Wind-light-hydrogen system energy management method and device for optimizing voltage power of electrolytic cell

Abstract

The invention belongs to the field of electric power, and discloses a wind-light-hydrogen system energy management method and device for optimizing voltage power of an electrolytic bath, energy management is wind-light-hydrogen system energy management, and the energy management method comprises the steps of adjusting active power output of a power grid, coordinating photovoltaic power, fan power and reactive power of the power grid, and optimizing the voltage power of the electrolytic bath. The line loss is optimized, and the voltage and power fluctuation of the access point of the electrolytic cell are optimized, so that the hydrogen production efficiency of the electrolytic cell is improved. According to the method, the hydrogen production efficiency can be improved by optimizing the voltage and the power of the access point of the electrolytic cell module, the line loss can be reduced through reactive power coordination output, the power grid output can be adjusted, the output is increased for improving the hydrogen production efficiency, and then more hydrogen is produced.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to an energy management method for a wind-solar-hydrogen system for optimizing the voltage and power of an electrolyzer. Background Art

[0002] At present, the production of hydrogen energy focuses on the development of wind and solar power generation to produce hydrogen. Using renewable energy to produce hydrogen can reduce pollution to the environment and is sustainable. With the continuous development of new energy technologies, the cost of hydrogen production is decreasing year by year. At present, the cost of hydrogen production from new energy is still higher than that of traditional hydrogen production methods, and continuous optimization research is needed.

[0003] Improving the hydrogen production efficiency in the wind-solar hydrogen system is an effective way to reduce the cost of hydrogen production from renewable energy sources. It can produce more hydrogen under the same power generation conditions, which is conducive to improving the economic benefits of the system. The main factors affecting the hydrogen production efficiency of the electrolyzer include the voltage and power fluctuations at the access point. Voltage and power fluctuations will cause changes in the actual hydrogen production power of the electrolyzer, thereby affecting the electrolysis current. Sudden changes in the electrolyzer current will cause the electrolyzer to deviate from the steady state and reduce the hydrogen production efficiency. Optimizing the voltage and power fluctuations at the access point of the electrolyzer can effectively improve the hydrogen production efficiency.

[0004] The existing energy management of wind-solar hydrogen systems focuses on solving algorithms and improving optimization targets, while ignoring the operating characteristics of the electrolyzer itself. Research on hydrogen production systems often focuses on increasing hydrogen production power to increase hydrogen production. However, fluctuations in the voltage and power of the electrolyzer will cause sudden changes in the electrolysis current, causing the electrolyzer to deviate from the steady-state operating condition and reduce hydrogen production efficiency. The reduction in hydrogen production efficiency will reduce the economic benefits of the system, which is not conducive to the promotion of new energy hydrogen production systems. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a wind-solar-hydrogen system energy management method that optimizes the voltage and power of the electrolyzer, so as to reduce the voltage and power fluctuations at the access point of the electrolyzer module in the wind-solar-hydrogen system and improve the hydrogen production efficiency.

[0006] According to a first aspect of an embodiment of the present application, there is provided a wind-solar-hydrogen system energy management method for optimizing the voltage and power of an electrolyzer, comprising:

[0007] Step 1: Obtain the photovoltaic active power and wind power active power of the wind-solar-hydrogen system;

[0008] Step 2: Construct an electrolyzer model and a hydrogen production efficiency model based on the electrolyzer operation characteristics;

[0009] Step 3: Determine the constraints based on the actual operation of photovoltaic, wind power and electrolyzer;

[0010] Step 4: Determine the objective function of the wind-solar-hydrogen system based on the optimization requirements of the wind-solar-hydrogen system;

[0011] Step 5: Obtain wind-solar-hydrogen system parameters;

[0012] Step 6: Initialize the population according to the intensity Pareto evolutionary algorithm in combination with the constraints;

[0013] Step 7: Calculate reactive power using the electrolyzer model, combine population, photovoltaic active power, wind power active power, and wind-solar-hydrogen system parameters, and determine active power, reactive power, and node voltage of each node through power flow calculation;

[0014] Step 8: According to the objective function, combined with the active power, reactive power, node voltage and hydrogen production efficiency model of each node of the wind-solar-hydrogen system, the target indicators of the wind-solar-hydrogen system are calculated;

[0015] Step 9: The populations are compared with each other according to the target indicators, the selection principle in the intensity Pareto evolutionary algorithm is used to form the optimal solution set, and the crossover and mutation model in the intensity Pareto evolutionary algorithm is used to regenerate the population;

[0016] Step 10: Determine whether the iteration of the intensity Pareto evolutionary algorithm reaches the upper limit, if so, execute step 11, if not, execute step 7;

[0017] Step 11: Assign weights to each target indicator, calculate the target fitness of the optimal solution set according to the target function multiplied by the weight, select the minimum value of the target fitness to form the optimal population, and obtain the optimal energy management solution.

