A method and system for optimizing hybrid energy storage capacity of photovoltaic distribution network based on VMD

Abstract

The present invention discloses a method and system for optimizing the hybrid energy storage capacity of a photovoltaic distribution network based on VMD. The method includes: obtaining a hybrid energy storage reference power based on the difference between the photovoltaic field output power and the grid-connected power; determining the number of decomposition layers through an optimized variational modal decomposition algorithm, decomposing the hybrid energy storage reference power according to the determined number of decomposition layers to obtain multiple modal components; determining the minimum cost of the hybrid energy storage system over its entire life cycle, and determining the rated power and rated capacity of the hybrid energy storage system under the minimum cost condition based on the charge and discharge consistency of power-type energy storage and energy-type energy storage and the energy storage working characteristics of energy-type energy storage according to the multiple modal components, thereby obtaining an optimal configuration scheme. The present invention can more accurately determine the number of modal decomposition layers and has better overall performance, thereby balancing energy storage capacity, extending energy storage life, improving energy utilization efficiency, reducing the number of energy storage replacements and replacement costs, and further improving economic efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic distribution network hybrid energy storage capacity optimization configuration technology, and in particular to a photovoltaic distribution network hybrid energy storage capacity optimization method and system based on VMD. Background Art

[0002] As the proportion of photovoltaic power in distribution networks increases, the impact of power fluctuations on the safe operation of distribution networks is also increasing. However, configuring photovoltaic power stations with only energy storage not only fails to meet the technical requirements for flexible grid integration, but also increases investment costs. Therefore, in recent years, hybrid energy storage systems (HESSs), which combine power and energy storage systems, have gained widespread adoption and are considered a promising solution for simultaneously meeting the flexible technical requirements and economic requirements for photovoltaic station grid integration. By rationally configuring HESS hybrid energy storage to smooth out photovoltaic power output fluctuations, it is possible to promote photovoltaic grid integration, increase grid connection benefits, and reduce energy storage deployment costs.

[0003] Most existing hybrid energy storage capacity configuration methods use the VMD (Variational Mode Decomposition) algorithm to decompose the hybrid energy storage output power. These components are then allocated to power and energy storage based on frequency, and the HESS capacity and power configuration is determined based on operating costs and constraints. However, when allocating power between power and energy storage, the potential for inconsistent charging and discharging states of power and energy storage at the same time, as well as the operating characteristics of energy storage, are not fully considered in actual operation. This results in additional charge and discharge conversions, shortening the energy storage lifespan and leading to increased replacement frequency and overall costs. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: In response to the above-mentioned problems of the prior art, a method and system for optimizing the hybrid energy storage capacity of a photovoltaic distribution network based on VMD is provided, which comprehensively considers the charging and discharging status and energy storage working characteristics, so as to extend the energy storage life, reduce the overall cost, and improve the economy and overall performance.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A hybrid energy storage capacity optimization method for photovoltaic distribution network based on VMD, comprising:

[0007] Step S1: Calculate the hybrid energy storage reference power; obtain the hybrid energy storage reference power according to the difference between the photovoltaic field output power and the grid-connected power;

[0008] Step S2: variational modal decomposition; determining the number of decomposition layers by an optimized variational modal decomposition algorithm, and decomposing the hybrid energy storage reference power according to the determined number of decomposition layers to obtain multiple modal components;

[0009] Step S3: Determine the optimal configuration; determine the minimum cost of the hybrid energy storage system over its entire life cycle, determine the rated power and rated capacity of the hybrid energy storage system under the minimum cost condition based on the charge and discharge consistency of the power-type energy storage and the energy-type energy storage and the energy storage working characteristics of the energy-type energy storage according to the multiple modal components, and obtain the optimal configuration solution.

[0010] Furthermore, the step S1 includes:

[0011] Monitor the maximum photovoltaic fluctuation of the photovoltaic field according to the output power of the photovoltaic field;

[0012] When the maximum photovoltaic fluctuation of the photovoltaic field is monitored to exceed the grid-connected standard limit, the Gaussian filtering algorithm is used to obtain the grid-connected power, and the hybrid energy storage reference power is calculated based on the difference between the photovoltaic field output power and the grid-connected power.

[0013] Furthermore, the step S2 includes:

[0014] The FTTA-VMD algorithm is used to optimize the mode number K and the quadratic penalty term α in the variational mode decomposition algorithm by taking the minimum value of envelope entropy as the fitness function for optimizing VMD parameters.

[0015] The hybrid energy storage reference power is subjected to variational modal decomposition according to the found optimal combination of the modal number K and the quadratic penalty term α to obtain K modal components.

[0016] Furthermore, step S3 includes:

[0017] Step S31: Establishing a full life cycle cost model for the hybrid energy storage system, calculating the full life cycle cost of the hybrid energy storage system under different high and low frequency cutoff points of the modal components, and obtaining the corresponding high and low frequency cutoff points under the optimal cost;

[0018] Step S32: According to the high-low frequency dividing point at the optimal cost, the modal components above the high-low frequency dividing point among the multiple modal components are allocated to the power-type energy storage, and the modal components below the high-low frequency dividing point are allocated to the energy-type energy storage, thereby obtaining an initial power allocation result;

[0019] Step S33: optimizing the distribution of the power-type energy storage and the energy-type energy storage after the initial power distribution based on the charge-discharge consistency of the power-type energy storage and the energy-type energy storage and the energy storage working characteristics of the energy-type energy storage, to obtain a secondary power distribution result;

[0020] Step S34: Determine the rated power and rated capacity of the hybrid energy storage system according to the secondary power distribution result to obtain an optimal configuration solution.

