Wind-solar hydrogen production hybrid energy storage capacity configuration method based on star-graffiti optimization algorithm

Abstract

The invention discloses a wind-solar hydrogen production hybrid energy storage capacity configuration method based on a star-graffiti optimization algorithm, and the method comprises the steps: inputting wind-solar data into an energy management system, and obtaining the unbalanced power of the system; a mathematical model of hybrid energy storage renewable energy source off-grid hydrogen production system equipment is established, and a storage battery and a super capacitor are controlled to be charged / discharged according to the unbalanced power and a system operation strategy; establishing a hybrid energy storage capacity optimization configuration total objective function based on a star-graffiti optimization algorithm by taking minimum system unit hydrogen production cost and minimum hydrogen production power relative standard deviation as objectives; corresponding constraint conditions are formulated in combination with actual operation conditions of the system; and solving and obtaining an optimal capacity configuration scheme of the storage battery and the super capacitor by utilizing a star-graffiti optimization algorithm. According to the method, hybrid energy storage of the optimal capacity scheme is configured for the wind-solar off-grid hydrogen production system by means of the star-graffiti optimization algorithm, and the two targets of system economy and hydrogen production stability can be balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of capacity planning of renewable energy microgrid systems, and in particular to a method for configuring the capacity of a wind-solar-hydrogen hybrid energy storage system based on a Nutcracker optimization algorithm. Background Art

[0002] Hydrogen energy is known as the "ultimate energy of the 21st century". It is also a clean energy that is being accelerated in the context of carbon peak and carbon neutrality. As an important conversion hub for various types of renewable energy, it is an important carrier for energy users to achieve green and low-carbon transformation. Among them, off-grid renewable energy on-site water electrolysis hydrogen production, combined with hydrogen storage and transportation technology, can achieve cross-regional transfer of renewable energy, improve the deep absorption capacity of wind and solar power, and minimize the impact on large power grids. It will become the core development direction of new energy hydrogen production in the future. However, the volatility and intermittency of renewable energy seriously affect the efficiency and safety of electrolyzer hydrogen production. Although hybrid energy storage can effectively smooth the power fluctuations of renewable energy off-grid hydrogen production systems, the configuration of hybrid energy storage capacity will increase costs. The unreasonable configuration of the capacity of each device in the renewable energy off-grid hydrogen production system will seriously affect the economic efficiency of system construction and hinder the development of renewable energy off-grid hydrogen production. When configuring the hybrid energy storage capacity, the two goals of system economy and hydrogen production stability are mutually exclusive, and it is very difficult to achieve a balance between the two.

[0003] At present, many scholars have conducted research on the optimization of capacity configuration of renewable energy hydrogen production. However, most of the existing research results have not considered adding energy storage to improve the performance of hydrogen production systems. Their hydrogen production systems still face challenges such as high cost, poor operating stability, and low efficiency. In response to this, some scholars have added energy storage to renewable energy hydrogen production systems and performed capacity configuration. However, the renewable energy hydrogen production systems in these research results all use a single form of energy storage to suppress hydrogen power fluctuations, which cannot meet the requirements of fast response speed and low cost of renewable energy hydrogen production systems, and it is still difficult to achieve a balance between the economy of system construction and stability and reliability. Summary of the invention

[0004] The purpose of the present invention is to provide a wind-solar-hydrogen production hybrid energy storage capacity configuration method based on the Star Crow optimization algorithm, with the lowest unit hydrogen production cost and the smallest relative standard deviation of hydrogen production power as the goals, and with power shortage rate and wind and solar abandonment rate as constraints, to construct a renewable energy off-grid hydrogen production hybrid energy storage capacity configuration model, and add a multi-objective particle swarm algorithm with inertia factors and random variation factors to solve the optimal capacity configuration plan of the hybrid energy storage system, so as to achieve a balance between the economy and hydrogen production stability of the renewable energy off-grid hydrogen production system.

[0005] The present invention adopts the following technical solution:

[0006] The method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm includes the following steps:

[0007] S1. Input the wind speed, light and temperature data with a time scale of minutes into the energy management system of the hybrid energy storage renewable energy off-grid hydrogen production system to obtain the corresponding output power and unbalanced power.

[0008] S2. Establish a mathematical model of all equipment in the hybrid energy storage renewable energy off-grid hydrogen production system and formulate the coupling relationship between the equipment; wherein, the hybrid energy storage renewable energy off-grid hydrogen production system includes a renewable energy power generation system, a hydrogen production and storage system, a hybrid energy storage system, an energy management system, a battery management system and a hybrid energy storage inverter system.

[0009] S3. According to the size of the unbalanced power, the operation strategy of the hybrid energy storage renewable energy off-grid hydrogen production system is used to control the charging / discharging of the batteries and supercapacitors in the hybrid energy storage system.

[0010] S4. With the goal of minimizing the unit hydrogen production cost and the relative standard deviation of hydrogen production power of the hybrid energy storage renewable energy off-grid hydrogen production system, the overall objective function of the hybrid energy storage capacity optimization model is designed.

[0011] S5. Based on the actual operating conditions of the hybrid energy storage renewable energy off-grid hydrogen production system, formulate the constraint conditions for solving the hybrid energy storage capacity optimization model.

[0012] S6. Taking the capacity of the battery and supercapacitor in the hybrid energy storage system as decision variables, combined with the constraints of step S5, the optimal capacity configuration scheme of the battery and supercapacitor is obtained using the Nudibranch optimization algorithm.

[0013] Furthermore, in step S1, the power calculation formula includes:

[0014] The formula for calculating the output power is:

[0015]

[0016] Among them, P wt (t) is the real-time output power of the wind turbine at the tth moment, P pv (t) is the real-time output power of the photovoltaic generator at the tth moment, P r is the rated power of the wind turbine, v(t) is the real-time wind speed at the hub height at the tth moment, v in is the wind turbine cut-in wind speed, v out is the cut-out wind speed, v r is the rated wind speed of the fan, P sta is the rated power of the photovoltaic panel under standard parameters, f pv is the power decay coefficient, G sta is the standard light intensity, G(t) is the light intensity at the tth moment, α Tis the power temperature coefficient, T a (t) is the temperature of the photovoltaic panel at the tth moment, T a,sta is the standard ambient temperature.

[0017] The calculation formula for unbalanced power is:

[0018] △P(t)=P wt (t)+P pv (t)-P ecmax

[0019] Among them, △P(t) is the unbalanced power at the tth moment, P ecmax is the rated power of the electrolyzer.