[0018] According to a second aspect of an embodiment of the present application, there is provided an energy management device for a wind-solar-hydrogen system for optimizing the voltage and power of an electrolyzer, comprising:

[0019] The first acquisition module is used to acquire the photovoltaic active power and wind power active power of the wind-solar-hydrogen system;

[0020] A model building module is used to build an electrolyzer model and a hydrogen production efficiency model based on the electrolyzer operation characteristics;

[0021] The constraint determination module is used to determine the constraint conditions according to the actual operation of photovoltaic, wind power and electrolyzer;

[0022] An objective function determination module is used to determine the objective function of the wind-solar-hydrogen system according to the optimization requirements of the wind-solar-hydrogen system;

[0023] The second acquisition module is used to obtain the wind-solar-hydrogen system parameters;

[0024] A population initialization module, used to initialize the population according to the intensity Pareto evolutionary algorithm in combination with the constraint conditions;

[0025] The first calculation module is used to calculate reactive power using the electrolyzer model, and determine the active power, reactive power, and node voltage of each node through power flow calculation in combination with population, photovoltaic active power, wind power active power, and wind-solar-hydrogen system parameters;

[0026] The second calculation module is used to calculate the target index of the wind-solar hydrogen system according to the objective function and in combination with the active power, reactive power, node voltage and hydrogen production efficiency model of each node of the wind-solar hydrogen system;

[0027] A population regeneration module is used to compare populations with each other according to the target indicators, form an optimal solution set using the selection principle in the intensity Pareto evolutionary algorithm, and regenerate the population using the crossover and mutation model in the intensity Pareto evolutionary algorithm;

[0028] An iteration module is used to determine whether the iteration of the intensity Pareto evolutionary algorithm reaches an upper limit, and if so, execute the solution acquisition module; if not, execute the first calculation module;

[0029] The scheme acquisition module is used to assign weights to each target indicator, calculate the target fitness of the optimal solution set according to the target function multiplied by the weight, select the minimum value of the target fitness to form the optimal population, and obtain the optimal energy management scheme.

[0030] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0031] It can be seen from the above embodiments that the present application adopts an energy management method for optimizing the voltage and power at the access point of the electrolyzer module, coordinates the active power and reactive power output of the photovoltaic module, wind turbine module, energy storage module, and hydrogen production module in the wind-solar hydrogen system, takes into account the impact of voltage and power fluctuations on electrolyzer hydrogen production, reduces voltage and power fluctuations at the access point of the electrolyzer, overcomes the problem of reduced hydrogen production efficiency of the electrolyzer caused by voltage and power fluctuations, takes into account the line loss of the wind-solar hydrogen system, improves the hydrogen production efficiency while reducing the loss as much as possible, and also takes into account the goal of increasing hydrogen production output, and increases the hydrogen production power as much as possible, thereby achieving the effect of increasing system revenue and reducing hydrogen production costs.

[0032] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0034] Figure 1 is a structural diagram of a wind-solar-hydrogen system according to an exemplary embodiment.

[0035] Figure 2 It is a flow chart of a method for energy management of a wind-solar-hydrogen system for optimizing the voltage and power of an electrolyzer according to an exemplary embodiment.

[0036] Figure 3 The active power of photovoltaic power and wind power is shown according to an exemplary embodiment.

[0037] Figure 4 The photovoltaic reactive power output is shown according to an exemplary embodiment.

[0038] Figure 5 The wind power reactive output is shown according to an exemplary embodiment.

[0039] Figure 6 The reactive power output of a power grid is shown according to an exemplary embodiment.

[0040] Figure 7 The active output of a power grid is shown according to an exemplary embodiment.

[0041] Figure 8 is an electrolyzer active power requirement shown according to an exemplary embodiment.

[0042] Fig. 9 FIG. 4 is a diagram showing reactive power demand of an electrolyzer according to an exemplary embodiment.

[0043] Fig.10 is a node voltage shown according to an exemplary embodiment.

[0044] Fig.11 1 is the active and reactive losses of the line 12 shown according to an exemplary embodiment.

[0045] Fig.12 2 is the active and reactive losses of line 23 shown according to an exemplary embodiment.

[0046] Fig.13 2 is the active and reactive losses of the line 24 according to an exemplary embodiment.

