A large-scale photovoltaic hydrogen production station equivalent modeling method and system

Abstract

The application provides a large-scale photovoltaic hydrogen production station equivalent modeling method and system, comprising the following steps: constructing a basic structure of a large-scale photovoltaic hydrogen production station grid-connected system; establishing an electrolytic cell system model taking single electrolytic cell operation as a unit and a photovoltaic power generation system model taking single photovoltaic array operation as a unit; based on the working principles of the electrolytic cell system and the photovoltaic power generation system and the interaction mechanism thereof with the power grid, analyzing key factors reflecting dynamic characteristics of the photovoltaic hydrogen production station, reducing dimensions of the key factors by using a sparse self-encoder to remove redundant information, and grouping electrolytic cell units and photovoltaic power generation units in the photovoltaic hydrogen production station by using a clustering algorithm; and establishing a multi-machine equivalent model of the large-scale photovoltaic hydrogen production station by using a capacity weighting, equal power loss method, etc. The application effectively fuses physical characteristics and data analysis of the large-scale photovoltaic hydrogen production station grid-connected system, and the equivalent model thereof can adapt to steady-state, transient-state and various working conditions, and reduce system simulation calculation amount.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of equivalent modeling of new energy stations, in particular to a method and system for equivalent modeling of a large-scale photovoltaic hydrogen production station. BACKGROUND

[0002] Based on the characteristics of renewable resources such as wind and light and the reverse distribution of energy demand, the grid-connected system of a large-scale new energy station is an inevitable development trend of energy and power layout. The use of a high proportion of power electronic equipment in a large-scale photovoltaic hydrogen production station, the intermittency, randomness and volatility of photovoltaic new energy output, and the wide power range operation characteristics of water electrolysis hydrogen production devices will all change the dynamic characteristics of the system. Detailed modeling of each unit in the station can reflect its operating characteristics, but it cannot meet the simulation requirements under the development of large-scale new energy stations. How to construct a reasonable and accurate station equivalent model for system steady-state and transient analysis is one of the current research focuses.

[0003] New energy station equivalent modeling often aggregates units with similar dynamic characteristics to reduce system simulation calculation. Existing researches mostly determine the grouping index and construct the equivalent model for a certain scene and specific characteristics, lacking comprehensiveness. Modeling based on the physical characteristics of new energy stations cannot fully reflect the dynamic characteristics of the system, and modeling based on data analysis often requires a large amount of high-quality data to find the inherent rules hidden therein, while the model lacks interpretability. Therefore, it is urgent to consider the physical-data characteristics of the grid-connected system of a large-scale photovoltaic hydrogen production station to construct its multi-machine equivalent model to adapt to varying operating conditions and make the modeling more accurate. SUMMARY

[0004] To solve the above technical problems, the present application proposes a method and system for equivalent modeling of a large-scale photovoltaic hydrogen production station. Based on the interaction mechanism of the electrolysis cell system, the photovoltaic power generation system and the power grid, the electrolysis cell units and the photovoltaic power generation units in the station are grouped based on physical characteristics-data analysis, and a multi-machine equivalent model of a large-scale photovoltaic hydrogen production station is constructed.

[0005] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0006] A method for equivalent modeling of a large-scale photovoltaic hydrogen production station, comprising the following steps:

[0007] Step 1: Based on the capacity of the photovoltaic hydrogen production station and the actual application of the water electrolysis hydrogen production demonstration project, the basic structure of the grid-connected system of a large-scale photovoltaic hydrogen production station is constructed;

[0008] Step 2: According to the working principle of the electrolytic cell system and the photovoltaic array and the control mode of the converter thereof, an electrolytic cell system model taking single electrolytic cell operation as a unit and a photovoltaic power generation system model taking single photovoltaic array operation as a unit are established;

[0009] Step 3: Based on the working principle of the electrolytic cell system and the photovoltaic power generation system and the interaction mechanism thereof with the power grid, key factors reflecting the dynamic characteristics of the photovoltaic hydrogen production station are analyzed, the key factors are reduced in dimension by using a sparse autoencoder to remove redundant information, and the electrolytic cell units and the photovoltaic power generation units in the photovoltaic hydrogen production station are clustered by using a clustering algorithm;

[0010] Step 4: The capacity weighting and equal power loss method are used to construct equivalent models for the devices in the same cluster, and a multi-machine equivalent model of the large-scale photovoltaic hydrogen production station is formed.

[0011] The application further provides a large-scale photovoltaic hydrogen production station equivalent modeling system, comprising the following modules:

[0012] A structure construction module determines the basic structure of the large-scale photovoltaic hydrogen production station grid-connected system by considering the capacity size of the photovoltaic hydrogen production station;

[0013] A model construction module establishes an electrolytic cell system model taking single electrolytic cell operation as a unit and a photovoltaic power generation system model taking single photovoltaic array operation as a unit according to the working principle of the electrolytic cell system and the photovoltaic array and the control mode of the converter thereof;

[0014] A clustering module analyzes key factors reflecting the dynamic characteristics of the photovoltaic hydrogen production station based on the working principle of the electrolytic cell system and the photovoltaic power generation system and the interaction mechanism thereof with the power grid, reduces the key factors in dimension by using a sparse autoencoder to remove redundant information, and clusters the electrolytic cell units and the photovoltaic power generation units in the photovoltaic hydrogen production station by using a clustering algorithm;

[0015] A multi-machine equivalent construction module constructs equivalent models for the devices in the same cluster by using the capacity weighting and equal power loss method, and forms a multi-machine equivalent model of the large-scale photovoltaic hydrogen production station.

[0016] The application further provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the large-scale photovoltaic hydrogen production station equivalent modeling method described above when executing the program.

[0017] The application further provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the large-scale photovoltaic hydrogen production station equivalent modeling method described above.

[0018] Compared with the prior art, the application has the following beneficial effects:

[0019] 1、The present application effectively fuses the physical characteristics and data analysis of the large-scale photovoltaic hydrogen production station grid-connected system, that is, the data is screened and processed in a targeted manner on the basis of deep understanding of the physical characteristics of the system, which can reduce the redundancy of system information and improve the accuracy of the established model.