[0020] Furthermore, in step S2, establishing a mathematical model includes the following contents:

[0021] S201. The mathematical model of the hybrid energy storage renewable energy off-grid hydrogen production system includes a mathematical model of the power generation power of the renewable energy power generation system, a mathematical model of the hydrogen production and storage system, and a mathematical model of the hybrid energy storage system.

[0022] S202. The expression of the mathematical model of the power generation of the renewable energy power generation system is:

[0023] P pw (t) = P pv (t)+P wt (t)

[0024] Among them, P pw (t) is the total power generation of the renewable energy power generation system.

[0025] S203. The mathematical model of the hydrogen production and storage system includes the mathematical model of the electrolyzer and the mathematical model of the hydrogen storage tank.

[0026] The expression of the electrolytic cell mathematical model is:

[0027]

[0028] in, is the amount of hydrogen substance; η F is the hydrogen production efficiency, I is the electrolysis current; F is the Faraday constant; N ec is the number of electrolytic cells.

[0029] The mathematical model of the hydrogen storage tank is expressed as:

[0030] S HS,t+△t =S HS,t +M EL,t △tS H,t

[0031] Among them, S HS,t , S HS,t+△t are the hydrogen storage capacity of the hydrogen storage tank at time t and time t+△t, respectively, EL,t is the hydrogen storage rate at time t, S H,t is the amount of hydrogen sold at the tth moment, and △t is the time step.

[0032] S204. The mathematical model of the hybrid energy storage system includes a battery mathematical model and a supercapacitor mathematical model, and the specific expression is:

[0033]

[0034] Among them, S SOE,t , S SOE,t+△t are the charge states of the energy storage device at time t and time t+△t, respectively, P t ch , P t dis are the charging and discharging power of the energy storage device at the tth moment, η is the charging and discharging efficiency of the energy storage device, and E is the configuration capacity of the energy storage device.

[0035] S205. The coupling relationship between the devices in the hybrid energy storage renewable energy off-grid hydrogen production system is as follows: the renewable energy power generation system provides power to the hydrogen production and storage system through power generation, and the hybrid energy storage system is responsible for absorbing the surplus power of power generation and supplementing the power shortage.

[0036] Furthermore, in step S3, the charge / discharge strategy includes the following contents:

[0037] (1) When △P ≥ P b +P cmax When the battery and supercapacitor are charged / discharged at maximum power.

[0038] (2) When △P ≥ P b , and △P <P b +P cmax When the battery is charged / discharged at rated power, the supercapacitor is charged / discharged as needed.

[0039] (3) When △P<P b When the battery is not charged / discharged, the supercapacitor is charged / discharged.

[0040] Among them, △P is the unbalanced power, P b is the rated working power of the battery, P cmax is the maximum power of the supercapacitor.

[0041] Furthermore, in step S4, the overall objective function for designing hybrid energy storage capacity optimization includes the following contents:

[0042] S401. The cost of hydrogen production includes equipment investment cost and equipment operation and maintenance cost. The minimum unit hydrogen production cost in the hybrid energy storage renewable energy off-grid hydrogen production system is the objective function 1. The expression of objective function 1 is:

[0043]

[0044] Among them, f1 is the objective function 1; is the annual hydrogen production mass; C inv is the annualized investment cost,

[0045] y is the equipment life, r is the annual discount rate, They are the unit capacity investment costs of photovoltaic panels, wind turbines, electrolyzers, hydrogen storage tanks, batteries, and supercapacitors.

[0046] E pv 、E wt 、E ec 、E hst 、E b 、E c are the configuration capacities of photovoltaic panels, wind turbines, electrolyzers, hydrogen storage tanks, batteries, and supercapacitors; C op is the annual operation and maintenance cost,

[0047]

[0048] α1, β1, γ1, λ1, μ1, and ρ1 are the annual maintenance cost ratio coefficients of photovoltaic panels, wind turbines, electrolyzers, hydrogen storage tanks, batteries, and supercapacitors, respectively.

[0049] S402, taking the minimum relative standard deviation of hydrogen production power in the hybrid energy storage renewable energy off-grid hydrogen production system as the objective function 2, the expression of the objective function 2 is:

[0050]

[0051] Among them, f2 is the second objective function, P ec (q) is the electrolytic cell input power at the qth data point, P ec,mean is the average value of the electrolyzer input power throughout the year, and Q is the total number of data points.

[0052] S403. The overall objective function of hybrid energy storage capacity optimization is:

[0053]

[0054] Among them, f is the total objective function, λ2 and λ3 are the weights of objective function one and objective function two respectively.

[0055] Furthermore, in step S5, the constraints include power balance constraints, battery power constraints, supercapacitor power constraints, energy storage state constraints, wind and solar power abandonment rate constraints, and power shortage rate constraints.

[0056] The power balance constraint is:

[0057] P ec (t)+P bc (t)+P cc (t) = P pv (t)+P wt (t)+P bd (t)+P cd (t)-P was (t)P ec (t) = P ecmax -P bre (t)

[0058] Among them, P ec (t), P was (t), P bre (t) are the input power of the electrolyzer group, the abandoned wind and solar power, and the load shedding power at the t moment, respectively. bc (t), P cc (t) are the charging power of the battery and supercapacitor at the tth moment, P bd (t), P cd (t) are the discharge power of the battery and supercapacitor at the tth moment, P ecmax is the rated power of the electrolyzer.

[0059] According to the hybrid energy storage system operation strategy, the battery power constraint is:

[0060]

[0061] Among them, P b is the rated working power of the battery.

[0062] The supercapacitor power constraint is:

[0063]

[0064] Among them, P cmax is the maximum operating power of the supercapacitor.

[0065] The energy storage state constraint is:

[0066] 0.1≤S SOE,t ≤0.9.

[0067] The wind and solar curtailment rate constraints are:

[0068] EWR≤0.2

[0069]

[0070] Among them, EWR is the wind and solar power abandonment rate.

[0071] The power failure rate constraint is:

[0072] LPSP≤0.1

[0073]

[0074] Among them, LPSP is the power failure rate, P L is the rated power requirement of the electrolyzer.

[0075] Further, in step S6, the optimal capacity configuration scheme of the battery and supercapacitor is obtained by using the Nutcracker optimization algorithm, including the following contents:

[0076] S601, set the maximum number of iterations of the Nutcracker optimization algorithm and the size of the Nutcracker particle population, and initialize various parameters, including:

[0077] Set the size of the nutcracker particle population to N, the dimension of the optimization problem to D, and the position of the initial population to:

[0078]

[0079] in, is the position of the j-dimensional vector of the i-th Star Crow particle at the k-th iteration, i=1,2,...,N,j=1,2,...,D,U j and L j are the upper and lower limits of the j-th dimension vector, represents a random vector.