[0021] Furthermore, in step S31, the full life cycle cost model of the hybrid energy storage system is calculated based on the sum of investment cost, operation and maintenance cost, decommissioning cost, replacement cost and auxiliary cost minus the recovered residual value.

[0022] Furthermore, the step S33 includes:

[0023] Step S331: adjusting the initial power allocation result according to the charge and discharge states of the power-type energy storage and the energy-type energy storage. If the charge and discharge states of the two are inconsistent, allocating all the total power to the power-type energy storage and the energy-type energy storage with the larger power value to obtain an intermediate power allocation result;

[0024] Step S332: Adjust the intermediate power allocation result according to the power value of the energy-type energy storage in the intermediate power allocation result. If the power value of the energy-type energy storage is lower than the preset threshold, allocate all the total power to the power-type energy storage to obtain the secondary power allocation result.

[0025] Furthermore, the step S331 includes:

[0026] The initial power allocation result is judged and adjusted according to the charge-discharge consistency coefficient, and the expression is:

[0027]

[0028]

[0029] In the above formula, and are the power of energy type energy storage and power type energy storage at time t after charge and discharge consistency optimization, μ is the charge and discharge consistency coefficient, e is a natural constant, and are the powers of energy type energy storage and power type energy storage at time t after the initial distribution respectively.

[0030] Furthermore, the expression of step S332 is:

[0031]

[0032] In the above formula, and are the powers of energy-type energy storage and power-type energy storage at time t after adjustment considering the energy storage working characteristics, and d is the preset threshold.

[0033] Furthermore, the step S34 includes:

[0034] The configured rated power should absorb or supplement the maximum excess power or maximum power shortage of the energy storage power during the assessment period. The rated power is calculated by combining the converter efficiency and the charge and discharge efficiency of the energy storage element. The expression is:

[0035] In the above formula, and are the rated power of lithium battery and supercapacitor respectively, are the discharge power and charging power of the lithium battery at time t after the secondary power distribution is optimized through charge and discharge consistency and energy storage working characteristics, They are the discharge power and charging power of the supercapacitor at time t after the secondary power distribution is optimized through charge and discharge consistency and energy storage working characteristics, is the initial moment, are the efficiencies of DC-DC and DC-AC respectively, are the charging and discharging efficiencies of lithium batteries, are the charging and discharging efficiencies of the supercapacitor, respectively, and T is the operating cycle;

[0036] The rated capacity is calculated based on the state of charge of the energy storage itself, and the expression is:

[0037]

[0038] In the above formula, and are the rated capacities of lithium batteries and supercapacitors respectively, Energy storage reference state of charge for lithium batteries and supercapacitors respectively, The upper and lower limits of the state of charge of the lithium battery, The upper and lower limits of the supercapacitor's state of charge.

[0039] The present invention further provides a VMD-based photovoltaic distribution network hybrid energy storage capacity optimization system, comprising an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the above-mentioned VMD-based photovoltaic distribution network hybrid energy storage capacity optimization method.

[0040] Compared with the prior art, the advantages of the present invention are:

[0041] The present invention decomposes the hybrid energy storage reference power into multiple modal components through an optimized variational modal decomposition algorithm. Compared with the conventional VMD algorithm, the degree of aliasing between the modal components can be further reduced, making the determination of the number of modal decomposition layers more accurate and the overall performance better. The rated power and rated capacity configuration scheme of the hybrid energy storage system under the lowest cost conditions is determined through a two-layer power optimization allocation strategy based on charge and discharge consistency and energy storage working characteristics. This can balance the energy storage capacity, extend the energy storage life, improve energy utilization efficiency, reduce the number of energy storage replacements and replacement costs, and further improve economic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the flow of a method for optimizing hybrid energy storage capacity in a photovoltaic distribution network based on VMD according to an embodiment of the present invention.

[0043] Figure 2 Detailed diagram of the system structure and optimization process of the VMD-based photovoltaic distribution network hybrid energy storage capacity optimization method according to an embodiment of the present invention.

[0044] Figure 3 1 is a graph showing actual output power on a typical day in an embodiment of the present invention.

[0045] Figure 4 This is a comparison diagram of photovoltaic power before and after using Gaussian filtering and other filtering methods in an embodiment of the present invention.

[0046] Figure 5 is the probability distribution of 1-minute volatility on a typical day in the embodiment of the present invention.

[0047] Figure 6 : is the probability distribution of the 10-minute volatility on a typical day in an embodiment of the present invention.

[0048] Figure 7 This is a specific flow chart of optimizing VMD parameters using the FTTA-VMD algorithm in an embodiment of the present invention.

[0049] Figure 8 1 is a comparison chart of the algorithm iteration results of the FTTA-VMD algorithm and other optimization algorithms in an embodiment of the present invention.

[0050] Figure 9 This is the Hilbert marginal spectrum using the EMD algorithm in an embodiment of the present invention.

[0051] Figure 10 This is the Hilbert marginal spectrum using the FTTA-VMD algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0053] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a method for optimizing hybrid energy storage capacity of a photovoltaic distribution network based on VMD, comprising:

[0054] Step S1: Calculate the hybrid energy storage reference power; obtain the hybrid energy storage reference power according to the difference between the photovoltaic field output power and the grid-connected power;

[0055] Step S2: variational modal decomposition; determining the number of decomposition layers using an optimized variational modal decomposition algorithm, and decomposing the hybrid energy storage reference power according to the determined number of decomposition layers to obtain multiple modal components;

[0056] Step S3: Determine the optimal configuration; determine the minimum cost of the hybrid energy storage system over its entire life cycle, determine the rated power and rated capacity of the hybrid energy storage system under the lowest cost condition based on multiple modal components, the charge and discharge consistency of power-type energy storage and energy-type energy storage, and the energy storage working characteristics of energy-type energy storage, and obtain the optimal configuration solution.

