Off-grid wind-solar hydrogen production system capacity configuration optimization method based on wind-solar optimal configuration ratio

Abstract

The invention relates to a wind-solar optimal configuration ratio-based capacity configuration optimization method for an off-grid wind-solar hydrogen production system, which comprises the following steps of: firstly, solving an optimal wind-solar ratio by taking a wind-solar complementary principle as a basis and taking economic cost and fluctuation of a wind-solar power generation overall curve as optimization targets according to the wind-solar complementary principle; then, on the basis of the optimal configuration ratio, an improved particle swarm optimization algorithm is adopted for an off-grid wind-solar hydrogen production system which has been successfully modeled according to hydrogen production demand data and historical data such as wind speed and illumination of a target area, so that the optimal configuration ratio of the wind-solar hydrogen production system is obtained, and the optimal configuration ratio of the wind-solar hydrogen production system is obtained. In the optimization process, optimization parameters such as inertia weights and learning factors change adaptively according to system parameters, capacity configuration optimization of the off-grid wind and light hydrogen production system is carried out, and finally an optimal capacity configuration result is obtained.

Description

Technical Field

[0001] The present invention belongs to the field of DC microgrids, and relates to an optimization method for the capacity configuration of an off-grid wind-solar hydrogen production system based on the optimal configuration ratio of wind and light. Background Art

[0002] With the transformation of the global energy structure and the continuous growth of the demand for clean energy, hydrogen energy, as an efficient and clean energy carrier, has received extensive attention in its development and utilization. Especially in the context of the "dual-carbon goal", using renewable energy for electrolytic water hydrogen production has become an important way to achieve the transformation of the energy structure and reduce carbon emissions.

[0003] Wind power has the advantages of rich resources, high power generation utilization efficiency, no occupation of land resources, and suitability for large-scale development. For off-grid hydrogen production systems, there are currently severe problems such as low energy utilization efficiency and poor economic benefits. The output power of wind and light is random and fluctuating, with characteristics such as frequent power fluctuations. If these problems cannot be solved, it may lead to errors and shutdowns during the operation of the hydrogen production system, and even cause the entire system to collapse.

[0004] Off-grid wind-solar hydrogen production projects also face challenges in terms of economy. The change in hydrogen price has a great impact on the system economy. If the economy of the hydrogen production system cannot be effectively improved, it may lead to the inability of the project to operate in the long term. Against these backgrounds, the problem of optimizing the capacity configuration of off-grid hydrogen production systems is an urgent problem to be solved.

[0005] There are currently many technical challenges in the optimization of the capacity configuration of off-grid hydrogen production systems, mainly including how to ensure the stable operation of the hydrogen production system under unstable wind and light conditions, and how to improve the adaptability of the electrolyzer to the fluctuations of renewable energy, that is, to meet the target hydrogen production volume even in the face of uncertain light and wind. The economic challenge involves how to configure sufficient energy storage devices to balance energy supply and demand without adding too much cost to ensure the normal operation of the off-grid hydrogen production system. In addition, the system stability challenge focuses on how to ensure the long-term reliable operation of the hydrogen production system in the absence of grid support. Considering the cost problem of large-capacity consumption by the energy storage system, the coordinated control and energy management of the hydrogen production system have become another research difficulty. Hydrogen production can not only be used as a way of energy storage, but also provide support for other energy demands, such as transportation, industry, and household use. Therefore, how to find a balance between cost and benefit and how to optimize the capacity configuration of off-grid wind-solar hydrogen production systems are the key points of current research. Summary of the Invention

[0006] In order to solve the problem of what is the optimal solution for the capacity configuration of each component in an off-grid wind-solar hydrogen production system to meet the target hydrogen production demand in the target area while ensuring economy and stability, the technical solution adopted in the present invention is: an optimization method for the capacity configuration of an off-grid wind-solar hydrogen production system based on the optimal wind-solar ratio, comprising the following steps:

[0007] Construct an overall model of the off-grid wind-solar hydrogen production system including a wind turbine system, a photovoltaic system, a hydrogen production electrolyzer system, and an energy storage system;

[0008] Based on the overall model of the off-grid wind-solar hydrogen production system, in accordance with the principle of wind-solar complementarity, with economic cost and the volatility of the overall wind-solar power generation curve as the optimization objectives, solve for the optimal wind-solar ratio to obtain the optimal configuration quantity ratio of the wind turbine and photovoltaic per unit power generation unit capacity;

[0009] Based on the historical wind and light data of the target area, the hydrogen production demand data of the area, and the optimal configuration quantity ratio of the wind turbine and photovoltaic, on the premise of meeting the hydrogen demand of the area and ensuring the stable operation of the off-grid wind-solar hydrogen production system, with the economy of the off-grid wind-solar hydrogen production system as the optimization objective, use an adaptive particle swarm optimization algorithm to optimize the capacity configuration of the off-grid wind-solar hydrogen production system, determine the quantities of the wind turbine, photovoltaic, hydrogen production electrolyzer, and energy storage system in the off-grid wind-solar hydrogen production system, and obtain the optimal capacity configuration result of the off-grid wind-solar hydrogen production system.