[0047] Fig.14 2 is the active and reactive losses of the line 25 shown according to an exemplary embodiment.

[0048] Fig.15 The total active and reactive power losses of a line are shown according to an exemplary embodiment.

[0049] Fig.16 Graph 1 is an electrolyzer hydrogen production efficiency shown according to an exemplary embodiment.

[0050] Fig.17 It is a block diagram of an energy management device for a wind-solar-hydrogen system for optimizing the voltage and power of an electrolyzer according to an exemplary embodiment. DETAILED DESCRIPTION

[0051] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0052] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0053] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0054] like Figure 1 As shown, the energy management object of the present invention is a wind-solar-hydrogen system, which is mainly composed of a photovoltaic power generation module, a wind power generation module, and an electrolyzer module, and adopts a grid-connected operation mode; the active output of the photovoltaic power generation module and the wind power generation module is selected according to the actual situation, and the reactive output participates in the system voltage, line loss, and reactive balance adjustment; the electrolyzer module includes a transformer, an inverter, and a DC electrolysis module, and the actual hydrogen production power is affected by the voltage and power of the access point; the energy management method is to adjust the active power output of the power grid, coordinate the reactive power of photovoltaics, wind turbines, and the power grid, optimize the voltage and power fluctuations at the electrolyzer access point while optimizing the line loss, and thereby improve the hydrogen production efficiency of the electrolyzer.

[0055] Figure 2 is a flow chart showing a method for energy management of a wind-solar-hydrogen system for optimizing the voltage and power of an electrolyzer according to an exemplary embodiment. Figure 2 As shown, the method is applied in a terminal and may include the following steps:

[0056] Step S1: Obtaining the photovoltaic active power and wind power active power of the wind-solar-hydrogen system;

[0057] Specifically, photovoltaic active power and wind power active power are the electrical energy inputs of the wind-solar-hydrogen system, and are the prerequisites for the system to carry out energy management. They are affected by factors such as light, temperature, wind speed, and wind direction in the area where the wind-solar-hydrogen system is located. Energy management can control the power generation and consumption of photovoltaic and wind power modules. Improving the power generation and consumption rate as much as possible is also one of the goals of the wind-solar-hydrogen system. Improving the consumption rate can increase the hydrogen production power and the grid-connected power, which increases the system revenue.

[0058] Step S2: constructing an electrolyzer model and a hydrogen production efficiency model according to the electrolyzer operation characteristics;

[0059] The active power of the alkaline electrolyzer is affected by the voltage at the access point, reactive power demand, etc. Voltage fluctuations will cause active power fluctuations. The electrolyzer model is as follows:

[0060]

[0061] In the formula, is the reactive power of the electrolyzer, in Mvar; Run the system minute; is the voltage at the access point of the electrolyzer, in kV; is the equivalent reactance of the electrolytic cell, in Ω; Allocate electrolyzer active power to the system in MW.

[0062] The efficiency of hydrogen production in the electrolyzer is affected by the fluctuation of hydrogen production power. The hydrogen production power is affected by voltage fluctuation and energy management allocation. The greater the fluctuation, the lower the efficiency. The rate of decline of hydrogen production efficiency is slow at first and then fast. After a short-term decline, the hydrogen production efficiency will gradually recover, and the recovery speed is fast at first and then slow. The characteristic point surface fitting is taken. The effect of power fluctuation on the efficiency of the electrolyzer is instantaneous. For the comparison of hydrogen production efficiency and hydrogen production output within a unit time scale, the integral method of solving the average value of hydrogen production efficiency is used to obtain the hydrogen production efficiency model as follows:

[0063]

[0064] In the formula, For voltage fluctuation ,t s The ratio of hydrogen production efficiency restored at the moment, in %; For voltage fluctuation ,t s The actual hydrogen production efficiency restored at the moment, in %; is the actual maximum hydrogen production efficiency of the electrolyzer, in %; , is an intermediate variable; is the power fluctuation ratio of the electrolytic cell, in %; For electrolytic cell Minute active power, in MW; For electrolytic cell -1 minute active power, in MW; is the rated power of the electrolyzer, in MW; is the average hydrogen production efficiency at time t, in %.

[0065] The hydrogen production efficiency model conforms to the law of reduced hydrogen production efficiency caused by voltage and power fluctuations. The model can quantify the problem of reduced hydrogen production efficiency and facilitate system energy management.

[0066] Step S3: determining constraint conditions according to the actual operation of photovoltaic power, wind power and electrolyzer;

[0067] Specifically, the constraints include photovoltaic constraints, wind power constraints, and electrolyzer constraints.