[0057] It can be understood that this embodiment decomposes the hybrid energy storage reference power through an optimized variational modal decomposition algorithm to obtain multiple modal components. Compared with the conventional VMD algorithm, it can further reduce the degree of aliasing between the modal components, making the determination of the number of modal decomposition layers more accurate and the overall performance better. The two-layer power optimization allocation strategy based on charge and discharge consistency and energy storage working characteristics is used to determine the rated power and rated capacity configuration scheme of the hybrid energy storage system under the lowest cost conditions. This can balance the energy storage capacity, extend the energy storage life, improve energy utilization efficiency, reduce the number of energy storage replacements and replacement costs, and further improve economic efficiency.

[0058] In this embodiment, step S1 includes:

[0059] Monitor the maximum photovoltaic fluctuation of the photovoltaic field according to the output power of the photovoltaic field;

[0060] When it is monitored that the maximum photovoltaic fluctuation of the photovoltaic field exceeds the grid-connected standard limit, the Gaussian filtering algorithm (preferably the adaptive Gaussian filtering algorithm AGF) is used to obtain the grid-connected power, and the hybrid energy storage reference power is calculated based on the difference between the photovoltaic field output power and the grid-connected power.

[0061] Specifically, the expression of the maximum photovoltaic fluctuation is:

[0062] (1)

[0063] In the above formula, is the maximum photovoltaic fluctuation, is the photovoltaic field output power at time t, is the photovoltaic field output power at time (t+1), and max is the maximum value.

[0064] The expression of grid-connected power is:

[0065] (2)

[0066] In the above formula, is the grid-connected power at time t, i is the original data number, j is the data number in the window, L is the sliding window length at time i, is the AGF weight coefficient (preferably set to 1).

[0067] The expression of hybrid energy storage power is:

[0068] (3)

[0069] In the above formula, is the reference power of hybrid energy storage at time t.

[0070] In an exemplary application, the actual value of a typical day in a 30MW photovoltaic field is as follows: Figure 3 The output power data shown in the figure (sampling time is 1 minute) is obtained by passing Gaussian filtering, sliding average and exponential average algorithms on the raw data obtained. Figure 4 The graphs of the filtered PV power and raw PV power using various algorithms are shown. It can be seen that the grid-connected power after AGF filtering is significantly smoother and the power fluctuation rate is significantly reduced, indicating that AGF can effectively assist in the safe operation of high-proportion PV distribution networks. This also verifies that the adaptive Gaussian filtering algorithm can accurately extract the reference power of the HESS hybrid energy storage system, significantly improving the efficiency of PV power tracking, reducing energy storage pressure, and enhancing economic efficiency.

[0071] Subsequently, the Gaussian filtered data was simulated and analyzed in Matlab, and the probability distribution of power fluctuation rate for 10 minutes and 1 minute was calculated as follows: Figure 5 and Figure 6 As shown, the data shown in Table 1 are obtained through analysis:

[0072]

[0073] Table 1

[0074] From the data in Table 1, we can see that the maximum fluctuation rate of photovoltaic power in 1 minute is 10.49%, and the maximum fluctuation rate of photovoltaic power in 10 minutes is 40.05%, both of which are lower than my country's photovoltaic power station grid connection standards of 20% (maximum fluctuation rate upper limit in 1 minute) and 100% (maximum fluctuation rate upper limit in 10 minutes), meeting the requirements of the grid connection standards.

[0075] In this embodiment, step S2 includes:

[0076] The FTTA-VMD algorithm is used to optimize the mode number K and the quadratic penalty term α in the variational mode decomposition algorithm by taking the minimum value of envelope entropy as the fitness function for optimizing VMD parameters.

[0077] According to the optimal combination of the found mode number K and the quadratic penalty term α, the hybrid energy storage reference power is subjected to variational modal decomposition to obtain K modal components.

[0078] Specifically, to maximize the operational characteristics of the lithium batteries and supercapacitors in the HESS during charging and discharging, the FTTA-VMD (Football Team Training Algorithm) algorithm is preferably used to decompose the HESS power obtained after smoothing the AGF filter. FTTA is a metaheuristic algorithm that simulates the player behavior during high-level football team training. FTTA's practical application far surpasses other optimization algorithms, effectively improving the utilization efficiency of renewable energy. Furthermore, its use in optimizing variational mode decomposition (VMD) can further enhance data denoising. FTTA simulates the three components of a football training session: collective training, small group training, and individual additional training. Traditional VMD algorithms require a predefined number of modes, K, and a quadratic penalty term, α. The values ​​of K and α determine the adequacy of the original signal decomposition. Envelope entropy is an important indicator of the original signal's sparsity; smaller envelope entropy values ​​indicate lower aliasing between the decomposed modal components. Applying FTTA to optimize the VMD parameter combination [K, α] significantly improves the objectivity of VMD parameter selection.

[0079] The FTTA optimization algorithm can be divided into four stages:

[0080] 1. Initialization phase

[0081] Set population size N ′, maximum number of iterations , the dimension of the search space , search the initial individual set and calculate the fitness value.