[0020] 2、The present application is suitable for transient and steady state conditions, and reflects the dynamic characteristics of the system operation process, while effectively reducing the simulation calculation amount while ensuring the invariability of the equivalent model output external characteristics. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 It is a flowchart of an equivalent modeling method of a large-scale photovoltaic hydrogen production station of the present application;

[0022] Figure 2 It is a topological structure and electric energy flow path diagram of a large-scale photovoltaic hydrogen production station grid-connected power system of the present application;

[0023] Figure 3 It is an equivalent circuit diagram of an alkaline electrolytic cell of the present application;

[0024] Figure 4 It is a control block diagram of an electrolytic cell side Buck converter of the present application;

[0025] Figure 5 It is a control block diagram of an electrolytic cell side rectifier of the present application;

[0026] Figure 6 It is an equivalent circuit diagram of a photovoltaic cell of the present application;

[0027] Figure 7 It is a control block diagram of a photovoltaic side Boost converter of the present application;

[0028] Figure 8 It is a control block diagram of a photovoltaic side inverter of the present application;

[0029] Figure 9 It is a factor set diagram for characterizing the dynamic characteristics of a photovoltaic hydrogen production station of the present application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0031] As shown in the drawings, Figure 1 A large-scale photovoltaic hydrogen production station equivalent modeling method of the present application comprises the following steps:

[0032] Step 1: Construct the basic structure of the grid-connected system of the large-scale photovoltaic hydrogen production station, including:

[0033] According to the topology structure and electric energy flow path diagram of the grid-connected system of the large-scale photovoltaic hydrogen production station as shown in Figure 2 , the green electricity generated by the photovoltaic power generation system passes through the Boost circuit and the inverter, is stepped up by the step-up transformer, passes through the collection line, and then is connected to the 220kV line through the main step-up transformer to be transmitted to the hydrogen production plant area; the electric energy consumed by the water electrolysis hydrogen production device is connected to the step-down transformer through the main step-down transformer and the multi-loop line from the 220kV line, and then is supplied through the rectifier and the Buck circuit; the paths of the electric energy generated by the photovoltaic system and the electric energy consumed by the electrolytic cell are basically opposite. When the electric energy generated by the hundred-megawatt photovoltaic hydrogen production station is too much, the excess electric energy is consumed by the power grid; when the electric energy generated by the photovoltaic system is insufficient to support the minimum power operation of the electrolytic cell, the power grid needs to supply energy to support the continuous operation of the electrolytic cell;

[0034] Step 2: According to the structure of the station and the working principle of the electrolytic cell and the photovoltaic array and the control mode of the converter thereof, an electrolytic cell system model taking the operation of a single electrolytic cell as a unit and a photovoltaic power generation system model taking the operation of a single photovoltaic array as a unit are established, including:

[0035] 1) Establishing an electrolytic cell system model

[0036] 1.1) Electrolytic cell model

[0037] The electrolytic cell is a key component of the hydrogen production system, which decomposes the electrolyte solution into hydrogen and oxygen through direct current. In order to accurately simulate the electrical characteristics of the electrolytic cell, a dynamic model is established as shown in Figure 3 , the reversible voltage U rev is the basic voltage required for the water electrolysis reaction, which can be represented by a voltage source U rev ; the ohmic overvoltage U ohm is caused by the resistance of the electrolytic cell component to the flow of electric charge, which can be represented by an ohmic resistor R ohm ; the activation overvoltage U act is the potential difference caused by the electrode electron transfer process, which can be represented by a controlled current source I act in parallel with a capacitor C act ; the sum of the reversible voltage U rev , the ohmic overvoltage U ohm , and the activation overvoltage U act , i.e., the above three are connected in series, is the electrolytic cell voltage U el , which can be represented as:

[0038] (1)

[0039] wherein, U acta , Uactc Anode activation overvoltage, cathode activation overvoltage, respectively.

[0040] Each electrolyzer stack is composed of multiple electrolytic cells. The reversible voltage can be expressed as:

[0041] (2)

[0042] Where, N el is the number of electrolytic cells; AG is the Gibbs free energy; z is the number of electrons transferred to produce 1 mole of hydrogen; F is the Faraday constant; R is the ideal gas constant; T el is the working temperature of the electrolyzer; P H2 , P O2 are the partial pressures of the reaction products hydrogen and oxygen, respectively; a H2O is the water activity; ln() is the logarithmic function.

[0043] The ohmic overvoltage can be expressed as:

[0044] (3)

[0045] Where, A el is the surface area of the electrolyzer; r1, r2, r3, r4 are the area specific resistance coefficients of the electrolyzer monomer; I el is the current of the electrolyzer.

[0046] According to the modified Tafel equation, the activation overvoltage is a nonlinear function of the activation current, which can be expressed as:

[0047] (4)

[0048] (5)

[0049] Where, I acta , I actc are the anode activation current and the cathode activation current, respectively; a el , b el , c el , d el are the Tafel empirical coefficients, which can be specifically expressed as:

[0050] (6)

[0051] (7)

[0052] (8)

[0053] (9)

[0054] Wherein, a1, a2, a3, b1, b2, b3 are electrolytic cell anode activation resistance empirical coefficient; c1, c2, c3, d1, d2, d3 are electrolytic cell cathode activation resistance empirical coefficient.

[0055] 1.2) Electrolytic cell side DC / DC converter and its control model:

[0056] The required DC voltage amplitude of electrolytic cell equipment operation is low, usually needs to be reduced to the required voltage of electrolytic cell through DC / DC converter, that is, Buck circuit. The control block diagram of DC / DC converter is shown in Figure 4 , which adopts double-loop control mode of power outer loop and current inner loop, that is, according to system running state and running range P eln of electrolytic cell, the active power reference value P elref of electrolytic cell is given, which is compared with the actual active power value P el of electrolytic cell, and the current reference value I elref is obtained through PI regulator, which is compared with the current value I el of electrolytic cell, and the Buck circuit modulation information D eldc is obtained through PI regulator after current closed-loop control, so as to adjust the duty cycle of the circuit and realize real-time control of electrolytic cell power.

[0057] Since electrolytic cell can flexibly adjust system unbalanced power in a wide power range, the minimum running power can reach 5%~25% of the rated power, and the maximum running power can reach 110%~130% of the rated power. In the case of large fluctuation of photovoltaic output or system fault state, the response adjustment characteristics of electrolytic cell can be fully utilized, and a voltage response link can be added in the outer loop control, that is, the DC bus voltage reference value U eldcref of electrolytic cell is compared with the actual voltage value U eldc , and the value after PI regulator is used as outer loop control parameter, so that electrolytic cell can run outside the normal running interval (25%~100%) for a short time.