[0010] Furthermore: The process of solving for the optimal wind-solar ratio based on the overall model of the off-grid wind-solar hydrogen production system, in accordance with the principle of wind-solar complementarity, with economic cost and the volatility of the overall wind-solar power generation curve as the optimization objectives, is as follows:

[0011] Based on the historical wind speed and light data of the target area, obtain the overall wind-solar power generation power curve under different wind-solar power generation unit configuration ratios;

[0012] According to the overall wind-solar power generation power curve, calculate the positive volatility and negative volatility in one-hour steps, record the positive volatility and negative volatility exceeding 2% as effective volatility, and obtain the cumulative volatility in chronological order,

[0013] Calculate the difference between the maximum value and the minimum value of the cumulative volatility to obtain the cumulative volatility difference;

[0014] Calculate the economic cost under different configuration ratios of the total installed wind power capacity;

[0015] According to the principle of wind-solar complementary, with the economic cost and the volatility of the overall curve of wind-solar power generation as two objective functions for optimization, the multi-objective particle swarm algorithm is used to optimize and solve the economic cost and the volatility of the overall curve of wind-solar power generation for the optimal wind-solar configuration ratio, and the point of the optimal solution is obtained, that is, the ratio of the installed capacity of the fan to the photovoltaic, which is also the optimal fan and photovoltaic configuration ratio.

[0016] Furthermore: The objective function of the volatility of the overall curve of wind-solar power generation is characterized by the cumulative volatility difference;

[0017] The formula for the cumulative volatility difference is as follows in formula (3):

[0018] CFD = max(CF(1, 2, …, N)) - min(CF(1, 2, …, N)) (3)

[0019] In the formula: CF(1, 2, …, N) represents the cumulative volatility value within the time series from 1 to N.

[0020] Furthermore, the objective function of the economic cost under different configuration ratios of the total installed capacity of wind power is as follows:

[0021] C1 = N wind C wind + N pv C pv (4)

[0022] In the formula: C1 is the total cost of the wind-solar power generation unit, N wind is the installed capacity of the fan, C wind is the cost per unit capacity of the fan, N pv is the installed capacity of the photovoltaic, C pv is the cost per unit capacity of the photovoltaic.

[0023] Furthermore: The constraint conditions for optimizing and solving the economic cost and the volatility of the overall curve of wind-solar power generation using the multi-objective particle swarm algorithm are as follows:

[0024]

[0025] In the formula: N wind is the installed capacity of the fan, N pv is the installed capacity of the photovoltaic.

[0026] Furthermore: The update formula for the particle velocity in the adaptive particle swarm optimization algorithm is as follows:

[0027]

[0028] In the formula, is the velocity of the particle in the next generation, w is the inertia weight, c1 and c2 are the learning factors, and rand1() and rand2() are random numbers in the interval [0, 1];

[0029] Among them: The specific change formula of the inertia weight w is as follows:

[0030]

[0031] In the formula, w max is the maximum value of the inertia weight, w min is the minimum value of the inertia weight, p ec is the actual hydrogen production power of the electrolyzer, is the maximum hydrogen production power of the electrolyzer;

[0032] The individual learning factor c1: is used to control the dependence degree of the particle on its own historical best position, and the social learning factor c2: is used to control the dependence degree of the particle on the group best position;

[0033]

[0034] In the formula, c base is the base value of c1 and c2, c max is the maximum value of c1 and c2, N ess is the current capacity value of the energy storage system, N essmax is the maximum capacity value of the energy storage system.

[0035] Furthermore: The stable operation of the off-grid wind-solar hydrogen production system is characterized by the abnormality rate of the off-grid wind-solar hydrogen production system, and the formula for the abnormality rate of the off-grid wind-solar hydrogen production system is as follows:

[0036]

[0037] In the formula: M e is the abnormality rate of the system, P wind，pv (t) is the total power generation of the wind turbine and photovoltaic in the t period, C br is the total load interruption power during the simulated operation duration, C wa is the total wind and light abandonment power during the simulated operation duration.

[0038] Furthermore: The economy of the off-grid wind-solar hydrogen production system is characterized by the fitness value of the overall off-grid wind-solar hydrogen production system, and the calculation formula for the fitness value of the overall off-grid wind-solar hydrogen production system is as follows:

[0039]

[0040] In the formula, M total is the fitness value of the overall off-grid wind-solar hydrogen production system, M H is the hydrogen sales revenue, Mec is the investment, installation, operation and maintenance costs of the electrolyzer, M wind，pv is the total investment, installation, operation and maintenance costs of the fan and photovoltaic power generation unit, M ess is the investment, installation, operation and maintenance costs of the energy storage unit.

[0041] An off-grid wind-solar hydrogen production system capacity configuration optimization method based on the optimal wind-solar configuration ratio provided by the present invention; first, according to the complementary characteristics of the fan and photovoltaic power generation unit, under the condition that the total capacity of the wind-solar power generation unit is certain, with economy and the overall power volatility of the power generation unit as the goal, first use the particle swarm optimization algorithm to solve, and obtain the optimal configuration ratio under the unit power generation unit capacity. On the basis of this optimal configuration ratio, for the successfully modeled off-grid wind-solar hydrogen production system, according to the hydrogen production demand data of the target area and historical data such as wind speed and sunlight, use the adaptive particle swarm optimization algorithm to optimize and solve the capacity configuration of the entire off-grid wind-solar hydrogen production system. Finally, perform an actual example analysis on the solved capacity configuration plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0043] Figure 1 is the flowchart of the method of this application;

[0044] Figure 2 is the off-grid wind-solar hydrogen production system which is the research object of the present invention;

[0045] Figure 3 is the capacity configuration optimization flowchart of the adaptive particle swarm optimization algorithm proposed by the present invention;

[0046] Figure 4 is the overall capacity configuration optimization flowchart of the off-grid wind-solar hydrogen production system based on the wind-solar complementary principle and the adaptive particle swarm algorithm proposed by the present invention;

