Capacity configuration method and system for off-grid wind and light storage combined hydrogen production system

Abstract

The invention discloses a capacity configuration method and system for an off-grid wind and light storage combined hydrogen production system, and belongs to the technical field of renewable energy and hydrogen energy utilization. The method comprises the steps that a multi-time-scale coupling mathematical model including a wind power generation system, a photovoltaic power generation system, an energy storage system and an alkaline electrolytic cell is constructed, and a multi-target optimization model is constructed with the minimization of the annual wind and light abandoning rate, the minimization of the leveling hydrogen production cost and the minimization of the cold start proportion of the electrolytic cell as targets; and solving the multi-objective optimization model by adopting a multi-objective marine predator algorithm to obtain a Pareto solution set, determining an objective function entropy weight of the multi-objective optimization model by utilizing an improved entropy weight method, and selecting an optimal compromise solution as a system capacity configuration scheme. According to the method, the problem that the influence of wind and light output multi-time scale coupling, energy storage, dynamic response characteristics of the electrolytic cell and cold start of the electrolytic cell is ignored in the existing technical scheme is solved, and the purposes of efficiently absorbing wind and light resource fluctuation by the off-grid hydrogen production system, enhancing the operation reliability and optimizing the economic performance are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of renewable energy and hydrogen energy utilization, in particular to a capacity configuration method and system of an off-grid wind-solar-storage combined hydrogen production system. BACKGROUND

[0002] With the rapid popularization of renewable energy hydrogen production technology and off-grid energy systems, large-scale wind turbine generators, photovoltaic components, energy storage devices and electrolyzer devices are deeply integrated in off-grid wind-solar hydrogen production systems. The power interaction and dynamic response mechanism between different subsystems are increasingly complex. Once the capacity configuration is unreasonable, it is easy to cause hydrogen production fluctuation, frequent cold start of equipment and even hydrogen production stagnation, which seriously affects the reliable operation of the system.

[0003] The off-grid wind-solar-storage combined hydrogen production system is mainly composed of wind power generation, photovoltaic power generation, energy storage batteries and alkaline electrolyzers. The output of wind power and photovoltaic power generation is affected by meteorological conditions and has significant randomness and volatility; the energy storage system needs to balance the power between power generation and electrolyzer load, and is limited by the state of charge constraint; the electrolyzer is sensitive to the dynamic response of input power, heat preservation and cold start frequency, and its efficiency changes with the power ratio and is limited by overload and climbing rate. The above multi-time scale coupling characteristics make the capacity configuration need to meet the requirements of safe operation and economy.

[0004] Existing off-grid wind-solar-storage hydrogen production capacity configuration researches are mostly based on deterministic optimization or robust optimization framework, focusing on minimizing system investment and operation and maintenance costs or maximizing hydrogen production efficiency, and commonly using single-objective or double-objective optimization methods such as genetic algorithm and particle swarm optimization. However, these methods often ignore the multi-time scale coupling model of wind and light output and electrolyzer, energy storage system, do not fully consider the dynamic factors such as electrolyzer efficiency curve fitting, cold start cost, hydrogen rejection rate, and lack of research on the cold start proportion of electrolyzer after energy storage, making it difficult to balance between system operation stability and economy.

[0005] Therefore, there is an urgent need for an off-grid wind-solar combined hydrogen production capacity configuration method that takes into account the fluctuation characteristics of wind and light resources, the dynamic behavior of electrolyzers and the coordination constraints of energy storage, and comprehensively considers economy and safety, to overcome the limitations of existing technology and provide a systematic solution for reliable and efficient operation of off-grid hydrogen production systems. SUMMARY

[0006] The present application aims to overcome the problems of the prior art and provide an off-grid wind-solar-storage combined hydrogen production system capacity configuration method and system, which solves the problem that existing off-grid wind-solar hydrogen production capacity configuration schemes ignore the multi-time scale coupling of wind and light output, the dynamic response characteristics of energy storage and electrolyzer, and the influence of electrolyzer cold start, and achieves the invention purpose of efficient consumption of wind and light resources, enhanced operation reliability and optimized economic performance of off-grid hydrogen production system.

[0007] To solve the above technical problems, the present application is realized by adopting the following technical solutions:

[0008] In a first aspect, the present application provides a capacity configuration method for an off-grid wind-solar-storage combined hydrogen production system, comprising:

[0009] A multi-time scale coupling mathematical model is constructed, including wind power generation, photovoltaic power generation, energy storage system and alkaline electrolyzer;

[0010] According to the multi-time scale coupling mathematical model, a multi-objective optimization model is constructed with the minimum annual abandoned wind and light rate, the minimum hydrogen production cost of flat equalization, and the minimum electrolyzer cold start proportion as the target;

[0011] The multi-objective optimization model is solved by using a multi-objective marine predator algorithm to obtain a Pareto solution set;

[0012] According to the Pareto solution set, an improved entropy weight method is used to determine the entropy weight of the objective function of the multi-objective optimization model, and the best compromise solution is selected as the system capacity configuration scheme.