[0058] 1.3) Electrolytic cell side rectifier and its control model:

[0059] The required of electrolytic cell equipment operation is DC, in order to meet the requirement of large-scale electrolytic cell equipment grid connection, usually through AC / DC converter to convert source side AC into DC. The control block diagram of electrolytic cell side rectifier is shown in Figure 5 , which adopts double-loop control mode of voltage, power outer loop and current inner loop, that is, according to the running state of electrolytic cell and the stability requirement of system, the DC bus voltage reference value U eldcref of electrolytic cell, the active power reference value P elref and the reactive power reference value Q elref of electrolytic cell are given, which are compared with the actual DC bus voltage value U eldc, the actual active power value P of the electrolytic cell el , the actual reactive power value Q el , the d-axis and q-axis current reference values I eldref , I elqref generated by the PI regulator eldref , I elqref are compared with the actual values I eld , I elq , and then through the PI regulator, the d-axis and q-axis actual voltage values e eld , e elq and the voltage across the equivalent inductance L els of the AC side are superimposed to obtain the d-axis and q-axis modulation voltages u eld , u elq , and then through Park inverse transformation and pulse width modulation technology, the rectifier modulation signal D elac is obtained to realize system power balance.

[0060] 2) Establish a model of the photovoltaic power generation system

[0061] 2.1) Photovoltaic array model:

[0062] Photovoltaic cells convert light energy into electrical energy based on the "photovoltaic effect", and their output characteristics are easily affected by external environmental factors such as ambient temperature and light intensity. At present, the widely used equivalent circuit of photovoltaic cells is as shown in Figure 6 , the photovoltaic cell generates a photogenerated current I sc after being illuminated, which can be represented by a constant current source; a part of it is used to offset the junction current I d of the P-N junction, i.e., a parallel ideal diode is used to shunt; a shunt resistor R sh is connected in parallel to represent the short circuit caused by the edge leakage of the battery and the loss of the metal bridge; a parasitic resistor R s is connected in series to represent the loss caused by the resistivity of the battery material. Based on this circuit, the model of the photovoltaic cell can be obtained as follows:

[0063] (10)

[0064] where U pc , I pc are the output voltage and output current of the photovoltaic cell, respectively; I0 is the reverse saturation current of the parallel diode; q is the electronic charge constant; n is the ideal quality factor of the diode; K is the Boltzmann constant; T pv is the temperature of the photovoltaic cell; exp() is the exponential function.

[0065] Photovoltaic array is usually connected by a plurality of photovoltaic cells through series and parallel connection, and its model can be expressed as:

[0066] (11)

[0067] wherein, U pv , I pv are output voltage and output current of photovoltaic array; N s , N p are number of series and parallel connection of photovoltaic cells.

[0068] 2.2) Photovoltaic side DC / DC converter and its control model:

[0069] Photovoltaic array output voltage amplitude is low, and usually needs to pass through DC / DC converter, i.e. Boost circuit voltage. Photovoltaic power generation system output is affected by environmental temperature and light intensity, and presents nonlinear characteristics. Control block diagram of Boost converter is shown in Figure 7 , during normal operation of the system, usually maximum power tracking technology is adopted, i.e. voltage value U pvref at maximum power output of photovoltaic array is compared with actual output voltage value U pv , modulation signal D pvdc is generated through PI regulator, and maximum power output of photovoltaic array is realized; during system failure, Boost converter usually adopts double-loop control mode of power outer loop and current inner loop, i.e. active power reference value P pvref of photovoltaic array is given according to system operation state, and compared with actual active power value P pv of photovoltaic array, current reference value I pvref is generated through regulator, and current inner loop control is realized, i.e. compared with actual output current value I pv of photovoltaic array, and Boost circuit modulation signal D pvdc is obtained through PI regulator, and power balance of the whole system is realized.

[0070] 2.3) Photovoltaic side inverter and its control model:

[0071] In order to meet the grid connection requirements of large-scale photovoltaic power generation system, usually DC / AC converter is used to convert direct current generated by photovoltaic system into alternating current, and at the same time, amplitude, voltage, frequency and phase of grid connection are kept consistent. Control block diagram of inverter is shown in Figure 8 , during normal operation state, photovoltaic side inverter adopts double-loop control mode of voltage, power outer loop and current inner loop, and its control process is similar to electrolytic cell, i.e. direct current bus voltage reference value U pvdcref of photovoltaic array, active power reference value P pvref of photovoltaic array and reactive power reference value Qpvref , the actual voltage value of the photovoltaic array DC bus U pvdc , the actual active power value of the photovoltaic array P pv , the actual reactive power value Q pv , the photovoltaic array active and reactive current reference value I pvdref , I pvqref , the modulation signal D pvac , the photovoltaic power generation system unit power factor operation is realized.

[0072] In the system fault state, that is, when the voltage of the photovoltaic side port drops or rises, the photovoltaic inverter will switch to the fault ride-through control mode. The photovoltaic inverter ensures off-grid operation within a certain time while absorbing and emitting certain reactive power to support the recovery of the grid voltage. The voltage at the grid side will drop due to short-circuit fault, and the dynamic reactive current should track the voltage change of the grid point in real time, and the output current can be expressed as:

[0073] (12)

[0074] (13)

[0075] Where, k1, k2 are the reactive current coefficients; V pcc is the voltage at the photovoltaic side grid point, which is the per unit value; I pvN is the photovoltaic rated current; I pvd is the active current of the photovoltaic in normal operation state; I max is the inverter current limit, generally 1.2~1.5 times the rated current.

[0076] Step 3: Based on the working principle of electrolytic cell system and photovoltaic power generation system and its interaction mechanism with the power grid, the key factors reflecting the dynamic characteristics of the photovoltaic hydrogen production station are analyzed, and the sparse self-encoder is used to remove redundant information, and the electrolytic cell unit and photovoltaic power generation unit in the photovoltaic hydrogen production station are clustered by clustering algorithm, including:

[0077] 1) Key factor analysis:

[0078] For the grid-connected system of large-scale photovoltaic hydrogen production station, the physical characteristics at a certain time and space scale are often difficult to accurately characterize the dynamic characteristics of the whole system. When the system operating state changes, the dynamic characteristics of the photovoltaic array unit and the electrolytic cell unit show many changes, such as changes in DC bus voltage, terminal voltage, active power, reactive power, and changes in converter control mode. Therefore, it is necessary to sort out the internal and external key influencing factors that can reflect the dynamic characteristics of photovoltaic power generation unit, electrolytic cell unit and photovoltaic hydrogen production station from multiple time and space scales.