[0047] Figure 5 is the optimal wind-solar configuration ratio result diagram based on the wind-solar complementary principle proposed by the present invention;

[0048] Figure 6 is the final capacity configuration optimization result diagram based on the optimal wind-solar configuration ratio of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0049] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and in no way constitutes a limitation on the present invention and its application or use. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0051] Figure 1 is a flowchart of the method of this application;

[0052] An optimization method for the capacity configuration of an off-grid wind-solar hydrogen production system based on the optimal wind-solar ratio includes the following steps:

[0053] S1: Construct an overall model of the off-grid wind-solar hydrogen production system including a wind turbine system, a photovoltaic system, a hydrogen production electrolyzer system, and an energy storage system;

[0054] S2: Based on the overall model of the off-grid wind-solar hydrogen production system, in accordance with the principle of wind-solar complementarity, with the economic cost and the volatility of the overall wind-solar power generation curve as the optimization objectives, solve for the optimal wind-solar ratio to obtain the optimal configuration quantity ratio under the unit power generation capacity of the wind turbine and the photovoltaic;

[0055] S3: Based on the historical wind-solar data of the target area, the hydrogen production demand data of this area, and the optimal configuration quantity ratio of the wind turbine and the photovoltaic, on the premise of meeting the hydrogen demand of this area and ensuring the stable operation of the off-grid wind-solar hydrogen production system, with the economy of the off-grid wind-solar hydrogen production system as the optimization objective, use the adaptive particle swarm optimization algorithm to optimize the capacity configuration of the off-grid wind-solar hydrogen production system, determine the quantities of the wind turbine, the photovoltaic, the hydrogen production electrolyzer, and the energy storage system in the off-grid wind-solar hydrogen production system, and obtain the optimal capacity configuration result of the off-grid wind-solar hydrogen production system.

[0056] Steps S1 / S2 / S3 are executed in sequence;

[0057] As Figure 2 shown, the off-grid wind-solar hydrogen production system, which is the object for implementing the optimized capacity configuration, is composed of equipment such as a wind turbine power generation unit, a photovoltaic power generation unit, an electrolyzer, and an energy storage system. The power sources of the off-grid wind-solar hydrogen production system are the wind turbine and the photovoltaic power generation unit, and the energy storage system provides assistance and realizes the function of peak shaving and valley filling.

[0058] Build the models of the wind turbine and the photovoltaic power generation unit, and build the models of the electrolyzer and the energy storage system, specifically including: build the models of the wind turbine and the photovoltaic power generation unit through the wind power generation formula, the equivalent circuit and equations of photovoltaic power generation; build the state-of-charge model of the energy storage system according to the ampere-hour integration method; build the electrolyzer model corresponding to the hydrogen production amount and power according to the basic electrolysis formula.

[0059] Model each component of the off-grid wind-solar hydrogen production system. The wind turbine array, the photovoltaic array and the energy storage unit are connected in parallel to the DC bus through the corresponding DC / DC converters; the electrolyzer is also connected in parallel to the DC bus through the corresponding DC / DC converter. The energy storage unit consists of multiple lithium batteries and their bidirectional DC / DC converters. Usually, multiple groups of energy storage units are connected in parallel to the DC bus to improve the total capacity and reliability of the energy storage system.

[0060] According to the characteristic that photovoltaic power generation cannot provide power at night, a wind power generation unit is added. Because the power generation characteristics of the wind turbine unit and the photovoltaic unit are naturally complementary, wind power generation can not only make up for the shortage at night during photovoltaic power generation, but also its construction cost is lower than that of photovoltaic power generation. It can further reduce the construction cost of the power generation unit to improve the economy. However, the volatility of wind power generation is greater than that of photovoltaic power generation, and a greater volatility needs to be suppressed, so the capacity demand for the energy storage system is greater. The reasonable configuration of the wind power generation unit and the photovoltaic power generation unit can not only improve the economy, but also reduce the power volatility of the entire power generation unit. Under the condition that the total capacity of the wind-solar power generation unit is certain, aiming at the economy of the power generation unit construction and the overall power volatility of the power generation unit, first use the multi-objective particle swarm optimization algorithm to solve, and obtain the optimal configuration ratio per unit power generation unit capacity. On this basis, further optimize and solve the capacity configuration of the entire system. To reduce the capacity requirements of the energy storage system and the electrolyzer in the off-grid wind-solar hydrogen production system, reduce redundancy, and improve the economy of the overall system.

[0061] As Figure 3 shown in the optimization strategy flowchart of the optimal configuration ratio of the wind-solar power generation unit,

[0062] According to the characteristic that photovoltaic power generation cannot provide power at night, a wind power generation unit is added. Because the power generation characteristics of the wind turbine unit and the photovoltaic unit are naturally complementary, wind power generation can not only make up for the shortage at night during photovoltaic power generation, but also its construction cost is lower than that of photovoltaic power generation. It can further reduce the construction cost of the power generation unit to improve the economy. However, the volatility of wind power generation is greater than that of photovoltaic power generation, and a greater volatility needs to be suppressed, so the capacity demand for the energy storage system is greater. The reasonable configuration of the wind power generation unit and the photovoltaic power generation unit can not only improve the economy, but also reduce the power volatility of the entire power generation unit.