[0013] Optionally, the multi-time scale coupling mathematical model constructed by including wind power generation, photovoltaic power generation, energy storage system and alkaline electrolyzer comprises:

[0014] A wind power generation output power expression is established based on the rated wind speed, cut-in wind speed and cut-out wind speed of the wind turbine to obtain a wind power generation model;

[0015] A photovoltaic power generation model is established based on the influence of irradiance and temperature changes of the photovoltaic module on power;

[0016] An energy storage model is established according to the charge and discharge characteristics of the battery;

[0017] An electrolyzer power model is established based on the working characteristics of the electrolyzer;

[0018] The wind power generation model, the photovoltaic power generation model, the energy storage model and the electrolyzer power model are solved to obtain a multi-time scale coupling mathematical model.

[0019] Optionally, the expression of the wind power generation model is:

[0020] ,

[0021] wherein, Pw represents the wind power generation output power, Ptotal represents the total rated power of the wind turbine, Vr represents the rated wind speed of the wind turbine, Vc represents the cut-in wind speed of the wind turbine, Vco represents the cut-out wind speed of the wind turbine, actual wind speed of the wind turbine;

[0022] The expression of the photovoltaic power generation model is:

[0023] ,

[0024] wherein, P(t) represents the photovoltaic power generation output power, E(t) represents the standard irradiance, T(t) represents the reference temperature, Pstd represents the total rated power of the photovoltaic panel under the standard irradiance and the reference temperature , I(t) represents the solar radiation intensity, Tc(t) represents the cell temperature when the component is working, Kp represents the power temperature coefficient;

[0025] The expression of the energy storage model is:

[0026] ,

[0027] wherein, C(t) represents the battery capacity of the energy storage model at time t, C(t-1) represents the battery capacity of the energy storage model at time t-1, ηd represents the self-discharge rate of the battery, ηc represents the charging efficiency of the battery, ηd represents the discharging efficiency of the battery, Ctotal represents the total capacity of the energy storage, PS(t) represents the energy storage output, PS(t)>0 represents the charging power of the battery, and PS(t)<0 represents the discharging power of the battery, t represents the charging and discharging time;

[0028] The expression of the electrolytic tank power model is:

[0029] ,

[0030] wherein, P(t) represents the electrolytic tank output power at time t, η(t) represents the electrolytic conversion efficiency at time t, β(t) represents the cold start penalty coefficient at time t, P(t) represents the wind-solar-storage input power at time t.

[0031] Optionally, the cold start penalty coefficient The cold start ratio is obtained by the following formula:

[0032] ,

[0033] wherein, represents the time when the cold start electrolyzer restarts heating, represents the heating time, represents the downtime of the electrolyzer when the electrolyzer restarts at time t, represents the holding time;

[0034] The wind-solar-storage input power is obtained by the following formula:

[0035] ,

[0036] wherein, represents the wind-solar-storage common output, represents the safety lower limit coefficient, represents the rated power of a single electrolyzer, represents the overload coefficient, represents the number of electrolyzers;

[0037] The wind-solar-storage common output is obtained by the following formula:

[0038] .

[0039] Optionally, the objective function of the multi-objective optimization model is:

[0040] ,

[0041] wherein, represents the minimum value function, represents the installed capacity of the electrolyzer, represents the energy storage capacity, represents the annual wind and light abandonment rate, i.e., the first objective function, represents the total time of the expected optimization, represents the wind and light power at time t, represents the total abandoned wind and light power; represents the life cycle leveling hydrogen production cost, i.e., the second objective function, represents the initial investment cost, represents the operation and maintenance cost, represents the expected service life, represents the hydrogen production amount in the life cycle; represents the cold start ratio, i.e., the third objective function, represents the number of cold starts, represents the number of times of hot start.

[0042] Optionally, the multi-objective optimization model is subject to capacity configuration constraints, power balance constraints and energy storage system constraints.

[0043] The capacity configuration constraints are:

[0044] ,

[0045] wherein, represents the installed capacity of wind power generation, represents the installed capacity of photovoltaic power generation, represents the installed number of electrolytic cells, represents the rated power of a single electrolytic cell;

[0046] The power balance constraints are:

[0047] ,

[0048] wherein, represents the wind power generation output power at time t, represents the photovoltaic power generation output power at time t, is the energy storage output at time t, represents the wind-solar-storage input power at time t, represents the curtailed wind-solar power at time t;

[0049] The energy storage system constraints are:

[0050] ,

[0051] wherein, represents the minimum state of charge of the energy storage system, represents the maximum state of charge of the energy storage system, represents the state of charge of the energy storage system at time t, represents the preset target state of charge.

[0052] Optionally, the total curtailed wind-solar power is obtained by the following formula:

[0053] ,

[0054] The initial investment cost is obtained by the following formula:

[0055] ,

[0056] wherein, represents the unit installed investment cost of wind power generation, represents the unit installed investment cost of photovoltaic power generation, represents the unit installed investment cost of electrolytic cell, represents the unit installed investment cost of energy storage;

[0057] The operation and maintenance cost is obtained by the following formula:

[0058] ,

[0059] wherein, represents the unit installed operation and maintenance cost of wind power generation, represents the unit installed operation and maintenance cost of photovoltaic power generation, represents the unit installed operation and maintenance cost of electrolytic cell, represents the unit installed operation and maintenance cost of energy storage.