[0082] 2. Group training phase

[0083] FTTA simulates the process of players training together under the guidance of a coach. Individuals are categorized as: Followers, Discoverers, Thinkers, and Fluctuators. In each iteration, the individual's type randomly changes. The mathematical model for the four types of individuals is as follows:

[0084] (1) Followers

[0085] Followers have average performance and improve their performance by imitating the best players. The follower mathematical model is as follows:

[0086] (4)

[0087] In the above formula, k is the number of iterations, is the value of the current best player, is the value of the i-th player on the current dimension j, is the state of the i-th player in dimension j before training, is the state of the i-th player in dimension j after training.

[0088] (2) Discoverer

[0089] The Discoverer has the potential to surpass the current best player by exploring new strategies. The Discoverer mathematical model is as follows:

[0090] (5)

[0091] In the above formula, The worst player currently.

[0092] (3) Thinkers

[0093] Thinkers excel in theory and strategy, improving performance by analyzing the key differences between the current best and worst solutions. The Thinker mathematical model is as follows:

[0094] (6)

[0095] (4) Fluctuators

[0096] Volatility performance is poor, and by randomly searching in the solution space, it brings innovation and unexpected possibilities to the entire team. The mathematical model of the volatile player is as follows:

[0097] (7)

[0098] In the above formula, t(k) is a random number with a t-distribution, whose degrees of freedom are the current number of iterations. As the degrees of freedom increase, the probability of the t-distribution approaching the middle value (0) increases, while the distribution at both ends gradually decreases, becoming closer to a normal distribution. Therefore, as the number of iterations increases, the degree of fluctuation decreases, and the search gradually shifts from a global search to a local search.

[0099] 3. Group training stage

[0100] FTTA simulates the process of players entering group training after collective training. Individuals are divided into forwards, midfielders, defenders, and goalkeepers. During group training, FTTA uses the MGEM adaptive clustering method (MixGaussEM) to divide individuals into four groups. The specific classification format is as follows:

[0101] (8)

[0102] The number of people in each group must be greater than or equal to 2, otherwise group training cannot be implemented. At this time, the coach will conduct a second grouping, using a uniform random grouping strategy to randomly and evenly divide the players into four groups.

[0103] After the grouping is completed, the players in the group learn or communicate, and group training is defined as three states: optimal learning, random learning and random communication. The learning probability is defined as , the communication probability is , the player randomly selects a state in each iteration, and the mathematical model of the three states is as follows:

[0104] (1) Optimal Learning

[0105] In each dimension, a player has a certain probability of directly learning the ability of the best player in the group. The mathematical model is as follows:

[0106] (9)

[0107] In the above formula, Is the best player in group i It is the state in which players have undergone optimal learning.

[0108] (2) Stochastic Learning

[0109] In each dimension, a player has a certain probability of directly learning the ability of any random player in the group. The mathematical model is as follows:

[0110] (10)

[0111] In the above formula, is a random player of dimension j in group i, It is the state of the player after random learning.

[0112] (3) Random communication

[0113] In training, learning is only part of it, and communication between two players is more important for improving their abilities. In each dimension, a player has a certain probability of communicating with any player in the group. The mathematical model is as follows:

[0114] (11)

[0115] In the above formula, is a random player in j dimensions in group i, and the players exchanged their abilities in j dimensions; Random is a normally distributed random number, multiplied by Indicates the two players' understanding of the others' abilities.

[0116] (4) Random error

[0117] During training, there is a certain probability that an error will occur, that is, a player accidentally learns the content of another player in another dimension. The probability of this happening is low, but it is real and objective. Define the error probability as , the mathematical model is as follows:

[0118] (12)

[0119] 4. Personal additional training

[0120] FTTA simulates the process of recalculating new physical fitness values ​​after group training and updating the player's status with better physical fitness values. The mathematical model is as follows:

[0121] (13)

[0122] In the above formula, (Cauchy) and (Gaussian) joint variation is used to describe individual additional training. The reason for choosing Gaussian Cauchy distribution is that it can effectively provide a large range of improvements to players in the early stages of training, which is beneficial for global search. As the number of iterations increases, the Gaussian distribution accounts for a larger proportion, which is more conducive to local search.

[0123] The minimum value of envelope entropy is used as the fitness function of FTTA to optimize VMD parameters. The flow chart of FTTA to optimize VMD parameters is as follows: Figure 7 As shown:

[0124] The first step is to initialize the FTTA optimization parameters and randomly generate the [K, α] combination as the initial input of the optimization process; the second step is to calculate the fitness of the current parameter combination to measure the quality of the current solution and provide a reference for subsequent optimization; the third step is to enter the main loop, and the loop variable i starts from 1 until the maximum number of iterations In each iteration, find the best and worst solutions in the current population, improve the fitness of all individuals by training the population as a whole, and group the population into multiple clusters to facilitate subsequent collaboration and optimization; the fourth step is to calculate the population size and determine whether the current population size is less than the number of teams. number. If the population is smaller than the number of teams, random error, random communication, random learning, and learning optimization steps are executed. After each training and learning, the fitness is recalculated and the [K, α] combination with better fitness is updated to keep the population evolving in a better direction. If the population is larger than the number of teams, the optimal additional training phase is directly entered. In the fifth step, the optimal additional training is performed. The system tries to find a better solution and conducts additional training on individuals with higher fitness to further improve the overall optimization effect. In the sixth step, it is determined whether the new solution is better than the current solution. If the new solution is better than the current solution, the current population is updated to the new solution, and the population is continued to be updated and optimized. If the new solution is not better than the current solution, the current solution is kept unchanged to avoid degradation. In the seventh step, when the maximum number of iterations is reached or the termination condition is met, the algorithm ends and outputs the optimized [K, α] combination as the final optimal solution.