[0080] S602, setting parameter P a1 and P a2 , P a1 As the number of iterations increases, P decreases linearly from 1 to 0. a2 Pick

[0081] 0.2, generate random numbers σ, σ1,

[0082] S603, according to σ, σ1, P a1 , P a2 Determine the stage of the Star Crow particle. The four stages are as follows:

[0083] (1) When σ>σ1 and When , the Nutcracker particle is in the exploration phase of the foraging storage strategy;

[0084] (2) When σ>σ1 and When , the Nutcracker particle is in the development stage of the foraging storage strategy;

[0085] (3) When σ≤σ1 and At , the Nutcracker particles are in the exploration phase of searching for storage areas to retrieve food;

[0086] (4) When σ≤σ1 and At the time, the Nutcracker Particles were in the development stage of searching for storage areas to retrieve food.

[0087] S604. Update the position of the Nutcracker particle according to the stage of the Nutcracker particle, specifically:

[0088] (1) When the Nutcracker particle is in the exploration phase of the foraging storage strategy, the Nutcracker particle position update formula is:

[0089]

[0090] in, is the position of the i-th Nudibranch particle at the k+1th iteration; is the k-th iteration position of the i-th Star Crow particle; is the average value of the j-dimensional vector positions of all Star Crow particles at the k-th iteration; γ 11 is the first random number obtained according to the levy flight function; is the position of the j-th dimensional vector of the star crow particle A; is the position of the j-th dimensional vector of the star crow particle B; is the position of the j-th dimension vector of the star crow particle C; μ is a random number generated based on normal distribution, levy flight function and [0,1], τ 14 is the normal distribution parameter, τ 15 is the levy flight parameter, τ 13 , r1, r2, r3, τ 11 , τ 12 and r 11 are all random numbers between [0,1]; K max is the maximum number of iterations.

[0091] (2) When the Nutcracker particle is in the development stage of the foraging storage strategy, the Nutcracker particle position update formula is:

[0092]

[0093] in, is the optimal position at the kth iteration, λ 22 is the second random number obtained according to the levy flight function, τ 21 , τ 22 , τ23 are all random numbers between [0,1], and l is the decreasing factor.

[0094] (3) When the Nutcracker particle is in the exploration phase of searching for food in the storage area, two reference points are generated. The calculation formula for the specific position is:

[0095]

[0096] in, is the first reference point of the i-th Star Crow particle at the k-th iteration; α is the convergence coefficient of the reference point, θ is a random radian between [0,π]; RP is the random position of the Nutcracker particle at the kth iteration; is the second reference point of the i-th Nudibranch particle at the k-th iteration; are the upper and lower limits of the position of the Star Crow particle respectively; are random values ​​of 0 and 1, is a random vector between [0,1], P rp To determine the percentage of the global area that the search space occupies.

[0097] If the star crow particle is based on Update the optimal position, and the position update calculation formula is:

[0098]

[0099] in, is the position of the j-dimensional vector of the ith Star Crow particle at the k+1th iteration, is the optimal position of the j-dimensional vector of all Nudibranch particles at the k-th iteration.

[0100] If the star crow particle is based on If the optimal position is not found, Exploration, the position update calculation formula is:

[0101]

[0102] Among them, τ 16 is a random number in the interval [0,1].

[0103] (4) When the Nutcracker particle is in the development stage of searching for food in the storage area, the Nutcracker particle position update formula includes:

[0104] when When , the update formula of the position of the star crow particle is:

[0105]

[0106] when When , the particle position update formula is:

[0107]

[0108] S605. Repeat steps S602-S604 until the maximum number of iterations is reached, and the position where the total objective function value is minimum is obtained, that is, the optimal capacity configuration scheme of the battery and supercapacitor.

[0109] Furthermore, the method further includes step S7: analyzing the performance indicators of the optimal battery and supercapacitor capacity configuration solution obtained in step S6.

[0110] Furthermore, in step S7, the performance indicators analyzed include power shortage rate and wind and solar power abandonment rate. When the power shortage rate and wind and solar power abandonment rate are lower than 10%, the operation requirements are met.

[0111] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for configuring the capacity of a hybrid energy storage system of wind and solar power and hydrogen production based on the Nudibranch optimization algorithm are implemented.

[0112] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0113] The method proposed in the present invention can comprehensively consider the construction economy and safety and stability of the hydrogen production system, so that both can be optimized at the same time. In addition, the wind and solar power abandonment rate under this scheme is also relatively low, and the annual operation times of the battery are significantly reduced by 60.18%, which means that the battery life is extended. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figure 1 It is a structural diagram of the hybrid energy storage system renewable energy off-grid hydrogen production system of the present invention.

[0115] Figure 2 It is a flow chart for implementing the optimal capacity configuration scheme based on the Nutcracker optimization algorithm of the present invention.

[0116] Figure 3 1 is a diagram showing the effects of unbalanced power smoothing in three schemes according to an embodiment of the present invention.

[0117] Figure 4 4 is a relationship diagram between the power shortage rate and the hybrid energy storage capacity according to an embodiment of the present invention. DETAILED DESCRIPTION

[0118] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0119] The working principle of the hybrid energy storage renewable energy off-grid hydrogen production system is as follows: the renewable energy power generation system supplies power to the electrolyzer to produce hydrogen at rated power; the battery management system monitors the battery and supercapacitor charge state, current, voltage and other data in real time; the energy management system can calculate the power generation of wind turbines and photovoltaic generators based on wind speed, light intensity and temperature data, and calculate the unbalanced power of the system, and then issue charging and discharging instructions to the hybrid energy storage inverter system based on the data monitored by the battery management system; the hybrid energy storage inverter system controls the hybrid energy storage system to charge or discharge at a certain power according to the instructions, and the power shortage is used as load shedding, and the power surplus is used as wind and solar power abandonment.