[0060] The cold start number is obtained by the following formula:

[0061] ,

[0062] wherein, represents the cold start state, represents the downtime of the electrolytic cell when the electrolytic cell is restarted at time t, represents the holding time.

[0063] The hot start number is obtained by the following formula:

[0064] ,

[0065] wherein, represents the hot start state.

[0066] Optionally, the multi-objective marine predator algorithm is used to solve the multi-objective optimization model to obtain a Pareto solution set, including:

[0067] An initial population meeting the capacity configuration constraints of the multi-objective optimization model is randomly generated, and elite solutions in the population are screened out by fast non-dominated sorting and crowding distance, an elite matrix is constructed, and then the Pareto solution set is solved according to the elite matrix guidance and a three-section breeding strategy.

[0068] Optionally, according to the Pareto solution set, an improved entropy weight method is used to determine the entropy weight of the objective function of the multi-objective optimization model, and the best compromise solution is selected as the system capacity configuration scheme, including:

[0069] Construct a decision matrix based on the Pareto solution set, and normalize the decision matrix by minimizing the objective to obtain the normalized decision matrix.

[0070] The information entropy of the objective function is calculated based on the normalized decision matrix, and the entropy weight of the objective function is obtained by normalizing the information entropy.

[0071] Based on the normalized decision matrix and the entropy weight of the objective function, the Pareto solution set is comprehensively evaluated, and the Pareto solution with the highest comprehensive evaluation score is selected as the best compromise solution.

[0072] The normalized decision matrix is ​​obtained by the following formula:

[0073] ,

[0074] in, Indicates the row number of the decision matrix. Indicates the column number of the decision matrix. The decision matrix represents the first... Line number The value of the column, i.e., the first The Pareto solution of the first... The value of the objective function, This represents the function that takes the minimum value. This represents the function that takes the maximum value. Represents the decision matrix. The normalized decision matrix represents the first... Line number The value of the column;

[0075] The information entropy of the objective function is obtained by the following formula:

[0076] ,

[0077] in, Indicates the first Information entropy of an objective function Indicates the first The Pareto solution of the first... The weighting coefficient of each objective function Indicates the number of objective functions. Represents the natural logarithm;

[0078] The entropy weight of the objective function is obtained by the following formula:

[0079] ,

[0080] in, Indicates the first Entropy weights of objective functions Indicates the first a difference coefficient of the objective function;

[0081] The comprehensive evaluation score is obtained by the following formula:

[0082]

[0083] wherein, represents the comprehensive evaluation score of the first Pareto solution.

[0084] In a second aspect, the present application provides a capacity configuration system of an off-grid wind-solar-storage combined hydrogen production system, which is suitable for the capacity configuration method of the off-grid wind-solar-storage combined hydrogen production system according to any one of the first aspect, and comprises:

[0085] a model construction module, configured to construct a multi-time scale coupling mathematical model of wind power generation, photovoltaic power generation, energy storage system and alkaline electrolyzer;

[0086] a multi-objective optimization module, configured to construct a multi-objective optimization model according to the multi-time scale coupling mathematical model, with the minimum annual abandoned wind and light rate, the minimum cost of hydrogen production by flat rate and the minimum proportion of electrolyzer cold start as the objectives;

[0087] a multi-objective solution module, configured to solve the multi-objective optimization model by using a multi-objective marine predator algorithm to obtain a Pareto solution set;

[0088] a system capacity configuration module, configured to determine the entropy weight of the objective function of the multi-objective optimization model by using an improved entropy weight method according to the Pareto solution set, and select the best compromise solution as the system capacity configuration scheme.

[0089] Compared with the prior art, the present application has the following beneficial effects:

[0090] 1. It can effectively consume fluctuating wind and light resources and improve the stable operation ability of the hydrogen production system: the annual abandoned wind and light rate is used as the renewable energy consumption performance evaluation index, and the flat rate hydrogen production cost and the electrolyzer cold start proportion are used as the economic and operation stability evaluation standards, so that obvious advantages in economic benefits can be achieved while ensuring the continuous and stable operation of the system;

[0091] 2. The present application solves the problem that the existing off-grid wind-solar hydrogen production capacity configuration scheme ignores the multi-time scale coupling of wind and light output, the dynamic response characteristics of energy storage and electrolyzer, and the influence of electrolyzer cold start, and realizes the invention purpose of efficient consumption of wind and light resource fluctuations, enhanced operation reliability and optimized economic performance of the off-grid hydrogen production system. BRIEF DESCRIPTION OF DRAWINGS

[0092] Figure 1 ​A flow chart of a capacity configuration method of an off-grid wind-solar-storage combined hydrogen production system according to an embodiment of the present application is provided.

[0093] Figure 2 A three-dimensional Pareto frontier chart according to an embodiment of the present application is provided.

[0094] Figure 3 A two-dimensional Pareto frontier chart of trade-off between economy and energy utilization efficiency according to an embodiment of the present application is provided.

[0095] Figure 4 A two-dimensional Pareto frontier chart of trade-off between energy utilization efficiency and system stability according to an embodiment of the present application is provided.

[0096] Figure 5 A two-dimensional Pareto frontier chart of trade-off between economy and system stability according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0097] The technical solutions of the present application will be described in detail below with the aid of the accompanying drawings and specific embodiments. It should be understood that the specific features in the embodiments and the specific embodiments are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application. In the case of no conflict, the technical features in the embodiments and the specific embodiments can be combined with each other.