[0125] In an exemplary application, the initial parameters of the FTTA algorithm are set as follows: the dimension of the search space Dim = 2, the number of populations N' = 10, the maximum number of iterations , and its iterative results are compared with other algorithms. Figure 8 As shown in the figure, it can be seen that other optimization algorithms need to be iterated at least twice to obtain the minimum fitness function value of 0.0779543, and there is a phenomenon of falling into local optimality, while FTTA only needs to be iterated once to obtain the minimum fitness function value, the optimal K value is 10, and the optimal α value is 2000. In other words, the optimization speed and accuracy of the FTTA algorithm are better than other algorithms, and the global optimization ability is stronger. In addition, please refer to Figure 9 and Figure 10 , for hybrid energy storage power A comparative analysis was conducted using the traditional EMD method and the FTTA-VMD method. From the Hilbert marginal spectrum after decomposition, it can be seen that the results obtained by EMD decomposition have serious modal aliasing, while the use of FTTA-VMD decomposition effectively avoids this problem, further demonstrating that the FTTA-VMD method has better performance.

[0126] Subsequently, the hybrid energy storage power is decomposed according to the optimal solution to obtain the corresponding number of sub-components IMF, and the IMF is Hilbert transformed. According to the minimum allocation of adjacent mode aliasing of the Hilbert marginal spectrum, the low-frequency power component is allocated to the lithium battery, and the high-frequency power component and the margin are allocated to the supercapacitor according to the difference in characteristics between power-type energy storage and energy-type energy storage. The initial power allocation result is shown in the following formula (14):

[0127] (14)

[0128] In the above formula, and The power of the lithium battery and supercapacitor after allocation at time t, Represents the high and low frequency critical points of the decomposition (refer to step S31 for specific determination method), is the Xth modal component of the decomposition, K is the total number of decomposed modes, and res is the decomposed modal residual.

[0129] In this embodiment, step S3 includes:

[0130] Step S31: Establishing a full life cycle cost model for the hybrid energy storage system, calculating the full life cycle cost of the hybrid energy storage system under different high and low frequency cutoff points of the modal components, and obtaining the corresponding high and low frequency cutoff points under the optimal cost;

[0131] Step S32: Based on the high- and low-frequency dividing points at the optimal cost, the modal components above the high- and low-frequency dividing points among the multiple modal components are allocated to the power-type energy storage, and the modal components below the high- and low-frequency dividing points are allocated to the energy-type energy storage, thereby obtaining an initial power allocation result;

[0132] Step S33: optimizing the distribution of the power-type energy storage and the energy-type energy storage after the initial power distribution based on the charge-discharge consistency of the power-type energy storage and the energy-type energy storage and the energy storage working characteristics of the energy-type energy storage, to obtain a secondary power distribution result;

[0133] Step S34: Determine the rated power and rated capacity of the hybrid energy storage system according to the secondary power distribution result to obtain the optimal configuration solution.

[0134] In step S31 of this embodiment, the full life cycle cost model of the hybrid energy storage system is obtained by subtracting the recovery residual value from the sum of the investment cost, operation and maintenance cost, decommissioning disposal cost, replacement cost, and auxiliary cost. The specific expression is:

[0135] (15)

[0136] In the above formula, is the HESS life cycle cost, is the investment cost incurred during the entire life cycle, For operation and maintenance costs, is the decommissioning disposal cost, To update the replacement cost, For auxiliary costs, To recover the residual value.

[0137] The expression of investment cost is as follows:

[0138] (16)

[0139] In the above formula, is the investment cost of HESS, are the investment costs of lithium batteries and supercapacitors respectively, are the unit power investment costs of lithium batteries and supercapacitors, are the unit capacity investment costs of lithium batteries and supercapacitors, r is the base discount rate, k is the kth year in the entire life cycle, and n is the number of replacements of the energy storage equipment ( The number of times the lithium battery is replaced, is the number of supercapacitor replacements), The full life cycle period, is the rated power of the lithium battery in the HESS, is the rated power of the supercapacitor in the HESS, is the rated capacity of the lithium battery in the HESS, is the rated capacity of the supercapacitor in the HESS.

[0140] The expression of operation and maintenance cost is as follows:

[0141] (17)

[0142] In the above formula, is the operation and maintenance cost of HESS, are the operation and maintenance costs of lithium batteries and supercapacitors respectively, The unit power operation and maintenance costs of lithium batteries and supercapacitors are respectively, They are the unit capacity operation and maintenance costs of lithium batteries and supercapacitors respectively. They are the annual charge and discharge capacity of lithium batteries and supercapacitors respectively.

[0143] The expression of decommissioning disposal cost is as follows:

[0144] (18)

[0145] In the above formula, is the decommissioning and disposal cost of HESS, are the decommissioning costs of lithium batteries and supercapacitors, are the unit power decommissioning disposal costs of lithium batteries and supercapacitors, are the unit capacity retirement disposal costs of lithium batteries and supercapacitors respectively, and j is the jth year in the entire life cycle.

[0146] The expression for the update replacement cost is as follows:

[0147] (19)

[0148] In the above formula, is the replacement cost of HESS, The replacement costs of lithium batteries and supercapacitors respectively; The unit power replacement costs of lithium batteries and supercapacitors are respectively, They are the unit capacity update and replacement costs of lithium batteries and supercapacitors respectively.

