Wind-solar off-grid hydrogen production two-stage power optimization method considering reserved energy of energy storage

Abstract

The invention discloses a wind and light off-grid hydrogen production two-stage power optimization method considering reserved energy of energy storage, and belongs to the technical field of wind and light off-grid hydrogen production system power optimization, and the technical scheme is that the method comprises the following steps: S1, establishing a wind and light off-grid hydrogen production system mathematical model; s2, inputting minute-level wind and light historical data; s3, formulating a power distribution rule considering deep coupling of an electrolytic bath operation condition and energy storage preparation energy in a first operation stage, and determining hydrogen production power and hybrid energy storage initial distribution power; and S4, in the second operation stage, with the minimum unit hydrogen production cost and the minimum average charge and discharge depth of the storage battery as targets, the hybrid energy storage power is optimized by adopting an improved multi-target particle swarm algorithm. The method has the beneficial effects that the hydrogen production stability and the energy storage and power supply capacity can be improved, and the wind and light abandoning rate and the unit hydrogen production cost can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power optimization of off-grid wind and solar power hydrogen production systems, and in particular to a two-stage power optimization method for off-grid wind and solar power hydrogen production taking into account energy storage reserve energy. Background Art

[0002] To reduce the impact of wind and solar renewable energy on the power system, new approaches are needed to increase wind and solar power consumption. Hydrogen is a clean secondary energy source with high energy density, enabling large-capacity, long-term storage and transportation. Using electricity generated by wind and photovoltaic power to electrolyze water to produce hydrogen in an off-grid manner and store the energy as hydrogen energy can not only achieve a high proportion of wind and solar power consumption in a clean and pollution-free manner, but also minimize the impact on the main power grid. However, the inherent volatility and intermittency of wind and solar power resources pose a threat to the safe and stable operation of electrolyzers. When the electrolyzer power is too low, it is prone to explosion. Therefore, energy storage is often incorporated into off-grid hydrogen production systems to balance the imbalance between wind and solar power generation and hydrogen production power demand. With the addition of energy storage, a wind, solar, and hydrogen storage system is a complex multi-energy system integrating electricity and hydrogen. Effective power coordination between system components is crucial for the stability of the hydrogen production equipment, the wind and solar power consumption rate, the economic cost, and the energy storage power supply capacity.

[0003] Numerous studies have been conducted on the power optimization of hydrogen energy systems (HESs), including approaches such as intelligent algorithm optimization, data-driven machine learning-based power allocation, and rule-based power allocation. Intelligent algorithm optimization approaches construct multi-objective functions and seek the optimal solution within certain constraints. These approaches demonstrate strong global optimization capabilities for power allocation in complex multi-energy systems. However, these approaches often suffer from high computational complexity and strong hardware dependence. Artificial intelligence machine learning algorithms offer data-driven solutions for power allocation, but they place stringent demands on data quality and hardware performance. These approaches require the collection of at least 2,000 sets of operating condition data for policy training, and GPU-accelerated computing modules increase hardware costs by over 40%, presenting significant obstacles to practical deployment. Deterministic rule-based power allocation approaches, which formulate deterministic allocation logic based on system characteristics, have demonstrated unique advantages in engineering practice. However, existing power allocation rules lack flexibility and adaptability, making it difficult to achieve global optimization, resulting in reduced energy storage power supply capacity and compromised electrolyzer operational stability. A comprehensive comparison reveals that several commonly used HESs power allocation approaches each have their own strengths and weaknesses. Therefore, combining several methods for multi-stage power allocation can combine the advantages of different methods to more ideally complete multiple tasks and improve the overall performance of the system. Therefore, research on two-stage power optimization of off-grid wind and solar hydrogen production systems is of great significance. Summary of the Invention

[0004] The purpose of the present invention is to overcome the problems in the background technology and provide a two-stage power optimization method for off-grid hydrogen production of wind and solar power taking into account energy storage and reserve energy.

[0005] The technical idea of the present invention is: based on deterministic power allocation rules, considering the deep coupling between the electrolyzer operating conditions and the energy storage reserve energy, giving priority to avoiding the harsh operating conditions of the electrolyzer, improving the global hydrogen production stability of the electrolyzer, and thus formulating power allocation rules that are deeply coupled with the real-time operating conditions of the electrolyzer and the energy storage charge state, thereby improving the power adaptability of the system; at the same time, combining the allocation rules with intelligent algorithms, and adopting two-stage collaborative power optimization to ensure the rapidity of power allocation while achieving the optimal global power performance balance.

[0006] The present invention adopts the following technical solution: a two-stage power optimization method for off-grid hydrogen production using wind and solar power, taking into account energy storage and reserve energy, comprising the following steps:

[0007] S1, establish a mathematical model for the off-grid wind and solar power hydrogen production system, including: determining the structure of the off-grid wind and solar power hydrogen production system, establishing a mathematical model for the wind and solar power generation unit, establishing a mathematical model for the hydrogen production and storage unit, and establishing a mathematical model for the hybrid energy storage unit;

[0008] S2, input minute-level wind and solar historical data;

[0009] S3, based on the system mathematical model established in step S1, formulate a power allocation rule in the first operation phase that takes into account the deep coupling between the electrolyzer operating conditions and the energy storage reserve energy, and determine the hydrogen production power and the initial allocation power of the hybrid energy storage;

[0010] S4. Based on the hydrogen production power and the initial hybrid energy storage allocation power obtained in step S3, in the second operation phase, the hybrid energy storage power is optimized using an improved multi-objective particle swarm algorithm with the goal of minimizing the unit hydrogen production cost and the average battery charge and discharge depth.

[0011] Furthermore, in step S1, the mathematical model of the wind-solar off-grid hydrogen production system is established as follows:

[0012] The structure of the wind-solar off-grid hydrogen production system is determined, including a wind-solar power generation unit, a hydrogen production and storage unit, and an energy storage unit. The wind-solar power generation unit includes a wind turbine and a photovoltaic panel. The hydrogen production and storage unit includes an alkaline electrolyzer and a hydrogen storage tank. The energy storage unit includes a hybrid energy storage composed of a battery and a supercapacitor. The wind-solar power generation unit supplies hydrogen to the electrolyzer, and the system power shortage and power surplus are smoothed by the energy storage unit.