[0098] It should be noted that the term "and / or" in this paper is only to describe the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the existence of A alone, the existence of A and B, and the existence of B alone. In addition, the character " / " in this paper generally represents a "or" relationship between the associated objects before and after it.

[0099] Embodiment one:

[0100] The embodiments of the present application disclose a capacity configuration method of an off-grid wind-solar-storage combined hydrogen production system, as shown in Figure 1 The capacity configuration method comprises the following steps:

[0101] S1, a multi-time scale coupling mathematical model of wind power generation, photovoltaic power generation, energy storage system and alkaline electrolytic cell is constructed;

[0102] S2, according to the multi-time scale coupling mathematical model, a multi-objective optimization model is constructed with the minimum annual abandoned wind and light rate, the minimum hydrogen production cost of flat equalization and the minimum electrolytic cell cold start proportion as the target;

[0103] S3, a multi-objective marine predator algorithm is used to solve the multi-objective optimization model to obtain a Pareto solution set;

[0104] S4, according to the Pareto solution set, the improved entropy weight method is used to determine the target function entropy weight of the multi-objective optimization model, and the best compromise solution is selected as the system capacity configuration scheme.

[0105] Specifically, in step S1, the wind power generator converts wind energy into mechanical energy, and then converts the mechanical energy into electrical energy. In this embodiment, a wind power generation output power expression is established based on the rated wind speed, cut-in wind speed and cut-out wind speed of the wind power generator, and a wind power generation model is obtained:

[0106] ,

[0107] wherein, P represents the wind power generation output power, Ptotal represents the total rated power of the wind power generator, Vr represents the rated wind speed of the wind power generator, Vc represents the cut-in wind speed of the wind power generator, Vco represents the cut-out wind speed of the wind power generator, V represents the actual wind speed of the wind power generator;

[0108] A photovoltaic generation output power expression is established based on the influence of irradiance and temperature changes of the photovoltaic module on power, and a photovoltaic generation model is obtained:

[0109] ,

[0110] wherein, P represents the photovoltaic generation output power, I0 represents the standard irradiance, T0 represents the reference temperature, Ptotal represents the total rated power of the photovoltaic panel under the standard irradiance and the reference temperature , I represents the solar radiation intensity, T represents the cell temperature when the module is working, K represents the power temperature coefficient;

[0111] According to the charge and discharge characteristics of the battery (lithium iron phosphate battery), the charge and discharge efficiency is set to , and an energy storage model is established:

[0112] ,

[0113] wherein, P represents the battery capacity of the energy storage model at , P represents the battery capacity of the energy storage model at , K represents the self-discharge rate of the battery, represents the charging efficiency of the battery, represents the discharging efficiency of the battery, represents the total capacity of the energy storage, represents the output of the energy storage, PS(t) > 0 represents the charging power of the battery, and PS(t) < 0 represents the discharging power of the battery, represents the charging and discharging time;

[0114] The embodiment adopts an alkaline electrolytic cell as a hydrogen production device, and the characteristics and working constraints are as follows:

[0115] The electrolytic cell needs high power for heating when starting, and after reaching the hydrogen production working temperature, it is converted to a temperature maintaining power for maintaining the temperature. When stopping, the power supply can be instantaneously cut off to realize an interruptible load. After exiting operation, the environmental control device can maintain the temperature for 10 hours, and at any time, the hydrogen production can be restored without the need for re-heating. From high-temperature high-power to low-temperature low-power, it can be rapidly adjusted in milliseconds, and the reverse temperature increase and power increase need minutes, and at the same time, the single power increment should not exceed 30% of the rated power to avoid triggering the climbing mechanism.

[0116] At the same time, in order to prevent hydrogen-oxygen mutual stringing accidents when running below 20%-25% of the rated power for a long time, the minimum operating power needs to be maintained. In normal operation, it can be overloaded to 120% of the rated power for a short time, and after the time limit, only the rated power can be used. Its efficiency can be approximated based on the load rate, taking into account the energy efficiency trend and the simplicity of scheduling calculation. Based on the load rate, the approximation is made to take into account the energy efficiency trend and the simplicity of scheduling calculation.

[0117] The expression of the electrolytic cell is:

[0118] ,

[0119] wherein, represents the electrolytic cell output power at the moment t, represents the electrolytic conversion efficiency at the moment t, represents the cold start penalty coefficient at the moment t, represents the wind-solar-storage input power at the moment t.

[0120] The cold start penalty coefficient is related to the shutdown time of the electrolytic cell when the electrolytic cell is restarted at the moment t, that is:

[0121] ,

[0122] wherein, represents the moment when the cold start electrolytic cell starts heating again, represents the heating time, which is usually 1 hour, This indicates the time the electrolytic cell has been offline when it restarts at time t. Indicates the heat preservation time;

[0123] Power input to the electrolytic cell at time t The combined efforts of wind, solar and energy storage at that moment This is determined by the working characteristics of the electrolytic cell itself, namely:

[0124] ,

[0125] in, This indicates that wind, solar, and energy storage will work together. This represents the lower safety limit factor, which is approximately 20%. This indicates the rated power of a single electrolytic cell. This indicates the overload factor, which is approximately 120%. Indicates the number of electrolytic cells;

[0126] As can be seen from the above, wind, solar and energy storage work together. Not all wind and solar power is fed into the electrolytic cell, resulting in a certain amount of wind and solar power being wasted. That is: .