[0079] 1.1)Voltage variation:

[0080] The power information between the photovoltaic array, the electrolytic cell and the grid is exchanged through the two-stage converter. When the grid side fails, such as single-phase short-circuit fault, two-phase short-circuit fault, the grid voltage will change, and the active power absorbed by the grid will decrease. Due to the time delay of the two-stage converter control system, the photovoltaic power generation unit and the electrolytic cell unit cannot immediately track the power change of the grid side. At this time, the active power of the grid side, the photovoltaic side and the electrolytic cell side can be expressed as:

[0081] (14)

[0082] (15)

[0083] (16)

[0084] where P g is the active power of the grid side; e gd , e gq are the d-axis and q-axis components of the grid voltage, respectively; i gd , i gq are the d-axis and q-axis components of the grid current, respectively.

[0085] According to the power balance principle, we get:

[0086] (17)

[0087] where P g1 , P g2 are the interactive power of the grid side and the photovoltaic side and the electrolytic cell side, respectively; ΔP1, ΔP2 are the unbalanced power of the photovoltaic side and the electrolytic cell side, respectively; C pvdc , C eldc are the DC bus capacitors of the photovoltaic side and the electrolytic cell side, respectively.

[0088] It can be seen that the unbalanced power between the photovoltaic side, the electrolytic cell side and the grid side is absorbed by the DC bus capacitor, which causes the DC bus voltage U pvdc , U eldc to change, and further causes the control strategy of the converter in the photovoltaic power generation system and the electrolytic cell system to change to eliminate the DC bus voltage change. Therefore, the DC bus voltage U pvdc , U eldc can be selected to describe the dynamic behavior of the photovoltaic array, the electrolytic cell and its converter, and the photovoltaic side voltage U pvt , the electrolytic cell side voltage U elt can be selected to describe the fault degree of the system.

[0089] 1.2)Power variation:

[0090] One of the main external characteristics of the photovoltaic power generation system and the electrolyzer system is the photovoltaic output power and the electrolyzer absorption power, which is achieved through power control. In normal operation of the system, the power balance among the photovoltaic power generation system, the electrolyzer system and the power grid; when the system fails, the photovoltaic side inverter and the electrolyzer side rectifier will change their control strategy to respond to the change of the grid side power, so that the whole system is in power balance again, therefore, the photovoltaic active power P pv , the photovoltaic reactive power Q pv , the electrolyzer active power P el can be selected to describe the dynamic behavior of the photovoltaic system and the electrolyzer system.

[0091] The grid voltage is a directional vector for vector control, which converts three-phase voltage and current into two-phase rotating dq coordinate system, so that the grid voltage vector e g is coincident with the d-axis, that is, e gd =E g , e gq =0, the grid side active power and reactive power can be expressed as:

[0092] (18)

[0093] Wherein, Q g is the grid side reactive power; E g is the grid side voltage.

[0094] From the above formula, the grid side active power can be controlled by the d-axis current, and the grid side reactive power can be controlled by the q-axis current, and the photovoltaic side end current I pvt and the electrolyzer side end current I elt can be selected to represent the change of the grid side power.

[0095] 1.3) Spatial information:

[0096] In normal operation of the system, the photovoltaic power generation system often maintains maximum power output, the electrolyzer system often maintains rated power operation, and the power grid absorbs and emits certain power to maintain the power balance of the whole system. In the event of system failure, the spatial distribution of large-scale photovoltaic hydrogen production station is large, and the operating state of photovoltaic power generation system and electrolyzer system at different positions will change, the photovoltaic array and electrolyzer near the fault point are deeply affected by the fault, and the power fluctuation is large; the photovoltaic array and electrolyzer far from the fault point are less affected by the fault or even not affected. The photovoltaic power P pv , Q pv , and the electrolyzer power P el can be selected to quantitatively represent the dynamic influence of spatial information on the photovoltaic hydrogen production station.

[0097] From the above analysis, Upvdc , U eldc , U pvt , U elt , I pvt , I elt , P pv , Q pv , P el The comprehensive consideration of factors such as the above can relatively comprehensively represent the dynamic characteristics of the photovoltaic hydrogen production station, that is, according to the control process of the photovoltaic hydrogen production station in step 2, the key factor set representing the dynamic characteristics of the station can be represented as shown in Figure 9 However, the above factors exhibit strong coupling, nonlinearity, and dimensional differences, and if all factors are considered together, it is easy to have low modeling efficiency, poor interpretability, and poor implementation possibility, so it is necessary to screen and refine them to reduce the information redundancy of the input features and facilitate subsequent photovoltaic hydrogen production station clustering.

[0098] 2) Data preprocessing:

[0099] To improve the accuracy and convergence speed of the model, the key factors of different attributes and physical characteristics need to be preprocessed. Since the data units of different factors are inconsistent and the numerical differences are large, the data of each factor is normalized before analyzing each factor, that is, the p-norm of the sample data is first calculated, and then all elements of the sample data are divided by the norm, which can reduce the equal error and avoid data overfitting.

[0100] For a vector x = [x1, x2, …, xn] composed of original sample data, n T (n is the number of samples), the p-norm can be expressed as:

[0101] (19)

[0102] The normalization of each sample data can be expressed as:

[0103] (20)

[0104] Where x ir is the result of the normalization of each sample data, which is distributed between [0, 1]; x i is the i-th original sample data.

[0105] 3) Data feature construction:

[0106] ​Autoencoders is a kind of neural network model for unsupervised learning using back propagation algorithm, which can be used for data dimensionality reduction and feature extraction. It mainly includes two parts of encoding and decoding, in which the encoder is to compress the input high-dimensional data into low-dimensional data representation to extract the most informative features in the input data; the decoder is to reconstruct the original input data from the low-dimensional data representation. For the sample data that has been normalized, the operations of the encoder and the decoder can be represented as:

[0107] (21)

[0108] wherein z en , x de are low-dimensional data, reconstructed data respectively; f, g are activation functions of the encoder and the decoder respectively; W en , W de are weight matrices of the encoder and the decoder respectively; b en , b de are bias terms of the encoder and the decoder respectively.