[0120] To achieve the above purpose, the present invention proposes a method for configuring the capacity of a wind-solar-hydrogen hybrid energy storage system based on a Nudibranch optimization algorithm, and the specific steps are as follows:

[0121] S1. Input the wind speed, light and temperature data with a time scale of minutes into the energy management system of the hybrid energy storage renewable energy off-grid hydrogen production system to obtain the corresponding output power and unbalanced power; the specific contents are as follows:

[0122] The formula for calculating the output power is:

[0123]

[0124] Among them, P wt (t) is the real-time output power of the wind turbine at the tth moment, P pv (t) is the real-time output power of the photovoltaic generator at the tth moment, P r is the rated power of the wind turbine, v(t) is the real-time wind speed at the hub height at the tth moment, v in is the wind turbine cut-in wind speed, v out is the cut-out wind speed, v r is the rated wind speed of the fan, P sta is the rated power of the photovoltaic panel under standard parameters, f pv is the power decay coefficient, G sta is the standard light intensity, G(t) is the light intensity at the tth moment, α T is the power temperature coefficient, T a (t) is the temperature of the photovoltaic panel at the tth moment, T a,sta is the standard ambient temperature.

[0125] The calculation formula for unbalanced power is:

[0126] △P(t)=P wt (t)+P pv (t)-P ecmax

[0127] Among them, △P(t) is the unbalanced power at the tth moment, Pecmax is the rated power of the electrolyzer.

[0128] The positive or negative nature of the unbalanced power determines whether the hybrid energy storage renewable energy off-grid hydrogen production system should be charged or discharged: when the unbalanced power is positive and the system energy is not fully charged, the system is charged; when the unbalanced power is negative and the system energy is not fully discharged, the system is discharged.

[0129] S2. Establish mathematical models of all equipment in the hybrid energy storage renewable energy off-grid hydrogen production system and formulate the coupling relationship between the equipment; the hybrid energy storage renewable energy off-grid hydrogen production system includes a renewable energy power generation system, a hydrogen production and storage system, a hybrid energy storage system, an energy management system, a battery management system and a hybrid energy storage converter system; the specific contents are:

[0130] S201, such as Figure 1 As shown, the hybrid energy storage renewable energy off-grid hydrogen production system includes a renewable energy power generation system, a hydrogen production and storage system, a hybrid energy storage system, an energy management system, a battery management system, and a hybrid energy storage inverter system. Among them, the renewable energy power generation system includes a wind turbine and a photovoltaic generator, and the hybrid energy storage system includes batteries and supercapacitors.

[0131] The mathematical model of the hybrid energy storage renewable energy off-grid hydrogen production system includes a mathematical model of the power generation of the renewable energy power generation system, a mathematical model of the hydrogen production and storage system, and a mathematical model of the hybrid energy storage system.

[0132] S202. The expression of the mathematical model of the power generation of the renewable energy power generation system is:

[0133] P pw (t) = P pv (t)+P wt (t)

[0134] Among them, P pw (t) is the total power generation of the renewable energy power generation system.

[0135] S203. The mathematical model of the hydrogen production and storage system includes the mathematical model of the electrolyzer and the mathematical model of the hydrogen storage tank. Since the alkaline electrolyzer has good performance, low price and wide application, the alkaline electrolyzer is selected here. After the electrolyzer produces hydrogen, it is stored in a low-pressure hydrogen storage tank for a short time, and hydrogen is sold regularly every day to obtain income.

[0136] The expression of the electrolytic cell mathematical model is:

[0137]

[0138] in, is the amount of hydrogen substance; η F is the hydrogen production efficiency, I is the electrolysis current; F is the Faraday constant; N ec is the number of electrolytic cells.

[0139] The mathematical model of the hydrogen storage tank is expressed as:

[0140] S HS,t+△t =S HS,t +M EL,t △tS H,t

[0141] Among them, S HS,t , S HS,t+△t are the hydrogen storage capacity of the hydrogen storage tank at time t and time t+△t, respectively, EL,t is the hydrogen storage rate at time t, S H,t is the amount of hydrogen sold at the tth moment, and △t is the time step.

[0142] S204. The mathematical model of the hybrid energy storage system includes a battery mathematical model and a supercapacitor mathematical model, and the specific expression is:

[0143]

[0144] Among them, S SOE,t , S SOE,t+△t are the charge states of the energy storage device at time t and time t+△t, respectively, P t ch , P t dis are the charging and discharging power of the energy storage device at the tth moment, η is the charging and discharging efficiency of the energy storage device, and E is the configuration capacity of the energy storage device.

[0145] S205. The coupling relationship between the devices in the hybrid energy storage renewable energy off-grid hydrogen production system is as follows: the renewable energy power generation system provides power to the hydrogen production and storage system through power generation, and the hybrid energy storage system is responsible for absorbing the surplus power of power generation and supplementing the power shortage.

[0146] S3. According to the size of the unbalanced power, the operation strategy of the hybrid energy storage renewable energy off-grid hydrogen production system is used to control the charging / discharging of the battery and supercapacitor in the hybrid energy storage system; the specific contents are:

[0147] (1) When △P ≥ P b +P cmax When the battery and supercapacitor are charged / discharged at maximum power.

[0148] (2) When △P ≥ P b , and △P <P b +P cmax When the battery is charged / discharged at rated power, the supercapacitor is charged / discharged as needed.

[0149] (3) When △P<P b When the battery is not charged / discharged, the supercapacitor is charged / discharged.

[0150] Among them, △P is the unbalanced power, P b is the rated working power of the battery, P cmax is the maximum power of the supercapacitor.

[0151] The hybrid energy storage system mainly uses batteries and supplemented by supercapacitors. In order to avoid frequent and irregular charging and discharging of batteries, the batteries are ensured to work only at rated power.

[0152] S4. With the goal of minimizing the unit hydrogen production cost and the relative standard deviation of hydrogen production power of the hybrid energy storage renewable energy off-grid hydrogen production system, the overall objective function of the hybrid energy storage capacity optimization model is designed; the specific contents are:

[0153] S401. The cost of hydrogen production includes equipment investment cost and equipment operation and maintenance cost. The minimum unit hydrogen production cost in the hybrid energy storage renewable energy off-grid hydrogen production system is the objective function 1. The expression of objective function 1 is:

[0154]

[0155] Among them, f1 is the objective function 1; m H2 is the annual hydrogen production mass; C inv is the annualized investment cost,

[0156] y is the equipment life, r is the annual discount rate, are the unit capacity investment costs of photovoltaic panels, wind turbines, electrolyzers, hydrogen storage tanks, batteries, and supercapacitors, respectively. pv 、E wt 、E ec 、E hst 、E b 、E c are the configuration capacities of photovoltaic panels, wind turbines, electrolyzers, hydrogen storage tanks, batteries, and supercapacitors; C op is the annual operation and maintenance cost,

[0157] α1, β1, γ1, λ1, μ1, and ρ1 are the annual maintenance cost ratio coefficients of photovoltaic panels, wind turbines, electrolyzers, hydrogen storage tanks, batteries, and supercapacitors, respectively.