[0127] During the energy storage process, the energy storage system adjusts the SOC to the target value at the end of each day: if the SOC is too high, excess energy is released; if the SOC is too low, it is prioritized for charging the next day without affecting the normal operation of the electrolyzer. When insufficient wind and solar power generation causes the electrolyzer to shut down and the shutdown time is about to reach the cold start threshold, the energy storage can temporarily supply power to maintain its operation. When wind and solar power generation is sufficient, the short-term overload operation requirements of the electrolyzer are met first, and the excess is absorbed by the energy storage.

[0128] In step S2, the objective function of the multi-objective optimization model is:

[0129] ,

[0130] in, This represents the function that takes the minimum value. Indicates the installed capacity of the electrolytic cell. Indicates energy storage capacity, This represents the annual wind and solar curtailment rate, i.e., the first objective function. This indicates the estimated total optimization time. express Real-time wind and solar power output This indicates that the total power output of wind and solar power has been discarded. This represents the levelized cost of hydrogen production over its lifecycle, i.e., the second objective function. Indicates the initial investment cost. Indicates operation and maintenance costs. represents the life expectancy, represents the hydrogen production amount in the life cycle; represents the cold start proportion, i.e., the third target function, represents the number of cold starts, represents the number of hot starts.

[0131] the total power of the abandoned wind and light, is obtained by the following formula:

[0132] ,

[0133] the initial investment cost is obtained by the following formula:

[0134] ,

[0135] wherein, represents the unit installation investment cost of wind power generation, represents the unit installation investment cost of photovoltaic power generation, represents the unit installation investment cost of electrolytic cell, represents the unit installation investment cost of energy storage;

[0136] the operation and maintenance cost is obtained by the following formula:

[0137] ,

[0138] wherein, represents the unit installation operation and maintenance cost of wind power generation, represents the unit installation operation and maintenance cost of photovoltaic power generation, represents the unit installation operation and maintenance cost of electrolytic cell, represents the unit installation operation and maintenance cost of energy storage.

[0139] the number of cold starts is obtained by the following formula:

[0140] ,

[0141] wherein, represents the cold start state, represents the downtime of the electrolytic cell when the electrolytic cell is restarted at time t, represents the holding time;

[0142] the number of hot starts is obtained by the following formula:

[0143] ,

[0144] wherein, denotes the hot start state.

[0145] The multi-objective optimization model needs to meet a series of technical and operational constraints:

[0146] Capacity configuration constraints:

[0147] ,

[0148] wherein, denotes the wind power installed capacity, denotes the photovoltaic power installed capacity, denotes the number of electrolyzer installed, denotes the rated power of a single electrolyzer;

[0149] Power balance constraints:

[0150] ,

[0151] wherein, denotes the wind power output at time t, denotes the photovoltaic power output at time t, is the energy storage output at time t, denotes the wind-solar-storage input at time t, denotes the curtailed wind-solar power at time t;

[0152] Energy storage system constraints:

[0153] ,

[0154] wherein, denotes the minimum state of charge of the energy storage system, denotes the maximum state of charge of the energy storage system, denotes the state of charge of the energy storage system at time t, denotes the preset target state of charge.

[0155] The specific economic investment costs in the embodiment are as shown in Table 1.

[0156] Table 1 Economic costs of each device of renewable energy hydrogen production

[0157]

[0158] In step S3, the MOMPA algorithm is used to obtain the Pareto solution set of capacity configuration; compared with the traditional genetic algorithm, the MOMPA has stronger global search ability and faster convergence speed, and is particularly suitable for processing engineering optimization problems with complex constraints.

[0159] The MOMPA algorithm first randomly generates an initial population that satisfies the electrolytic cell and energy storage capacity constraints, and filters out elite solutions through fast non-dominated sorting and crowding distance to construct an elite matrix. Then it enters the three-stage adaptive hunting: in the exploration stage, it combines the mixed search of Levy flight and Brown motion to quickly tour the solution space; in the development stage, it introduces an adaptive convergence factor to smoothly switch between global and local, and converges to a high-quality area in a random or elite-guided manner; in the refinement stage, it uses small-step Levy flight to perform high-precision local optimization around the elite solution. After each update, the new individuals are corrected for constraints, and the wind-solar-storage hydrogen simulation model is called to calculate the objective function.

[0160] After each generation iteration is completed, the parent and child are combined, and the new generation population is selected through fast non-dominated sorting and crowding distance, while the better solutions in the elite matrix on any objective are updated. The algorithm takes the maximum number of iterations as the termination condition, and in this embodiment it is set to 1000 times, and finally outputs the Pareto frontier containing multiple non-dominated solutions, providing the decision maker with multiple capacity configuration schemes for multi-objective optimization.