[0109] The weight matrices and the bias terms in the encoding and decoding process are updated by the stochastic gradient descent method to minimize the error between the reconstructed data and the original high-dimensional data, and the objective function can be represented as:

[0110] (22)

[0111] wherein R re is the reconstruction error; L lo is the loss function.

[0112] When using autoencoders to reduce the dimensionality of the key factors representing the dynamic characteristics of large-scale photovoltaic hydrogen production stations, the hidden layer in the autoencoder, i.e. the result of data dimensionality reduction, is often the focus of attention. Due to the large amount of data and high complexity of the key factors, overfitting problems are prone to occur. Sparse autoencoders introduce a sparse penalty term, which forces the sparse activation of hidden layer neurons, so that they can better learn the features and structure of the input data. Compared with traditional autoencoders, sparse autoencoders have no limit on the amount of input data, can filter out noise and redundant information in the data, extract the most critical features for classification and identification, and improve the robustness and generalization ability of the system. The control objective of sparse autoencoders is to add a sparse penalty term to the loss error, which can be represented as:

[0113] (23)

[0114] wherein R sa is the control objective of sparse autoencoders; Ω is the penalty term.

[0115] The penalty term is constructed using KL divergence, which can be expressed as:

[0116] (24)

[0117] where K l is the number of hidden layer nodes; ρ s is the set activation value; ρ kav is the average activation value of the hidden layer.

[0118] The objective function of the sparse autoencoder can be expressed as:

[0119] (25)

[0120] where β pf is the penalty factor.

[0121] To avoid losing too much information in the data dimensionality reduction process, an information retention rate is introduced to reduce redundant information while ensuring the amount of information, which can be expressed as:

[0122] (26)

[0123] where η ir is the information retention rate; d en is the feature dimension after data dimensionality reduction; x ir,j , x de,j are the jth feature of the original data and reconstructed data, respectively; m is the feature dimension of the original data.

[0124] 4) Clustering and grouping:

[0125] After using the autoencoder to obtain the data characteristics of the large-scale photovoltaic hydrogen production station grid-connected system, it can be used as a grouping index, and a clustering algorithm is used to group the photovoltaic system and the electrolyzer system. The k-means algorithm has the characteristics of fast calculation speed and simple understanding, and is widely used in cluster division. The k-means algorithm is an iterative clustering algorithm, and its core idea is to divide the data set into k groups, so that each data point belongs to the group with the closest distance, that is, the most similar characteristics in the photovoltaic hydrogen production station are divided into a group. There are many studies on the k-means algorithm, which will not be repeated here.

[0126] Step 4: Capacity weighting, equal power loss method, etc. are used to construct equivalent models for equipment in the same cluster, forming a large-scale photovoltaic hydrogen production station multi-machine equivalent model, including:

[0127] Based on the dynamic characteristics of large-scale photovoltaic hydrogen production station, photovoltaic power generation units and electrolyzer units with similar characteristics are divided into a cluster, and several groups can be used to equivalent the entire photovoltaic hydrogen production station. The group can accurately represent the operating characteristics of a cluster, and the external characteristics of the photovoltaic hydrogen production station at the grid connection point remain the same before and after the equivalent. The equivalent group model construction mainly includes electrolyzer system equivalent, photovoltaic system equivalent, transformer equivalent, etc.

[0128] 1) Electrolyzer system equivalent:

[0129] The equivalent principle of electrolyzer system is to keep the required power unchanged before and after the equivalent, that is, the required power of electrolyzer sub-cluster is equal to the sum of the required power of all electrolyzer units in the cluster, which can be expressed as:

[0130] (27)

[0131] Where, P tel,s is the equivalent power of the s-th electrolyzer sub-cluster; P el,as is the power required by the a-th electrolyzer unit in the s-th electrolyzer sub-cluster; is the number of electrolyzer units in the s-th electrolyzer sub-cluster.

[0132] 2) Photovoltaic system equivalent:

[0133] The equivalent principle of photovoltaic system is to keep the output power unchanged before and after the equivalent, that is, the output power of photovoltaic sub-cluster is equal to the sum of the output power of all photovoltaic power generation units in the cluster, which can be expressed as:

[0134] (28)

[0135] Where, P tpv,t , Q tpv,t are the equivalent active and reactive power of the t-th photovoltaic sub-cluster; P pv,bt , Q pv,bt are the active and reactive power of the b-th photovoltaic power generation unit in the t-th photovoltaic sub-cluster; is the number of photovoltaic power generation units in the t-th photovoltaic sub-cluster.

[0136] 3) Transformer equivalent:

[0137] The equivalent principle of transformer is to keep the power loss unchanged before and after the equivalent, and the total power loss of the transformer in a cluster is equal to the sum of the power loss of each transformer, which can be expressed as:

[0138] (29)

[0139] Where, P ttr,w、 S ttr,wrespectively, are equivalent active power loss and rated capacity of the transformer in the wth sub-cluster; tr,cw , S tr,cw respectively, are active power loss and rated capacity of the cth transformer in the wth sub-cluster; is the number of transformers in the wth sub-cluster.

[0140] In addition, the application also provides a large-scale photovoltaic hydrogen production station equivalent modeling system, comprising the following modules:

[0141] The structure construction module determines the basic structure of the large-scale photovoltaic hydrogen production station grid-connected system by considering the capacity size of the photovoltaic hydrogen production station.

[0142] The model construction module establishes an electrolytic cell system model taking single electrolytic cell operation as a unit and a photovoltaic power generation system model taking single photovoltaic array operation as a unit according to the station structure and the working principles of the electrolytic cell and the photovoltaic array and the control mode of the transformer thereof.

[0143] The grouping module analyzes key factors reflecting the dynamic characteristics of the photovoltaic hydrogen production station based on the working principles of the electrolytic cell system and the photovoltaic power generation system and the interaction mechanism thereof with the power grid, reduces the key factors by using a sparse auto-encoder to remove redundant information, and groups the electrolytic cell units and the photovoltaic power generation units in the photovoltaic hydrogen production station by using a clustering algorithm.