[0068] The reactive power output of the photovoltaic module is limited by the converter capacity and active power output, that is, the photovoltaic constraint condition is:

[0069]

[0070] In the formula, The upper limit of photovoltaic reactive power output at time t, in Mvar; is the apparent power of the photovoltaic inverter, in MVA; is the photovoltaic active output at time t, in MW.

[0071] The reactive output of the wind turbine module is limited by the power factor and active output, that is, the wind power constraint condition is:

[0072]

[0073] In the formula, The upper limit of wind turbine reactive power output at time t, in Mvar; is the active output of the wind turbine at time t, in MW; It is the lower limit of fan power factor.

[0074] The active and reactive power in the hydrogen production system must be balanced, that is, the constraints of the electrolyzer are:

[0075]

[0076] In the formula, is the reactive power of the line at time t, in MW, Mvar; is the photovoltaic reactive power output at time t, in Mvar; is the reactive power output of the wind turbine at time t, in Mvar; is the active power output of the power grid at time t, in MW; It is the reactive power output of the power grid at time t, in Mvar.

[0077] Step S4: Determine the objective function of the wind-solar-hydrogen system according to the optimization requirements of the wind-solar-hydrogen system;

[0078] Specifically, the objective function includes voltage fluctuation at the electrolyzer access point, electrolyzer power fluctuation, line loss, grid power support, and hydrogen production.

[0079] The voltage fluctuation at the access point of the electrolyzer reduces the efficiency of hydrogen production by electrolysis. The objective function with the minimum voltage fluctuation at the access point of the electrolyzer as the goal is:

[0080] ;

[0081] In the formula, is the voltage fluctuation value of the electrolytic cell; T is the system calculation period.

[0082] The power fluctuation of the electrolyzer reduces the efficiency of hydrogen production by electrolysis. The objective function with the minimum power fluctuation of the electrolyzer as the goal is:

[0083] ;

[0084] Reducing line loss can increase the hydrogen production power of the system, thereby increasing the economic benefits of the system, which is also one of the important indicators. The objective function with the minimum line loss as the goal is:

[0085] ;

[0086] In the formula, is the total active power loss of the line, in MW; is the active power loss of line 23, in MW; is the active power loss of line 24, in MW; is the active power loss of line 25, in MW;

[0087] The active power of the power grid supports the production of hydrogen in the system, which will increase the cost of hydrogen production. Making full use of solar energy and wind energy to produce hydrogen is conducive to improving the economic benefits of the system. The system aims to minimize the active power of the power grid. In the wind-solar hydrogen system, both photovoltaic and wind power modules have reactive power regulation capabilities, and reactive power is expected to be balanced locally. The objective function with the goal of minimizing the power support of the power grid is:

[0088] ;

[0089] In the formula, is the total active power output of the power grid, in MW; is the total reactive power output of the power grid, in Mvar;

[0090] The main revenue of the wind-solar hydrogen system comes from the sale of hydrogen. Increasing hydrogen production is conducive to improving the economic efficiency of the system. The objective function with the maximum hydrogen production as the goal is:

[0091] ;

[0092] In the formula, is the total hydrogen production of the system, in Nm 3 ; is the maximum hydrogen production of the system per unit time, in Nm 3 .

[0093] The hydrogen production efficiency correction is introduced into the hydrogen production objective function, which can be closer to the actual hydrogen production of the electrolyzer. The system takes the maximum hydrogen production as one of its goals, and can coordinate the output of each module to ensure a high level of hydrogen production to improve the economic benefits of the system.

[0094] Step S5: Obtaining wind-solar-hydrogen system parameters;

[0095] Specifically, the wind-solar-hydrogen system parameters include system rated voltage, line impedance, photovoltaic inverter apparent power, wind turbine power factor, and electrolyzer rated power.

[0096] The wind-solar-hydrogen system parameters represent the actual operating system data. Different parameters mean that energy management needs to adapt to different systems. The system rated voltage determines the level of the system's access to the grid. Voltage levels such as 220kV, 110kV, and 35kV are all acceptable. The line impedance is determined based on the system line selection and operating environment, and is the main factor affecting line losses. The apparent power of the photovoltaic inverter is determined based on the equipment options and is used to determine the maximum reactive power output of the photovoltaic module. The fan power factor is selected based on the fan model and is used to determine the maximum reactive power output of the fan module. The rated power of the electrolyzer is determined based on the electrolyzer model and is the maximum hydrogen production power of the hydrogen production module.