[0063] Based on the overall model of the off-grid wind-solar hydrogen production system, in accordance with the principle of wind-solar complementarity, taking the economic cost and the volatility of the overall wind-solar power generation curve as the optimization objectives, the optimal wind-solar ratio is solved to obtain the optimal configuration quantity ratio under the unit power generation capacity of the wind turbine and photovoltaic. The total installed capacity of the overall wind-solar power generation part can be increased or decreased according to the actual demand in proportion to the optimal wind-solar configuration ratio. On the basis of this optimal wind-solar configuration ratio, the solution of the overall capacity configuration optimization of the off-grid wind-solar hydrogen production system is further carried out, which can further reduce the demand and redundancy of the electrolyzer and energy storage system on the premise of meeting the demand and improve the economy of the system;

[0064] Furthermore, based on the overall model of the off-grid wind-solar hydrogen production system, in accordance with the principle of wind-solar complementarity, taking the economic cost and the volatility of the overall wind-solar power generation curve as the optimization objectives, the process of solving the optimal wind-solar ratio to obtain the optimal configuration quantity ratio under the unit power generation capacity of the wind turbine and photovoltaic is as follows:

[0065] According to the historical wind speed and light data of the target area, the overall wind-solar power generation power curve under different wind-solar power generation unit configuration ratios is obtained;

[0066] Then, the positive volatility and negative volatility are calculated in one-hour steps based on the overall wind-solar power generation power curve, and the values exceeding 2% in the positive volatility and negative volatility are recorded as effective volatility,

[0067] Then, the cumulative volatility (CF) is obtained in chronological order. The cumulative volatility is the effective volatility, and the values not exceeding 2% are not counted as cumulative volatility, while the values exceeding 2% are cumulative volatility;

[0068] Finally, the difference between the maximum value and the minimum value of the cumulative volatility is calculated to obtain the cumulative volatility deviation (CFD);

[0069] Then, the economic costs under different configuration ratios of the total installed capacity of wind power are calculated;

[0070] In accordance with the principle of wind-solar complementarity, taking the economic costs under different configuration ratios of the total installed capacity of wind power and the volatility of the overall wind-solar power generation curve as the two objective functions for optimization, the multi-objective particle swarm optimization algorithm is used to solve the two-objective optimization of the optimal wind-solar configuration ratio. The point of the optimal solution, that is, the ratio of the installed capacity of the wind turbine to the photovoltaic corresponding to it, is the optimal wind-solar configuration ratio.

[0071] The positive volatility reflects the power change from the maximum value of the cumulative volatility that appears first to the minimum value that appears later within one minute. The positive volatility is used to evaluate the fluctuation situation where the power rapidly drops from the peak to the trough during the wind-solar power generation process.

[0072] Negative volatility reflects the power change from the minimum value of the cumulative volatility that appears first to the maximum value that appears later within one minute.

[0073] Negative volatility is used to evaluate the fluctuation of the overall wind and solar power generation process, where the overall wind and solar power generation rapidly rises from the low point to the high point.

[0074] The cumulative volatility difference can well evaluate the power volatility of the wind and solar power generation unit. The smaller the cumulative volatility difference, the lower the output power volatility of the wind and solar power generation unit, the lower the capacity demand of the off-grid wind and solar hydrogen production system for the energy storage system, and the lower the redundancy of the electrolyzer. Calculate the positive volatility γ + and the negative volatility γ - The formulas are as follows:

[0075]

[0076]

[0077] In the formula: (1): maxP(t) represents the maximum power that appears first within the current 1 hour of the wind and solar power generation unit, minP(t) represents the minimum power that appears later within the current 1 hour, and P rated is the power corresponding to the total installed capacity.

[0078] (2): minP(t) represents the minimum power that appears first within the current 1 hour of the wind and solar power generation unit, maxP(t) represents the maximum power that appears later within the current 1 hour, and P rated is the power corresponding to the total installed capacity.

[0079] The cumulative volatility difference is an index used to evaluate the volatility of the total wind and solar power generation under the current wind and solar configuration ratio. The smaller the cumulative volatility difference, the smaller the volatility of the total power generation.

[0080] The objective function of the volatility of the overall wind and solar power generation curve is characterized by the cumulative volatility difference;

[0081] The cumulative volatility difference is the first optimization objective of the first layer of optimization;

[0082] The formula for calculating the cumulative volatility difference CFD is as follows, formula (3):

[0083] CFD = max(CF(1, 2, …, N)) - min(CF(1, 2, …, N)) (3)

[0084] In the formula: CF(1, 2, …, N) represents the cumulative volatility value within the time series from 1 to N. The time series selected for this optimization is from the 1st hour to the 720th hour.

[0085] The economic cost of the wind power and photovoltaic power generation units under different configuration ratios is the second optimization objective of the first-layer optimization.

[0086] The calculation formula for the economic cost of the wind power and photovoltaic power generation units under different configuration ratios is as follows:

[0087] C1 = N wind C wind + N pv C pv (4)

[0088] In the formula: C1 is the total cost of the wind-solar power generation unit, N wind is the configured capacity of the wind turbine, C wind is the cost per unit capacity of the wind turbine, N pv is the configured capacity of the photovoltaic, C pv is the cost per unit capacity of the photovoltaic.

[0089] The configured capacities of the wind power generation unit and the photovoltaic power generation unit need to be set within a constraint range during the optimization solution process. The specific constraint conditions are as follows:

[0090]

[0091] In the formula: N wind is the configured capacity of the wind turbine, N pv is the configured capacity of the photovoltaic.

[0092] The calculation formula for the optimal configuration ratio of the wind-solar power generation unit is as follows:

[0093]

[0094] In the formula: K p is the optimal configuration ratio of the wind-solar power generation unit, N wind is the configured capacity of the wind turbine, N pv is the configured capacity of the photovoltaic.