[0149] The expression for auxiliary equipment cost is as follows:

[0150] (20)

[0151] In the above formula, is the cost of auxiliary equipment for HESS, are the auxiliary equipment costs of lithium batteries and supercapacitors, are the unit power auxiliary equipment costs of lithium batteries and supercapacitors, They are the unit capacity auxiliary equipment costs of lithium batteries and supercapacitors respectively.

[0152] The expression of the recovery residual value is as follows:

[0153] (twenty one)

[0154] In the above formula, is the recovery value rate.

[0155] In addition, the following constraints need to be considered to calculate the optimal value of the life cycle cost of the hybrid energy storage system:

[0156] (1) Energy storage power constraint, the specific expression is as follows:

[0157] (twenty two)

[0158] In the above formula, is the rated power of the lithium battery, is the rated power of the supercapacitor, are the powers of lithium battery and supercapacitor at time t after considering the charging and discharging efficiency and converter efficiency.

[0159] (2) Energy storage SOC constraint, the specific expression is as follows:

[0160] (twenty three)

[0161] In the above formula, are the charge states of lithium battery and supercapacitor at time t, They are the upper and lower limits of the state of charge of the lithium battery, They are the upper and lower limits of the state of charge of the supercapacitor respectively.

[0162] In this embodiment, step S33 includes:

[0163] Step S331: Adjust the initial power allocation result based on the charge and discharge status of the power-type energy storage and the energy-type energy storage. If the charge and discharge status of the two are inconsistent, the total power is allocated to the power-type energy storage and the energy-type energy storage with the larger power value, thereby obtaining an intermediate power allocation result.

[0164] Step S332: Adjust the intermediate power allocation result according to the power value of the energy storage in the intermediate power allocation result. If the power value of the energy storage is lower than the preset threshold, the total power is allocated to the power storage to obtain a secondary power allocation result.

[0165] Specifically, after the initial power allocation, there may be inconsistent charge and discharge states between lithium batteries and supercapacitors, irrational distribution of small, high-frequency power fluctuations, and battery storage operating at extreme states of charge (SoCs). These issues can affect the lifespan of the battery storage. To address these issues, the initially allocated hybrid energy storage power needs to be further optimized. This is done through a two-tiered optimization strategy: one that considers charge and discharge consistency and one that considers the operating characteristics of the energy storage system.

[0166] Since the charging and discharging states of lithium batteries and supercapacitors are inconsistent, the number of charge and discharge times of hybrid energy storage will increase, the life of hybrid energy storage will be shortened, the number of replacements will increase, the replacement and update costs will increase, and the economic efficiency will be deteriorated. Based on the above problems, a strategy considering the consistency of charge and discharge is proposed. The expression of the HESS optimization strategy considering the consistency of charge and discharge is as follows:

[0167] (twenty four)

[0168] In the above formula, and are the power of lithium battery and supercapacitor at time t after charge and discharge consistency optimization, μ is the charge and discharge consistency coefficient, and its expression is as follows:

[0169] (25)

[0170] In the above formula, e is a natural constant. If 1 ≤ μ ≤ e, the charge and discharge states of the two are identical; otherwise, the charge and discharge states of the lithium battery and supercapacitor are opposite. If μ < 1, the absolute power of the supercapacitor exceeds that of the lithium battery, causing the supercapacitor to carry the total power and the lithium battery to have no effect. Similarly, if μ > e, the total power is carried by the lithium battery and the supercapacitor to have no effect.

[0171] Since the initial distribution of lithium battery power contains some high-frequency and small-amplitude components, the lithium battery is frequently charged and discharged, which affects the battery service life. Based on the above problems, a strategy considering the working characteristics of energy storage is proposed. The expression of the HESS optimization strategy considering the working characteristics of energy storage is as follows:

[0172] (26)

[0173] In the above formula, and They are the power of lithium battery and supercapacitor at time t after adjustment considering the energy storage working characteristics, and d is the set threshold, which can be selected according to the actual situation. ,in is the rated power of the lithium battery.

[0174] It can be understood that by optimizing the power after the initial power distribution through the consistency of charge and discharge and the working characteristics of energy storage, it is possible to balance the energy storage capacity, extend the energy storage life, improve energy utilization efficiency, reduce the number of energy storage replacements and replacement costs, and further improve economic efficiency.

[0175] In this embodiment, the maximum excess power or maximum power shortage of the energy storage power that should be absorbed or supplemented by the configured rated power during the assessment period is calculated by combining the converter efficiency and the charge and discharge efficiency of the energy storage element (including power-type energy storage and energy-type energy storage). The expression is:

[0176] (27)

[0177] In the above formula, and are the rated power of lithium battery and supercapacitor respectively, are the discharge power and charging power of the lithium battery at time t after the secondary power distribution is optimized through charge and discharge consistency and energy storage working characteristics, They are the discharge power and charging power of the supercapacitor at time t after the secondary power distribution is optimized through charge and discharge consistency and energy storage working characteristics, is the initial moment, are the efficiencies of DC-DC and DC-AC respectively, are the charging and discharging efficiencies of lithium batteries, are the charging and discharging efficiencies of the supercapacitor, respectively, and T is the operating cycle.

[0178] The rated capacity is calculated based on the state of charge of the energy storage itself, and its expression is:

[0179] (28)

[0180] In the above formula, and are the rated capacities of lithium batteries and supercapacitors respectively, Energy storage reference state of charge for lithium batteries and supercapacitors respectively, The upper and lower limits of the state of charge of the lithium battery, The upper and lower limits of the supercapacitor's state of charge.