[0144] The multi-machine equivalent construction module constructs an equivalent model for the devices in the same cluster by using the capacity weighting and equivalent power loss method, thereby forming a multi-machine equivalent model of the large-scale photovoltaic hydrogen production station.

[0145] The application also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the large-scale photovoltaic hydrogen production station equivalent modeling method described above when executing the program.

[0146] The application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program is executable on a processor to implement the steps of the large-scale photovoltaic hydrogen production station equivalent modeling method described above.

[0147] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a computer to perform any of the methods. The software implementation can be initialized by loading and executing a set of instructions arranged to perform one of the methods into the computer's memory. Alternatively, hard-wired circuitry can be used in place of, or in combination with, software instructions. Thus, the

[0148] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams.

[0149] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams.

[0151] While preferred embodiments of the application have been described, modifications and variations can be apparent to those skilled in the art once aware of the general underlying concepts. Accordingly, the appended claims are intended to embrace all such modifications and variations as fall within the scope of the application.

[0152] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method for equivalent modeling of a large-scale photovoltaic hydrogen production plant, characterized in that, Comprising the following steps: Step 1: Based on the capacity of the photovoltaic hydrogen production station and the actual application of the water electrolysis hydrogen production demonstration project, the basic structure of the grid-connected system of the large-scale photovoltaic hydrogen production station is constructed; Step 2: According to the station structure and the working principle of the electrolyzer and the photovoltaic array and the control mode of the converter, an electrolyzer system model taking single electrolyzer operation as a unit and a photovoltaic power generation system model taking single photovoltaic array operation as a unit are established; Step 3: Based on the working principle of the electrolyzer system and the photovoltaic power generation system and their interaction mechanism with the power grid, the key factors reflecting the dynamic characteristics of the photovoltaic hydrogen production station are analyzed, the key factors are reduced in dimension by using sparse autoencoder to remove redundant information, and the electrolyzer units and photovoltaic power generation units in the photovoltaic hydrogen production station are clustered by using clustering algorithm, including: 1) The power information interaction between the photovoltaic array, the electrolyzer and the power grid is carried out through two-stage converters, when the grid side fails, the power emitted and absorbed by the photovoltaic side and the electrolyzer side cannot immediately track the power change of the grid side, the active power of the grid side, the photovoltaic side and the electrolyzer side is represented as: （12） （13） （14） where P g is the grid-side active power; e gd , e gq are the d-axis and q-axis components of the grid-side voltage; i gd , i gq are the d-axis and q-axis components of the grid-side current; P pv is the photovoltaic active power; P el is the electrolyzer active power; I pv is the output current of the photovoltaic array, I el is the electrolyzer current; According to the power balance principle, the following is obtained: （15） wherein P g1 , P g2 are the exchanged powers at the grid side and the PV side, respectively, and at the electrolyzer side, respectively; ΔP1, ΔP2 are the unbalanced powers at the PV side and at the electrolyzer side, respectively; C pvdc , C eldc are the DC bus capacitances at the PV side and at the electrolyzer side, respectively; U pvdc , U eldc are the DC bus voltages at the PV side and at the electrolyzer side, respectively. The unbalanced power between the photovoltaic side, the electrolytic cell side, and the grid side is absorbed by the DC bus capacitor. The DC bus voltage U on the photovoltaic side is selected. pvdc DC bus voltage U on the electrolytic cell side eldc To describe the dynamic behavior of the photovoltaic array, electrolytic cell, and its converter, the photovoltaic side voltage U is selected. pvt Electrolytic cell side voltage U elt To describe the degree of system failure; 2) Power balance among photovoltaic power generation system, electrolytic cell system and power grid when the system is running normally; when the system fails, the photovoltaic hydrogen production station will change the control strategy to respond to the change of grid-side power, so that the system power reaches a new balance state, and the photovoltaic active power P pv , photovoltaic reactive power Q pv , and electrolytic cell active power P el are selected to describe the dynamic behavior of the photovoltaic system and the electrolytic cell system; The grid voltage is directed as a vector control, the three-phase voltage and current are converted into two-phase rotating dq coordinate system, the grid voltage vector e g is coincident with the d-axis, i.e. e gd =E g , e gq =0, the grid-side active power and reactive power are expressed as: （16） wherein Q g is the grid-side reactive power; E g is the grid-side voltage; From the above formula, the grid-side active power is controlled by the d-axis current, the grid-side reactive power is controlled by the q-axis current, and the photovoltaic-side terminal current I pvt , the electrolytic cell-side terminal current I elt to represent the grid-side power change; 3) the light intensity and the degree of failure are different in different spatial positions, and the photovoltaic power P pv , Q pv , the power of electrolytic tank P el Quantify the dynamic influence of spatial information on the photovoltaic hydrogen production station; In summary, Upvdc, U eldc , Upvt, U elt , Ipvt, Ielt, Ppv, Qpv, Pel comprehensive consideration of the dynamic characteristics of photovoltaic hydrogen production station is relatively comprehensive; Step 4: The capacity weighting and equal power loss method is used to construct the equivalent model of the equipment in the same cluster to form the multi-machine equivalent model of the large-scale photovoltaic hydrogen production station.

2. The method of claim 1, wherein In step 1, the basic structure of the grid-connected system of the large-scale photovoltaic hydrogen production station is constructed, including: The green electricity generated by the photovoltaic power generation system passes through the boost circuit, the inverter, the step-up transformer, the collection line, the main step-up transformer and the 220kV line to be transmitted to the hydrogen production plant area; The electrical energy consumed by the water electrolysis hydrogen production device is connected to the step-down transformer through the main step-down transformer and the multi-loop line from the 220kV line, and then supplied through the rectifier and the buck circuit; The paths of the electrical energy generated by the photovoltaic system and the electrical energy consumed by the electrolyzer are basically opposite; The power grid is connected to the 220kV line, and when the electrical energy generated by the photovoltaic hydrogen production station is too much, the excess electrical energy is consumed by the power grid; When the electrical energy generated by the photovoltaic system is insufficient to support the minimum power operation of the electrolyzer, the power grid is supplied to support the continuous operation of the electrolyzer.