[0013] In the wind-solar power generation unit, the wind turbine output power is:

[0014]

[0015] Where, P wt(t) is the wind power generation power at time t, v(t) is the real-time wind speed, v ci is the wind turbine cut-in wind speed, v co is the cut-out wind speed, v rate is the rated wind speed, is the rated power;

[0016] In the wind and solar power generation unit, the calculation formula for the photovoltaic panel power generation is:

[0017]

[0018] Where, P pv (t) is the power generated by the photovoltaic generator at time t, P pvn is the rated power of the photovoltaic panel under standard conditions, δ is the power temperature coefficient, G n , G(t) are the standard light intensity and the light intensity at time t, T n 、T pv (t) are the standard temperature and the photovoltaic panel temperature at time t respectively;

[0019] In the hydrogen production and storage unit, the mathematical model of the alkaline electrolyzer includes:

[0020] The hydrogen production of the electrolyzer is expressed as:

[0021] Q el (t) = P el (t)*η el (t)

[0022] Where Q el (t) is the hydrogen production, P el (t) and η el (t) are the electrolyzer power and hydrogen production efficiency, respectively;

[0023] The electrolyzer power is expressed as:

[0024] P el (t) = P elN -P bre (t)

[0025] Where, P elN is the rated power of the electrolytic cell, P bre (t) is the cut-off power of the electrolytic cell;

[0026] The hydrogen production efficiency of the electrolyzer is expressed as:

[0027]

[0028] Where η el (t) is the hydrogen production efficiency of the electrolyzer, U el (t) is the electrolysis voltage, U tnis the thermal neutral voltage, η F (t) is the Faraday efficiency of the electrolytic cell;

[0029] The calculation formula for the hydrogen production rate of the electrolyzer is:

[0030]

[0031] Where, is the hydrogen production rate of the electrolytic cell at time t, F is the Faraday constant, and I(t) is the electrolysis current of the electrolytic cell;

[0032] Divide the electrolytic cell power interval working conditions:

[0033] (1), [100% P N , 120%P N ]: Overload hydrogen production condition;

[0034] (2), [70% P N , 100% P N ]:For high-efficiency and high-speed hydrogen production conditions;

[0035] (3), [50%P N , 70%P N ]: It is a high-efficiency and medium-speed hydrogen production condition;

[0036] (4), [20%P N , 50%P N ]: High-efficiency and low-speed hydrogen production conditions;

[0037] (5), [0, 20% P N ]: The operating condition is not allowed;

[0038] The hybrid energy storage unit includes a battery and a supercapacitor. The mathematical model of the battery and the supercapacitor is:

[0039]

[0040] Where S SOE,t 、S SOE,t+Δt are the battery or supercapacitor charge states at time t and t+Δt respectively, P t ch 、P t dis are the charging and discharging power of the battery or supercapacitor, η is the charging and discharging efficiency of the energy storage device, Δt is the time step, E is the configured capacity of the battery or supercapacitor, and dt represents the integral over t.

[0041] Furthermore, in step S2, the input of minute-level wind and solar historical data specifically includes: inputting temperature, light intensity and wind speed data.

[0042] Furthermore, the specific method in step S3 is:

[0043] The wind and solar power generation powers are obtained according to the formula in step S1 respectively, and the sum of wind and solar power generation powers is expressed as:

[0044] P fg (t) = P wt (t)+P pv (t)

[0045] Where, P wt (t) is the power generated by the wind turbine at time t, P pv (t) is the power generated by the photovoltaic panel at the tth moment, P fg (t) is the sum of wind and solar power generation at time t;

[0046] The power allocation rule that considers the deep coupling between the electrolyzer operating conditions and the energy storage reserve energy is formulated to determine the hydrogen production power. The specific implementation steps include:

[0047] S3.1: Determine whether the battery and supercapacitor reserve energy is sufficient based on the hybrid energy storage state of charge. The initial state of charge of the energy storage is 0.5, and when the battery or supercapacitor state of charge is greater than 0.5, the reserve energy is considered sufficient.

[0048] S3.2, when the sum of wind and solar power generation power P fg (t) is greater than or equal to the rated power P of the electrolytic cell N When , the electrolyzer produces hydrogen at rated power;

[0049] When P fg (t) is less than P N When the electrolyzer is in a non-ideal hydrogen production condition or is not allowed to operate, the energy storage unit needs to be discharged. When the energy storage unit is discharged, the hydrogen production power of the electrolyzer can be divided into four cases, specifically:

[0050] a. When the electrolytic cell is not allowed to operate, the electrolytic cell power instruction is 0.5P N , the electrolyzer does not stop, making the electrolyzer enter the high-efficiency medium-speed hydrogen production condition;

[0051] b. When the electrolyzer is in high-efficiency and low-speed hydrogen production mode, the electrolyzer power instruction is set to 0.5P. N , so that the electrolyzer enters the high-efficiency and medium-speed hydrogen production mode;

[0052] c. When the electrolyzer is in high-efficiency and medium-speed hydrogen production mode, the electrolyzer power instruction is set to 0.7P. N , so that the electrolyzer enters the high-efficiency and high-speed hydrogen production mode;

[0053] d. When the electrolyzer is already in high-efficiency and high-speed hydrogen production mode, the electrolyzer power instruction is set to Pfg (t), maintain high-efficiency and high-speed hydrogen production conditions, and store energy to retain energy without discharging;

[0054] S3.3, based on the electrolyzer power command determined in the above steps and considering whether the battery and supercapacitor reserve energy is sufficient, the hybrid energy storage power is initially allocated. The specific rules are as follows:

[0055] S3.31, when hybrid energy storage is discharged, the energy storage element with sufficient reserve energy bears the discharge power;

[0056] S3.32: If both have sufficient reserve energy, the battery will bear the small power fluctuations, and the supercapacitor will bear the large power fluctuations.

[0057] S3.33, when the reserve energy of both is insufficient, the two energy storages are discharged together within their respective minimum capacity constraints to supplement the power shortage of the electrolytic cell, and the power shortage part that cannot be supplemented is cut off.