[0181] In the calculation of the full life cycle cost of a hybrid energy storage system, the number of replacements of the energy storage equipment is a key point to consider. Since the traditional battery life estimation model only considers the cycle life, it ignores the impact of the SoC overcharge and over-discharge state on the life, and at the same time increases the estimation of the battery life loss caused by micro-circulation under the SoC floating charge state. Based on the above problems, a battery life estimation model that considers cycle life and calendar life is proposed. This model takes into account the effects of solid-electrolyte interface (SEI) film formation, calendar aging, cycle aging and temperature on the chemical composition of many lithium-ion batteries. Taking lithium batteries as an example, the model analyzes the battery's energy storage capacity attenuation characteristics as shown below:

[0182] (29)

[0183] In the above formula, and is the fitting parameter of lithium battery (NMC battery), f is the function of cycle life and calendar life, is the cycle life function, is the calendar life function; It is the SoC factor, is the temperature factor based on the Arrhenius equation, where:

[0184] (30)

[0185] In the above formula, 3000 and 0.8 mean that the cycle life is 3000 cycles at 80% dod; is the calendar life fitting coefficient; is the SOC influencing factor coefficient; σ is the average SOC value; is the reference SOC value; is the temperature influence factor coefficient; T is the actual temperature; Is the reference temperature. Preferably the reference SOC value is 0.5, reference temperature is 25°C, the reference condition is not affected by the decrease, at this time , , .

[0186] Based on the battery life calculation model, the HESS life prediction steps are as follows:

[0187] The first step is to calculate the capacity attenuation:

[0188] Input the SoC sequence and use the rain flow counting method to calculate the battery charge and discharge depth dod and the corresponding number of cycles for each cycle , the dod of each cycle and the corresponding Substituting into formula (29) we get the function f about cycle life and calendar life, and using formula (29) to get the capacity attenuation of the energy storage battery: .

[0189] The second step is to calculate the operating life of the HESS:

[0190] Calculate the annual capacity attenuation of battery energy storage based on the annual photovoltaic fluctuation data , substituting into formula (31) we can get the operating life of the configured energy storage battery :

[0191] (31)

[0192] In the above formula, is the battery failure capacity attenuation, and its preferred value is 20%.

[0193] Since supercapacitors are less affected by DOD, the number of charge and discharge cycles is the main factor affecting their lifespan. Their service life can be estimated based on their actual daily charge and discharge cycles. for:

[0194] (32)

[0195] In the above formula, is the maximum number of charge and discharge times during the life cycle of the supercapacitor, is the actual number of charge and discharge times of the supercapacitor per day, and Day is the number of days the supercapacitor operates in a year.

[0196] The third step is to calculate the number of replacements during the entire life cycle of the HESS:

[0197] (33)

[0198] In the above formula, is the number of battery replacements, is the number of supercapacitor replacements, is the full life cycle of HESS, For battery operating life, is the operating life of the supercapacitor.

[0199] The number of replacements between lithium batteries and supercapacitors is calculated using a life prediction model and Then, substitute it into the full life cycle cost model of the hybrid energy storage system for subsequent calculations.

[0200] In an exemplary application, the life cycle cost values ​​of the hybrid energy storage system calculated at different frequency cutoff points (i.e., high and low frequency cutoff points) are shown in Table 2:

[0201]

[0202] Table 2

[0203] From the data in Table 2, we can see that the hybrid energy storage system has the lowest life cycle cost at the frequency cutoff point 1. Therefore, taking the modal component IMF1 as the lithium battery power, the modal components IMF2-IMF9 and res as the supercapacitor power, the optimal cost is 8.02×10 7 Yuan.

[0204] Subsequently, the power is distributed according to steps S32 and S33, and the rated power and rated capacity of the hybrid energy storage system are calculated as shown in Table 3:

[0205]

[0206] Table 3

[0207] In Table 3, Scheme 1 is the configuration result after FTTA-VMD decomposition, Scheme 2 is the configuration result after further consideration of charge-discharge consistency optimization, and Scheme 3 is the configuration result after further consideration of charge-discharge consistency optimization and energy storage working characteristics optimization. b is the cost of lithium battery, C lcc is the hybrid energy storage cost (HESS full life cycle cost), which includes the cost of lithium batteries and supercapacitors. As shown in Table 3, considering the optimization of charge and discharge consistency and energy storage operating characteristics can effectively reduce the cost of lithium batteries, and thus reduce the cost of hybrid energy storage systems, which has economic benefits.

[0208] The present invention further provides a VMD-based photovoltaic distribution network hybrid energy storage capacity optimization system, comprising an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the above-mentioned VMD-based photovoltaic distribution network hybrid energy storage capacity optimization method.

[0209] The system of the present invention corresponds to the above method and also has the advantages of the above method, which will not be described in detail here.