[0097] Step S6: Initialize the population according to the intensity Pareto evolutionary algorithm in combination with the constraint conditions;

[0098] Specifically, the population includes photovoltaic reactive output, wind turbine reactive output, electrolyzer active power, and electrolyzer access point voltage variables. The wind-solar hydrogen system uses photovoltaic reactive output, wind turbine reactive output, electrolyzer active power, and electrolyzer access point voltage as unknown solutions, which are randomly generated through the intensity Pareto evolutionary algorithm and combined to form a population, and the system flow can be calculated.

[0099] Step S7: Calculate reactive power using the electrolyzer model, combine population, photovoltaic active power, wind power active power, and wind-solar-hydrogen system parameters, and determine active power, reactive power, and node voltage of each node through power flow calculation;

[0100] Specifically, the electrolyzer model can calculate the electrolyzer reactive power based on the existing voltage and active power, and combine the electrolyzer reactive power, active power, voltage, photovoltaic reactive power, wind turbine reactive power, energy storage active power, energy storage reactive power in the population, system input photovoltaic active power, wind power active power, system line impedance parameters, and use the power flow formula to calculate the voltage and power of each node in the system as follows:

[0101] The node voltage is determined by the active and reactive output of wind power, photovoltaic power and power grid. The change of reactive power of electrolyzer will also affect the voltage of each node of the system. The voltage formula of nodes 1 and 2 is as follows:

[0102]

[0103] In the formula, is the voltage of node 2 at time t, in kV; is the voltage of node 1 at time t, in kV; is the longitudinal component of the voltage drop at node 1, in kV; is the voltage drop transverse component of node 1, in kV; is the active power of node 1, in MW; is the resistance of line 12, in Ω; is the reactive power of node 1, in Mvar; is the line 12 reactance, in Ω.

[0104] The voltage formula of node 3 is as follows:

[0105]

[0106] In the formula, is the voltage of node 3 at time t, in kV; is the longitudinal component of the voltage drop at node 2, in kV; is the voltage drop transverse component of node 2, in kV; The active power transmitted from node 2 to node 3 is in MW; is the resistance of line 23, in Ω; The reactive power transmitted from node 2 to node 3 is in Mvar; is the reactance of line 23, in Ω.

[0107] The voltage formula of node 4 is as follows:

[0108]

[0109] In the formula, is the voltage of node 4 at time t, in kV; The active power transmitted from node 2 to node 4 is in MW; is the resistance of line 24, in Ω; The reactive power transmitted from node 2 to node 4 is in Mvar; is the line 24 reactance, in Ω.

[0110] The voltage formula of node 5 is as follows:

[0111]

[0112] In the formula, is the voltage of node 5 at time t, in kV; The active power transmitted from node 2 to node 5 is in MW; is the resistance of line 25, in Ω; The reactive power transmitted from node 2 to node 5 is in Mvar; is the line 25 reactance, in Ω.

[0113] The line 12 loss formula is as follows:

[0114]

[0115] In the formula, is the active power of line 12, in MW; is the reactive power of line 12, in Mvar.

[0116] The line 23 loss formula is as follows:

[0117]

[0118] In the formula, is the active power of line 23, in MW; is the reactive power of line 23, in Mvar.

[0119] The line 24 loss formula is as follows:

[0120]

[0121] In the formula, is the active power of line 24, in MW; is the reactive power of line 24, in Mvar.

[0122] The line 25 loss formula is as follows:

[0123]

[0124] In the formula, is the active power of line 25, in MW; is the reactive power of line 25, in Mvar.

[0125] Step S8: According to the objective function, combined with the active power, reactive power, node voltage and hydrogen production efficiency model of each node of the wind-solar-hydrogen system, the target index of the wind-solar-hydrogen system is calculated;

[0126] Specifically, the target indicators include grid support, line loss, hydrogen production efficiency, and hydrogen production. The grid active power and reactive power are obtained according to the calculation formula of grid active power and reactive power in step S4, which is the grid support. The degree of dependence of the wind-solar-hydrogen system on the grid can be compared. The smaller the grid support, the smaller the degree of dependence of the system on the grid; the line loss calculation formula in step S4 can be used to calculate the system line loss, and the rationality of the reactive power output scheme can be judged by comparing the line loss. The smaller the line loss, the more reasonable the reactive power output scheme; the hydrogen production efficiency of the system is calculated according to the hydrogen production efficiency model in step S2, and the hydrogen production output of the same power can be compared. The more hydrogen production, the higher the hydrogen production efficiency; the hydrogen production of the system is calculated according to the hydrogen production calculation formula in step S4. The rationality of the active power and reactive power distribution scheme can be judged by comparing the hydrogen production. The higher the hydrogen production, the more reasonable the scheme.