[0058] Furthermore, it is characterized in that the specific method of step S4 includes: determining the objective function, determining the constraint conditions, determining the evaluation index and determining the solution algorithm;

[0059] S4.1, determine the objective function:

[0060] The first objective is to minimize the unit hydrogen production cost of the off-grid wind and solar power hydrogen production system, and the second objective is to minimize the deviation between the battery charge and discharge depth and the initial discharge depth. The overall objective function expression is:

[0061]

[0062] Where f is the total objective function, f1 and f2 are objective function one and objective function two respectively, λ1 and λ2 are the weights of objective one and objective two respectively, and the weights are set as:

[0063]

[0064] The expression of the objective function 1 is:

[0065]

[0066] Where, is the unit hydrogen production cost, C inv is the annualized investment cost, C op is the annual operation and maintenance cost, C rep is the annualized replacement cost, is the annual hydrogen production mass;

[0067] The expression of the second objective function is:

[0068]

[0069] Where D ave is the deviation between the comprehensive charge and discharge depth of the battery and the initial discharge depth, D(t) is the charge and discharge depth of the battery, 0.5 is the initial discharge depth, and T is the number of sampling points;

[0070] S4.2, determining constraints: the constraints include: power balance constraints, battery power constraints, supercapacitor power constraints, and energy storage state constraints;

[0071] S4.21, Power balance constraints

[0072] The power balance constraints are limited to:

[0073]

[0074] Where, P wt (t), P pv (t), P el (t), P was (t), P bre (t) represents the wind power generation power, photovoltaic power generation power, electrolyzer power, wind and solar power curtailment power and load shedding power at time t, respectively. bc (t), P bd (t) represents the battery charging and discharging power, P cc (t), P cd (t) represents the supercapacitor charging and discharging power, P elmax is the maximum power of the electrolyzer;

[0075] S4.22, battery power constraint

[0076] The charging and discharging power of the battery is subject to the following constraints:

[0077]

[0078] Where, P b is the rated power of the battery, P bc (t), P bd (t) represents the battery charging and discharging power respectively;

[0079] S4.23, supercapacitor power constraint

[0080] The constraints on the supercapacitor charging and discharging power are:

[0081]

[0082] Where, P cmax is the rated power of the supercapacitor, P cc (t), P cd(t) represents the supercapacitor charging and discharging power respectively;

[0083] S4.24, Energy Storage State Constraints

[0084] The constraints are:

[0085] 0.1≤S SOE,t ≤0.9

[0086] Where S SOE,t is the charge state of the battery or supercapacitor at time t;

[0087] S4.3, determine the evaluation indicators:

[0088] The system evaluation indicators are set as follows:

[0089] Abandonment rate of wind and solar power: used to evaluate the system's wind and solar power absorption capacity, and its expression is:

[0090]

[0091] Where, EWR is the wind and solar power curtailment rate, P was (t), P fg (t) is the sum of the wind and solar power curtailment and wind and solar power generation at time t;

[0092] Average fluctuation rate of hydrogen production power: used to evaluate the stability of the hydrogen production system, and its expression is:

[0093]

[0094] Where WAVE is the average fluctuation rate of hydrogen production power, T is the number of sampling points, P ecr Represents the actual hydrogen production power of the electrolyzer, P N Represents the rated power of the electrolyzer;

[0095] Average hydrogen production efficiency of electrolyzer: used to measure the energy utilization rate of hydrogen production system, its expression is:

[0096]

[0097] Where η ave is the average hydrogen production efficiency of the electrolyzer, η ec (t) is the hydrogen production efficiency of the electrolyzer at time t;

[0098] S4.4, determine the solution algorithm: the solution algorithm used is the improved multi-objective particle swarm optimization algorithm, which includes:

[0099] Adaptive inertia factor and adaptive mutation factor are introduced to improve the multi-objective particle swarm optimization algorithm. The algorithm solution steps are as follows:

[0100] S4.41, establish the equipment operation and optimization target model of the wind-solar off-grid hydrogen production system and set the system related parameters;

[0101] S4.42, initialize the population and external archive, set the initial value of the equilibrium point, the initial position of the particle and the flying speed and determine their upper and lower limits; set the maximum number of iterations to 50 and the population size to 100;

[0102] S4.43, calculate the inertia factor of i and introduce the adaptive inertia factor ω self , whose expression is:

[0103]

[0104] Where, ω max 、ω min are the upper and lower limits of the inertia factor, i is the current number of iterations, MAX i is the maximum number of iterations;

[0105] S4.44, calculate the fitness of each particle, compare and obtain the local optimal solution and the global optimal solution, and form a non-inferior solution set;

[0106] S4.45, introduces an adaptive mutation factor to perform mutation operations on non-inferior solutions, the mutation factor p i for:

[0107]

[0108] Where v is the mutation rate;

[0109] S4.46, update and maintain the set of non-inferior solutions;

[0110] S4.47, updates the particle's velocity and position;

[0111] S4.48, repeat steps S4.43-S4.47 until the maximum number of iterations is reached, and output the non-inferior solution set.

[0112] The present invention also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the two-stage power optimization method for off-grid hydrogen production of wind and solar power taking into account energy storage and reserve energy are implemented.

[0113] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0114] 1. In view of the problems of poor global energy storage power supply capacity and unstable electrolyzer power caused by the simple interaction between the upper and lower limit constraints of the electrolyzer and the upper and lower limit constraints of energy storage, the traditional power allocation rules only consider the simple interaction between the upper and lower limit constraints of the electrolyzer and the upper and lower limit constraints of energy storage. The present invention considers the deep interaction between various working conditions of the electrolyzer and the energy storage reserve energy, and formulates a more refined and adaptable power allocation rule base, focusing on reserving different sizes of energy storage reserve energy when the electrolyzer is in different working conditions, so as to give priority to avoiding harsh working conditions of the electrolyzer and improve the global hydrogen production stability of the electrolyzer.