[0210] The present invention can implement all or part of the process steps in the above-described method embodiments by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable media include any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory and accessing data stored in the memory. The memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0211] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing hybrid energy storage capacity of photovoltaic distribution network based on VMD, characterized in that: include: Step S1, calculating the hybrid energy storage reference power: obtaining the hybrid energy storage reference power according to the difference between the photovoltaic field output power and the grid-connected power; Step S2, variational modal decomposition: determining the number of decomposition layers by using an optimized variational modal decomposition algorithm, and decomposing the hybrid energy storage reference power according to the determined number of decomposition layers to obtain multiple modal components; Step S3, determining the optimal configuration: determining the minimum cost of the hybrid energy storage system over its entire life cycle, determining the rated power and rated capacity of the hybrid energy storage system under the minimum cost condition based on the charge and discharge consistency of the power-type energy storage and the energy-type energy storage and the energy storage operating characteristics of the energy-type energy storage according to the multiple modal components, and obtaining the optimal configuration solution; Wherein, the step S3 includes: Step S31: Establishing a full life cycle cost model for the hybrid energy storage system, calculating the full life cycle cost of the hybrid energy storage system under different high and low frequency cutoff points of the modal components, and obtaining the corresponding high and low frequency cutoff points under the optimal cost; Step S32: According to the high-low frequency dividing point at the optimal cost, the modal components above the high-low frequency dividing point among the multiple modal components are allocated to the power-type energy storage, and the modal components below the high-low frequency dividing point are allocated to the energy-type energy storage, thereby obtaining an initial power allocation result; Step S33: optimizing the distribution of the power-type energy storage and the energy-type energy storage after the initial power distribution based on the charge-discharge consistency of the power-type energy storage and the energy-type energy storage and the energy storage working characteristics of the energy-type energy storage, to obtain a secondary power distribution result; Step S34: determining the rated power and rated capacity of the hybrid energy storage system according to the secondary power allocation result to obtain an optimal configuration solution; The step S33 includes: Step S331: adjusting the initial power allocation result according to the charge and discharge states of the power-type energy storage and the energy-type energy storage. If the charge and discharge states of the two are inconsistent, allocating all the total power to the power-type energy storage and the energy-type energy storage with the larger power value to obtain an intermediate power allocation result; Step S332: Adjust the intermediate power allocation result according to the power value of the energy-type energy storage in the intermediate power allocation result. If the power value of the energy-type energy storage is lower than the preset threshold, allocate all the total power to the power-type energy storage to obtain the secondary power allocation result.

2. The method for optimizing hybrid energy storage capacity of photovoltaic distribution network based on VMD according to claim 1, characterized in that: The step S1 comprises: Monitor the maximum photovoltaic fluctuation of the photovoltaic field according to the output power of the photovoltaic field; When the maximum photovoltaic fluctuation of the photovoltaic field is monitored to exceed the grid-connected standard limit, the Gaussian filtering algorithm is used to obtain the grid-connected power, and the hybrid energy storage reference power is calculated based on the difference between the photovoltaic field output power and the grid-connected power.

3. The method for optimizing hybrid energy storage capacity of photovoltaic distribution network based on VMD according to claim 1, characterized in that: The step S2 comprises: The FTTA-VMD algorithm is used to optimize the mode number K and the quadratic penalty term α in the variational mode decomposition algorithm by taking the minimum value of envelope entropy as the fitness function for optimizing VMD parameters. The hybrid energy storage reference power is subjected to variational modal decomposition according to the found optimal combination of the modal number K and the quadratic penalty term α to obtain K modal components.

4. The method for optimizing hybrid energy storage capacity of photovoltaic distribution network based on VMD according to claim 1, characterized in that: In step S31, the full life cycle cost model of the hybrid energy storage system is calculated based on the sum of investment cost, operation and maintenance cost, decommissioning cost, replacement cost and auxiliary cost minus the recovered residual value.

5. The method for optimizing hybrid energy storage capacity of photovoltaic distribution network based on VMD according to claim 1, characterized in that: The step S331 includes: The initial power allocation result is judged and adjusted according to the charge-discharge consistency coefficient, and the expression is: In the above formula, and are the power of energy type energy storage and power type energy storage at time t after charge and discharge consistency optimization, μ is the charge and discharge consistency coefficient, e is a natural constant, and are the powers of energy type energy storage and power type energy storage at time t after the initial distribution respectively.

6. The method for optimizing hybrid energy storage capacity of photovoltaic distribution network based on VMD according to claim 1, characterized in that: The expression of step S332 is: In the above formula, and are the powers of energy-type energy storage and power-type energy storage at time t after adjustment considering the energy storage working characteristics, and d is the preset threshold.

7. The method for optimizing hybrid energy storage capacity of photovoltaic distribution network based on VMD according to claim 1, characterized in that: The step S34 includes: The configured rated power should absorb or supplement the maximum excess power or maximum power shortage of the energy storage power during the assessment period. The rated power is calculated by combining the converter efficiency and the charge and discharge efficiency of the energy storage element. The expression is: In the above formula, and are the rated power of lithium battery and supercapacitor respectively, are the discharge power and charging power of the lithium battery at time t after the secondary power distribution is optimized through charge and discharge consistency and energy storage working characteristics, They are the discharge power and charging power of the supercapacitor at time t after the secondary power distribution is optimized through charge and discharge consistency and energy storage working characteristics, is the initial moment, are the efficiencies of DC-DC and DC-AC respectively, are the charging and discharging efficiencies of lithium batteries, are the charging and discharging efficiencies of the supercapacitor, respectively, and T is the operating cycle; The rated capacity is calculated based on the state of charge of the energy storage itself, and the expression is: In the above formula, and are the rated capacities of lithium batteries and supercapacitors respectively, Energy storage reference state of charge for lithium batteries and supercapacitors respectively, The upper and lower limits of the state of charge of the lithium battery, The upper and lower limits of the supercapacitor's state of charge.

8. A photovoltaic distribution network hybrid energy storage capacity optimization system based on VMD, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the VMD-based photovoltaic distribution network hybrid energy storage capacity optimization method according to any one of claims 1 to 7.

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