[0127] Step S9: the populations are compared with each other according to the target indicators, the selection principle in the intensity Pareto evolutionary algorithm is used to form the optimal solution set, and the crossover and mutation model in the intensity Pareto evolutionary algorithm is used to regenerate the population;

[0128] Specifically, the target indicator comparison refers to a group of populations calculating the power grid support, line loss, hydrogen production efficiency, and hydrogen production targets according to the calculation formula, and deleting the populations whose four targets are all smaller than the other group. Through deletion and selection, a single-target optimal and comprehensive target optimal solution set can be formed, and unreasonable active power and reactive power distribution schemes can be eliminated.

[0129] Step S10: Determine whether the iteration of the intensity Pareto evolution algorithm reaches the upper limit, if so, execute step S11, if not, execute step S7;

[0130] Specifically, the intensity Pareto evolutionary algorithm iteration is a process of continuously generating a new population by using the intensity Pareto evolutionary algorithm crossover and mutation rules for the optimal solution set, comparing the new population with the original optimal population in terms of grid support, line loss, hydrogen production efficiency, and hydrogen production volume, and selecting a new optimal solution set.

[0131] Step S11: assign a weight to each target indicator, calculate the target fitness of the optimal solution set according to the target function multiplied by the weight, select the minimum value of the target fitness to form the optimal population, and obtain the optimal energy management solution.

[0132] Specifically, the weights allocated to the target indicators can be changed according to demand, representing the importance of grid support, line loss, hydrogen production efficiency, and hydrogen production volume in the current wind-solar-hydrogen system. The target fitness is formed by adding the product of the target and the weight. The target fitness data is compared to select the smallest corresponding population to form the optimal solution. Different weights can select different solutions from the solution set to meet different energy management needs.

[0133] It can be seen from the above embodiments that the present application adopts an energy management method for optimizing the voltage and power at the access point of the electrolyzer module, coordinates the active power and reactive power output of the photovoltaic module, wind turbine module, energy storage module, and hydrogen production module in the wind-solar hydrogen system, takes into account the impact of voltage and power fluctuations on electrolyzer hydrogen production, and reduces the voltage and power fluctuations at the access point of the electrolyzer. Therefore, the problem of reduced hydrogen production efficiency of the electrolyzer caused by voltage and power fluctuations is overcome, and the line loss of the wind-solar hydrogen system is taken into account. While improving the hydrogen production efficiency, the loss is reduced as much as possible, and the goal of increasing hydrogen production output is also taken into account. The hydrogen production power is increased as much as possible, thereby achieving the effect of increasing system revenue and reducing hydrogen production costs.

[0134] Example:

[0135] Wind and solar system Figure 1 The system parameters are shown in Table 1.

[0136] Table 1 Parameters of wind-solar-hydrogen system

[0137] The energy management optimization method of the wind-solar hydrogen system is used to verify the effectiveness of improving the hydrogen production efficiency by optimizing the electrolyzer voltage and power fluctuations.

[0138] The wind-solar-hydrogen system selects the actual photovoltaic and wind turbine active output as follows Figure 3 As shown. The energy management method of optimizing voltage and power is introduced to arrange the reactive power of photovoltaic, wind turbine, power grid and electrolyzer and the active power of power grid and electrolyzer as shown. Figure 4-Figure 9 As shown; the voltage of nodes 2-5 is as Fig.10 As shown in the figure, after optimization, the hydrogen production power of the electrolyzer has increased, the reactive power demand has increased, and the difficulty of optimizing voltage fluctuation has increased. On this basis, the voltage fluctuation after optimization is still slightly improved compared with that before optimization; the losses of lines 12, 23, 24, and 25 are as follows Figure 11-Figure 14 As shown, the total line loss is Fig.15 As shown in the figure, the line loss increases after optimization, mainly because the hydrogen production power increases, and the increase is partly due to the power grid output. At the same time, the line loss corresponding to the unit hydrogen production power before optimization is 1.37%, and after optimization it is 1.36%. It can be seen that the reactive power coordination output has optimized the line loss. The efficiency optimization results are shown in the figure below. Fig.16As shown in the figure, the efficiency of hydrogen production has been significantly improved, and the same hydrogen production power can produce more hydrogen. The energy management strategy significantly optimizes the voltage and power fluctuations of the electrolyzer on the basis of taking into account line losses. Although the power grid output has increased, the hydrogen production has increased more, which has improved the economy of the system. The energy management method of the wind-solar hydrogen system that optimizes the voltage and power of the electrolyzer is effective.