[0095] Furthermore, based on the historical wind and solar data of the target area, the hydrogen production demand data of this area, and the optimal configuration quantity ratio of the wind turbines and photovoltaics, while meeting the hydrogen demand of this area and ensuring the stable operation of the off-grid wind-solar hydrogen production system, with the economy of the off-grid wind-solar hydrogen production system as the optimization objective, an adaptive particle swarm optimization algorithm is used to optimize the capacity configuration of the off-grid wind-solar hydrogen production system, determine the quantities of the wind turbines, photovoltaics, hydrogen production electrolyzers, and energy storage systems in the off-grid wind-solar hydrogen production system, and obtain the optimal capacity configuration result of the off-grid wind-solar hydrogen production system.

[0096] Further, based on the historical wind and light data of the target area, the hydrogen production demand data of the area, and the optimal configuration quantity ratio of wind turbines and photovoltaics, while meeting the hydrogen demand of the area and ensuring the stable operation of the off-grid wind and light hydrogen production system, with the economy of the off-grid wind and light hydrogen production system as the optimization goal, and with the two objective functions of the maximum economic fitness value and the minimum system failure rate of the off-grid wind and light hydrogen production system as the optimization goals, an adaptive particle swarm optimization algorithm is used to optimize the capacity configuration of the off-grid wind and light hydrogen production system, determine the quantities of wind turbines, photovoltaics, hydrogen production electrolyzers, and energy storage systems in the off-grid wind and light hydrogen production system, and obtain the optimal capacity configuration result of the off-grid wind and light hydrogen production system;

[0097] Such as Figure 4 is the flow chart of the capacity configuration optimization of the adaptive particle swarm optimization algorithm proposed by the present invention;

[0098] Based on the previously obtained optimal configuration ratio of the wind and light power generation unit, an adaptive particle swarm algorithm is used to optimize and solve with the goal of maximizing the fitness value of the overall off-grid wind and light hydrogen production system according to the historical wind speed, light data, and hydrogen production load data of the target area. The specific process is as follows:

[0099] Input the relevant parameters of the system, including the maximum output power of the wind turbine, and then the price. All components included in the system, such as the relevant parameters of the photovoltaic electrolyzer, need to be input, and initialize the number of capacity configurations participating in the optimization;

[0100] Randomly generate the positions and velocities of the particles;

[0101] Associate the inertia weight and learning factor with the parameters in the off-grid wind and light hydrogen production system;

[0102] Add penalties for curtailed wind and light and load interruption penalties;

[0103] Perform the following capacity configuration optimization based on the existing optimal configuration ratio of wind and light (optimize and solve the overall capacity configuration of the second layer, and finally solve how much the capacity of each component should be configured);

[0104] Construct the failure rate and fitness of the off-grid wind and light hydrogen production system as the objective functions, and solve the optimal capacity configuration corresponding to the optimal fitness.

[0105] The adaptive particle swarm optimization algorithm has many advantages. The adaptive particle swarm optimization algorithm can search within the entire solution space, which helps to find the global optimal solution, especially when dealing with multi-dimensional, non-linear, and non-convex optimization problems. During the optimization process of the improved particle swarm optimization algorithm, w is the inertia weight, which is used to control the influence of the previous iteration of the particle velocity. Its value usually decreases with the number of iterations to improve the search accuracy;

[0106] c1 and c2 are learning factors that control the tendency of particles to move towards the personal best position and the global best position;

[0107] In the process of solving the usual particle swarm optimization algorithm, usually only an inertial weight w is set within a range, and the c1 and c2 learning factors are also set to a fixed value. This may lead to the loss of the optimal solution during the solving process.

[0108] The adaptive particle swarm algorithm associates the inertial weight w with the actual operating power of the electrolyzer and associates the c1 and c2 learning factors with the capacity of the energy storage system. This can achieve the self - adaptation of the algorithm optimization parameters with the change of the specific parameters of the system during the solving process, making the solving process more detailed and comprehensive, thus reducing the possibility of losing the optimal solution and greatly improving the reliability of the solving result.

[0109] The specific implementation steps of the adaptive particle swarm algorithm can be divided into the following steps:

[0110] (1) Initialization: Randomly generate the positions and velocities of a group of particles;

[0111] (2) Evaluation: Calculate the fitness of each particle;

[0112] (3) Update personal best: If the fitness of the current particle is better than the personal best fitness, then update the personal best position. In the adaptive particle swarm optimization algorithm, the update of the particle's velocity and position are two key steps, which determine the search behavior of the particle in the solution space.

[0113] The update of the particle's velocity involves three main factors: (1) the current velocity of the particle (denoted as ); (2) the difference between the particle's own historical best position (personal best, denoted as pbest i ) and the current position; (3) the difference between the global best position (or neighborhood best position, denoted as gbest) and the particle's current position;

[0114] Velocity update formula:

[0115]

[0116] In the formula, is the velocity of the particle in the next generation, w is the inertial weight, c1 and c2 are learning factors, rand1() and rand2() are random numbers within the range of [0, 1].

[0117] Among them, the inertial weight w: controls the tendency of the particle to maintain the previous velocity. A larger value is helpful for global search, while a smaller value is helpful for local search. It corresponds to the hydrogen production power of the electrolyzer and changes with the actual hydrogen production power of the electrolyzer. The specific change formula is as follows:

[0118]

[0119] Wherein, w max is the maximum value of the inertia weight, w min is the minimum value of the inertia weight, p ec is the actual hydrogen production power of the electrolyzer, is the maximum hydrogen production power of the electrolyzer.