[0115] 2. To address the difficulties of intelligent optimization algorithms in solving problems and slow response speeds, as well as the poor adaptability of deterministic rule-based power allocation methods under wind and solar fluctuations, making it difficult to achieve global optimization, the present invention adopts a two-stage collaborative power optimization. In the first stage, based on refined rules that consider energy storage reserve energy, the power of the electrolyzer is quickly allocated, as well as the initial power allocation of hybrid energy storage, reducing the complexity of the nonlinear problem of power optimization. In the second stage, with the goal of minimizing the unit hydrogen production cost and the battery charge and discharge depth, an improved multi-objective particle swarm optimization algorithm is used to optimize the power distribution between hybrid energy storage (batteries, supercapacitors), achieving global equilibrium optimization of electrolyzer power and energy storage power. This method can ensure both rapid power allocation and global power optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1 It is a structural schematic diagram of the wind-solar off-grid hydrogen production system of the present invention.

[0117] Figure 2 This is a data chart of minute-level wind speed, light intensity, and temperature for a typical day throughout the year used in the present invention.

[0118] Figure 3 This is a diagram of power allocation rules for the first operation phase of the present invention.

[0119] Figure 4 This is a flow chart of the improved multi-objective particle swarm algorithm in the second operation phase of the present invention.

[0120] Figure 5 This is a typical daily operating power diagram of the electrolytic cell according to an embodiment of the present invention. DETAILED DESCRIPTION

[0121] 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 solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0122] The structure of the wind-solar off-grid hydrogen production system used in the present invention is as follows: Figure 1As shown, it includes a wind and solar power generation unit, a hydrogen production and storage unit, and an energy storage unit. The wind and solar power generation unit includes a wind turbine and photovoltaic panels; the hydrogen production and storage unit includes an alkaline electrolyzer and a hydrogen storage tank; the energy storage unit is a hybrid energy storage composed of batteries and supercapacitors; the wind and solar power generation unit supplies hydrogen to the electrolyzer, and the energy storage unit smooths out power shortages and surpluses in the system. The specific parameters of the system are:

[0123] Table 1 Specific parameters of wind and solar off-grid hydrogen production system

[0124]

[0125]

[0126] To achieve the above objectives, the present invention proposes a two-stage power optimization method for off-grid hydrogen production using wind and solar power, taking into account energy storage reserve. The specific steps are as follows:

[0127] S1. Establishing a mathematical model for off-grid wind and solar power hydrogen production systems includes the following:

[0128] In the wind-solar power generation unit, the wind turbine output power is:

[0129]

[0130] Where, P wt (t) is the wind power generation power at time t, v(t) is the real-time wind speed, v ci is the wind turbine cut-in wind speed, v co is the cut-out wind speed, v rate is the rated wind speed, is the rated power;

[0131] In the wind and solar power generation unit, the calculation formula for the photovoltaic panel power generation is:

[0132]

[0133] Where, P pv (t) is the power generated by the photovoltaic generator at time t, P pvn is the rated power of the photovoltaic panel under standard conditions, δ is the power temperature coefficient, G n , G(t) are the standard light intensity and the light intensity at time t, T n 、T pv (t) are the standard temperature and the photovoltaic panel temperature at time t respectively;

[0134] In the hydrogen production and storage unit, the mathematical model of the alkaline electrolyzer includes:

[0135] The hydrogen production of the electrolyzer is expressed as:

[0136] Q el (t) = Pel (t)*η el (t)

[0137] Where Q el (t) is the hydrogen production, P el (t) and η el (t) are the electrolyzer power and hydrogen production efficiency, respectively;

[0138] The electrolyzer power is expressed as:

[0139] P el (t) = P elN -P bre (t)

[0140] Where, P elN is the rated power of the electrolytic cell, P bre (t) is the cut-off power of the electrolytic cell;

[0141] The hydrogen production efficiency of the electrolyzer is expressed as:

[0142]

[0143] Where U el (t) is the electrolysis voltage, η el (t) is the hydrogen production efficiency of the electrolyzer, U tn is the thermal neutral voltage, the theoretical value is 1.23V, η F (t) is the Faraday efficiency of the electrolyzer; the formula for calculating the hydrogen production rate of the electrolyzer is:

[0144]

[0145] Where, is the hydrogen production rate of the electrolyzer at time t, and F is the Faraday constant. Based on the two indicators of hydrogen production efficiency and hydrogen production rate, this paper divides the electrolyzer power range operating conditions into:

[0146] (1)[100%P N , 120%P N ]: In overload hydrogen production conditions, the hydrogen production rate is the highest, which can increase the hydrogen production. To avoid damaging the fuel cell materials, it can only be operated for a short time;

[0147] (2) [70% P N , 100% P N ]: High-efficiency and high-speed hydrogen production conditions, with hydrogen production efficiency greater than 70% and hydrogen production rate greater than 7.28 mol / s, are the most ideal;

[0148] (3) [50%P N , 70%P N ]: High-efficiency and medium-speed hydrogen production conditions, with hydrogen production efficiency greater than 70% and moderate hydrogen production rate, which is relatively ideal;

[0149] (4)[20%P N , 50%P N ]: High-efficiency and low-speed hydrogen production conditions, with hydrogen production efficiency greater than 70%, but the hydrogen production rate is low and the power shortage rate is high, affecting the benefits;

[0150] (5)[0, 20%P N ]: Operation is not allowed. Although it contains some high-efficiency hydrogen production ranges, the hydrogen production rate is less than 2 mol / s, which is prone to explosion and is not allowed to operate;

[0151] The hybrid energy storage unit includes batteries and supercapacitors. The mathematical models of batteries and supercapacitors are:

[0152]

[0153] Where S SOE,t 、S SOE,t+Δt are the battery or supercapacitor charge states at time t and t+Δt respectively, P t ch 、P t dis are the charging and discharging power of the battery or supercapacitor, η is the charging and discharging efficiency of the energy storage device, Δt is the time step; E is the configured capacity of the battery or supercapacitor.

[0154] S2. Input minute-level wind and solar history data including: input temperature, light intensity, wind speed data, typical day minute-level temperature, light intensity, wind speed data such as Figure 2 As shown in the figure, the wind and solar power generation resources are relatively weak on the 3rd to 5th typical days, which can verify the effect of the method proposed in this work when continuous wind and solar resources are insufficient.