3. The method of claim 1, wherein, In step 2, the electrolyzer system model taking single electrolyzer operation as a unit is established, including: 1) The electrolyzer model is constructed as: （1） wherein U el is the cell voltage; U rev is the reversible voltage; U ohm is the ohmic overvoltage; U acta , U actc are the anodic and cathodic activation overvoltages, respectively; Each electrolyzer stack is composed of multiple electrolytic cells; The reversible voltage is represented as: （2） where N el is the number of electrolytic cells; AG is the Gibbs free energy; z is the number of electrons transferred to produce 1 mole of hydrogen; F is the Faraday constant; R is the ideal gas constant; T el is the operating temperature of the electrolytic cell; P H2 , P O2 are the partial pressures of the reaction products, hydrogen and oxygen, respectively; a H2O is the water activity; and ln() is the logarithm function. The ohmic overvoltage is represented as: （3） wherein A el is the surface area of the electrolytic cell; r1, r2, r3, r4 are the area specific resistance coefficients of the electrolytic cell monomer; I el is the electrolytic cell current; The activation overvoltage is represented as: （4） （5） where I acta , I actc are the anodic and cathodic activation currents, respectively; a el , b el , c el , d el are the Tafel empirical coefficients, expressed as: （6） （7） （8） （9） Wherein, a1, a2, a3, b1, b2, b3 are empirical coefficients of the anode activation resistance of the electrolyzer; c1, c2, c3, d1, d2, d3 are empirical coefficients of the cathode activation resistance of the electrolyzer; 2) Constructing the electrolyzer side converter and its control model, the electrolyzer adopts two-stage control of rectifier and Buck converter, that is, the rectifier converts the source side alternating current into direct current, and the Buck converter reduces the voltage to the required voltage of the electrolyzer; during normal operation of the system, according to the operating state of the system and the operating range of the electrolyzer, both controllers adopt double-loop control mode of power, voltage outer loop and current inner loop to realize real-time control of the reference power and direct current voltage of the electrolyzer; when the photovoltaic output appears large fluctuation or the system is in a fault state, a voltage response link is added in the outer loop control to fully exert the response and adjustment characteristics of the electrolyzer.

4. The method of claim 1, wherein In step 2, a photovoltaic power generation system model is established, taking the operation of a single photovoltaic array as a unit, including: 1) Constructing a photovoltaic array model, including: The photovoltaic cell model is represented as: （10） wherein U pc , I pc are the output voltage and output current of the photovoltaic cell, respectively; I sc is the photogenerated current; I0is the reverse saturation current of the parallel diode; q is the electron charge constant; R sh , R s are the shunt resistance and the parasitic resistance, respectively; n is the diode ideality factor; K is the Boltzmann constant; T pv is the photovoltaic cell temperature; exp() is the exponential function; The photovoltaic array is connected by multiple photovoltaic cells in series and parallel, and its model is represented as: （11） Wherein, U pv , I pv are output voltage and output current of the photovoltaic array respectively; N s , N p are series and parallel numbers of the photovoltaic cells respectively; 2) Constructing a photovoltaic side converter and its control model, the photovoltaic array adopts two-stage control of Boost converter and inverter, that is, the Boost converter raises the voltage at the photovoltaic array side to the required voltage at the grid side, and the inverter converts direct current into alternating current; during normal operation of the system, the Boost circuit usually adopts maximum power point tracking technology control to achieve maximum power output of the photovoltaic array; the inverter adopts double-loop control mode of voltage, power outer loop and current inner loop to make the amplitude, voltage, frequency and phase of the grid consistent, so that the photovoltaic power generation system operates at unit power factor; when the system is in a fault state, that is, when the voltage at the photovoltaic side port drops or rises, the photovoltaic inverter will switch to fault ride-through control mode to absorb and emit certain reactive power to support the recovery of the grid voltage; the Boost converter will switch to double-loop control mode of power outer loop and current inner loop to achieve power balance of the overall system.

5. The method of claim 1, wherein, In step 3, the electrolyzer unit and photovoltaic power generation unit in the photovoltaic hydrogen production station are grouped, including dimensionality reduction of key factors, including: 1) Regularization processing is performed on each factor data, the p-norm of the sample data is first calculated, and then all elements of the sample data are divided by the norm; For a vector x = [x1, x2, ..., xn] composed of the original sample data n ] T Where n is the number of samples, its p-norm is expressed as: （17） The normalized processing is performed on each sample data, which is represented as: （18） wherein x ir is the result of the regularization of each sample data, which is distributed in [0, 1]; x i is the i-th original sample data; 2) For the sample data that has been normalized, an autoencoder is used for data dimensionality reduction and feature extraction, the operations of the encoder and the decoder are represented as: （19） wherein z en , x de are low-dimensional data, reconstructed data, respectively; f, g are activation functions of the encoder, decoder, respectively; W en , W de are weight matrices of the encoder, decoder, respectively; b en , b de are bias terms of the encoder, decoder, respectively. The weight matrix and bias term in the encoding and decoding process are updated using the stochastic gradient descent method to minimize the error between the reconstructed data and the original high-dimensional data, and the objective function is represented as: （20） where R re is the reconstruction error; L lo is the loss function; A sparse autoencoder is used, and a sparsity penalty term is introduced to force the sparse activation of hidden layer neurons, so that they can better learn the features and structure of the input data. The control objective of the sparse autoencoder is to add a sparsity penalty term to the loss error, which is represented as: （21） wherein R sa is the control target of the sparse autoencoder; Ω is the penalty term; A KL divergence is used to construct the penalty term, which is represented as: （22） where K l is the number of hidden layer nodes; p s is the set activation value; p kav is the average hidden layer activation value; The objective function of the sparse autoencoder is represented as: （23） where β pf is a penalty factor; In order to avoid losing too much information during data dimensionality reduction, an information retention rate is introduced to reduce redundant information while ensuring the amount of information, which is represented as: （24） wherein η ir is the information retention rate; d en is the feature dimension after data dimension reduction; x ir,j , x de,j are the jth feature of the original data and the reconstructed data, respectively; and m is the feature dimension of the original data. 3) After using the self-encoder to obtain the data characteristics of the grid-connected system of the large-scale photovoltaic hydrogen production plant, the data characteristics are used as a clustering index, and a clustering algorithm is used to cluster the photovoltaic system and the electrolyzer system, and the most similar characteristics in the photovoltaic hydrogen production plant are divided into the same cluster.