[0158] S402. The fluctuation of electrolyzer hydrogen production power will seriously affect the hydrogen production efficiency and device life. The relative standard deviation of electrolyzer power indicates the fluctuation of electrolyzer power. The smaller the value, the smaller the fluctuation of electrolyzer power and the higher the working stability of electrolyzer. The minimum relative standard deviation of hydrogen production power in hybrid energy storage renewable energy off-grid hydrogen production system is taken as objective function 2. The expression of objective function 2 is:

[0159]

[0160] Among them, f2 is the second objective function, P ec (q) is the electrolytic cell input power at the qth data point, P ec,mean is the average value of the electrolyzer input power throughout the year, and Q is the total number of data points.

[0161] S403. The overall objective function of hybrid energy storage capacity optimization is:

[0162]

[0163] Among them, f is the total objective function, λ2 and λ3 are the weights of objective function one and objective function two respectively.

[0164] Here, economy is taken as the main goal and stability as the secondary goal. Therefore, the weights are set to λ2=0.9 and λ3=0.1 in multi-objective function processing.

[0165] In actual situations, the target weight can be determined according to the decision maker's importance preference to obtain more satisfactory results.

[0166] S5. Based on the actual operating conditions of the hybrid energy storage renewable energy off-grid hydrogen production system, formulate the constraint conditions for solving the hybrid energy storage capacity optimization model; the specific contents are:

[0167] The constraints include power balance constraints, battery power constraints, supercapacitor power constraints, energy storage status constraints, wind and solar power abandonment rate constraints and power shortage rate constraints.

[0168] In order to ensure the stable operation of the hybrid energy storage renewable energy off-grid hydrogen production system, the power balance constraints that need to be met are:

[0169] P ec (t)+P bc (t)+P cc (t) = P pv (t)+P wt (t)+P bd (t)+P cd (t)-P was (t)P ec (t) = P ecmax -Pbre (t)

[0170] Among them, P ec (t), P was (t), P bre (t) are the input power of the electrolyzer group, the abandoned wind and solar power, and the load shedding power at the t moment, respectively. bc (t), P cc (t) are the charging power of the battery and supercapacitor at the tth moment, P bd (t), P cd (t) are the discharge power of the battery and supercapacitor at the tth moment, P ecmax is the rated power of the electrolyzer.

[0171] According to the hybrid energy storage system operation strategy, the battery power constraint is:

[0172]

[0173] Among them, P b is the rated working power of the battery.

[0174] The supercapacitor power constraint is:

[0175]

[0176] Among them, P cmax is the maximum operating power of the supercapacitor.

[0177] In order to prevent the battery and supercapacitor from life degradation caused by overcharging and over-discharging, the energy storage state is constrained. The energy storage state constraint is:

[0178] 0.1≤S SOE,t ≤0.9.

[0179] The wind and solar curtailment rate constraints are:

[0180] EWR≤0.2

[0181]

[0182] Among them, EWR is the wind and solar power abandonment rate.

[0183] The power failure rate constraint is:

[0184] LPSP≤0.1

[0185]

[0186] Among them, LPSP is the power failure rate, P L is the rated power requirement of the electrolyzer.

[0187] S6. Taking the capacity of the battery and supercapacitor in the hybrid energy storage system as decision variables, combined with the constraints of step S5, the optimal capacity configuration scheme of the battery and supercapacitor is obtained by using the Nudibranch optimization algorithm, such as Figure 2 As shown, the specific contents are:

[0188] S601, set the maximum number of iterations of the Nutcracker optimization algorithm and the size of the Nutcracker particle population, and initialize various parameters, including:

[0189] Set the size of the nutcracker particle population to N, the dimension of the optimization problem to D, and the position of the initial population to:

[0190]

[0191] in, is the position of the j-dimensional vector of the i-th Star Crow particle at the k-th iteration, i=1,2,...,N,j=1,2,...,D,U j and L j are the upper and lower limits of the j-th dimension vector, represents a random vector between [0,1], and each star crow particle represents a feasible solution to a problem.

[0192] S602, set the parameters and P used to determine the stage of the Star Crow particle a1 and P a2 , P a1 As the number of iterations increases, P decreases linearly from 1 to 0. a2 Take 0.2 and generate random numbers σ, σ1,

[0193] S603. The process of the nutcracker particles searching for the optimal solution simulates the living habits of the nutcracker, including two strategies: the foraging storage strategy and the strategy of finding storage areas to retrieve food. Both strategies include exploration and development stages. Therefore, the behavior of the nutcracker particles includes four stages in total, namely the exploration stage in the foraging storage strategy (exploration stage 1), the development stage in the foraging storage strategy (development stage 1), the exploration stage in finding storage areas to retrieve food (exploration stage 2), and the development stage in finding storage areas to retrieve food (development stage 2). In each iteration, all particles of the nutcracker can only be in one stage, according to σ, σ1, P a1 , P a2 Determine the stage of the Star Crow particle. The four stages are as follows:

[0194] (1) When σ>σ1 and When , the Star Crow particle is in the exploration stage 1;

[0195] (2) When σ>σ1 and At the time, the Star Crow particle was in development stage 1;

[0196] (3) When σ≤σ1 and When , the Star Crow particle is in the exploration stage 2;

[0197] (4) When σ≤σ1 and At the time, the Star Crow Particle was in the development stage 2.

[0198] S604. Update the position of the Nutcracker particle according to the stage of the Nutcracker particle, specifically:

[0199] (1) When the Nutcracker particle is in exploration stage 1, the Nutcracker particle position update formula is:

[0200]

[0201] in, is the position of the i-th Nudibranch particle at the k+1th iteration; is the k-th iteration position of the i-th Star Crow particle; is the average value of the j-dimensional vector positions of all Star Crow particles at the k-th iteration; γ 11 is the first random number obtained according to the levy flight function; is the position of the j-th dimensional vector of the star crow particle A; is the position of the j-th dimensional vector of the star crow particle B; is the position of the nutcracker particle C in the j-th dimension vector; nutcracker particles A, B, and C are three randomly selected nutcracker particles to promote the extensiveness of the search; μ is a random number generated based on normal distribution, levy flight function and [0,1], τ 14 is the normal distribution parameter, τ 15 is the levy flight parameter, τ 13 , r1, r2, r3 are all random numbers between [0,1]; τ 11 , τ 12 and r 11 are all random numbers between [0,1]; K max is the maximum number of iterations.