[0155] S3. In the first operation phase, a power allocation rule is formulated that takes into account the deep coupling between the electrolyzer operating conditions and the energy storage reserve energy, and the hydrogen production power and the initial allocation power of the hybrid energy storage are determined. The power allocation rule for the first phase is shown in the figure below. Figure 3 As shown, the electrolyzer power instruction and the hybrid energy storage initial distribution power instruction can be determined through the allocation rule; the specific process includes:

[0156] The wind and solar power generation powers are obtained according to the formula in step S1 respectively, and the sum of wind and solar power generation powers is expressed as:

[0157] P fg (t) = P wt (t)+P pv (t)

[0158] Where, P wt (t) is the power generated by the wind turbine at time t, P pv(t) is the power generated by the photovoltaic panel at the tth moment, P fg (t) is the sum of wind and solar power generation at time t;

[0159] Formulate a power allocation rule that takes into account the deep coupling between the electrolyzer operating conditions and the energy storage reserve energy, and determine the hydrogen production power including:

[0160] First, a power allocation rule is formulated that takes into account the deep interaction between wind and solar power generation, electrolyzer operating conditions, and energy storage reserve energy. The energy storage power supply priority is formulated for different electrolyzer operating conditions. When the electrolyzer operating conditions allow, appropriate energy storage energy is retained to cope with severe operating conditions, thereby improving the overall energy storage power supply capacity and ensuring the stable operation of the electrolyzer.

[0161] The specific implementation steps include:

[0162] (1) Determine whether the reserve energy of the battery and supercapacitor is sufficient based on the hybrid energy storage state of charge. Here, the initial state of charge of the energy storage is 0.5 as the judgment standard. When the state of charge of the battery or supercapacitor is greater than 0.5, it is considered that the reserve energy is sufficient to withstand the adverse working conditions of the electrolyzer that may occur in the future;

[0163] (2) When the sum of wind and solar power generation power P fg (t) is greater than or equal to the rated power P of the electrolytic cell N When the electrolyzer produces hydrogen at rated power, the electrolyzer is in a high-efficiency and high-speed hydrogen production state, with the maximum hydrogen production rate and the highest profit; P fg (t) is greater than P N When P fg (t) is less than P N When relying solely on wind and solar power generation, the electrolyzer may be in a non-ideal hydrogen production condition, or even not allowed to operate. At this time, the energy storage unit needs to discharge to improve the electrolyzer operating conditions. For different operating conditions, it is necessary to flexibly allocate the electrolyzer optimal power instruction based on the current energy storage charge state, so that the energy storage can reserve sufficient capacity while discharging to avoid electrolyzer shutdown and low-speed hydrogen production conditions. This situation can be specifically divided into four cases. The rules for determining the electrolyzer hydrogen production power in these four cases are as follows:

[0164] 1) When the electrolytic cell is not allowed to operate, the electrolytic cell power instruction is 0.5P N First, ensure that the electrolyzer does not stop, and secondly, make the electrolyzer enter the relatively ideal high-efficiency medium-speed hydrogen production condition;

[0165] 2) When the electrolyzer is in high-efficiency and low-speed hydrogen production mode, the electrolyzer power instruction is set to 0.5P N , making it enter a relatively ideal high-efficiency medium-speed hydrogen production condition;

[0166] 3) When the electrolyzer is in an ideal high-efficiency medium-speed hydrogen production condition, the electrolyzer power instruction is set to 0.7P N , making it enter the most ideal high-efficiency and high-speed hydrogen production condition;

[0167] 4) When the electrolyzer is already in the most ideal high-efficiency and high-speed hydrogen production condition, the electrolyzer power instruction is set to P fg (t), maintain high-efficiency and high-speed hydrogen production conditions, and store energy to retain energy without discharging;

[0168] In this way, when the reserve energy storage is insufficient, only the priority power supply needs of the electrolyzer under each operating condition are met, retaining appropriate storage capacity, thereby improving the electrolyzer's ability to cope with harsh operating conditions throughout the year. In addition, when wind and solar power generation continuously exceeds the rated power of the electrolyzer and the hybrid energy storage reserve capacity is sufficient, the electrolyzer temporarily enters an overload hydrogen production condition to increase hydrogen production and reduce wind and solar curtailment.

[0169] Based on the electrolyzer power command determined in the above steps, combined with whether the reserve energy of the battery and supercapacitor is sufficient, the hybrid energy storage power is initially allocated, which can also reduce the complexity of the second-stage optimization problem in step S4. The specific rules are as follows:

[0170] 1) When hybrid energy storage is discharged, the energy storage element with sufficient reserve energy bears the discharge power;

[0171] 2) If both have sufficient reserve energy, the battery will bear the small power fluctuations and the supercapacitor will bear the large power fluctuations;

[0172] 3) When both reserve energies are insufficient, the two energy storages are discharged together within their respective minimum capacity constraints to supplement the power shortage of the electrolytic cell. The power shortage that cannot be supplemented can only be cut off;

[0173] This energy storage power allocation method can not only ensure that the real-time operating conditions of the electrolyzer are improved, but also reserve energy for energy storage to cope with the overall harsh operating conditions of the electrolyzer, thereby improving the stability of hydrogen production in the electrolyzer.

[0174] S4. In the second operation stage, with the goal of minimizing the unit hydrogen production cost and the average depth of charge and discharge of the battery, an improved multi-objective particle swarm optimization algorithm is used to optimize the hybrid energy storage power, including: determining the objective function, determining the constraint conditions, determining the evaluation index, and determining the solution algorithm;

[0175] S401, determining the objective function includes:

[0176] In order to improve the economic efficiency of the system, the first goal is to minimize the unit hydrogen production cost of the wind-solar off-grid hydrogen production system. At the same time, the energy storage loss is related to the charge and discharge depth, and the number of supercapacitor cycles can cover the entire cycle, so only the battery loss is considered. Therefore, the second goal is to minimize the deviation between the battery charge and discharge depth and the initial discharge depth. The overall goal expression is:

[0177]

[0178] Where f is the total objective function, f1 and f2 are objective function 1 and objective function 2, respectively, and λ1 and λ2 are the weights of objective 1 and objective 2, respectively. Here, improving economy and reducing battery loss are considered equally important, and the weights are set as:

[0179]

[0180] The expression of objective function 1 is:

[0181]

[0182] Where, is the unit hydrogen production cost, C inv is the annualized investment cost, C op is the annual operation and maintenance cost, C rep is the annualized replacement cost, is the annual hydrogen production mass;

[0183] The expression of objective function 2 is:

[0184]

[0185] Where D ave is the deviation between the comprehensive charge and discharge depth of the battery and the initial discharge depth, D(t) is the charge and discharge depth of the battery, 0.5 is the initial discharge depth, and T is the number of sampling points;

[0186] S402. Constraints include:

[0187] (1) Power balance constraints

[0188] In order to ensure the stable operation of the wind-solar off-grid hydrogen production system, the following constraints must be met:

[0189]

[0190] Where, P wt (t), P pv (t), P el (t), P was (t), P bre (t) represents the wind power generation power, photovoltaic power generation power, electrolyzer power, wind and solar power curtailment power, and load shedding power at time t, respectively.bc (t), P bd (t) represents the battery charging and discharging power, P cc (t), P cd (t) represents the supercapacitor charging and discharging power, P elmax is the maximum power of the electrolyzer;

[0191] (2) Battery power constraints

[0192] According to the hybrid energy storage system operation strategy, the battery charging and discharging power is subject to the following constraints:

[0193]

[0194] Where, P b is the rated power of the battery, P bc (t), P bd (t) represents the battery charging and discharging power respectively;

[0195] (3) Supercapacitor power constraints

[0196] The constraints on the supercapacitor charging and discharging power are:

[0197]

[0198] Where, P cmax is the rated power of the supercapacitor, P cc (t), P cd (t) represents the supercapacitor charging and discharging power respectively;

[0199] (4) Energy storage state constraints

[0200] To prevent battery and supercapacitor lifespan degradation caused by overcharging and over-discharging, the energy storage state is constrained:

[0201] 0.1≤S SOE,t ≤0.9

[0202] Where S SOE,t is the charge state of the battery or supercapacitor at time t;

[0203] S403. Evaluation indicators include:

[0204] In order to measure the performance of the off-grid wind and solar power hydrogen production system, the system evaluation indicators are set as follows:

[0205] (1) Wind and solar power curtailment rate: The wind and solar power curtailment rate can evaluate the system's wind and solar power absorption capacity, and its expression is:

[0206]

[0207] Where, EWR is the wind and solar power curtailment rate, P was (t), P fg (t) is the sum of the wind and solar power curtailment and wind and solar power generation at time t;

[0208] (2) Average fluctuation rate of hydrogen production power: The average fluctuation rate of hydrogen production power can be used to evaluate the stability of the hydrogen production system. Its expression is:

[0209]

[0210] Where WAVE is the average fluctuation rate of hydrogen production power, T is the number of sampling points;

[0211] (3) Average hydrogen production efficiency of electrolyzer: The average hydrogen production efficiency of electrolyzer can measure the energy utilization rate of hydrogen production system, and its expression is:

[0212]

[0213] Where η ave is the average hydrogen production efficiency of the electrolyzer, η ec (t) is the hydrogen production efficiency of the electrolyzer at time t;

[0214] S404: The solution algorithm used is an improved multi-objective particle swarm optimization algorithm, which specifically includes:

[0215] In order to avoid the optimization result falling into the local optimum, the adaptive inertia factor and adaptive mutation factor are introduced to improve the multi-objective particle swarm algorithm. The algorithm flow chart is as follows: Figure 4 As shown, the algorithm solution steps are as follows:

[0216] (1) Establish the equipment operation and optimization target model of the wind-solar off-grid hydrogen production system and set the system related parameters;

[0217] (2) Initialize the population and external archive, set the initial value of the equilibrium point, the initial position of the particle and the flying speed, and determine their upper and lower limits; set the maximum number of iterations to 50 and the population size to 100;

[0218] (3) Calculate the inertia factor of i. To avoid falling into local optimum, the adaptive inertia factor ω is introduced self , whose expression is:

[0219]

[0220] Where, ω max 、ω min are the upper and lower limits of the inertia factor, i is the current number of iterations, MAX i is the maximum number of iterations;

[0221] (4) Calculate the fitness of each particle, compare the local optimal solution and the global optimal solution, and form a non-inferior solution set.

[0222] (5) In order to expand the shrinking search range, an adaptive mutation factor is introduced to perform mutation operations on non-inferior solutions. The mutation factor p i for:

[0223]

[0224] Where v is the mutation rate;

[0225] (6) Update and maintain the non-inferior solution set;

[0226] (7) Update the particle's velocity and position;

[0227] (8) Repeat steps (3)-(7) until the maximum number of iterations is reached and output the set of non-inferior solutions.

[0228] In order to verify the superiority of the two-stage collaborative power optimization strategy proposed in this work, four schemes are set for comparison: (1) the electrolytic cell is set at 0.2P N The traditional strategy of the above operation; (2) Make the electrolytic cell at 0.4P N The traditional strategy of the above operation; (3) Make the electrolytic cell P N Operation strategy; (4) Two-stage collaborative power optimization strategy proposed in this paper. Of the four schemes mentioned above, the first three are traditional power allocation rules that only consider the simple interaction between the upper and lower limit constraints of the electrolyzer and the upper and lower limit constraints of the energy storage. The fourth scheme considers the deep interaction between the various working conditions of the electrolyzer and the reserve energy of the energy storage, and combines intelligent algorithms to optimize the power. The various indicators of the four schemes are shown in Table 4.

[0229] Table 24 Comparison of indicators of various schemes

[0230]

[0231] As shown in Table 4, Scheme 4, proposed in this work, offers the lowest unit hydrogen production cost and average fluctuation in hydrogen production power, demonstrating the best economy and stability. Furthermore, Scheme 4 achieves the highest average battery state of charge, minimizes battery loss, and maximizes hydrogen production. Furthermore, Scheme 4 maintains moderate levels of annual outages, hydrogen production efficiency, power shortage rate, and wind and solar curtailment rates.

[0232] Figure 5 The power comparison of the electrolytic cells of the four schemes is shown in the figure. It can be seen from the figure that the electrolytic cell power of scheme 1 and scheme 2 is 0.2P N With 1.2P N Between, 0.4P N With 1.2P NScheme 2 occasionally shuts down, and the hydrogen production reliability is higher, but the power fluctuation and power shortage rate are too high, and the hydrogen production stability is poor; Scheme 3 electrolyzer shuts down too many times, and the power is between 0 and 1.2P N The power of scheme 4 fluctuates between 0.5P and 0.6P, and the reliability and stability of hydrogen production are poor. N With 1.2P N The number of shutdowns is within an acceptable range, far less than that of Scheme 3, and the power outage rate is also the lowest. The stability and reliability of hydrogen production are both optimal. N is the rated power of the electrolyzer.