[0139] Corresponding to the aforementioned embodiment of the wind-solar-hydrogen system energy management method for optimizing the voltage and power of the electrolyzer, the present application also provides an embodiment of the wind-solar-hydrogen system energy management device for optimizing the voltage and power of the electrolyzer.

[0140] Fig.17 is a block diagram of an energy management device for a wind-solar-hydrogen system for optimizing the voltage and power of an electrolyzer according to an exemplary embodiment. Fig.17 , the device comprises:

[0141] The first acquisition module 1 is used to acquire the photovoltaic active power and wind power active power of the wind-solar-hydrogen system;

[0142] Model building module 2, used to build an electrolyzer model and a hydrogen production efficiency model according to the electrolyzer operation characteristics;

[0143] The constraint condition determination module 3 is used to determine the constraint conditions according to the actual operation of photovoltaic, wind power and electrolyzer;

[0144] The objective function determination module 4 is used to determine the objective function of the wind-solar-hydrogen system according to the optimization requirements of the wind-solar-hydrogen system;

[0145] The second acquisition module 5 is used to obtain wind-solar-hydrogen system parameters;

[0146] A population initialization module 6 is used to initialize the population according to the intensity Pareto evolutionary algorithm in combination with the constraint conditions;

[0147] The first calculation module 7 is used to calculate reactive power using the electrolyzer model, and determine the active power, reactive power and node voltage of each node through power flow calculation in combination with the population, photovoltaic active power, wind power active power and wind-solar-hydrogen system parameters;

[0148] The second calculation module 8 is used to calculate the target index of the wind-solar hydrogen system according to the objective function and in combination with the active power, reactive power, node voltage and hydrogen production efficiency model of each node of the wind-solar hydrogen system;

[0149] A population regeneration module 9 is used to compare populations with each other according to the target indicators, form an optimal solution set using the selection principle in the intensity Pareto evolutionary algorithm, and regenerate the population using the crossover and mutation model in the intensity Pareto evolutionary algorithm;

[0150] Iteration module 10, used to determine whether the iteration of the intensity Pareto evolutionary algorithm reaches the upper limit, if so, execute the solution acquisition module 11, if not, execute the first calculation module 7;

[0151] The scheme acquisition module 11 is used to assign weights to each target indicator, calculate the target fitness of the optimal solution set according to the target function multiplied by the weight, select the minimum value of the target fitness to form the optimal population, and obtain the optimal energy management scheme.

[0152] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0153] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0154] Correspondingly, the present application also provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the wind-solar-hydrogen system energy management method for optimizing the voltage and power of the electrolyzer as described above.

[0155] Correspondingly, the present application also provides a computer-readable storage medium on which computer instructions are stored. When the instructions are executed by a processor, the wind-solar-hydrogen system energy management method for optimizing the voltage and power of the electrolyzer as described above is implemented.

[0156] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0157] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A wind-solar hydrogen system energy management method for optimizing the voltage and power of the electrolyzer, characterized in that: include: Step 1: Obtain the photovoltaic active power and wind power active power of the wind-solar-hydrogen system; Step 2: Construct an electrolyzer model and a hydrogen production efficiency model based on the electrolyzer operation characteristics; Step 3: Determine the constraints based on the actual operation of photovoltaic, wind power and electrolyzer; Step 4: Determine the objective function of the wind-solar-hydrogen system based on the optimization requirements of the wind-solar-hydrogen system; Step 5: Obtain wind-solar-hydrogen system parameters; Step 6: Initialize the population according to the intensity Pareto evolutionary algorithm in combination with the constraints; Step 7: Calculate reactive power using the electrolyzer model, combine population, photovoltaic active power, wind power active power, and wind-solar-hydrogen system parameters, and determine active power, reactive power, and node voltage of each node through power flow calculation; Step 8: According to the objective function, combined with the active power, reactive power, node voltage and hydrogen production efficiency model of each node of the wind-solar-hydrogen system, the target indicators of the wind-solar-hydrogen system are calculated; Step 9: The populations are compared with each other according to the target indicators, the selection principle in the intensity Pareto evolutionary algorithm is used to form the optimal solution set, and the crossover and mutation model in the intensity Pareto evolutionary algorithm is used to regenerate the population; Step 10: Determine whether the iteration of the intensity Pareto evolutionary algorithm reaches the upper limit, if so, execute step 11, if not, execute step 7; Step 11: Assign weights to each target indicator, calculate the target fitness of the optimal solution set according to the target function multiplied by the weight, select the minimum value of the target fitness to form the optimal population, and obtain the optimal energy management solution.