[0202] (2) When the Star Crow particle is in development stage 1, the formula for updating the position of the Star Crow particle is:

[0203]

[0204] in, is the optimal position at the kth iteration; 22 is the second random number obtained according to the levy flight function; τ 21 , τ 22 , τ23 is a random number in [0,1]; l is a factor that decreases linearly from 1 to 0, which helps to avoid the optimization result from falling into the local optimum.

[0205] (3) When the Nutcracker particle is in exploration stage 2, the Nutcracker particle position update is based on two reference points. Therefore, before updating the particle position, two reference points need to be generated. The calculation formulas for the positions of the two reference points include:

[0206]

[0207] in, is the first reference point of the i-th Starling particle at the k-th iteration; α is the convergence coefficient of the reference point, and α decreases linearly from 1 to 0. θ is a random radian between [0,π]; RP is the random position of the Nutcracker particle at the kth iteration; is the second reference point of the i-th Nudibranch particle at the k-th iteration; are the upper and lower limits of the position of the Star Crow particle respectively; are random values ​​of 0 and 1, is a random vector between [0,1], P rp To determine the percentage of the global area that the search space occupies.

[0208] If the star crow particle is based on Update the optimal position, and the position update calculation formula is:

[0209]

[0210] in, is the position of the j-dimensional vector of the ith Star Crow particle at the k+1th iteration, is the optimal position of the j-dimensional vector of all Nudibranch particles at the k-th iteration.

[0211] If the star crow particle is based on If the optimal position is not found, Exploration, the position update calculation formula is:

[0212]

[0213] Among them, τ 16 is a random number in the interval [0,1].

[0214] (4) When the Nutcracker particle is in the development stage 2, the Nutcracker particle searches for the optimal solution based on two reference points. The particle position update formula includes:

[0215] when When , the update formula of the position of the star crow particle is:

[0216]

[0217] when When , the particle position update formula is:

[0218]

[0219] S605. Repeat steps S602-S604 until the maximum number of iterations is reached, and the position where the total objective function value is minimum is obtained, that is, the optimal capacity configuration scheme of the battery and supercapacitor.

[0220] S7, analyzing the performance indicators of the optimal battery and supercapacitor capacity configuration scheme obtained in step S6, and proving the effectiveness and superiority of the method; the specific contents are:

[0221] The analysis includes whether it is possible to effectively balance the economic efficiency with the safety and stability of hydrogen production, as well as the effect of unbalanced power smoothing.

[0222] The evaluation indicators involved include relative standard deviation of hydrogen production power, unit hydrogen production cost, power shortage rate, wind and solar power abandonment rate and battery action ratio. The relative standard deviation of hydrogen production power and unit hydrogen production cost are sub-objective functions in step S4. The smaller these two indicators are, the better. Power shortage rate and wind and solar power abandonment rate are constraints in step S5. When the power shortage rate and wind and solar power abandonment rate are lower than 10%, the operation requirements are met. The lower the battery action ratio, the longer the battery service life. The expression of the battery action ratio is:

[0223]

[0224] Among them, rate represents the battery action rate, num_b represents the number of data points where the battery operating power is not zero, and NUM represents the total number of sampling points.

[0225] It is proved that the final capacity plan achieves a balance between economic constructiveness and stable and safe hydrogen production, and both goals are optimized.

[0226] When the total objective function value is the minimum, the corresponding non-inferior solution is the optimal battery and supercapacitor capacity configuration scheme, which is shown in Table 1. Among them, the three schemes are all solved using the Nudibranch optimization algorithm, and the energy storage systems used are batteries, supercapacitors, and hybrid energy storage.

[0227] Table 1 Capacity configuration scheme of batteries and supercapacitors

[0228]

[0229]

[0230] As can be seen from Table 1, when hybrid energy storage is used, the battery capacity is much larger than the supercapacitor capacity, indicating that the hybrid energy storage system is mainly based on batteries and supplemented by supercapacitors when suppressing unbalanced power. This is because the cost of batteries is much lower than that of supercapacitors. In the process of capacity configuration optimization, the model fully considers the economic efficiency of system construction and optimizes the capacity ratio of batteries and supercapacitors. Therefore, under this capacity configuration scheme, the unit hydrogen production cost is only 35.964 yuan / kg, which is significantly reduced by 42.71% compared with 62.777 yuan / kg when only supercapacitors are used. It can also be seen from Table 1 that the relative standard deviation and power shortage rate of hydrogen production power when hybrid energy storage are used are 22.15% and 32.95% lower than when only batteries are used. This is because the battery has a slow charging and discharging speed and cannot suppress the large fluctuations in hydrogen power in an instant. The supercapacitor in the hybrid energy storage system can make up for this defect and can well suppress the long-term and instantaneous fluctuations of hydrogen power. Accordingly, the stability and safety reliability of hydrogen production are improved when hybrid energy storage is used. It can be seen that Scheme 3 using hybrid energy storage can take into account both system economy and the safety and stability of hydrogen production.

[0231] The unbalanced power smoothing effects of the three solutions are as follows: Figure 3 As shown, Figure 3 (a) is the unbalanced power smoothing effect when using batteries. Figure 3 (b) is the unbalanced power smoothing effect when using supercapacitors. Figure 3 (c) shows the unbalanced power smoothing effect when hybrid energy storage is used. It can be seen that when the unbalanced power is positive, it means that wind and solar power generation cannot meet the rated hydrogen demand of the electrolyzer, and the energy storage system needs to discharge to supplement the power supply; when the unbalanced power is negative, it means that wind and solar power generation is abundant, and the energy storage system needs to charge and absorb electricity. When supercapacitors are used, the positive value part after unbalanced power smoothing is the narrowest and the line is the sparsest, followed by hybrid energy storage. This shows that when supercapacitors are used, the power shortage time of the hydrogen production system is shorter and the power shortage value is smaller.

[0232] Supercapacitors and batteries have a good inhibitory effect on power shortages and wind and solar power abandonment. Figure 4 This is a relationship diagram of the power shortage rate, wind and solar power abandonment rate and hybrid energy storage capacity of the present invention. From the figure, it can be seen that the supercapacitor has an inhibitory effect on power shortage, the battery has an inhibitory effect on the wind and solar power abandonment rate, and considering the system economy and hydrogen production stability, the hybrid energy storage system composed of batteries and supercapacitors has the best effect, which also verifies the effectiveness and superiority of the method proposed in the present invention.