[0120] The individual learning factor c1: is used to control the degree of dependence of the particle on its own historical best position.

[0121] The social learning factor c2: is used to control the degree of dependence of the particle on the group best position.

[0122]

[0123] Wherein, c base is the base value of c1 and c2, c max is the maximum value of c1 and c2, N ess is the current capacity value of the energy storage system, N essmax is the maximum capacity value of the energy storage system.

[0124] Once the velocity of the particle is updated, the position of the particle can be obtained through the following position update formula:

[0125]

[0126] is the position of the particle in the next generation. is the position of the particle in the current iteration. is the updated velocity.

[0127] The income of the off-grid wind-solar hydrogen production system has only one source, which is selling the produced hydrogen. The income formula for selling hydrogen is:

[0128]

[0129] Wherein, M H is the hydrogen selling income, T is the period of the algorithm simulation, taking 720 hours, P ec (t) is the operating power of the electrolyzer at time t, η ec is the hydrogen production efficiency of the electrolyzer, λ is the mass of hydrogen generated per degree of electricity electrolysis, unit kg, C ec is the price of per kg of hydrogen.

[0130] The input cost of the off-grid wind-solar hydrogen production system has multiple sources. Among them, the annualized investment installation cost and operation and maintenance cost of the electrolyzer are calculated as follows:

[0131]

[0132] In the formula: M ec is the annualized investment installation cost and operation and maintenance cost of the annualized electrolyzer, N ec is the total installed capacity of the electrolyzer, C ec-inv is the installation cost per unit power of the electrolyzer, C ec-run is the operation and maintenance cost per unit power of the electrolyzer, r is the depreciation rate taken as 6%, and m is the service life taken as 15 years.

[0133] The calculation formula for the annualized investment installation cost and operation and maintenance cost of the fan photovoltaic power generation unit in the input cost of the off-grid wind-solar hydrogen production system is:

[0134]

[0135] In the formula: M wind，pv is the annualized investment installation cost and operation and maintenance cost of the fan photovoltaic power generation unit, N wind，pv is the total capacity of the wind-solar power generation unit, C wind-inv is the installation cost per unit power of the wind power generation unit, C wind-run is the operation and maintenance cost per unit power of the wind power generation unit, K p is the optimal configuration ratio of the wind-solar power generation unit, C pv-inv is the installation cost per unit power of the photovoltaic power generation unit, C pv-run is the operation and maintenance cost per unit power of the photovoltaic power generation unit, r is the depreciation rate taken as 6%, and m is the service life taken as 15 years.

[0136] The calculation formula for the annualized investment installation cost and operation and maintenance cost of the energy storage system in the input cost of the off-grid wind-solar hydrogen production system is:

[0137]

[0138] In the formula: M ess is the annualized investment installation cost and operation and maintenance cost of the energy storage system, N ess is the total installed capacity of the energy storage system, C ess-inv is the installation cost per unit power of the energy storage system, C ess-run is the operation and maintenance cost per unit power of the energy storage system, r is the depreciation rate taken as 6%, and m is the service life taken as 15 years.

[0139] In the actual operation process of the off-grid wind-solar hydrogen production system, because the overall wind-solar power generation power has large fluctuations, even though there is an energy storage system to suppress the fluctuations, there may still be wind and light abandonment and load interruption phenomena in extreme cases, and these two extreme phenomena are used to evaluate the stability of the system.

[0140] The normal range of the SOC of the energy storage system is defined as 0.2 - 0.8. Only when the SOC of the energy storage system is in a normal state can the functions of suppressing fluctuations and shaving peaks and filling valleys be achieved. Therefore, when the SOC of the energy storage system is in an abnormal state and the total power generation of wind and light is higher or lower than the upper and lower limits of the electrolyzer operating power, phenomena of wind and light abandonment or load interruption will occur. The total operation time of the simulated off-grid hydrogen production system is 720 hours, and the time step is 1 hour. The entire operation process is divided into 720 time periods.

[0141] When the SOC of the energy storage system is in an abnormal state and the total power generation of wind and light in this t time period is higher than the maximum value of the electrolyzer operating power, the excess power formula of the off-grid wind-light hydrogen production system at this time is as follows:

[0142]

[0143] In the formula: P EXC (t) is the excess power of the off-grid wind-light hydrogen production system in the t time period when the SOC is in an abnormal state, P wind，pv (t) is the total power generation of the wind turbine and photovoltaic in the t time period, is the maximum operating power of the electrolyzer.

[0144] When the SOC of the energy storage system is in a normal state and the total power generation of wind and light in this t time period is higher than the sum of the maximum operating power of the electrolyzer and the maximum charging power of the energy storage system, the excess power formula of the off-grid wind-light hydrogen production system at this time is as follows:

[0145]

[0146] In the formula: P EXC (t) is the excess power of the off-grid wind-light hydrogen production system in the t time period, P wind，pv (t) is the total power generation of the wind turbine and photovoltaic in the t time period, is the maximum operating power of the electrolyzer, is the maximum charging power of the energy storage system.

[0147] If the excess power of the off-grid wind-light hydrogen production system in the t time period is positive, it means that the total power generation of wind and light is relatively large at this time, exceeding the maximum values of the power that the electrolyzer can consume and the energy storage system can absorb under the current configuration. If the excess power of the off-grid wind-light hydrogen production system in the t time period is negative, it means that the total power generation of wind and light is within the normal range that the electrolyzer can consume and the energy storage system can absorb under the current configuration, and this negative value has no meaning for recording. The total formula for wind and light abandonment power is as follows:

[0148]

[0149]

[0150] Where: P wa (t) is the positive part of the excess power of the off-grid wind-solar hydrogen production system in the t period, P EXC (t) is the excess power of the off-grid wind-solar hydrogen production system in the t period, C wa is the total power of abandoned wind and light during the simulated operation duration, and T is the period of the algorithm simulation, taking 720 hours.