[0233] An embodiment of the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. It should be noted that when the processor executes the computer program, it corresponds to the specific steps of the method provided in the embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. For technical details not fully described 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 two-stage power optimization method for off-grid hydrogen production using wind and solar power considering energy storage reserve, characterized in that: The steps include: S1, establish a mathematical model for the off-grid wind and solar power hydrogen production system, including: determining the structure of the off-grid wind and solar power hydrogen production system, establishing a mathematical model for the wind and solar power generation unit, establishing a mathematical model for the hydrogen production and storage unit, and establishing a mathematical model for the hybrid energy storage unit; S2, input minute-level historical wind and solar data; S3, based on the system mathematical model established in step S1, formulate a power allocation rule in the first operation phase that takes into account the deep coupling between the electrolyzer operating conditions and the energy storage reserve energy, and determine the hydrogen production power and the initial allocation power of the hybrid energy storage; S4. Based on the hydrogen production power and the initial hybrid energy storage allocation power obtained in step S3, in the second operation phase, the hybrid energy storage power is optimized using an improved multi-objective particle swarm algorithm with the goal of minimizing the unit hydrogen production cost and the average battery charge and discharge depth.

2. A two-stage power optimization method for off-grid hydrogen production using wind and solar power considering energy storage reserve energy according to claim 1, characterized in that: In step S1, the mathematical model of the wind-solar off-grid hydrogen production system is established as follows: The structure of the wind-solar off-grid hydrogen production system is determined, including a wind-solar power generation unit, a hydrogen production and storage unit, and an energy storage unit. The wind-solar power generation unit includes a wind turbine and a photovoltaic panel. The hydrogen production and storage unit includes an alkaline electrolyzer and a hydrogen storage tank. The energy storage unit includes a hybrid energy storage composed of a battery and a supercapacitor. The wind-solar power generation unit supplies hydrogen to the electrolyzer, and the system power shortage and power surplus are smoothed by the energy storage unit. In the wind-solar power generation unit, the wind turbine output power is: Where, P wt (t) is the wind power generation power at time t, v(t) is the real-time wind speed, v ci is the wind turbine cut-in wind speed, v co is the cut-out wind speed, v rate is the rated wind speed, is the rated power; In the wind and solar power generation unit, the calculation formula for the photovoltaic panel power generation is: Where, P pv (t) is the power generated by the photovoltaic generator at time t, P pvn is the rated power of the photovoltaic panel under standard conditions, δ is the power temperature coefficient, G n , G(t) are the standard light intensity and the light intensity at time t, T n 、T pv (t) are the standard temperature and the photovoltaic panel temperature at time t respectively; In the hydrogen production and storage unit, the mathematical model of the alkaline electrolyzer includes: The hydrogen production of the electrolyzer is expressed as: Q el (t)=P el (t)*η el (t) Where Q el (t) is the hydrogen production, P el (t) and η el (t) are the electrolyzer power and hydrogen production efficiency, respectively; The electrolyzer power is expressed as: P el (t)=P elN -P bre (t) Where, P elN is the rated power of the electrolytic cell, P bre (t) is the cut-off power of the electrolytic cell; The hydrogen production efficiency of the electrolyzer is expressed as: Where η el (t) is the hydrogen production efficiency of the electrolyzer, U el (t) is the electrolysis voltage, U tn is the thermal neutral voltage, η F (t) is the Faraday efficiency of the electrolytic cell; The calculation formula for the hydrogen production rate of the electrolyzer is: Where, is the hydrogen production rate of the electrolyzer at time t, F is the Faraday constant, and I(t) is the electrolysis current of the electrolyzer; Divide the electrolytic cell power interval working conditions: (1), [100% P N , 120%P N ]: Overload hydrogen production condition; (2), [70% P N , 100% P N ]:For high-efficiency and high-speed hydrogen production conditions; (3), [50%P N , 70%P N ]: It is a high-efficiency and medium-speed hydrogen production condition; (4), [20%P N , 50%P N ]: High-efficiency and low-speed hydrogen production conditions; (5), [0, 20% P N ]: The operating condition is not allowed; The hybrid energy storage unit includes a battery and a supercapacitor. The mathematical model of the battery and the supercapacitor is: Where S SOE,t 、S SOE,t+Δt are the battery or supercapacitor charge states at time t and t+Δt respectively, P t ch 、P t dis are the charging and discharging power of the battery or supercapacitor, η is the charging and discharging efficiency of the energy storage device, Δt is the time step, E is the configured capacity of the battery or supercapacitor, and dt represents the integral over t.

3. The two-stage power optimization method for off-grid hydrogen production using wind and solar power considering energy storage reserve energy according to claim 1 is characterized in that: In step S2, the minute-level wind and light historical data are input, specifically: temperature, light intensity and wind speed data are input.

