility constraints. The SOC constraint is: Where, SOC a (t) represents the state of charge (SOC) of the lithium battery at time t. oclow and S ocup These represent the minimum and maximum SOC values ​​for lithium batteries; SOC f (t) represents the state of charge (SOC) of the flywheel, S OClow and S OCup These are the minimum and maximum SOC values ​​of the flywheel, respectively. The energy storage charging and discharging power constraint at time t is: and In the formula, P a(t) Let P be the power of the lithium battery at time t. ar For the stable range of lithium batteries, η a The charge / discharge efficiency of lithium batteries; P f(t) Let P be the power of the flywheel at time t. fr At the stable kilometer point of the flywheel, η f The charging and discharging efficiency of the flywheel; The volatility constraint is: In the formula, α1′ and α 10 The volatility α represents the 1-minute and 10-minute fluctuations required for grid connection, respectively. one and α ten These represent the maximum power changes over 1 minute and 10 minutes, respectively, in accordance with the standards.

5. The capacity optimization configuration method for a hybrid energy storage two-layer model according to claim 1, characterized in that, The formula for calculating the power demand of the primary frequency regulation of the wind farm is as follows: In the formula, P n P represents the power demand for primary frequency regulation in a wind farm, δ% represents the frequency regulation droop rate of the wind farm, and P represents the power demand for primary frequency regulation in a wind farm. N f is the rated power of the wind farm, f is the historical frequency of the power grid, f0 is the rated frequency of the power grid, and f d This is a frequency modulation dead zone.

6. A hybrid energy storage two-layer model capacity optimization configuration system, characterized in that, include: The model building module is used to build the upper-level model of hybrid energy storage capacity optimization configuration in the power smoothing stage and the lower-level model of hybrid energy storage capacity optimization configuration in the primary frequency regulation stage, respectively. The decomposition module is used to perform wavelet packet decomposition on the historical output power of the wind farm during the power smoothing phase, and select grid-connected power that meets the wind power grid connection fluctuation standard from the decomposition results for grid connection. Power other than grid-connected power in the decomposition results is used as hybrid energy storage power. A smoothing module is used to distribute the hybrid energy storage power to the lithium battery and flywheel for smoothing purposes. The power smoothing stage optimization module is used to solve the upper-level model of the hybrid energy storage capacity optimization configuration based on the smoothed power of the lithium battery and flywheel, with the goal of maximizing power utilization, and obtain the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the power smoothing stage. The optimization range determination module is used to determine the optimization range of lithium battery capacity and the optimization range of flywheel capacity based on the optimal capacity of lithium battery and flywheel during the power smoothing stage, respectively. The power demand calculation module is used to determine the power demand of the wind farm for primary frequency regulation based on the historical frequency of the power grid when the power grid has a primary frequency regulation demand. A distribution module is used to allocate the power demand to the lithium battery and flywheel; The primary frequency regulation stage optimization module is used to solve the lower-level model of the hybrid energy storage capacity optimization configuration based on the power output of the lithium battery and flywheel, with the goal of maximizing power utilization and the optimization range of lithium battery capacity and flywheel capacity as constraints. This results in obtaining the optimal capacity of the lithium battery and the optimal capacity of the flywheel during the primary frequency regulation stage. Distributing the hybrid energy storage power to the lithium battery and flywheel for smoothing out the impact specifically includes: An improved complete set empirical mode decomposition method is used to decompose the hybrid energy storage power to obtain all modal components and residuals; Based on all modal components and residuals, using the formula and The reconstruction yields high-frequency and low-frequency components; where P H For the reconstructed high-frequency components, P L For the reconstructed low-frequency components, imfi is the i-th mode component, imfj is the j-th mode component, r is the residual, k is the decomposition level, and n is the boundary point; The reconstructed low-frequency components are distributed to the lithium battery for smoothing, and the reconstructed high-frequency components are distributed to the flywheel for smoothing.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the capacity optimization configuration method for a hybrid energy storage two-layer model as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed, implements the capacity optimization configuration method for a hybrid energy storage two-layer model as described in any one of claims 1 to 5.

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