[0233] The embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. It should be noted that when the processor executes the computer program, the specific steps of the method provided in the embodiment of the present invention are corresponding to the specific steps of the method provided in the embodiment of the present invention, and the processor has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0234] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm, characterized in that: include: S1. Input the wind speed, light and temperature data with a time scale of minutes into the energy management system of the hybrid energy storage renewable energy off-grid hydrogen production system to obtain the corresponding output power and unbalanced power; S2. Establish mathematical models of all equipment in the hybrid energy storage renewable energy off-grid hydrogen production system and formulate coupling relationships between equipment; wherein the hybrid energy storage renewable energy off-grid hydrogen production system includes a renewable energy power generation system, a hydrogen production and storage system, a hybrid energy storage system, an energy management system, a battery management system and a hybrid energy storage converter system; S3. According to the size of the unbalanced power, the operation strategy of the hybrid energy storage renewable energy off-grid hydrogen production system is used to control the charging / discharging of the battery and supercapacitor in the hybrid energy storage system; S4. Design the overall objective function of the hybrid energy storage capacity optimization model with the goal of minimizing the unit hydrogen production cost and the relative standard deviation of hydrogen production power of the hybrid energy storage renewable energy off-grid hydrogen production system; S5. Based on the actual operating conditions of the hybrid energy storage renewable energy off-grid hydrogen production system, formulate the constraint conditions for solving the hybrid energy storage capacity optimization model; S6. Taking the capacity of the battery and supercapacitor in the hybrid energy storage system as decision variables, combined with the constraints of step S5, the optimal capacity configuration scheme of the battery and supercapacitor is obtained using the Nudibranch optimization algorithm.

2. The method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm according to claim 1 is characterized in that: In step S1, the power calculation formula includes: The formula for calculating the output power is: Among them, P wt (t) is the real-time output power of the wind turbine at the tth moment, P pv (t) is the real-time output power of the photovoltaic generator at the tth moment, P r is the rated power of the wind turbine, v(t) is the real-time wind speed at the hub height at the tth moment, v in is the wind turbine cut-in wind speed, v out is the cut-out wind speed, v r is the rated wind speed of the fan, P sta is the rated power of the photovoltaic panel under standard parameters, f pv is the power decay coefficient, G sta is the standard light intensity, G(t) is the light intensity at the tth moment, α T is the power temperature coefficient, T a (t) is the temperature of the photovoltaic panel at the tth moment, T a,sta is the standard ambient temperature; The calculation formula for unbalanced power is: △P(t)=P wt (t)+P pv (t)-P ecmax Among them, △P(t) is the unbalanced power at the tth moment, P ecmax is the rated power of the electrolyzer.

3. The method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm according to claim 1 is characterized in that: In step S2, establishing a mathematical model includes the following: S201. The mathematical model of the hybrid energy storage renewable energy off-grid hydrogen production system includes a mathematical model of the power generation of the renewable energy power generation system, a mathematical model of the hydrogen production and storage system, and a mathematical model of the hybrid energy storage system; S202. The expression of the mathematical model of the power generation of the renewable energy power generation system is: P pw (t)=P pv (t)+P wt (t) Among them, P pw (t) is the total power generation of the renewable energy power generation system, P wt (t) is the real-time output power of the wind turbine at the tth moment, P pv (t) is the real-time output power of the photovoltaic generator at the tth moment; S203, the mathematical model of the hydrogen production and storage system includes an electrolyzer mathematical model and a hydrogen storage tank mathematical model; The expression of the electrolytic cell mathematical model is: in, is the amount of hydrogen substance; η F is the hydrogen production efficiency, I is the electrolysis current; F is the Faraday constant; N ec is the number of electrolytic cells; The mathematical model of the hydrogen storage tank is expressed as: S HS,t+△t =S HS,t +M EL,t △t-S H,t Among them, S HS,t , S HS,t+△t are the hydrogen storage capacity of the hydrogen storage tank at time t and time t+△t, respectively, EL,t is the hydrogen storage rate at time t, S H,t is the hydrogen sales volume at time t, △t is the time step; S204. The mathematical model of the hybrid energy storage system includes a battery mathematical model and a supercapacitor mathematical model, and the specific expression is: Among them, S SOE,t , S SOE,t+△t are the charge states of the energy storage device at time t and time t+△t, respectively. are the charging and discharging power of the energy storage device at the tth moment, η is the charging and discharging efficiency of the energy storage device, and E is the configuration capacity of the energy storage device; S205. The coupling relationship between the devices in the hybrid energy storage renewable energy off-grid hydrogen production system is as follows: the renewable energy power generation system provides power to the hydrogen production and storage system through power generation, and the hybrid energy storage system is responsible for absorbing the surplus power of power generation and supplementing the power shortage.

4. The method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm according to claim 1 is characterized in that: In step S3, the charge / discharge strategy includes the following: (1) When △P ≥ P b +P cmax When , the battery and supercapacitor are charged / discharged at maximum power; (2) When △P ≥ P b , and △P <P b +P cmax When the battery is charged / discharged at rated power, the supercapacitor is charged / discharged as needed; (3) When △P<P b When the battery is not charged / discharged, the supercapacitor is charged / discharged; Among them, △P is the unbalanced power, P b is the rated working power of the battery, P cmax is the maximum power of the supercapacitor.

5. The method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm according to claim 1 is characterized in that: In step S4, the overall objective function for designing hybrid energy storage capacity optimization includes the following contents: S401. The cost of hydrogen production includes equipment investment cost and equipment operation and maintenance cost. The minimum unit hydrogen production cost in the hybrid energy storage renewable energy off-grid hydrogen production system is the objective function 1. The expression of objective function 1 is: Among them, f1 is the objective function 1; is the annual hydrogen production mass; C inv is the annualized investment cost, y is the equipment life, r is the annual discount rate, are the unit capacity investment costs of photovoltaic panels, wind turbines, electrolyzers, hydrogen storage tanks, batteries, and supercapacitors, respectively. pv 、E wt 、E ec 、E hst 、E b 、E c are the configuration capacities of photovoltaic panels, wind turbines, electrolyzers, hydrogen storage tanks, batteries, and supercapacitors; C op is the annual operation and maintenance cost, α1, β1, γ1, λ1, μ1, and ρ1 are the annual maintenance cost ratio coefficients of photovoltaic panels, wind turbines, electrolyzers, hydrogen storage tanks, batteries, and supercapacitors, respectively; S402, taking the minimum relative standard deviation of hydrogen production power in the hybrid energy storage renewable energy off-grid hydrogen production system as the objective function 2, the expression of the objective function 2 is: Among them, f2 is the second objective function, P ec (q) is the electrolytic cell input power at the qth data point, P ec,mean is the average annual input power of the electrolyzer, and Q is the total number of data points; S403. The overall objective function of hybrid energy storage capacity optimization is: Among them, f is the total objective function, λ2 and λ3 are the weights of objective function one and objective function two respectively.