[0161] In step S4, since the Pareto frontier has many non-dominated sets, the Pareto solution set needs to be further processed to obtain the optimal configuration, including:

[0162] According to the values of each solution in the Pareto solution set on the three objective functions, a decision matrix is constructed, and the decision matrix is normalized for the minimization target to obtain a normalized decision matrix;

[0163] According to the normalized decision matrix, the information entropy of the objective function is calculated, and the entropy weight of the objective function is obtained through information entropy normalization;

[0164] According to the normalized decision matrix and the entropy weight of the objective function, the Pareto solution set is comprehensively evaluated, and the Pareto solution with the highest comprehensive evaluation score is selected as the best compromise scheme;

[0165] The normalized decision matrix is obtained by the following formula:

[0166] ,

[0167] Wherein, represents the row number of the decision matrix, represents the column number of the decision matrix, represents the j-th column of the i-th row of the decision matrix the value of the first column in the first row of the decision matrix, the value of the first column in the first row of the decision matrix, the value of the first column in the first row of the decision matrix, the value of the first column in the first row of the decision matrix, denotes a minimum value function, denotes a maximum value function, denotes a decision matrix, denotes the value of the first column in the first row of the normalized decision matrix, the value of the first column in the first row of the decision matrix, the value of the first column in the first row of the decision matrix,

[0168] The information entropy of the objective function is obtained by the following formula:

[0169] ,

[0170] wherein, denotes the information entropy of the first objective function, denotes the information entropy of the first objective function, denotes the proportionality coefficient of the first objective function in the first Pareto solution, denotes the number of objective functions, which is 3 in this embodiment, denotes a natural logarithm; The entropy weight of the objective function is obtained by the following formula:

[0171] ,

[0172] wherein, denotes the entropy weight of the first objective function,

[0173] denotes the difference coefficient of the first objective function; The comprehensive evaluation score is obtained by the following formula: ,

[0174] wherein,

[0175] denotes the comprehensive evaluation score of the first Pareto solution. In order to verify the effectiveness of the off-grid type wind-solar-storage combined hydrogen production system capacity configuration method proposed in this embodiment, simulation verification is performed, and the simulation parameters are shown in Table 2.

[0176] Table 2 System simulation parameters

[0177]

[0178]

[0179]

[0180] ​​​​​According to the parameters in Table 2, the capacity configuration method of off-grid wind-solar combined hydrogen production is simulated and verified, and the simulation results are as shown in Figure 2 and Figures 3 to 5 ; Figure 2 The three-dimensional cumulative distribution of the Pareto frontier (the Pareto solution set is the set of all solutions that satisfy the Pareto optimal condition, and the Pareto frontier is the boundary or surface formed by the objective function values corresponding to these solutions) is shown, and the optimal solution marked by the red star is located in the compromise region; Figure 3 The scatter plot of the wind and light abandonment rate and the unit hydrogen production cost is shown, and the color scale represents the electrolyzer capacity and the bubble size represents the energy storage capacity. The wind and light abandonment rate and the unit hydrogen production cost show a positive correlation, and the increase of the wind and light abandonment rate will result in a higher hydrogen production cost; Figure 4 The relationship between the wind and light abandonment rate and the cold start ratio is shown, reflecting the negative correlation between the wind and light abandonment rate and the cold start ratio. The lower the wind and light abandonment rate, the higher the system cold start ratio; Figure 5 The scatter plot of the hydrogen production cost and the cold start ratio is shown, and the hydrogen production cost and the cold start ratio also show a negative correlation. In summary, the configuration of 8 sets of 5 MW electrolyzers (total 40 MW) and 16.57 MWh energy storage achieves a balance between the wind and light abandonment rate (13.89%), the unit hydrogen production cost (23304.00 yuan / t) and the system stability (cold start ratio: 2.05%), verifying the effectiveness of the proposed method.

[0181] In summary, the off-grid wind-solar combined hydrogen production capacity configuration method proposed in this embodiment constructs a multi-time scale coupled mathematical model of wind power generation, photovoltaic power generation, energy storage system and alkaline electrolyzer. The multi-objective optimization model is constructed with the minimum annual wind and light abandonment rate, the minimum hydrogen production cost and the minimum electrolyzer cold start ratio as the target. The MOMPA algorithm is used to solve the multi-objective optimization model to obtain the Pareto solution set through the three-stage adaptive hunting strategy and the elite matrix guidance. According to the multi-time scale coupled mathematical model and the three-objective optimization model, the Pareto solution set obtained by the MOMPA algorithm is used to determine the weight of each objective function by using the improved entropy weight method, and the best compromise solution is selected as the system capacity configuration scheme.

[0182] Embodiment Two

[0183] Based on the same inventive concept as Embodiment One, the present embodiment discloses a kind of off-grid wind-solar combined hydrogen production system capacity configuration system, suitable for any off-grid wind-solar combined hydrogen production system capacity configuration method in embodiment one, comprising:

[0184] A model construction module is used to construct a multi-time scale coupled mathematical model of wind power generation, photovoltaic power generation, energy storage system and alkaline electrolyzer;

[0185] The multi-objective optimization module is configured to: according to the multi-time scale coupling mathematical model, construct a multi-objective optimization model with the minimum annual wind and light curtailment rate, the minimum hydrogen production cost of the leveling, and the minimum electrolytic cell cold start proportion as the objectives;

[0186] The multi-objective solution module is configured to: solve the multi-objective optimization model by using a multi-objective marine predator algorithm to obtain a Pareto solution set;

[0187] The system capacity configuration module is configured to: according to the Pareto solution set, determine the entropy weight of the objective function of the multi-objective optimization model by using an improved entropy weight method, and select an optimal compromise solution as a system capacity configuration scheme.