[0151] When the SOC of the energy storage system is in an abnormal state, and the total power generation of wind and light in this t period is lower than the lower limit value of the electrolyzer operating power, the power deficit formula of the off-grid wind-solar hydrogen production system at this time is as follows:

[0152]

[0153] Where: P EXC (t) is the power deficit of the off-grid wind-solar hydrogen production system in the t period when the SOC is in an abnormal state, P wind，pv (t) is the total power generation of the wind turbine and photovoltaic in the t period, is the minimum operating power of the electrolyzer.

[0154] When the SOC of the energy storage system is in a normal state, and the total power generation of wind and light in this t period plus the maximum discharge power of the energy storage system is still lower than the lower limit value of the electrolyzer operating power, the power deficit formula of the off-grid wind-solar hydrogen production system at this time is as follows:

[0155]

[0156] Where: P BRE (t) is the power deficit of the off-grid wind-solar hydrogen production system in the t period, P wind，pv (t) is the total power generation of the wind turbine and photovoltaic in the t period, is the minimum operating power of the electrolyzer, is the maximum discharge power of the energy storage system.

[0157] If the power deficit of the off-grid wind-solar hydrogen production system in the t period is positive, it means that the total power generation of wind and light at this time is small, and even adding the maximum power that the energy storage system can release under the current configuration still cannot meet the minimum operating power of the electrolyzer. If the excess power of the off-grid wind-solar hydrogen production system in the t period is negative, it means that the total power generation of wind and light and the power that the energy storage system can release under the current configuration can meet the minimum operating power of the electrolyzer, and this negative value has no meaning for recording. The total power formula for load interruption is as follows:

[0158]

[0159] Where: P br(t) is the positive part of the deficit power of the off-grid wind-solar hydrogen production system during the t period, P BRE (t) is the deficit power of the off-grid wind-solar hydrogen production system during the t period, C br is the total power of load interruption during the simulated operation duration, and T is the period of algorithm simulation, taking 720 hours.

[0160] The stability of the off-grid wind-solar hydrogen production system operation is evaluated by the abnormal rate. The lower the abnormal rate, the higher the stability of the system. The formula for calculating the abnormal rate of the system is as follows:

[0161]

[0162] In the formula: M e is the abnormal rate of the system, P wind，pv (t) is the total power generation of the wind turbine and photovoltaic during the t period, C br is the total power of load interruption during the simulated operation duration, C wa is the total power of wind and light abandonment during the simulated operation duration.

[0163] The simulated operation period is one month. The fitness value of the overall off-grid wind-solar hydrogen production system is an index to evaluate the pros and cons of the whole system. The fitness value calculation formula is the objective function of the overall optimization, and the calculation formula is as follows:

[0164]

[0165] In the formula, M total is the fitness value of the overall off-grid wind-solar hydrogen production system, M H is the revenue from hydrogen sales, M ec is the investment, installation cost and operation and maintenance cost of the electrolyzer, M wind，pv is the total investment, installation cost and operation and maintenance cost of the wind turbine and photovoltaic power generation unit, M ess is the investment, installation cost and operation and maintenance cost of the energy storage unit.

[0166] Taking the maximum fitness value of the overall off-grid wind-solar hydrogen production system as the optimization goal, before using the adaptive particle swarm optimization algorithm for optimization and solution, it is also necessary to limit the maximum capacities of the wind turbine photovoltaic power generation unit, electrolyzer and energy storage system, add the overall power balance constraint and electrolyzer power constraint. In terms of energy management, the basic rule method principle is adopted for control to ensure the normal operation of the off-grid wind-solar hydrogen production system.

[0167] The capacity configuration of each component corresponding to the maximum fitness value of the overall off-grid wind-solar hydrogen production system obtained by optimization and solution is the optimal capacity configuration finally obtained.

[0168] Figure 5 This is the result graph of the optimal wind-solar configuration ratio based on the principle of wind-solar complementarity proposed by the present invention;

[0169] Figure 6 This is the final result diagram of the capacity configuration optimization based on the optimal wind-solar configuration ratio for the present invention.

[0170] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimization method for the capacity configuration of an off-grid wind-solar hydrogen production system based on the optimal wind-solar configuration ratio, characterized in that: It includes the following steps: Construct an overall model of an off-grid wind-solar-hydrogen production system including a wind turbine system, a photovoltaic system, a hydrogen production electrolyzer system, and an energy storage system; Based on the overall model of the off-grid wind-solar-hydrogen production system, in accordance with the principle of wind-solar complementarity, with the economic cost and the volatility of the overall wind-solar power generation curve as the optimization objectives, solve for the optimal wind-solar ratio to obtain the optimal configuration quantity ratio of the wind turbines and photovoltaic units per unit of power generation capacity; Based on the historical wind and light data of the target area, the hydrogen production demand data of the area, and the optimal configuration quantity ratio of the wind turbines and photovoltaic units, under the premise of meeting the hydrogen demand of the area and ensuring the stable operation of the off-grid wind-solar-hydrogen production system, with the economy of the off-grid wind-solar-hydrogen production system as the optimization objective, use an adaptive particle swarm optimization algorithm to optimize the capacity configuration of the off-grid wind-solar-hydrogen production system, determine the quantities of the wind turbines, photovoltaic units, hydrogen production electrolyzers, and energy storage systems in the off-grid wind-solar-hydrogen production system, and obtain the optimal capacity configuration result of the off-grid wind-solar-hydrogen production system.