6. The method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm according to claim 1 is characterized in that: In step S5, the constraints include power balance constraint, battery power constraint, supercapacitor power constraint, energy storage state constraint, wind and solar power abandonment rate constraint and power shortage rate constraint; The power balance constraint is: P ec (t)+P bc (t)+P cc (t)=P pv (t)+P wt (t)+P bd (t)+P cd (t)-P was (t)P ec (t)=P ecmax -P bre (t) Among them, P ec (t), P was (t), P bre (t) are the input power of the electrolyzer group, the abandoned wind and solar power, and the load shedding power at the t moment, respectively. bc (t), P cc (t) are the charging power of the battery and supercapacitor at the tth moment, P bd (t), P cd (t) are the discharge power of the battery and supercapacitor at the tth moment, P pv (t) is the real-time output power of the photovoltaic panel at the tth moment, P wt (t) is the real-time output power of the wind turbine at the tth moment, P ecmax is the rated power of the electrolyzer; According to the hybrid energy storage system operation strategy, the battery power constraint is: Among them, P b is the rated working power of the battery; The supercapacitor power constraint is: Among them, P cmax is the maximum operating power of the supercapacitor; The energy storage state constraint is: 0.1≤S SOE,t ≤0.9； Among them, S SOE,t is the state of charge of the energy storage device at time t; The wind and solar curtailment rate constraints are: EWR≤0.2 Among them, EWR is the wind and solar power abandonment rate; The power failure rate constraint is: LPSP≤0.1 Among them, LPSP is the power failure rate, P L is the rated power requirement of the electrolyzer.

7. The method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm according to claim 1 is characterized in that: In step S6, the optimal capacity configuration scheme of the battery and supercapacitor is obtained by using the Nudibranch optimization algorithm, including the following contents: S601, set the maximum number of iterations of the Nutcracker optimization algorithm and the size of the Nutcracker particle population, and initialize various parameters, including: Set the size of the nutcracker particle population to N, the dimension of the optimization problem to D, and the position of the initial population to: in, is the position of the j-dimensional vector of the i-th Star Crow particle at the k-th iteration, i=1,2,...,N,j=1,2,...,D,U j and L j are the upper and lower limits of the j-th dimension vector, represents a random vector; S602, setting parameter P a1 and P a2 , P a1 As the number of iterations increases, P decreases linearly from 1 to 0. a2 Take 0.2 and generate random numbers σ, σ1, S603, according to σ, σ1, P a1 , P a2 Determine the stage of the Star Crow particle. The four stages are as follows: (1) When σ>σ1 and When , the Nutcracker particle is in the exploration phase of the foraging storage strategy; (2) When σ>σ1 and When , the Nutcracker particle is in the development stage of the foraging storage strategy; (3) When σ≤σ1 and At , the Nutcracker particles are in the exploration phase of searching for storage areas to retrieve food; (4) When σ≤σ1 and At the time, the Nutcracker particles were in the development stage of searching for storage areas to retrieve food; S604. Update the position of the Nutcracker particle according to the stage of the Nutcracker particle, specifically: (1) When the Nutcracker particle is in the exploration phase of the foraging storage strategy, the Nutcracker particle position update formula is: in, is the position of the i-th Nudibranch particle at the k+1th iteration; is the k-th iteration position of the i-th Star Crow particle; is the average value of the j-dimensional vector positions of all Star Crow particles at the k-th iteration; γ 11 is the first random number obtained according to the levy flight function; is the position of the j-th dimensional vector of the star crow particle A; is the position of the j-th dimensional vector of the star crow particle B; is the position of the j-th dimension vector of the star crow particle C; μ is a random number generated based on normal distribution, levy flight function and [0,1], τ 14 is the normal distribution parameter, τ 15 is the levy flight parameter, τ 13 , r1, r2, r3, τ 11 , τ 12 and r 11 are all random numbers between [0,1]; K max is the maximum number of iterations; (2) When the Nutcracker particle is in the development stage of the foraging storage strategy, the Nutcracker particle position update formula is: in, is the optimal position at the kth iteration, λ 22 is the second random number obtained according to the levy flight function, τ 21 , τ 22 , τ 23 are all random numbers between [0,1], and l is the decreasing factor; (3) When the Nutcracker particle is in the exploration phase of searching for food in the storage area, two reference points are generated. The calculation formula for the specific position is: in, is the first reference point of the i-th Star Crow particle at the k-th iteration; α is the convergence coefficient of the reference point, θ is a random radian between [0,π]; RP is the random position of the Nutcracker particle at the kth iteration; is the second reference point of the i-th Nudibranch particle at the k-th iteration; are the upper and lower limits of the position of the Star Crow particle respectively; are random values ​​of 0 and 1, is a random vector between [0,1], P rp To determine the percentage of the search space in the global area; If the star crow particle is based on Update the optimal position, and the position update calculation formula is: in, is the position of the j-dimensional vector of the ith Star Crow particle at the k+1th iteration, is the optimal position of the j-dimensional vector of all Nudibranch particles at the k-th iteration; If the star crow particle is based on If the optimal position is not found, Exploration, the position update calculation formula is: Among them, τ 16 is a random number in the interval [0,1]; (4) When the Nutcracker particle is in the development stage of searching for food in the storage area, the Nutcracker particle position update formula includes: when When , the update formula of the position of the star crow particle is: Where f is the total objective function for hybrid energy storage capacity optimization; when When , the particle position update formula is: S605. Repeat steps S602-S604 until the maximum number of iterations is reached, and the position where the total objective function value is minimum is obtained, that is, the optimal capacity configuration scheme of the battery and supercapacitor.

8. The method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm according to claim 1 is characterized in that: The method further includes step S7: analyzing the performance indicators of the optimal battery and supercapacitor capacity configuration solution obtained in step S6.

9. The method for configuring the capacity of wind-solar-hydrogen hybrid energy storage based on the Nudibranch optimization algorithm according to claim 8 is characterized in that: In step S7, the performance indicators analyzed include power shortage rate and wind and solar power abandonment rate. When the power shortage rate and wind and solar power abandonment rate are lower than 10%, the operation requirements are met.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for configuring the capacity of a wind-solar-hydrogen hybrid energy storage system based on the Nudibranch optimization algorithm described in any one of claims 1 to 9 are implemented.

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