6. The method of claim 1, wherein In step 4, a multi-machine equivalent model of the large-scale photovoltaic hydrogen production plant is formed, including: 1) The equivalent principle of the electrolyzer system is that the required power remains unchanged before and after the equivalent, that is, the required power of the electrolyzer sub-cluster is equal to the sum of the required power of all electrolyzer units in the cluster, which is represented as: （25） P tel,s is the equivalent power of the s-th electrolytic cell sub-cluster; P el,as is the power required by the a-th electrolytic cell unit in the s-th electrolytic cell sub-cluster; is the number of electrolytic cell units in the s-th electrolytic cell sub-cluster; 2) The equivalent principle of the photovoltaic system is that the output power remains unchanged before and after the equivalent, that is, the output power of the photovoltaic sub-cluster is equal to the sum of the output power of all photovoltaic power generation units in the cluster, which is represented as: （26） wherein P tpv,t , Q tpv,t are the equivalent active and reactive power of the tth photovoltaic sub-cluster, respectively; P pv,bt , Q pv,bt are the active and reactive power of the bth photovoltaic power generation unit within the tth photovoltaic sub-cluster, respectively; is the number of photovoltaic power generation units within the tth photovoltaic sub-cluster. 3) The equivalent principle of the transformer is that the power loss remains unchanged before and after the equivalent, and the total power loss of the transformers in a cluster is equal to the sum of the power losses of the transformers, which is represented as: （27） wherein P ttr,w、 S ttr,w Pw and Sw are the equivalent active power loss and rated capacity of transformers in the wth sub-cluster, respectively. tr,cw Pw and Sw are the equivalent active power loss and rated capacity of transformers in the wth sub-cluster, respectively. tr,cw Pw and Sw are the equivalent active power loss and rated capacity of transformers in the wth sub-cluster, respectively. Nw is the number of transformers in the wth sub-cluster.

7. A large-scale photovoltaic hydrogen production plant equivalent modeling system, characterized in that, The following modules are included: A structure construction module determines the basic structure of the grid-connected system of the large-scale photovoltaic hydrogen production plant by considering the capacity of the photovoltaic hydrogen production plant; A model construction module establishes an electrolyzer system model taking single electrolyzer operation as a unit and a photovoltaic power generation system model taking single photovoltaic array operation as a unit according to the structure of the plant and the working principles of the electrolyzer and photovoltaic array and the control mode of their transformers; A clustering module analyzes key factors reflecting the dynamic characteristics of the photovoltaic hydrogen production plant based on the working principles of the electrolyzer system and the photovoltaic power generation system and their interaction mechanism with the grid, reduces the key factors using a sparse self-encoder to remove redundant information, and clusters the electrolyzer units and photovoltaic power generation units in the photovoltaic hydrogen production plant using a clustering algorithm, including: 1) The photovoltaic array, electrolyzer, and grid interact through two-stage transformers, and when the grid side fails, the power emitted and absorbed by the photovoltaic side and the electrolyzer side cannot immediately track the power change of the grid side, and the active power of the grid side, photovoltaic side, and electrolyzer side is represented as: （12） （13） （14） where P grid is the grid-side active power; e d and e q are the d-axis and q-axis components of the grid-side voltage; i d and i q are the d-axis and q-axis components of the grid-side current; P pv is the photovoltaic active power; P tank is the tank active power; I pv is the output current of the photovoltaic array; I tank is the tank current. g gd gq gd gq pv el pv el where P grid is the grid-side active power; e d and e q are the d-axis and q-axis components of the grid-side voltage; i d and i q are the d-axis and q-axis components of the grid-side current; P pv is the photovoltaic active power; P tank is the tank active power; I pv is the output current of the photovoltaic array; I tank is the tank current.​​​​​​​​ According to the power balance principle, the following is obtained: （15） wherein P g1 , P g2 are the exchanged powers at the grid side and the PV side, respectively, and at the electrolyzer side, respectively; ΔP1, ΔP2 are the unbalanced powers at the PV side and at the electrolyzer side, respectively; C pvdc , C eldc are the DC bus capacitances at the PV side and at the electrolyzer side, respectively; U pvdc , U eldc are the DC bus voltages at the PV side and at the electrolyzer side, respectively. The unbalanced power between the photovoltaic side, the electrolytic cell side, and the grid side is absorbed by the DC bus capacitor. The DC bus voltage U on the photovoltaic side is selected. pvdc DC bus voltage U on the electrolytic cell side eldc To describe the dynamic behavior of the photovoltaic array, electrolytic cell, and its converter, the photovoltaic side voltage U is selected. pvt Electrolytic cell side voltage U elt To describe the degree of system failure; 2) Power balance among photovoltaic power generation system, electrolytic cell system and power grid when the system is running normally; when the system fails, the photovoltaic hydrogen production station will change the control strategy to respond to the change of grid-side power, so that the system power reaches a new balance state, and the photovoltaic active power P pv , photovoltaic reactive power Q pv , electrolytic cell active power P el are selected to describe the dynamic behavior of the photovoltaic system and the electrolytic cell system; The grid voltage is taken as a directional vector for vector control, and three-phase voltage and current are converted into two-phase rotating dq coordinate system, so that the grid voltage vector e g is coincident with the d-axis, i.e. e gd =E g , e gq =0, and the active power and reactive power on the grid side are expressed as: （16） wherein Q g is the grid-side reactive power; E g is the grid-side voltage; From the above formula, the grid-side active power is controlled by the d-axis current, the grid-side reactive power is controlled by the q-axis current, and the photovoltaic-side terminal current I pvt , the electrolytic cell-side terminal current I elt to represent the grid-side power change; 3) the light intensity and the degree of failure are different in different spatial positions, and the photovoltaic power P pv , Q pv , the power of electrolytic tank P el Quantify the dynamic influence of spatial information on the photovoltaic hydrogen production station; In summary, Upvdc, U eldc , Upvt, U elt , Ipvt, Ielt, Ppv, Qpv, Pel comprehensive consideration of the dynamic characteristics of photovoltaic hydrogen production station relative comprehensive characterization; A multi-machine equivalent construction module constructs an equivalent model of the devices in the same cluster using the capacity weighting and equal power loss method to form a multi-machine equivalent model of the large-scale photovoltaic hydrogen production plant.

8. An electronic device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps of the large-scale photovoltaic hydrogen production plant equivalent modeling method according to any one of claims 1 to 6. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the large-scale photovoltaic hydrogen production plant equivalent modeling method according to any one of claims 1 to 6.

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