2. The method according to claim 1, characterized in that The electrolytic cell model is as follows: ， In the formula, is the reactive power of the electrolyzer, in Mvar; Run the system minute; is the voltage at the access point of the electrolyzer, in kV; is the equivalent reactance of the electrolytic cell, in Ω; Allocate electrolyzer active power to the system in MW.

3. The method according to claim 1, characterized in that The hydrogen production efficiency model is as follows: In the formula, For voltage fluctuation ,t s The ratio of hydrogen production efficiency restored at the moment, in %; For voltage fluctuation ,t s The actual hydrogen production efficiency restored at the moment, in %; is the actual maximum hydrogen production efficiency of the electrolyzer, in %; , is an intermediate variable; is the power fluctuation ratio of the electrolytic cell, in %; For electrolytic cell Minute active power, in MW; For electrolytic cell -1 minute active power, in MW; is the rated power of the electrolyzer, in MW; is the average hydrogen production efficiency at time t, in %.

4. The method according to claim 1, characterized in that: The constraints include photovoltaic constraints, wind power constraints, and electrolyzer constraints.

5. The method according to claim 1, characterized in that The objective function includes voltage fluctuation at the access point of the electrolyzer, power fluctuation of the electrolyzer, line loss, power support of the power grid, and hydrogen production.

6. The method according to claim 5, characterized in that The objective function with the goal of minimizing the voltage fluctuation at the electrolyzer access point is: ； In the formula, is the voltage fluctuation value of the electrolytic cell; T is the system calculation period; The objective function to minimize the electrolytic cell power fluctuation is: ； The objective function with the minimum line loss as the goal is: ； In the formula, is the total active power loss of the line, in MW; is the active power loss of line 23, in MW; is the active power loss of line 24, in MW; is the active power loss of line 25, in MW; The objective function to minimize the grid power support is: ； In the formula, is the total active power output of the power grid, in MW; is the total reactive power output of the power grid, in Mvar; The objective function for maximizing hydrogen production is: ； In the formula, is the total hydrogen production of the system, in Nm 3 ; is the maximum hydrogen production of the system per unit time, in Nm 3 .

7. The method according to claim 1, characterized in that The wind-solar-hydrogen system parameters include system rated voltage, line impedance, photovoltaic inverter apparent power, wind turbine power factor, and electrolyzer rated power.

8. The method according to claim 1, characterized in that The population includes photovoltaic reactive power output, wind turbine reactive power output, electrolyzer active power, and electrolyzer access point voltage variables.

9. The method according to claim 1, characterized in that: The target indicators include grid support, line loss, hydrogen production efficiency, and hydrogen production volume.

10. An energy management device for a wind-solar hydrogen system that optimizes the voltage and power of an electrolyzer, characterized in that: include: The first acquisition module is used to acquire the photovoltaic active power and wind power active power of the wind-solar-hydrogen system; A model building module is used to build an electrolyzer model and a hydrogen production efficiency model based on the electrolyzer operation characteristics; The constraint determination module is used to determine the constraint conditions according to the actual operation of photovoltaic, wind power and electrolyzer; An objective function determination module is used to determine the objective function of the wind-solar-hydrogen system according to the optimization requirements of the wind-solar-hydrogen system; The second acquisition module is used to obtain the wind-solar-hydrogen system parameters; A population initialization module, used to initialize the population according to the intensity Pareto evolutionary algorithm in combination with the constraint conditions; The first calculation module is used to calculate reactive power using the electrolyzer model, and determine the active power, reactive power, and node voltage of each node through power flow calculation in combination with population, photovoltaic active power, wind power active power, and wind-solar-hydrogen system parameters; The second calculation module is used to calculate the target index of the wind-solar hydrogen system according to the objective function and in combination with the active power, reactive power, node voltage and hydrogen production efficiency model of each node of the wind-solar hydrogen system; A population regeneration module is used to compare populations with each other according to the target indicators, form an optimal solution set using the selection principle in the intensity Pareto evolutionary algorithm, and regenerate the population using the crossover and mutation model in the intensity Pareto evolutionary algorithm; An iteration module is used to determine whether the iteration of the intensity Pareto evolutionary algorithm reaches an upper limit, and if so, execute the solution acquisition module; if not, execute the first calculation module; The scheme acquisition module is used to assign weights to each target indicator, calculate the target fitness of the optimal solution set according to the target function multiplied by the weight, select the minimum value of the target fitness to form the optimal population, and obtain the optimal energy management scheme.

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