[0188] The specific function implementation of each module is referred to the related content in the method of the first embodiment, and is not described herein.

[0189] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product in the form of being implemented on one or more computer usable storage media containing computer usable program codes (including but not limited to disk memory, CD-ROM, optical memory, etc.).

[0190] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The devices that implement the functions specified in one or more flows and / or blocks.

[0191] These computer program instructions can also be stored in a computer readable memory capable of guiding a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The devices that implement the functions specified in one or more flows and / or blocks.

[0192] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are generated to realize the computer-implemented processes, and the instructions executed on the computer or other programmable devices provide a process for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or the functions specified in the block Figure 1 one block or multiple blocks.

[0193] The embodiments of the present application are described above with reference to the accompanying drawings, but the present application is not limited to the specific embodiments described above, and the specific embodiments described above are merely illustrative, but not restrictive, and those of ordinary skill in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, which are all within the protection of the present application.

Claims

1. A capacity configuration method of an off-grid wind-solar-storage combined hydrogen production system, characterized in that, The method comprises the following steps: a multi-time scale coupling mathematical model including wind power generation, photovoltaic power generation, energy storage system and alkaline electrolyzer is constructed; a multi-objective optimization model is constructed according to the multi-time scale coupling mathematical model, with the minimum annual wind and light curtailment rate, the minimum hydrogen production cost of flatization and the minimum electrolyzer cold start proportion as the target; a multi-objective marine predator algorithm is used to solve the multi-objective optimization model to obtain a Pareto solution set; an improved entropy weight method is used to determine the entropy weight of the objective function of the multi-objective optimization model according to the Pareto solution set, and the best compromise solution is selected as the system capacity configuration scheme.

2. The off-grid wind-solar-storage combined hydrogen production system capacity configuration method according to claim 1, characterized in that, The multi-time scale coupling mathematical model including wind power generation, photovoltaic power generation, energy storage system and alkaline electrolyzer comprises: a wind power generation model is obtained by establishing a wind power generation output power expression based on the rated wind speed, cut-in wind speed and cut-out wind speed of a wind turbine; a photovoltaic power generation model is established based on the influence of irradiance and temperature change of a photovoltaic module on power; an energy storage model is established according to the charge and discharge characteristics of a battery; an electrolyzer power model is established based on the working characteristics of an electrolyzer; the wind power generation model, the photovoltaic power generation model, the energy storage model and the electrolyzer power model are solved to obtain the multi-time scale coupling mathematical model.

3. The off-grid wind-solar-storage combined hydrogen production system capacity configuration method according to claim 2, characterized in that, The expression of the wind power generation model is: ， wherein represents the wind power output, represents the total rated power of the wind power generator, represents the rated wind speed of the wind power generator, represents the cut-in wind speed of the wind power generator, represents the cut-out wind speed of the wind power generator, is the actual wind speed of the wind power generator; The expression of the photovoltaic power generation model is: ， wherein, represents the photovoltaic power output, represents the standard irradiance, represents the reference temperature, represents the total rated power of the photovoltaic panel at a standard irradiance of , at a reference temperature of , represents the solar irradiance, represents the cell temperature when the assembly is operating, represents the power temperature coefficient; The expression of the energy storage model is: ， wherein, represents the battery capacity of the energy storage model at the time t, represents the battery capacity of the energy storage model at the time t, represents the self-discharge rate of the battery, represents the charging efficiency of the battery, represents the discharging efficiency of the battery, represents the total capacity of the energy storage, represents the energy storage output, PS(t) > 0 represents the charging power of the battery and PS(t) < 0 represents the discharging power of the battery, represents the charging and discharging time; The expression of the electrolyzer power model is: ， wherein, represents the electrolyzer output power at the time instant, represents the electrolysis conversion efficiency at the time instant, represents the cold start penalty coefficient at the time instant, represents the wind-solar-storage input power at the time instant.

4. The off-grid wind-solar-storage combined hydrogen production system capacity configuration method according to claim 3, characterized in that, The cold start penalty coefficient is obtained by the following equation: ， wherein, represents the time at which the cold start electrolyzer resumes heating, represents the heating time, represents the time the electrolyzer has been off at the time t when the electrolyzer is restarted, represents the holding time; The wind-solar-storage input power This is obtained by the equation: ， wherein, represents the wind, light, and storage combined output, represents the safety lower limit coefficient, represents the rated power of a single electrolytic cell, represents the overload coefficient, represents the number of electrolytic cells; The wind-solar-storage co-generation This is obtained by the equation: 。 5. The off-grid wind-solar-storage combined hydrogen production system capacity configuration method according to claim 1, characterized in that, The objective function of the multi-objective optimization model is: ， wherein, represents a minimum function, represents the installed capacity of the electrolyzer, represents the energy storage capacity, represents the annual wind and light curtailment rate, i.e., the first objective function, represents the total time of the expected optimization, represents the wind and light power at the moment, represents the total wind and light power abandoned; represents the levelized hydrogen production cost in the life cycle, i.e., the second objective function, represents the initial investment cost, represents the operation and maintenance cost, represents the expected service life, represents the hydrogen production amount in the life cycle; represents the cold start proportion, i.e., the third objective function, represents the number of cold starts, represents the number of hot starts.