2. The capacity configuration optimization method of an off-grid wind-solar hydrogen production system based on the optimal wind-solar configuration ratio according to claim 1, wherein: The process of solving for the optimal wind-solar ratio based on the overall model of the off-grid wind-solar-hydrogen production system, in accordance with the principle of wind-solar complementarity, with the economic cost and the volatility of the overall wind-solar power generation curve as the optimization objectives, is as follows: Based on the historical wind speed and light data of the target area, obtain the overall wind-solar power generation power curve under different wind-solar power generation unit configuration ratios; Calculate the positive volatility and negative volatility at one-hour intervals based on the overall wind-solar power generation power curve, record the values of the positive volatility and negative volatility that exceed 2% as effective volatility, and obtain the cumulative volatility in chronological order; Calculate the difference between the maximum and minimum values of the cumulative volatility to obtain the cumulative volatility difference; Calculate the economic costs under different configuration ratios of the total installed wind power capacity; In accordance with the principle of wind-solar complementarity, with the economic cost and the volatility of the overall wind-solar power generation curve as the two objective functions for optimization, use a multi-objective particle swarm algorithm to optimize and solve the economic cost and the volatility of the overall wind-solar power generation curve of the optimal wind-solar configuration ratio, obtain the point of the optimal solution, that is, the ratio of the installed capacity of the wind turbines to the photovoltaic units, which is also the optimal wind-solar configuration ratio.

3. An optimization method for the capacity configuration of an off-grid wind-solar hydrogen production system based on the optimal wind-solar configuration ratio according to claim 2, characterized in that: The objective function of the volatility of the overall wind-solar power generation curve is characterized by the cumulative volatility difference; The formula for the cumulative volatility difference is as follows, formula (3): CFD = max(CF(1, 2, …, N)) - min(CF(1, 2, …, N)) (3) In the formula: CF(1, 2, …, N) represents the cumulative volatility value within the time series from 1 to N.

4. The capacity configuration optimization method of an off-grid wind-solar-hydrogen production system based on the optimal wind-solar ratio according to claim 2, characterized in that: The objective function of the economic costs under different configuration ratios of the total installed wind power capacity is as follows: C1 = N wind C wind + N pv C pv (4) Where: C1 is the total cost of the wind-solar power generation unit, N wind is the configured capacity of the wind turbine, C wind is the cost per unit capacity of the wind turbine, N pv is the configured capacity of the photovoltaic, C pv is the cost per unit capacity of the photovoltaic.

5. An optimization method for the capacity configuration of an off-grid wind-solar hydrogen production system based on the optimal wind-solar configuration ratio according to claim 2, characterized in that: The constraint conditions for using a multi-objective particle swarm algorithm to optimize and solve the economic cost and the volatility of the overall wind-solar power generation curve of the optimal wind-solar configuration ratio are as follows: Where: N wind is the configured capacity of the fan, and N pv is the configured capacity of the photovoltaic.

6. The capacity configuration optimization method of an off-grid wind-solar hydrogen production system based on the optimal configuration ratio of wind and light according to claim 1, characterized in that: The update formula for the particle velocity in the adaptive particle swarm optimization algorithm is as follows: In the formula, is the velocity of the particle in the next generation, w is the inertia weight, c1 and c2 are learning factors, and rand1() and rand2() are random numbers in the range of [0, 1]; Among them: The specific change formula for the inertia weight w is as follows: where w max is the maximum value of the inertia weight, w min is the minimum value of the inertia weight, p ec is the actual hydrogen production power of the electrolyzer, is the maximum hydrogen production power of the electrolyzer; Individual learning factor c1: used to control the degree of dependence of the particle on its own historical best position, social learning factor c2: used to control the degree of dependence of the particle on the group best position; Wherein, c basc is the base value of c1 and c2, and c max is the maximum value of c1 and c2, N ess is the current capacity value of the energy storage system, and N essmax is the maximum capacity value of the energy storage system.

7. An optimization method for the capacity configuration of an off-grid wind-solar hydrogen production system based on the optimal wind-solar configuration ratio according to claim 1, characterized in that: The stable operation of the off-grid wind-solar hydrogen production system is characterized by the abnormality rate of the off-grid wind-solar hydrogen production system. The formula for the abnormality rate of the off-grid wind-solar hydrogen production system is as follows: Where: Me is the system failure rate, P wind，pv (t) is the total power generation of the fan and photovoltaic in the t period, C br is the total load interruption power during the simulated operation duration, C wa is the total curtailment of wind and solar power during the simulated operation duration.

8. An optimization method for the capacity configuration of an off-grid wind-solar hydrogen production system based on the optimal wind-solar configuration ratio according to claim 1, characterized in that: The economy of the off-grid wind-solar hydrogen production system is characterized by the fitness value of the overall off-grid wind-solar hydrogen production system. The calculation formula for the fitness value of the overall off-grid wind-solar hydrogen production system is as follows: Where, M total is the fitness value of the overall off-grid wind-solar hydrogen production system, M H is the revenue from hydrogen sales, M ec is the investment, installation cost and operation and maintenance cost of the electrolyzer, M wind，pv is the total investment, installation cost and operation and maintenance cost of the wind turbine and photovoltaic power generation unit, M ess is the investment, installation cost and operation and maintenance cost of the energy storage unit.

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