4. The two-stage power optimization method for off-grid hydrogen production using wind and solar power considering energy storage reserve energy according to claim 1 is characterized in that: The specific method in step S3 is: The wind and solar power generation powers are obtained according to the formula in step S1 respectively, and the sum of wind and solar power generation powers is expressed as: P fg (t)=P wt (t)+P pv (t) Where, P wt (t) is the power generated by the wind turbine at time t, P pv (t) is the power generated by the photovoltaic panel at the tth moment, P fg (t) is the sum of wind and solar power generation at time t; The power allocation rule that considers the deep coupling between the electrolyzer operating conditions and the energy storage reserve energy is formulated to determine the hydrogen production power. The specific implementation steps include: S3.1: Determine whether the battery and supercapacitor reserve energy is sufficient based on the hybrid energy storage state of charge. The initial state of charge of the energy storage is 0.5, and when the battery or supercapacitor state of charge is greater than 0.5, the reserve energy is considered sufficient. S3.2, when the sum of wind and solar power generation power P fg (t) is greater than or equal to the rated power P of the electrolytic cell N When , the electrolyzer produces hydrogen at rated power; When P fg (t) is less than P N When the electrolyzer is in a non-ideal hydrogen production condition or is not allowed to operate, the energy storage unit needs to be discharged. When the energy storage unit is discharged, the hydrogen production power of the electrolyzer can be divided into four cases, specifically: a. When the electrolytic cell is not allowed to operate, the electrolytic cell power instruction is 0.5P N , the electrolyzer does not stop, making the electrolyzer enter the high-efficiency medium-speed hydrogen production condition; b. When the electrolyzer is in high-efficiency and low-speed hydrogen production mode, the electrolyzer power instruction is set to 0.5P. N , so that the electrolyzer enters the high-efficiency and medium-speed hydrogen production mode; c. When the electrolyzer is in high-efficiency and medium-speed hydrogen production mode, the electrolyzer power instruction is set to 0.7P. N , so that the electrolyzer enters the high-efficiency and high-speed hydrogen production mode; d. When the electrolyzer is already in high-efficiency and high-speed hydrogen production mode, the electrolyzer power instruction is set to P fg (t), maintain high-efficiency and high-speed hydrogen production conditions, and store energy to retain energy without discharging; S3.3, based on the electrolyzer power command determined in the above steps and considering whether the battery and supercapacitor reserve energy is sufficient, the hybrid energy storage power is initially allocated. The specific rules are as follows: S3.31, when hybrid energy storage is discharged, the energy storage element with sufficient reserve energy bears the discharge power; S3.32: If both have sufficient reserve energy, the battery will bear the small power fluctuations, and the supercapacitor will bear the large power fluctuations. S3.33, when the reserve energy of both is insufficient, the two energy storages are discharged together within their respective minimum capacity constraints to supplement the power shortage of the electrolytic cell, and the power shortage part that cannot be supplemented is cut off.

5. The two-stage power optimization method for off-grid hydrogen production using wind and solar power considering energy storage reserve energy according to claim 1 is characterized in that: The specific method of step S4 is: including determining the objective function, determining the constraint conditions, determining the evaluation index and determining the solution algorithm; S4.1, determine the objective function: The first objective is to minimize the unit hydrogen production cost of the off-grid wind and solar power hydrogen production system, and the second objective is to minimize the deviation between the battery charge and discharge depth and the initial discharge depth. The overall objective function expression is: Where f is the total objective function, f1 and f2 are objective function one and objective function two respectively, λ1 and λ2 are the weights of objective one and objective two respectively, and the weights are set as: The expression of the objective function 1 is: Where, is the unit hydrogen production cost, C inv is the annualized investment cost, C op is the annual operation and maintenance cost, C rep is the annualized replacement cost, is the annual hydrogen production mass; The expression of the objective function 2 is: Where D ave is the deviation between the comprehensive charge and discharge depth of the battery and the initial discharge depth, D(t) is the charge and discharge depth of the battery, 0.5 is the initial discharge depth, and T is the number of sampling points; S4.2, determining constraints: the constraints include: power balance constraints, battery power constraints, supercapacitor power constraints, and energy storage state constraints; S4.21, Power balance constraints The power balance constraints are limited to: Where, P wt (t), P pv (t), P el (t), P was (t), P bre (t) represents the wind power generation power, photovoltaic power generation power, electrolyzer power, wind and solar power curtailment power and load shedding power at time t, respectively. bc (t), P bd (t) represents the battery charging and discharging power, P cc (t), P cd (t) represents the supercapacitor charging and discharging power, P elmax is the maximum power of the electrolyzer; S4.22, battery power constraint The charging and discharging power of the battery is subject to the following constraints: Where, P b is the rated power of the battery, P bc (t), P bd (t) represents the battery charging and discharging power respectively; S4.23, supercapacitor power constraint The constraints on the supercapacitor charging and discharging power are: Where, P cmax is the rated power of the supercapacitor, P cc (t), P cd (t) represents the supercapacitor charging and discharging power respectively; S4.24, Energy Storage State Constraints The constraints are: 0.1≤S SOE,t ≤0.9 Where S SOE,t is the charge state of the battery or supercapacitor at time t; S4.3, determine the evaluation indicators: The system evaluation indicators are set as follows: Abandonment rate of wind and solar power: used to evaluate the system's wind and solar power absorption capacity, and its expression is: Where, EWR is the wind and solar power curtailment rate, P was (t), P fg (t) is the sum of the wind and solar power curtailment and wind and solar power generation at time t; Average fluctuation rate of hydrogen production power: used to evaluate the stability of the hydrogen production system, and its expression is: Where WAVE is the average fluctuation rate of hydrogen production power, T is the number of sampling points, P ecr Represents the actual hydrogen production power of the electrolyzer, P N Represents the rated power of the electrolyzer; Average hydrogen production efficiency of electrolyzer: used to measure the energy utilization rate of hydrogen production system, its expression is: Where η ave is the average hydrogen production efficiency of the electrolyzer, η ec (t) is the hydrogen production efficiency of the electrolyzer at time t; S4.4, determine the solution algorithm: the solution algorithm used is the improved multi-objective particle swarm optimization algorithm, which includes: Adaptive inertia factor and adaptive mutation factor are introduced to improve the multi-objective particle swarm optimization algorithm. The algorithm solution steps are as follows: S4.41, establish the equipment operation and optimization target model of the wind-solar off-grid hydrogen production system and set the system related parameters; S4.42, initialize the population and external archive, set the initial value of the equilibrium point, the initial position of the particle and the flying speed and determine their upper and lower limits; set the maximum number of iterations to 50 and the population size to 100; S4.43, calculate the inertia factor of i and introduce the adaptive inertia factor ω self , whose expression is: Where, ω max 、ω min are the upper and lower limits of the inertia factor, i is the current number of iterations, MAX i is the maximum number of iterations; S4.44, calculate the fitness of each particle, compare and obtain the local optimal solution and the global optimal solution, and form a non-inferior solution set; S4.45, introduces an adaptive mutation factor to perform mutation operations on non-inferior solutions, the mutation factor p i for: Where v is the mutation rate; S4.46, update and maintain the set of non-inferior solutions; S4.47, updates the particle's velocity and position; S4.48, repeat steps S4.43-S4.47 until the maximum number of iterations is reached, and output the non-inferior solution set.

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