6. The off-grid wind-solar-storage combined hydrogen production system capacity configuration method according to claim 5, characterized in that, The multi-objective optimization model is subject to capacity configuration constraints, power balance constraints and energy storage system constraints; The capacity configuration constraints are: , wherein, represents the installed capacity of wind power generation, represents the installed capacity of photovoltaic power generation, represents the installed number of electrolytic cells, represents the rated power of a single electrolytic cell; The power balance constraints are: ， wherein, represents the wind power generation output power at the time t, represents the photovoltaic power generation output power at the time t, is the energy storage output at the time t, represents the wind-solar-storage input power at the time t, represents the abandoned wind-solar power at the time t; The energy storage system constraints are: ， wherein, denotes a minimum state of charge of the energy storage system, denotes a maximum state of charge of the energy storage system, denotes the state of charge of the energy storage system at a time instant, denotes a preset target state of charge.

7. The off-grid wind-solar-storage combined hydrogen production system capacity configuration method according to claim 5, characterized in that, the total power of the discarded wind power is obtained by the following equation: ， the initial investment cost is obtained by the formula: ， wherein, represents the unit installed investment cost of wind power generation, represents the unit installed investment cost of photovoltaic power generation, represents the unit installed investment cost of electrolytic cell, represents the unit installed investment cost of energy storage; The operation and maintenance cost is obtained by the following equation: ， wherein, represents the unit installed operation and maintenance cost of wind power generation, represents the unit installed operation and maintenance cost of photovoltaic power generation, represents the unit installed operation and maintenance cost of electrolytic cell, represents the unit installed operation and maintenance cost of energy storage. the number of cold starts is obtained by the following equation: ， wherein, represents a cold start state, represents the time the electrolysis cell has been stopped at the time of restart of the electrolysis cell at time t, represents the holding time; the number of hot starts is obtained by the equation: ， wherein represents a hot start state. 8.The capacity configuration method of off-grid wind-solar-storage combined hydrogen production system according to claim 1, characterized in that, The multi-objective marine predator algorithm is used to solve the multi-objective optimization model to obtain a Pareto solution set, which comprises: An initial population satisfying the capacity configuration constraints of the multi-objective optimization model is randomly generated, and the elite solutions in the population are screened out by fast non-dominated sorting and crowding distance, to construct an elite matrix, and then the Pareto solution set is solved according to the elite matrix guidance and three-section breeding strategy. 9.The capacity configuration method of off-grid wind-solar-storage combined hydrogen production system according to claim 1, characterized in that, The improved entropy weight method is used to determine the entropy weight of the objective function of the multi-objective optimization model according to the Pareto solution set, and the best compromise solution is selected as the system capacity configuration scheme, which comprises: A decision matrix is constructed according to the Pareto solution set, and the decision matrix is normalized for the minimum target to obtain a normalized decision matrix; The information entropy of the objective function is calculated according to the normalized decision matrix, and the entropy weight of the objective function is obtained by information entropy normalization; The Pareto solution set is comprehensively evaluated according to the normalized decision matrix and the entropy weight of the objective function, and the Pareto solution with the highest comprehensive evaluation score is selected as the best compromise solution; The normalized decision matrix is obtained by the following formula: ， wherein, denotes the row number of the decision matrix, denotes the column number of the decision matrix, denotes the value of the decision matrix in the row and the column, i.e. the value of the th objective function in the th Pareto solution, denotes the minimum function, denotes the maximum function, denotes the decision matrix, denotes the value of the normalized decision matrix in the row and the column. The information entropy of the objective function is obtained by the following formula: ， wherein, represents the information entropy of the th objective function, represents the proportionality coefficient of the th objective function in the th Pareto solution, represents the number of objective functions, represents the natural logarithm; The entropy weight of the objective function is obtained by the following formula: ， wherein, denotes the entropy weight of the th objective function, denotes the difference coefficient of the th objective function; The comprehensive evaluation score is obtained by the following formula: ， wherein, represents the comprehensive evaluation score of the th Pareto solution.

10. An off-grid wind-solar-storage combined hydrogen production system capacity configuration system, characterized in that, The method comprises the following steps: a model construction module is used to construct a multi-time scale coupling mathematical model including wind power generation, photovoltaic power generation, energy storage system and alkaline electrolyzer; The multi-objective optimization module is configured to: according to the multi-time scale coupling mathematical model, construct a multi-objective optimization model with the minimum annual wind and light curtailment rate, the minimum hydrogen production cost of the leveling, and the minimum electrolytic cell cold start proportion as the objectives; The multi-objective solution module is configured to: solve the multi-objective optimization model by using a multi-objective marine predator algorithm to obtain a Pareto solution set; The system capacity configuration module is configured to: according to the Pareto solution set, determine the entropy weight of the objective function of the multi-objective optimization model by using an improved entropy weight method, and select an optimal compromise solution as a system capacity configuration scheme.

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